[ { "objectID": "about.html", "href": "about.html", "title": "区域水环境污染数据分析实践", "section": "", "text": "About this site." }, { "objectID": "SD/5.2_空间分析/index.html#目录", "href": "SD/5.2_空间分析/index.html#目录", "title": "空间分析", "section": "目录", "text": "目录\n\n空间数据基础概念\n\nSimple Features (SF) 核心规范\n\nSFEXT扩展功能实战\n\n空间关系与几何操作\n\n空间统计分析方法\n\n综合实战案例" }, { "objectID": "SD/5.2_空间分析/index.html#空间数据基础概念", "href": "SD/5.2_空间分析/index.html#空间数据基础概念", "title": "空间分析", "section": "1. 空间数据基础概念", "text": "1. 空间数据基础概念\n空间数据类型体系\nlibrary(sf)\n# 创建示例几何类型\nst_point(c(0, 0)) # 点\nst_linestring(rbind(c(0, 0), c(1, 1))) # 线\nst_polygon(list(rbind(c(0, 0), c(1, 0), c(1, 1), c(0, 0)))) # 面\n坐标参考系(CRS)\n# 常用坐标系定义\nwgs84 <- st_crs(4326) # 经纬度坐标\nutm50n <- st_crs(32650) # UTM 50N投影\ncat(st_crs(wgs84)$proj4string)" }, { "objectID": "SD/5.2_空间分析/index.html#simple-features-sf-核心规范", "href": "SD/5.2_空间分析/index.html#simple-features-sf-核心规范", "title": "空间分析", "section": "2. Simple Features (SF) 核心规范", "text": "2. Simple Features (SF) 核心规范\n创建SF对象\n# 从数据框转换\ndf <- data.frame(\n city = c(\"Beijing\", \"Shanghai\"),\n pop = c(2171, 2424),\n geometry = st_sfc(\n st_point(c(116.4, 39.9)),\n st_point(c(121.5, 31.2))\n )\n)\nsf_df <- st_as_sf(df, crs = 4326)\n数据IO操作\n# 写入/读取空间文件\nst_write(sf_df, \"cities.shp\")\nnew_sf <- st_read(\"cities.shp\")\n\n# 从内置数据集加载\nnc <- st_read(system.file(\"shape/nc.shp\", package = \"sf\"))" }, { "objectID": "SD/5.2_空间分析/index.html#sfext扩展功能实战", "href": "SD/5.2_空间分析/index.html#sfext扩展功能实战", "title": "空间分析", "section": "3. SFEXT扩展功能实战", "text": "3. SFEXT扩展功能实战\n高级几何生成\nremotes::install_github(\"r-spatial/sfext\")\nlibrary(sfext)\n\n# 生成渔网网格\ngrid <- st_regular_grid(\n xmin = 0,\n ymin = 0,\n xmax = 100,\n ymax = 100,\n cell_size = 10\n)\nplot(grid)\n空间索引加速\n# 创建空间索引\nsystem.time(\n without_idx <- st_intersects(nc, nc)\n)\n\nsystem.time(\n with_idx <- st_intersects(st_make_valid(nc), sparse = TRUE)\n)" }, { "objectID": "SD/5.2_空间分析/index.html#空间关系与操作", "href": "SD/5.2_空间分析/index.html#空间关系与操作", "title": "空间分析", "section": "4. 空间关系与操作", "text": "4. 空间关系与操作\n拓扑关系判断\np1 <- st_point(c(0, 0))\np2 <- st_point(c(1, 1))\nst_distance(p1, p2) # 距离计算\n\npoly <- st_polygon(list(rbind(c(0, 0), c(1, 0), c(1, 1), c(0, 0))))\nst_contains(poly, p1) # 包含关系\n几何变换操作\n# 缓冲区分析\nbuf <- st_buffer(nc[1, ], dist = 0.1)\nplot(buf)\nplot(nc[1, ]$geometry, add = TRUE, col = 'red')\n\n# 凸包计算\nconvex <- st_convex_hull(nc)\nplot(convex)" }, { "objectID": "SD/5.2_空间分析/index.html#空间统计分析方法", "href": "SD/5.2_空间分析/index.html#空间统计分析方法", "title": "空间分析", "section": "5. 空间统计分析方法", "text": "5. 空间统计分析方法\n点模式分析\nlibrary(spatstat)\n# 创建点模式对象\nppp <- ppp(x = runif(100), y = runif(100), window = owin(c(0, 1), c(0, 1)))\n\n# K函数分析\nK <- Kest(ppp)\nplot(K, main = \"Ripley's K函数\")\n空间自相关检验\nlibrary(spdep)\n# Moran's I检验\nnb <- poly2nb(nc)\nlw <- nb2listw(nb)\nmoran.test(nc$BIR74, lw)" }, { "objectID": "SD/5.2_空间分析/index.html#综合实战案例", "href": "SD/5.2_空间分析/index.html#综合实战案例", "title": "空间分析", "section": "6. 综合实战案例", "text": "6. 综合实战案例\n城市公园服务区分析\nlibrary(osmdata)\nlibrary(sfnetworks)\n\n# 获取OSM数据\nparks <- opq(\"New York\") %>%\n add_osm_feature(\"leisure\", \"park\") %>%\n osmdata_sf()\n\nroads <- opq(\"New York\") %>%\n add_osm_feature(\"highway\") %>%\n osmdata_sf()\n\n# 构建网络分析\nnet <- as_sfnetwork(roads$osm_lines) %>%\n st_transform(32618)\n\n# 计算服务范围（示例）\nservice_area <- st_network_blend(net, parks$osm_polygons) %>%\n st_buffer(500) # 500米服务区" }, { "objectID": "SD/5.2_空间分析/index.html#可视化增强技巧", "href": "SD/5.2_空间分析/index.html#可视化增强技巧", "title": "空间分析", "section": "可视化增强技巧", "text": "可视化增强技巧\n分层绘图\nlibrary(ggplot2)\nggplot() +\n geom_sf(data = nc, aes(fill = BIR74)) +\n geom_sf(data = service_area, alpha = 0.3) +\n scale_fill_viridis_c() +\n theme_minimal()\n交互可视化\nlibrary(mapview)\nmapview(nc, zcol = \"BIR74\", burst = TRUE) +\n mapview(service_area, alpha.regions = 0.2)" }, { "objectID": "SD/5.2_空间分析/index.html#中国地图", "href": "SD/5.2_空间分析/index.html#中国地图", "title": "空间分析", "section": "中国地图", "text": "中国地图\n\nCodeOutput\n\n\n\nlibrary(tidyverse)\nlibrary(sf)\n\n# 更稳定的预处理数据源（含港澳台）\n# china <- st_read(\"https://geo.datav.aliyun.com/areas_v3/bound/100000.json\")\n\nchina <- st_read(\"./中华人民共和国.json\", quiet = TRUE)\n\nlibrary(ggplot2)\n\nggplot(china) +\n geom_sf(fill = \"#F6F6F6\", color = \"#666666\", size = 0.3) +\n theme_void() +\n labs(title = \"China\")\n\n# 添加模拟经济数据（实际应用可连接统计年鉴）\nset.seed(123)\nchina$gdp <- runif(nrow(china), 100, 1000) # 模拟GDP数据（亿元）\n\nchina |>\n ggplot() +\n geom_sf(aes(fill = gdp), color = \"gray90\", size = 0.2) +\n scale_fill_gradientn(\n colours = c(\"#2c7bb6\", \"#abd9e9\", \"#ffffbf\", \"#fdae61\", \"#d7191c\"),\n name = NULL\n ) +\n theme_void() +\n theme(legend.position = c(0.85, 0.3))" }, { "objectID": "SD/5.2_空间分析/index.html#kriging", "href": "SD/5.2_空间分析/index.html#kriging", "title": "空间分析", "section": "Kriging", "text": "Kriging\n\nCodeOutput\n\n\n\nrequire(sf)\nrequire(sp)\nrequire(gstat)\nrequire(raster)\nrequire(ggplot2)\n# 读取太湖边界\nlake_boundary <- st_read(\"../../data/taihu.shp\") |>\n sf::st_make_valid()\nmain_water <- st_difference(\n lake_boundary[lake_boundary$Name == \"boundary\", ],\n st_union(lake_boundary[lake_boundary$Name != \"boundary\", ])\n)\n\nget_kriging <- function(indf, Yvarname, grid = NULL, boundsf = main_water) {\n insf <- indf |>\n sfext::df_to_sf(crs = 4326, coords = c(\"long\", \"lat\")) |>\n dplyr::select(-c(\"long\", \"lat\")) |>\n dplyr::rename(Y = tidyselect::all_of(Yvarname))\n if (is.null(grid)) {\n # 1. 将sf边界转为terra格式\n v <- terra::vect(boundsf)\n # 2. 创建基础网格\n base_grid <- terra::rast(v, resolution = 0.002) # 基础低分辨率\n # 3. 创建高分辨率区域（例如边界附近）\n buffer_zone <- buffer(v, width = 0.005) # 边界附近创建缓冲区\n hi_res_grid <- terra::rast(buffer_zone, resolution = 0.001)\n # 4. 合并网格\n final_grid <- merge(base_grid, hi_res_grid)\n # 5. 转为点并裁剪\n grid <- terra::as.points(final_grid) |>\n sf::st_as_sf() |>\n sf::st_filter(main_water)\n }\n insp <- sf::as_Spatial(insf)\n fit <- automap::autofitVariogram(Y ~ 1, insp)\n # 克里金插值\n m <- gstat::gstat(\n formula = Y ~ 1,\n data = insp,\n model = fit$var_model\n )\n predsf <- predict(m, grid) |>\n sf::st_as_sf()\n outsf <- predsf |>\n st_coordinates() |>\n as.data.frame() |>\n cbind(pred = predsf$var1.pred)\n return(outsf)\n}\n\n\nwqdf <- readxl::read_xlsx(\"../../data/wqdata.xlsx\")\n\nif (FALSE) {\n wqsf <- wqdf |>\n nest(datedf = -date) |>\n dplyr::mutate(\n krigingsf = purrr::map(datedf, \\(x) {\n get_kriging(x, \"conc\", boundsf = main_water)\n })\n )\n saveRDS(wqsf, \"./wqsf.rds\")\n}\nwqsf <- readRDS(\"./wqsf.rds\")\n\nwqsf |>\n unnest(krigingsf) |>\n ggplot(aes(X, Y)) +\n geom_contour_filled(aes(z = pred), bins = 20) +\n geom_sf(\n data = main_water,\n aes(x = NULL, y = NULL),\n fill = NA,\n colour = \"black\",\n linewidth = 1\n ) +\n # scale_fill_gradientn(colors = hcl.colors(100, \"RdYlBu\")) + # 设置颜色渐变\n # ggsci::scale_fill_aaas() +\n coord_sf() +\n theme_void()" }, { "objectID": "SD/5.2_空间分析/index.html#学习资源", "href": "SD/5.2_空间分析/index.html#学习资源", "title": "空间分析", "section": "学习资源", "text": "学习资源\n\nSF官方文档：https://r-spatial.github.io/sf/\n《Geocomputation with R》在线书\nR-spatial教程：https://github.com/r-spatial/workshops\n\n\n### 使用说明：\n1. 需要R 4.0+环境\n2. 推荐安装依赖：\n ```r\n install.packages(c(\"sf\", \"spatstat\", \"spdep\", \"osmdata\", \"sfnetworks\", \"ggplot2\", \"mapview\"))\n remotes::install_github(\"r-spatial/sfext\")\n\n实际案例数据可替换为本地shapefile\n交互可视化部分需要浏览器支持" }, { "objectID": "SD/5.2_空间分析/index.html#欢迎讨论", "href": "SD/5.2_空间分析/index.html#欢迎讨论", "title": "空间分析", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/4.2_实践部分/index.html#数据说明", "href": "SD/4.2_实践部分/index.html#数据说明", "title": "数据前处理+ggplot2画图实践", "section": "数据说明", "text": "数据说明\n\nmpg\n\n# A tibble: 234 × 11\n manufacturer model displ year cyl trans drv cty hwy fl class\n <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>\n 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…\n 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…\n 3 audi a4 2 2008 4 manu… f 20 31 p comp…\n 4 audi a4 2 2008 4 auto… f 21 30 p comp…\n 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…\n 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…\n 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…\n 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…\n 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…\n10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…\n# ℹ 224 more rows" }, { "objectID": "SD/4.2_实践部分/index.html#完成图1", "href": "SD/4.2_实践部分/index.html#完成图1", "title": "数据前处理+ggplot2画图实践", "section": "完成图1", "text": "完成图1" }, { "objectID": "SD/4.2_实践部分/index.html#完成图2", "href": "SD/4.2_实践部分/index.html#完成图2", "title": "数据前处理+ggplot2画图实践", "section": "完成图2", "text": "完成图2" }, { "objectID": "SD/4.2_实践部分/index.html#完成图3", "href": "SD/4.2_实践部分/index.html#完成图3", "title": "数据前处理+ggplot2画图实践", "section": "完成图3", "text": "完成图3" }, { "objectID": "SD/4.2_实践部分/index.html#完成图4", "href": "SD/4.2_实践部分/index.html#完成图4", "title": "数据前处理+ggplot2画图实践", "section": "完成图4", "text": "完成图4" }, { "objectID": "SD/4.2_实践部分/index.html#完成图5", "href": "SD/4.2_实践部分/index.html#完成图5", "title": "数据前处理+ggplot2画图实践", "section": "完成图5", "text": "完成图5" }, { "objectID": "SD/4.2_实践部分/index.html#综合实践三维荧光数据处理", "href": "SD/4.2_实践部分/index.html#综合实践三维荧光数据处理", "title": "数据前处理+ggplot2画图实践", "section": "综合实践：三维荧光数据处理", "text": "综合实践：三维荧光数据处理" }, { "objectID": "SD/4.2_实践部分/index.html#欢迎讨论", "href": "SD/4.2_实践部分/index.html#欢迎讨论", "title": "数据前处理+ggplot2画图实践", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/3.9_课后作业8/index.html#第8次课后作业", "href": "SD/3.9_课后作业8/index.html#第8次课后作业", "title": "课后作业8", "section": "第8次课后作业", "text": "第8次课后作业\n\n根据airqualitydf.xlsx，按采样点统计白天（8:00-20:00）与夜晚（20:00-8:00）中空气质量指数（AQI）中位数，按城市统计低于所有采样点AQI30%分位值的采样点占比，列出上述占比最高的10个城市（不考虑采样点数低于5个的城市）。\n按照不同城市分组，统计白天与夜晚AQI中位数是否具有显著差异。\n\n作业模板：第8次课后作业_模板.qmd" }, { "objectID": "SD/3.9_课后作业8/index.html#示例代码", "href": "SD/3.9_课后作业8/index.html#示例代码", "title": "课后作业8", "section": "示例代码", "text": "示例代码\n基于R的示例结果\n\n第8次课后作业R示例代码结果\n\n基于SAS的示例结果\n\n第8次课后作业SAS示例代码\n第8次课后作业SAS示例结果1\n第8次课后作业SAS示例结果2\n第8次课后作业SAS示例结果3\n第8次课后作业SAS示例结果4" }, { "objectID": "SD/3.9_课后作业8/index.html#欢迎讨论", "href": "SD/3.9_课后作业8/index.html#欢迎讨论", "title": "课后作业8", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/2.3_datatransform/index.html#tidyverse风格数据分析总体流程", "href": "SD/2.3_datatransform/index.html#tidyverse风格数据分析总体流程", "title": "Data Transform", "section": "tidyverse风格数据分析总体流程", "text": "tidyverse风格数据分析总体流程" }, { "objectID": "SD/2.3_datatransform/index.html#dplyr-cheatsheet", "href": "SD/2.3_datatransform/index.html#dplyr-cheatsheet", "title": "Data Transform", "section": "dplyr cheatsheet", "text": "dplyr cheatsheet" }, { "objectID": "SD/2.3_datatransform/index.html#查看数据", "href": "SD/2.3_datatransform/index.html#查看数据", "title": "Data Transform", "section": "查看数据", "text": "查看数据\n\nflights\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#选择列", "href": "SD/2.3_datatransform/index.html#选择列", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(year, month, day)" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-1", "href": "SD/2.3_datatransform/index.html#选择列-1", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(year:day)" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-2", "href": "SD/2.3_datatransform/index.html#选择列-2", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(3:5)\n\n# A tibble: 336,776 × 3\n day dep_time sched_dep_time\n <int> <int> <int>\n 1 1 517 515\n 2 1 533 529\n 3 1 542 540\n 4 1 544 545\n 5 1 554 600\n 6 1 554 558\n 7 1 555 600\n 8 1 557 600\n 9 1 557 600\n10 1 558 600\n# ℹ 336,766 more rows" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-3", "href": "SD/2.3_datatransform/index.html#选择列-3", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(!year:day)\n\n# A tibble: 336,776 × 16\n dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier\n <int> <int> <dbl> <int> <int> <dbl> <chr> \n 1 517 515 2 830 819 11 UA \n 2 533 529 4 850 830 20 UA \n 3 542 540 2 923 850 33 AA \n 4 544 545 -1 1004 1022 -18 B6 \n 5 554 600 -6 812 837 -25 DL \n 6 554 558 -4 740 728 12 UA \n 7 555 600 -5 913 854 19 B6 \n 8 557 600 -3 709 723 -14 EV \n 9 557 600 -3 838 846 -8 B6 \n10 558 600 -2 753 745 8 AA \n# ℹ 336,766 more rows\n# ℹ 9 more variables: flight <int>, tailnum <chr>, origin <chr>, dest <chr>,\n# air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-4", "href": "SD/2.3_datatransform/index.html#选择列-4", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(-(year:day))\n\n# A tibble: 336,776 × 16\n dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier\n <int> <int> <dbl> <int> <int> <dbl> <chr> \n 1 517 515 2 830 819 11 UA \n 2 533 529 4 850 830 20 UA \n 3 542 540 2 923 850 33 AA \n 4 544 545 -1 1004 1022 -18 B6 \n 5 554 600 -6 812 837 -25 DL \n 6 554 558 -4 740 728 12 UA \n 7 555 600 -5 913 854 19 B6 \n 8 557 600 -3 709 723 -14 EV \n 9 557 600 -3 838 846 -8 B6 \n10 558 600 -2 753 745 8 AA \n# ℹ 336,766 more rows\n# ℹ 9 more variables: flight <int>, tailnum <chr>, origin <chr>, dest <chr>,\n# air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-5", "href": "SD/2.3_datatransform/index.html#选择列-5", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(where(is.character))\n\n# A tibble: 336,776 × 4\n carrier tailnum origin dest \n <chr> <chr> <chr> <chr>\n 1 UA N14228 EWR IAH \n 2 UA N24211 LGA IAH \n 3 AA N619AA JFK MIA \n 4 B6 N804JB JFK BQN \n 5 DL N668DN LGA ATL \n 6 UA N39463 EWR ORD \n 7 B6 N516JB EWR FLL \n 8 EV N829AS LGA IAD \n 9 B6 N593JB JFK MCO \n10 AA N3ALAA LGA ORD \n# ℹ 336,766 more rows" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-6", "href": "SD/2.3_datatransform/index.html#选择列-6", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(!where(is.character)) |>\n select(contains(\"_\"))\n\n# A tibble: 336,776 × 8\n dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay air_time\n <int> <int> <dbl> <int> <int> <dbl> <dbl>\n 1 517 515 2 830 819 11 227\n 2 533 529 4 850 830 20 227\n 3 542 540 2 923 850 33 160\n 4 544 545 -1 1004 1022 -18 183\n 5 554 600 -6 812 837 -25 116\n 6 554 558 -4 740 728 12 150\n 7 555 600 -5 913 854 19 158\n 8 557 600 -3 709 723 -14 53\n 9 557 600 -3 838 846 -8 140\n10 558 600 -2 753 745 8 138\n# ℹ 336,766 more rows\n# ℹ 1 more variable: time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-7", "href": "SD/2.3_datatransform/index.html#选择列-7", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(tail_num = tailnum)\n\n# A tibble: 336,776 × 1\n tail_num\n <chr> \n 1 N14228 \n 2 N24211 \n 3 N619AA \n 4 N804JB \n 5 N668DN \n 6 N39463 \n 7 N516JB \n 8 N829AS \n 9 N593JB \n10 N3ALAA \n# ℹ 336,766 more rows" }, { "objectID": "SD/2.3_datatransform/index.html#选择列-8", "href": "SD/2.3_datatransform/index.html#选择列-8", "title": "Data Transform", "section": "选择列", "text": "选择列\n\nflights |>\n select(air_time, everything())\n\n# A tibble: 336,776 × 19\n air_time year month day dep_time sched_dep_time dep_delay arr_time\n <dbl> <int> <int> <int> <int> <int> <dbl> <int>\n 1 227 2013 1 1 517 515 2 830\n 2 227 2013 1 1 533 529 4 850\n 3 160 2013 1 1 542 540 2 923\n 4 183 2013 1 1 544 545 -1 1004\n 5 116 2013 1 1 554 600 -6 812\n 6 150 2013 1 1 554 558 -4 740\n 7 158 2013 1 1 555 600 -5 913\n 8 53 2013 1 1 557 600 -3 709\n 9 140 2013 1 1 557 600 -3 838\n10 138 2013 1 1 558 600 -2 753\n# ℹ 336,766 more rows\n# ℹ 11 more variables: sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,\n# flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#重命名", "href": "SD/2.3_datatransform/index.html#重命名", "title": "Data Transform", "section": "重命名", "text": "重命名\n\nflights |>\n rename(tail_num = tailnum)\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tail_num <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#重命名-1", "href": "SD/2.3_datatransform/index.html#重命名-1", "title": "Data Transform", "section": "重命名", "text": "重命名\n\nflights |>\n rename(年份 = 1) |>\n rename(月份 = 2)\n\n# A tibble: 336,776 × 19\n 年份月份 day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#重命名-2", "href": "SD/2.3_datatransform/index.html#重命名-2", "title": "Data Transform", "section": "重命名", "text": "重命名\n\nflights |> select(1:4) |> head(n = 3)\n\n# A tibble: 3 × 4\n year month day dep_time\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542\n\n# 重命名\nflights |> select(1:4) |> head(n = 3) |> rename_all(~ c(\"c1\", \"c2\", \"c3\", \"c4\"))\n\n# A tibble: 3 × 4\n c1 c2 c3 c4\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542" }, { "objectID": "SD/2.3_datatransform/index.html#重命名-3", "href": "SD/2.3_datatransform/index.html#重命名-3", "title": "Data Transform", "section": "重命名", "text": "重命名\n\nflights |> select(1:4) |> head(n = 3)\n\n# A tibble: 3 × 4\n year month day dep_time\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542\n\n# 重命名\nflights |> select(1:4) |> head(n = 3) |> rename_all(toupper)\n\n# A tibble: 3 × 4\n YEAR MONTH DAY DEP_TIME\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542" }, { "objectID": "SD/2.3_datatransform/index.html#重命名-4", "href": "SD/2.3_datatransform/index.html#重命名-4", "title": "Data Transform", "section": "重命名", "text": "重命名\n\nflights |> select(1:4) |> head(n = 3)\n\n# A tibble: 3 × 4\n year month day dep_time\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542\n\n# 重命名\nflights |>\n select(1:4) |>\n head(n = 3) |>\n rename_all(~ paste0(toupper(.), \"_NEW\"))\n\n# A tibble: 3 × 4\n YEAR_NEW MONTH_NEW DAY_NEW DEP_TIME_NEW\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542" }, { "objectID": "SD/2.3_datatransform/index.html#练习", "href": "SD/2.3_datatransform/index.html#练习", "title": "Data Transform", "section": "练习", "text": "练习\n将含有下划线的列名中的下划线去掉。\n\nflights |> select(1:4) |> head(n = 3)\n\n# A tibble: 3 × 4\n year month day dep_time\n <int> <int> <int> <int>\n1 2013 1 1 517\n2 2013 1 1 533\n3 2013 1 1 542" }, { "objectID": "SD/2.3_datatransform/index.html#练习-1", "href": "SD/2.3_datatransform/index.html#练习-1", "title": "Data Transform", "section": "练习", "text": "练习\n将airqualitydf中列名的单位信息去除（前5列）。\n\nairqualitydf <- readxl::read_xlsx(\"../../data/airquality.xlsx\", sheet = 2)\nairqualitydf |> select(1:5)\n\n# A tibble: 20,088 × 5\n datetime site `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`\n <dttm> <chr> <dbl> <dbl> <dbl>\n 1 2024-03-19 01:00:00 1001A 0.1 0.4 5\n 2 2024-03-19 01:00:00 1003A 0.2 0.4 9\n 3 2024-03-19 01:00:00 1004A 0.2 0.4 4\n 4 2024-03-19 01:00:00 1005A 0.1 0.3 6\n 5 2024-03-19 01:00:00 1006A 0.1 0.4 5\n 6 2024-03-19 01:00:00 1007A 0.3 0.5 6\n 7 2024-03-19 01:00:00 1008A 0.2 0.4 2\n 8 2024-03-19 01:00:00 1009A 0.2 0.4 2\n 9 2024-03-19 01:00:00 1010A 0.1 0.3 2\n10 2024-03-19 01:00:00 1011A 0.2 0.4 12\n# ℹ 20,078 more rows" }, { "objectID": "SD/2.3_datatransform/index.html#filter", "href": "SD/2.3_datatransform/index.html#filter", "title": "Data Transform", "section": "filter", "text": "filter\n\nflights |>\n filter(dep_delay > 120)\n\n# A tibble: 9,723 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 848 1835 853 1001 1950\n 2 2013 1 1 957 733 144 1056 853\n 3 2013 1 1 1114 900 134 1447 1222\n 4 2013 1 1 1540 1338 122 2020 1825\n 5 2013 1 1 1815 1325 290 2120 1542\n 6 2013 1 1 1842 1422 260 1958 1535\n 7 2013 1 1 1856 1645 131 2212 2005\n 8 2013 1 1 1934 1725 129 2126 1855\n 9 2013 1 1 1938 1703 155 2109 1823\n10 2013 1 1 1942 1705 157 2124 1830\n# ℹ 9,713 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#filter-练习", "href": "SD/2.3_datatransform/index.html#filter-练习", "title": "Data Transform", "section": "filter 练习", "text": "filter 练习\nFlights that departed on January 1.\n\n\n# A tibble: 842 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 832 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#filter-练习-1", "href": "SD/2.3_datatransform/index.html#filter-练习-1", "title": "Data Transform", "section": "filter 练习", "text": "filter 练习\nSelect flights that departed in January or February\n\n\n# A tibble: 51,955 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 51,945 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#filter-练习-2", "href": "SD/2.3_datatransform/index.html#filter-练习-2", "title": "Data Transform", "section": "filter 练习", "text": "filter 练习\n\njan1 <- flights |>\n filter(month == 1 & day == 1)" }, { "objectID": "SD/2.3_datatransform/index.html#filter-1", "href": "SD/2.3_datatransform/index.html#filter-1", "title": "Data Transform", "section": "filter", "text": "filter\n\nflights |>\n filter(month = 1)" }, { "objectID": "SD/2.3_datatransform/index.html#filter-2", "href": "SD/2.3_datatransform/index.html#filter-2", "title": "Data Transform", "section": "filter", "text": "filter\n\nflights |>\n filter(month == 1 | 2)\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#排序", "href": "SD/2.3_datatransform/index.html#排序", "title": "Data Transform", "section": "排序", "text": "排序\n\nflights |>\n arrange(year, month, day, dep_time)\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#排序-1", "href": "SD/2.3_datatransform/index.html#排序-1", "title": "Data Transform", "section": "排序", "text": "排序\n\nflights |>\n arrange(desc(dep_delay))\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 9 641 900 1301 1242 1530\n 2 2013 6 15 1432 1935 1137 1607 2120\n 3 2013 1 10 1121 1635 1126 1239 1810\n 4 2013 9 20 1139 1845 1014 1457 2210\n 5 2013 7 22 845 1600 1005 1044 1815\n 6 2013 4 10 1100 1900 960 1342 2211\n 7 2013 3 17 2321 810 911 135 1020\n 8 2013 6 27 959 1900 899 1236 2226\n 9 2013 7 22 2257 759 898 121 1026\n10 2013 12 5 756 1700 896 1058 2020\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#slice", "href": "SD/2.3_datatransform/index.html#slice", "title": "Data Transform", "section": "slice", "text": "slice\n\nflights |> head(n = 5)\n\n# A tibble: 5 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n1 2013 1 1 517 515 2 830 819\n2 2013 1 1 533 529 4 850 830\n3 2013 1 1 542 540 2 923 850\n4 2013 1 1 544 545 -1 1004 1022\n5 2013 1 1 554 600 -6 812 837\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>\n\nflights |> slice(1:5)\n\n# A tibble: 5 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n1 2013 1 1 517 515 2 830 819\n2 2013 1 1 533 529 4 850 830\n3 2013 1 1 542 540 2 923 850\n4 2013 1 1 544 545 -1 1004 1022\n5 2013 1 1 554 600 -6 812 837\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#slice-1", "href": "SD/2.3_datatransform/index.html#slice-1", "title": "Data Transform", "section": "slice", "text": "slice\n\nflights |>\n slice_max(dep_delay, n = 5)\n\n# A tibble: 5 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n1 2013 1 9 641 900 1301 1242 1530\n2 2013 6 15 1432 1935 1137 1607 2120\n3 2013 1 10 1121 1635 1126 1239 1810\n4 2013 9 20 1139 1845 1014 1457 2210\n5 2013 7 22 845 1600 1005 1044 1815\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#slice-2", "href": "SD/2.3_datatransform/index.html#slice-2", "title": "Data Transform", "section": "slice", "text": "slice\n\nflights |>\n slice_min(dep_delay, prop = 0.005)\n\n# A tibble: 2,257 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 12 7 2040 2123 -43 40 2352\n 2 2013 2 3 2022 2055 -33 2240 2338\n 3 2013 11 10 1408 1440 -32 1549 1559\n 4 2013 1 11 1900 1930 -30 2233 2243\n 5 2013 1 29 1703 1730 -27 1947 1957\n 6 2013 8 9 729 755 -26 1002 955\n 7 2013 10 23 1907 1932 -25 2143 2143\n 8 2013 3 30 2030 2055 -25 2213 2250\n 9 2013 3 2 1431 1455 -24 1601 1631\n10 2013 5 5 934 958 -24 1225 1309\n# ℹ 2,247 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#排序练习", "href": "SD/2.3_datatransform/index.html#排序练习", "title": "Data Transform", "section": "排序练习", "text": "排序练习\n根据origin、dest、air_time倒序排序。\n\n\n# A tibble: 336,776 × 19\n origin dest air_time year month day dep_time sched_dep_time dep_delay\n <chr> <chr> <dbl> <int> <int> <int> <int> <int> <dbl>\n 1 EWR ALB 50 2013 5 5 1950 2000 -10\n 2 EWR ALB 45 2013 1 13 1721 1619 62\n 3 EWR ALB 43 2013 1 20 1623 1619 4\n 4 EWR ALB 42 2013 4 1 1439 1340 59\n 5 EWR ALB 41 2013 12 4 1316 1310 6\n 6 EWR ALB 41 2013 2 1 2034 2000 34\n 7 EWR ALB 41 2013 5 7 1956 2000 -4\n 8 EWR ALB 38 2013 1 18 1824 1619 125\n 9 EWR ALB 38 2013 1 28 1636 1619 17\n10 EWR ALB 38 2013 11 10 2149 2159 -10\n# ℹ 336,766 more rows\n# ℹ 10 more variables: arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,\n# carrier <chr>, flight <int>, tailnum <chr>, distance <dbl>, hour <dbl>,\n# minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#去重", "href": "SD/2.3_datatransform/index.html#去重", "title": "Data Transform", "section": "去重", "text": "去重\n\n# Remove duplicate rows, if any\nflights |>\n distinct()\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#去重-1", "href": "SD/2.3_datatransform/index.html#去重-1", "title": "Data Transform", "section": "去重", "text": "去重\n\n# Find all unique origin and destination pairs\nflights |>\n distinct(origin, dest)\n\n# A tibble: 224 × 2\n origin dest \n <chr> <chr>\n 1 EWR IAH \n 2 LGA IAH \n 3 JFK MIA \n 4 JFK BQN \n 5 LGA ATL \n 6 EWR ORD \n 7 EWR FLL \n 8 LGA IAD \n 9 JFK MCO \n10 LGA ORD \n# ℹ 214 more rows" }, { "objectID": "SD/2.3_datatransform/index.html#去重-2", "href": "SD/2.3_datatransform/index.html#去重-2", "title": "Data Transform", "section": "去重", "text": "去重\n\nflights |>\n distinct(origin, dest, .keep_all = TRUE)\n\n# A tibble: 224 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 214 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/2.3_datatransform/index.html#欢迎讨论", "href": "SD/2.3_datatransform/index.html#欢迎讨论", "title": "Data Transform", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/2.1_codestyle/index.html#tidy-data", "href": "SD/2.1_codestyle/index.html#tidy-data", "title": "代码编写规则", "section": "tidy data", "text": "tidy data\n\nknitr::include_graphics(\"../../image/tidy-1.png\", dpi = 270)" }, { "objectID": "SD/2.1_codestyle/index.html#pipe管道", "href": "SD/2.1_codestyle/index.html#pipe管道", "title": "代码编写规则", "section": "pipe（管道） |>", "text": "pipe（管道） |>\n\nrequire(patchwork)\nplot(1:10)\n1:10 |> plot()\nplot(x = 1:10, y = sin(1:10))\n1:10 |> plot(y = sin(1:10))" }, { "objectID": "SD/2.1_codestyle/index.html#pipe管道-1", "href": "SD/2.1_codestyle/index.html#pipe管道-1", "title": "代码编写规则", "section": "pipe（管道）：%>%", "text": "pipe（管道）：%>%\nrequire(magrittr)\n1:10 %>% plot()\n1:10 %>% plot(y = sin(1:10))\nsin(1:10) %>% plot(1:10, .)\nsin(1:10) |> plot(x = 1:10, y = _)" }, { "objectID": "SD/2.1_codestyle/index.html#代码编写规则", "href": "SD/2.1_codestyle/index.html#代码编写规则", "title": "代码编写规则", "section": "代码编写规则", "text": "代码编写规则\n\n# Strive for:\nshort_flights <- flights |> filter(air_time < 60)\n# Avoid:\nSHORTFLIGHTS <- flights |> filter(air_time < 60)\n\n# Strive for\nz <- (a + b)^2 / d\n# Avoid\nz <- (a + b)^2 / d\n\n# Strive for\nmean(x, na.rm = TRUE)\n# Avoid\nmean(x, na.rm = TRUE)" }, { "objectID": "SD/2.1_codestyle/index.html#练习", "href": "SD/2.1_codestyle/index.html#练习", "title": "代码编写规则", "section": "练习", "text": "练习\n\nflights |>\n filter(dest == \"IAH\") |>\n group_by(year, month, day) |>\n summarize(n = n(), delay = mean(arr_delay, na.rm = TRUE)) |>\n filter(n > 10)" }, { "objectID": "SD/2.1_codestyle/index.html#练习-1", "href": "SD/2.1_codestyle/index.html#练习-1", "title": "代码编写规则", "section": "练习", "text": "练习\n\nflights |>\n filter(dest == \"IAH\") |>\n group_by(year, month, day) |>\n summarize(n = n(), delay = mean(arr_delay, na.rm = TRUE)) |>\n filter(n > 10)" }, { "objectID": "SD/2.1_codestyle/index.html#quarto", "href": "SD/2.1_codestyle/index.html#quarto", "title": "代码编写规则", "section": "quarto", "text": "quarto" }, { "objectID": "SD/2.1_codestyle/index.html#欢迎讨论", "href": "SD/2.1_codestyle/index.html#欢迎讨论", "title": "代码编写规则", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/1.3_Excel基础/index.html#下载excel文件", "href": "SD/1.3_Excel基础/index.html#下载excel文件", "title": "三、Excel基础", "section": "下载excel文件", "text": "下载excel文件\nhttps://git.drwater.net/course/RWEP/raw/branch/main/data/airquality.xlsx" }, { "objectID": "SD/1.3_Excel基础/index.html#tidy-data", "href": "SD/1.3_Excel基础/index.html#tidy-data", "title": "三、Excel基础", "section": "Tidy data", "text": "Tidy data\n由Hadley Wickham提出，tidy data 是一种结构化数据的组织方式，使得数据分析和可视化等操作更加简单、直观和有效。它有助于减少数据的混乱和复杂性，并促进数据的整洁性和易用性。\n\n每个变量一列\n每个观察一行\n每个表单一个表" }, { "objectID": "SD/1.3_Excel基础/index.html#excel主要学习内容", "href": "SD/1.3_Excel基础/index.html#excel主要学习内容", "title": "三、Excel基础", "section": "Excel主要学习内容", "text": "Excel主要学习内容\n\n常用函数\n数据筛选/排序\n条件格式化\n数据透视表/图" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数", "href": "SD/1.3_Excel基础/index.html#常用函数", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n引用位置\n\n相对引用\n\n相对引用是默认的引用类型。\n当公式被复制到其他单元格时，引用的单元格会相对移动。\n\n\n=A1 # 在 B1 单元格中的公式，引用了 A1 单元格的值\n=B1+C1 # 在 D1 单元格中的公式，引用了 B1 和 C1 单元格的值\n绝对引用\n\n绝对引用固定了单元格的地址，不会随公式的复制而改变。\n\n=$A$1 # 在 B1 单元格中的公式，始终引用 A1 单元格的值" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数-1", "href": "SD/1.3_Excel基础/index.html#常用函数-1", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n\nSUM：求和\nAVERAGE：求平均值\nMAX：求最大值\nMIN：求最小值" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数-2", "href": "SD/1.3_Excel基础/index.html#常用函数-2", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n\nCOUNT：计数非空单元格\nCOUNTA：计数非空单元格（包括文本）\nCOUNTIF：根据条件计数\nSUMIF：根据条件求和\nAVERAGEIF：根据条件求平均值" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数-3", "href": "SD/1.3_Excel基础/index.html#常用函数-3", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n\nVLOOKUP：垂直查找\nHLOOKUP：水平查找\nINDEX：返回指定位置的值\nMATCH：查找值在区域中的位置\nIF：条件判断函数" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数-4", "href": "SD/1.3_Excel基础/index.html#常用函数-4", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n\nCONCATENATE：连接多个文本字符串\nLEFT：提取文本左边的字符\nRIGHT：提取文本右边的字符\nMID：提取文本中间的字符\nTEXT：将数值格式化为文本" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数-5", "href": "SD/1.3_Excel基础/index.html#常用函数-5", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n\nROUND：四舍五入到指定位数\nROUNDUP：向上取整\nROUNDDOWN：向下取整" }, { "objectID": "SD/1.3_Excel基础/index.html#常用函数-6", "href": "SD/1.3_Excel基础/index.html#常用函数-6", "title": "三、Excel基础", "section": "常用函数", "text": "常用函数\n\nTODAY：返回当前日期\nNOW：返回当前日期和时间\nDATE：返回指定年、月、日的日期\nTIME：返回指定小时、分钟、秒的时间" }, { "objectID": "SD/1.3_Excel基础/index.html#欢迎讨论", "href": "SD/1.3_Excel基础/index.html#欢迎讨论", "title": "三、Excel基础", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/1.1_R语言介绍/index.html#r语言实践部分课程简介", "href": "SD/1.1_R语言介绍/index.html#r语言实践部分课程简介", "title": "一、R语言介绍", "section": "R语言实践部分课程简介", "text": "R语言实践部分课程简介\n接下来的课程由我来带着大家一起学习、实践基于R语言的数据分析。\n自我介绍\n\n苏命：来自中国科学院生态环境研究中心\n主要从事水源水质水生态方面的研究\n\n课件\n\n采用R语言+quarto完成\n网页公开：https://drc.drwater.net/course/public/su2026rwep/PUB/\n课件代码：https://git.drwater.net/course/su2026rwep.git" }, { "objectID": "SD/1.1_R语言介绍/index.html#如何学习接下来的内容", "href": "SD/1.1_R语言介绍/index.html#如何学习接下来的内容", "title": "一、R语言介绍", "section": "如何学习接下来的内容？", "text": "如何学习接下来的内容？\n\n上课形式：\n\n实践操作为主，目的是让每一位同学都能独立开展基于R的数据分析\n2-3人一组，自由分组，相互帮助\n\n准备工作：\n\n个人笔记本电脑，Microsoft Windows、GNU/Linux、MacOS系统\n安装好Microsoft Excel\n能联网\n如果电池不够用，建议带上插座\n\n上课规则\n\n可随时打断，不懂就问\n无特殊情况不迟到，否则操作过程容易跟不上" }, { "objectID": "SD/1.1_R语言介绍/index.html#rstudio-server", "href": "SD/1.1_R语言介绍/index.html#rstudio-server", "title": "一、R语言介绍", "section": "Rstudio server", "text": "Rstudio server\n\nServer 地址：https://rs1.drwater.net\n用户名: ruser01, ruser02, …, ruser06\n密码：rwep2026" }, { "objectID": "SD/1.1_R语言介绍/index.html#r语言的历史", "href": "SD/1.1_R语言介绍/index.html#r语言的历史", "title": "一、R语言介绍", "section": "R语言的历史", "text": "R语言的历史\n\n\n\n1976年：\n\n前身是S语言，由贝尔实验室John Chambers及其同事开发\n\n1993年：\n\nR语言由Ross Ihaka和Robert Gentleman在奥克兰大学开发\nR的诞生是为了提供一个强大的统计计算和图形显示的平台\n\n\n\n\n\n\n1995年：R发布第一个公开版本\n2000年代： R语言逐渐成为统计学和数据科学领域重要工具\n2010年：Hadley Wickham发布了ggplot2包，数据可视化方面更强\n2016年：创建CRAN（Comprehensive R Archive Network），R包中央库\n至今\n\nR语言已成为数据科学和统计学领域最受欢迎的工具之一，被广泛用于数据分析、机器学习、数据可视化等领域" }, { "objectID": "SD/1.1_R语言介绍/index.html#r的特点", "href": "SD/1.1_R语言介绍/index.html#r的特点", "title": "一、R语言介绍", "section": "R的特点", "text": "R的特点\n一般特点\n\n免费、开源、支持各个主要计算机系统。\n完整的程序设计语言，基于函数和对象。\n支持完善的数据类型，如向量、矩阵、数据框等。\n\n技术特点\n\n所有存在都是对象。\n所有动作都是函数调用。\n支持函数编程和对象类。\n是动态类型语言，运行速度相对较慢。" }, { "objectID": "SD/1.1_R语言介绍/index.html#r的参考资料", "href": "SD/1.1_R语言介绍/index.html#r的参考资料", "title": "一、R语言介绍", "section": "R的参考资料", "text": "R的参考资料\n推荐参考书\n\nHadley Wickham and Garrett Grolemund(2022). R for Data Science\nHadley Wickham(2019). Advanced R\nHadley Wickham(2016). ggplot2 Elegant Graphics for Data Analysis\nJohn M. Chambers(2008). Software for Data Analysis-Programming with R\nVenables, W. N. & Ripley, B. D.(2002). Modern Applied Statistics with S (MASS)" }, { "objectID": "SD/1.1_R语言介绍/index.html#r的参考资料-1", "href": "SD/1.1_R语言介绍/index.html#r的参考资料-1", "title": "一、R语言介绍", "section": "R的参考资料", "text": "R的参考资料\n中文参考书\n\n《R语言编程艺术》\n《R语言实战》\n《R语言教程与实践》\n《R语言数据可视化实战》\n《R语言从入门到精通》" }, { "objectID": "SD/1.1_R语言介绍/index.html#r的下载与安装", "href": "SD/1.1_R语言介绍/index.html#r的下载与安装", "title": "一、R语言介绍", "section": "R的下载与安装", "text": "R的下载与安装\nR的下载与安装\n\nR的官方网站：https://www.r-project.org/\nCRAN镜像网站：http://mirror.bjtu.edu.cn/cran/\n下载官方的R软件后按提示安装" }, { "objectID": "SD/1.1_R语言介绍/index.html#rstudio", "href": "SD/1.1_R语言介绍/index.html#rstudio", "title": "一、R语言介绍", "section": "RStudio", "text": "RStudio\n什么是RStudio？\n\nRStudio官方网站: https://posit.co/products/open-source/rstudio/\nRStudio是一个集成开发环境（IDE），专门用于R语言编程和数据分析。\n它提供了一个直观的界面，使得编写、调试和运行R代码变得更加容易。" }, { "objectID": "SD/1.1_R语言介绍/index.html#rstudio-1", "href": "SD/1.1_R语言介绍/index.html#rstudio-1", "title": "一、R语言介绍", "section": "RStudio", "text": "RStudio\nRStudio的功能\n\n代码编辑器：提供了语法高亮、自动补全和代码折叠等功能。\n控制台：用于直接执行R代码并查看结果。\n环境和历史记录：可以查看当前加载的数据、函数和变量，以及之前执行过的命令。\n图形和可视化：RStudio内置了绘图设备，可以方便地创建各种统计图表。\n文件管理器：可直接在RStudio中管理文件和项目。\n包管理器：方便地安装、更新和管理R包。\nMarkdown编辑器：支持Markdown格式，可以创建美观的文档和报告。" }, { "objectID": "SD/1.1_R语言介绍/index.html#如何获取rstudio", "href": "SD/1.1_R语言介绍/index.html#如何获取rstudio", "title": "一、R语言介绍", "section": "如何获取RStudio？", "text": "如何获取RStudio？\n\nRStudio可以从官方网站免费下载并安装：RStudio官网\nRStudio是一个强大的R编程环境，为R用户提供了丰富的功能和工具。\n它简化了R语言的使用，提高了数据分析和可视化的效率。" }, { "objectID": "SD/1.1_R语言介绍/index.html#扩展包r-package", "href": "SD/1.1_R语言介绍/index.html#扩展包r-package", "title": "一、R语言介绍", "section": "扩展包：R package", "text": "扩展包：R package\n\nR有一万多个扩展软件包，提供了各种各样的功能\n已安装的基本R包，如base, stats, graphics等，启动R时默认载入\n其它扩展包需要用library(.)函数载入运行；或者采用dplyr::filter(.)方式\n\n\nCodeOutput\n\n\n\n# load a R package\nlibrary(ggplot2)\n\n# plot it based on the functions from `ggplot2` package\nmtcars |>\n dplyr::filter(cyl != 8) |>\n ggplot(aes(hp, mpg, color = am)) +\n geom_point() +\n geom_smooth(formula = y ~ x, method = \"loess\")" }, { "objectID": "SD/1.1_R语言介绍/index.html#安装r包", "href": "SD/1.1_R语言介绍/index.html#安装r包", "title": "一、R语言介绍", "section": "安装R包", "text": "安装R包\n\n以安装sos包为例。\n在RStudio中调用“Tools”菜单的“Install Packages”，输入或选择sos即可安装。\nR图形界面安装\n如果不用RStudio，在R图形界面选菜单“程序包-安装程序包”进行安装。\n在CRAN镜像选择窗口中选择中国的镜像，如“China (Beijing 2)”，然后选择要安装的扩展软件包名称即可完成下载和安装。\n程序安装\n\n# 指定镜像网站并安装扩展包\noptions(repos = c(CRAN = \"https://mirror.tuna.tsinghua.edu.cn/CRAN/\"))\ninstall.packages(\"sos\")\n\n# 当R包不是CRAN标准包，代码放在[github](https://github.com)\nif (!require(devtools)) install.packages('devtools')\ndevtools::install_github(\"kjhealy/socviz\")" }, { "objectID": "SD/1.1_R语言介绍/index.html#安装r包-1", "href": "SD/1.1_R语言介绍/index.html#安装r包-1", "title": "一、R语言介绍", "section": "安装R包", "text": "安装R包\n视频" }, { "objectID": "SD/1.1_R语言介绍/index.html#结语", "href": "SD/1.1_R语言介绍/index.html#结语", "title": "一、R语言介绍", "section": "结语", "text": "结语\n\nR语言是一种强大的统计计算和数据分析工具，适用于各种领域。\n通过学习R语言，可以进行数据处理、统计分析和数据可视化。" }, { "objectID": "SD/1.1_R语言介绍/index.html#欢迎讨论", "href": "SD/1.1_R语言介绍/index.html#欢迎讨论", "title": "一、R语言介绍", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "version.html", "href": "version.html", "title": "区域水环境污染数据分析实践", "section": "", "text": "tibble::tibble(branch = system(\"git branch -a\", intern = TRUE)) |>\n dplyr::filter(grepl(\"remotes\", branch)) |>\n dplyr::filter(!grepl(\"HEAD\", branch)) |>\n dplyr::mutate(branch = gsub(\"^.*\\\\/\", \"\", branch)) |>\n dplyr::arrange(desc(branch)) |>\n dplyr::mutate(\n htmlstr = paste0(\n \"## [**Version**: \",\n branch,\n \"](https://drc.drwater.net/{{< var projtype >}}/\",\n ifelse(grepl(\"pub\", branch, ignore.case = TRUE), \"public\", \"protected\"),\n \"/{{< var reponame >}}/\",\n branch,\n \"/)\"\n )\n ) |>\n dplyr::pull(htmlstr) |>\n paste(collapse = \"\\n\\n\") |>\n cat()" }, { "objectID": "version.html#version-pub", "href": "version.html#version-pub", "title": "区域水环境污染数据分析实践", "section": "Version: PUB", "text": "Version: PUB\n\nVersions\n# 方法2：先获取分支列表，再逐个查询\nbranches <- system(\"git branch -r\", intern = TRUE)\nbranches <- trimws(branches) # 去除空白字符\nbranches <- branches[!grepl(\"HEAD\", branches)]\nbranches <- tibble::tibble(branch = branches) |>\n dplyr::arrange(dplyr::desc(branch)) |>\n dplyr::pull(branch)\n\ncat(\"\\n\")\nfor (branch in branches) {\n if (branch != \"\") {\n cat(\"### \", gsub(\"^.*/\", \"\", branch), \"\\n\\n\")\n commits <- system(\n paste0(\n \"git log --pretty=format:\\\"%h - %an, %ar : %s\\\" -5 \\\"\",\n branch,\n \"\\\"\"\n ),\n intern = TRUE\n )\n # all_commits[[branch]] <- paste0(\"- \", commits)\n cat(paste0(\"- \", commits), sep = \"\\n\")\n cat(\"\\n\")\n }\n}\n\n\nPUB\n\naeb5aab - ming, 5 days ago : render compile\n8288dac - ming, 5 days ago : render compile\n1dcd453 - ming, 5 days ago : render compile\n32a2989 - ming, 5 days ago : render compile\n845b840 - ming, 5 days ago : render compile" }, { "objectID": "index.html", "href": "index.html", "title": "区域水环境污染数据分析实践", "section": "", "text": "基本信息\n\n课程名称：区域水环境污染数据分析实践Data analysis practice of regional water environment pollution\n授课教师：王为东、苏命\n课程教室：教二楼-138\n课程时间：\n\n05月21日8:30-11:20 ✔\n05月26日8:30-11:20\n05月28日8:30-11:20\n06月02日8:30-11:20\n06月04日8:30-11:20\n\n\n\n\n课件\n\n网页公开：https://drc.drwater.net/course/public/su2026rwep/PUB/\n课件代码：https://git.drwater.net/course/su2026rwep.git" }, { "objectID": "SD/index.html#r语言语法基础", "href": "SD/index.html#r语言语法基础", "title": "区域水环境污染数据分析实践", "section": "1.2_R语言语法基础", "text": "1.2_R语言语法基础" }, { "objectID": "SD/index.html#excel基础", "href": "SD/index.html#excel基础", "title": "区域水环境污染数据分析实践", "section": "1.3_Excel基础", "text": "1.3_Excel基础" }, { "objectID": "SD/index.html#课后作业6", "href": "SD/index.html#课后作业6", "title": "区域水环境污染数据分析实践", "section": "1.9_课后作业6", "text": "1.9_课后作业6" }, { "objectID": 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"SD/index.html#实践部分", "href": "SD/index.html#实践部分", "title": "区域水环境污染数据分析实践", "section": "4.2_实践部分", "text": "4.2_实践部分" }, { "objectID": "SD/index.html#model", "href": "SD/index.html#model", "title": "区域水环境污染数据分析实践", "section": "5.1_model", "text": "5.1_model" }, { "objectID": "SD/index.html#空间分析", "href": "SD/index.html#空间分析", "title": "区域水环境污染数据分析实践", "section": "5.2_空间分析", "text": "5.2_空间分析" }, { "objectID": "SD/index.html#大数据分析工具", "href": "SD/index.html#大数据分析工具", "title": "区域水环境污染数据分析实践", "section": "5.3_大数据分析工具", "text": "5.3_大数据分析工具" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据类型", "href": "SD/1.2_R语言语法基础/index.html#数据类型", "title": "二、R语言语法基础", "section": "数据类型", "text": "数据类型\n数值型\nR中的数值型数据可以是整数或浮点数。\n\n(x <- 10)\n\n[1] 10\n\n(y <- 1.23e-2)\n\n[1] 0.0123\n\n(z <- pi)\n\n[1] 3.141593" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据类型-1", "href": "SD/1.2_R语言语法基础/index.html#数据类型-1", "title": "二、R语言语法基础", "section": "数据类型", "text": "数据类型\n字符串\n\nR 中的字符串用引号括起来，建议用双引号。\n中文编码主要有GBK编码和UTF-8编码，可能遇到编码错误造成乱码。RStudio软件默认采用UTF-8编码，在R程序运行时字符串一般用UTF-8编码保存。\n\n\n(str <- \"Hello, World!\")\n\n[1] \"Hello, World!\"\n\n(str <- 'Hello, World!')\n\n[1] \"Hello, World!\"\n\n(str <- 'He was very angry, and shouted: \"Stop!\"')\n\n[1] \"He was very angry, and shouted: \\\"Stop!\\\"\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据类型-2", "href": "SD/1.2_R语言语法基础/index.html#数据类型-2", "title": "二、R语言语法基础", "section": "数据类型", "text": "数据类型\n逻辑\n\nc(TRUE, FALSE)\n\n[1] TRUE FALSE" }, { "objectID": "SD/1.2_R语言语法基础/index.html#特殊值", "href": "SD/1.2_R语言语法基础/index.html#特殊值", "title": "二、R语言语法基础", "section": "特殊值", "text": "特殊值\n\n\n\nNA: 这是最常见的NA类型，表示缺失值\nNA_integer_: 这是NA的整数类型\nNA_real_: 这是NA的实数类型\nNA_character_: 这是NA的字符类型\nNA_complex_: 这是NA的复数类型\n\n\n\npi\n\n[1] 3.141593\n\nNA\n\n[1] NA\n\nNA_character_\n\n[1] NA\n\nInf\n\n[1] Inf" }, { "objectID": "SD/1.2_R语言语法基础/index.html#特殊值-1", "href": "SD/1.2_R语言语法基础/index.html#特殊值-1", "title": "二、R语言语法基础", "section": "特殊值", "text": "特殊值\n在 R 中，Inf 代表正无穷大（positive infinity），而 -Inf 则代表负无穷大（negative infinity）。这些值通常出现在数学计算中，例如除以零或对负数取对数等操作可能会导致无穷大的结果。\n\n# 正无穷大\n(x <- Inf)\n\n[1] Inf\n\n# 负无穷大\n(y <- -Inf)\n\n[1] -Inf\n\n# 无穷大的运算\n(a <- 5 / 0)\n\n[1] Inf\n\n(b <- log(0))\n\n[1] -Inf" }, { "objectID": "SD/1.2_R语言语法基础/index.html#变量赋值", "href": "SD/1.2_R语言语法基础/index.html#变量赋值", "title": "二、R语言语法基础", "section": "变量赋值", "text": "变量赋值\n在 R 中，可以使用 <- 或 = 运算符将值赋给变量，建议用<-。\n\n# 使用 `<-` 运算符\n(x <- 10)\n\n[1] 10\n\n(y <- \"hello\")\n\n[1] \"hello\"\n\n# 使用 `=` 运算符\n(z = c(1, 2, 3))\n\n[1] 1 2 3" }, { "objectID": "SD/1.2_R语言语法基础/index.html#变量赋值-1", "href": "SD/1.2_R语言语法基础/index.html#变量赋值-1", "title": "二、R语言语法基础", "section": "变量赋值", "text": "变量赋值\n\n# 向量赋值\n(vec <- c(1, 2, 3, 4, 5))\n\n[1] 1 2 3 4 5\n\n# 矩阵赋值\n(mat <- matrix(1:9, nrow = 3))\n\n [,1] [,2] [,3]\n[1,] 1 4 7\n[2,] 2 5 8\n[3,] 3 6 9" }, { "objectID": "SD/1.2_R语言语法基础/index.html#变量赋值-2", "href": "SD/1.2_R语言语法基础/index.html#变量赋值-2", "title": "二、R语言语法基础", "section": "变量赋值", "text": "变量赋值\n数据框赋值\n\n(df <- data.frame(\n Name = c(\"Alice\", \"Bob\", \"Charlie\"),\n Age = c(25, 30, 35),\n Married = c(TRUE, FALSE, TRUE)\n))\n\n Name Age Married\n1 Alice 25 TRUE\n2 Bob 30 FALSE\n3 Charlie 35 TRUE" }, { "objectID": "SD/1.2_R语言语法基础/index.html#变量赋值-3", "href": "SD/1.2_R语言语法基础/index.html#变量赋值-3", "title": "二、R语言语法基础", "section": "变量赋值", "text": "变量赋值\n列表赋值\n\n(lst <- list(\n numbers = c(1, 2, 3),\n strings = c(\"a\", \"b\", \"c\"),\n matrix = matrix(1:9, nrow = 3)\n))\n\n$numbers\n[1] 1 2 3\n\n$strings\n[1] \"a\" \"b\" \"c\"\n\n$matrix\n [,1] [,2] [,3]\n[1,] 1 4 7\n[2,] 2 5 8\n[3,] 3 6 9" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数学函数", "href": "SD/1.2_R语言语法基础/index.html#数学函数", "title": "二、R语言语法基础", "section": "数学函数", "text": "数学函数\n\nround(pi, digits = 3)\n\n[1] 3.142\n\nlog(10)\n\n[1] 2.302585\n\n\n\nabs(x): 返回 x 的绝对值\nsqrt(x): 返回 x 的平方根\nexp(x): 以e为底的指数函数值\nlog(x, base): 以指定底数的对数函数的值，默认底数为e\nlog10(x): 10为底的对数值\nlog2(x): 2为底的对数值\nfloor(x): 不大于x的最大整数\nceiling(x): 不小于x的最小整数" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数学函数-1", "href": "SD/1.2_R语言语法基础/index.html#数学函数-1", "title": "二、R语言语法基础", "section": "数学函数", "text": "数学函数\n\nsin(x), cos(x), tan(x): 返回 x 的正弦、余弦和正切值，其中 x 为弧度\nasin(x), acos(x), atan(x): x 的反正弦、反余弦和反正切值，返回弧度\nsinh(x), cosh(x), tanh(x): 返回 x 的双曲正弦、双曲余弦和双曲正切值\nasinh(x), acosh(x), atanh(x): 反双曲正弦、反双曲余弦和反双曲正切值\nround(x, digits): x 四舍五入，digits指定小数点后位数\ntrunc(x): 返回x截断值，即去掉小数部分\nsign(x): 返回符号" }, { "objectID": "SD/1.2_R语言语法基础/index.html#统计函数", "href": "SD/1.2_R语言语法基础/index.html#统计函数", "title": "二、R语言语法基础", "section": "统计函数", "text": "统计函数\n\nx <- c(5, 10, 15, 20, 25)\n# 计算向量的平均值\nmean(x)\n\n[1] 15\n\n# 计算向量的中位数\nmedian(x)\n\n[1] 15\n\n# 计算向量的最小值\nmin(x)\n\n[1] 5\n\n# 计算向量的最大值\nmax(x)\n\n[1] 25\n\n# 计算向量的总和\nsum(x)\n\n[1] 75" }, { "objectID": "SD/1.2_R语言语法基础/index.html#统计函数-1", "href": "SD/1.2_R语言语法基础/index.html#统计函数-1", "title": "二、R语言语法基础", "section": "统计函数", "text": "统计函数\n\n# 计算向量的标准差\nsd(x)\n\n[1] 7.905694\n\n# 计算向量的方差\nvar(x)\n\n[1] 62.5\n\n# 计算向量的分位数\nquantile(x, probs = c(0.25, 0.5, 0.75))\n\n25% 50% 75% \n 10 15 20 \n\n# 统计向量的频数\n(frequency <- table(x))\n\nx\n 5 10 15 20 25 \n 1 1 1 1 1" }, { "objectID": "SD/1.2_R语言语法基础/index.html#统计函数-2", "href": "SD/1.2_R语言语法基础/index.html#统计函数-2", "title": "二、R语言语法基础", "section": "统计函数", "text": "统计函数\n执行两样本或单样本 t 检验\n\ny <- c(3, 8, 13, 18, 23)\nt.test(x, y)\n\n\n Welch Two Sample t-test\n\ndata: x and y\nt = 0.4, df = 8, p-value = 0.6996\nalternative hypothesis: true difference in means is not equal to 0\n95 percent confidence interval:\n -9.530021 13.530021\nsample estimates:\nmean of x mean of y \n 15 13" }, { "objectID": "SD/1.2_R语言语法基础/index.html#统计函数-3", "href": "SD/1.2_R语言语法基础/index.html#统计函数-3", "title": "二、R语言语法基础", "section": "统计函数", "text": "统计函数\nWilcoxon-Mann-Whitney检验\n\nwilcox.test(x, y)\n\n\n Wilcoxon rank sum exact test\n\ndata: x and y\nW = 15, p-value = 0.6905\nalternative hypothesis: true location shift is not equal to 0\n\n\n[什么是 Wilcoxon-Mann-Whitney检验？](https://zhuanlan.zhihu.com/p/613524533）" }, { "objectID": "SD/1.2_R语言语法基础/index.html#统计函数-4", "href": "SD/1.2_R语言语法基础/index.html#统计函数-4", "title": "二、R语言语法基础", "section": "统计函数", "text": "统计函数\n创建向量的直方图\n\nhist(x)" }, { "objectID": "SD/1.2_R语言语法基础/index.html#函数调用-练习", "href": "SD/1.2_R语言语法基础/index.html#函数调用-练习", "title": "二、R语言语法基础", "section": "函数调用-练习", "text": "函数调用-练习\n题目：设有一组数据集合 x 包含了一些整数，请编写R语言代码计算并输出以下指标：\n\n平均值（mean）\n中位数（median）\n最大值（maximum）\n最小值（minimum）\n数据集合中所有元素的和（sum）\n数据集合的标准差（standard deviation）\n数据集合 x 为：x <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)\n\n要求：使用R语言编写函数，输入参数为数据集合 x，输出为以上指标的值。" }, { "objectID": "SD/1.2_R语言语法基础/index.html#控制流程", "href": "SD/1.2_R语言语法基础/index.html#控制流程", "title": "二、R语言语法基础", "section": "控制流程", "text": "控制流程\nif-else 语句\n\nx <- 10\n\nif (x > 10) {\n print(\"x 大于 10\")\n} else {\n print(\"x 不大于 10\")\n}\n\n[1] \"x 不大于 10\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#控制流程-1", "href": "SD/1.2_R语言语法基础/index.html#控制流程-1", "title": "二、R语言语法基础", "section": "控制流程", "text": "控制流程\nfor 循环\n\nfor (i in 1:5) {\n print(i)\n}\n\n[1] 1\n[1] 2\n[1] 3\n[1] 4\n[1] 5" }, { "objectID": "SD/1.2_R语言语法基础/index.html#自定义函数", "href": "SD/1.2_R语言语法基础/index.html#自定义函数", "title": "二、R语言语法基础", "section": "自定义函数", "text": "自定义函数\n定义函数\n使用 function 关键字定义函数，并使用 return 关键字返回结果。\n\nmy_function <- function(x, y) {\n return(x + y)\n}\n\n调用函数\n\nresult <- my_function(3, 4)\nprint(result)\n\n[1] 7" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据结构", "href": "SD/1.2_R语言语法基础/index.html#数据结构", "title": "二、R语言语法基础", "section": "数据结构", "text": "数据结构\n向量\n向量是一维数组，可以包含相同类型的元素。\n\n(v <- c(1, 2, 3, 4, 5))\n\n[1] 1 2 3 4 5\n\n\n列表\n列表可以包含不同类型的元素。\n\n(l <- list(a = 1, b = \"hello\", c = TRUE))\n\n$a\n[1] 1\n\n$b\n[1] \"hello\"\n\n$c\n[1] TRUE" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数值型向量", "href": "SD/1.2_R语言语法基础/index.html#数值型向量", "title": "二、R语言语法基础", "section": "数值型向量", "text": "数值型向量\n什么是数值型向量？\n\n在 R 中，向量是一种基本的数据结构。\n数值型向量包含相同类型的数值元素。\n\n创建数值型向量\n\n# 使用 c() 函数创建数值型向量\n(numeric_vector <- c(1, 2, 3, 4, 5))\n\n[1] 1 2 3 4 5" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数值型向量-1", "href": "SD/1.2_R语言语法基础/index.html#数值型向量-1", "title": "二、R语言语法基础", "section": "数值型向量", "text": "数值型向量\n向量运算\n\n# 创建两个数值型向量\n(vector1 <- c(1, 2, 3))\n\n[1] 1 2 3\n\n(vector2 <- c(4, 5, 6))\n\n[1] 4 5 6\n\n# 执行向量加法\n(result <- vector1 + vector2)\n\n[1] 5 7 9\n\n# 执行向量乘法\n(result <- vector1 * vector2)\n\n[1] 4 10 18" }, { "objectID": "SD/1.2_R语言语法基础/index.html#向量运算-1", "href": "SD/1.2_R语言语法基础/index.html#向量运算-1", "title": "二、R语言语法基础", "section": "向量运算", "text": "向量运算\n向量求和\n\n# 创建数值型向量\nvector <- c(1, 2, 3, 4, 5)\n\n# 求和\n(sum_result <- sum(vector))\n\n[1] 15" }, { "objectID": "SD/1.2_R语言语法基础/index.html#向量运算-2", "href": "SD/1.2_R语言语法基础/index.html#向量运算-2", "title": "二、R语言语法基础", "section": "向量运算", "text": "向量运算\n向量平均值\n\n# 创建数值型向量\nvector <- c(1, 2, 3, 4, 5)\n\n# 平均值\n(mean_result <- mean(vector))\n\n[1] 3" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-数值运算", "href": "SD/1.2_R语言语法基础/index.html#运算-数值运算", "title": "二、R语言语法基础", "section": "运算-数值运算", "text": "运算-数值运算\n\na 的平方。\nb 的立方。\na 除以 b 的商和余数。\n\n要求：使用R语言编写函数，输入参数为 a 和 b，输出为上述结果。" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-逻辑运算", "href": "SD/1.2_R语言语法基础/index.html#运算-逻辑运算", "title": "二、R语言语法基础", "section": "运算-逻辑运算", "text": "运算-逻辑运算\n\nall(c(FALSE, 2, 1:3, 3) > 1)\n\n[1] FALSE\n\nany(c(FALSE, 2, 1:3, 3) > 1)\n\n[1] TRUE\n\n(flag1 <- FALSE)\n\n[1] FALSE\n\n(flag2 <- (3 > 2))\n\n[1] TRUE\n\n(flag3 <- TRUE * TRUE)\n\n[1] 1\n\n(flag4 <- TRUE * FALSE)\n\n[1] 0\n\n(flag5 <- TRUE & FALSE)\n\n[1] FALSE\n\n(flag6 <- TRUE | FALSE)\n\n[1] TRUE" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-逻辑运算-1", "href": "SD/1.2_R语言语法基础/index.html#运算-逻辑运算-1", "title": "二、R语言语法基础", "section": "运算-逻辑运算", "text": "运算-逻辑运算\n\nwhich\n\n\nwhich(c(FALSE, TRUE, TRUE, FALSE, NA))\n\n[1] 2 3\n\nwhich((11:15) > 12)\n\n[1] 3 4 5\n\n\n\nidentical\n\n\nidentical(c(1, 2, 3), c(1, 2, NA))\n\n[1] FALSE\n\nidentical(c(1L, 2L, 3L), c(1, 2, 3))\n\n[1] FALSE" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-字符型", "href": "SD/1.2_R语言语法基础/index.html#运算-字符型", "title": "二、R语言语法基础", "section": "运算-字符型", "text": "运算-字符型\n\n特殊字符\n\n\nc(\"abc\", \"\", 'a cat', NA, '李明', \"\\n\")\n\n[1] \"abc\" \"\" \"a cat\" NA \"李明\" \"\\n\" \n\n\n\npaste\n\n\n(users <- paste(\"ruser\", 1:9))\n\n[1] \"ruser 1\" \"ruser 2\" \"ruser 3\" \"ruser 4\" \"ruser 5\" \"ruser 6\" \"ruser 7\"\n[8] \"ruser 8\" \"ruser 9\"\n\npaste(users, collapse = \", \")\n\n[1] \"ruser 1, ruser 2, ruser 3, ruser 4, ruser 5, ruser 6, ruser 7, ruser 8, ruser 9\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-字符型-1", "href": "SD/1.2_R语言语法基础/index.html#运算-字符型-1", "title": "二、R语言语法基础", "section": "运算-字符型", "text": "运算-字符型\n\n大小写\n\n\nletters[1:5]\n\n[1] \"a\" \"b\" \"c\" \"d\" \"e\"\n\ntoupper(letters[6:9])\n\n[1] \"F\" \"G\" \"H\" \"I\"\n\ntolower(month.abb)\n\n [1] \"jan\" \"feb\" \"mar\" \"apr\" \"may\" \"jun\" \"jul\" \"aug\" \"sep\" \"oct\" \"nov\" \"dec\"\n\nstringr::str_to_title(c(\"monday\", \"tuesday\"))\n\n[1] \"Monday\" \"Tuesday\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-字符型-2", "href": "SD/1.2_R语言语法基础/index.html#运算-字符型-2", "title": "二、R语言语法基础", "section": "运算-字符型", "text": "运算-字符型\n\n字符串截取\n\n\nsubstr(\"Monday\", 1, 3)\n\n[1] \"Mon\"\n\nstringr::str_sub(\"Monday\", 1, 3)\n\n[1] \"Mon\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-字符型-3", "href": "SD/1.2_R语言语法基础/index.html#运算-字符型-3", "title": "二、R语言语法基础", "section": "运算-字符型", "text": "运算-字符型\n\n类型转换\n\n\n100\n\n[1] 100\n\nas.character(100)\n\n[1] \"100\"\n\nas.numeric(c(\"0100\", \"0101\"))\n\n[1] 100 101\n\nsprintf('renamedfile%03d.png', c(3, 99, 100))\n\n[1] \"renamedfile003.png\" \"renamedfile099.png\" \"renamedfile100.png\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#运算-字符型-4", "href": "SD/1.2_R语言语法基础/index.html#运算-字符型-4", "title": "二、R语言语法基础", "section": "运算-字符型", "text": "运算-字符型\n\n字符串替换\n\n\n(mystr <- \"He was wrong!\")\n\n[1] \"He was wrong!\"\n\ngsub(\"wrong\", \"right\", mystr)\n\n[1] \"He was right!\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#索引", "href": "SD/1.2_R语言语法基础/index.html#索引", "title": "二、R语言语法基础", "section": "索引", "text": "索引\n向量\n\n# 创建一个向量\nvector <- c(\"apple\", \"banana\", \"cherry\", \"date\")\n# 访问第三个元素\nvector[3]\n\n[1] \"cherry\"\n\n# 访问多个元素\nvector[c(2, 4)]\n\n[1] \"banana\" \"date\" \n\nvector[c(2:4)]\n\n[1] \"banana\" \"cherry\" \"date\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#索引-1", "href": "SD/1.2_R语言语法基础/index.html#索引-1", "title": "二、R语言语法基础", "section": "索引", "text": "索引\n向量\n\n# 除了第2个元素\nvector[-2]\n\n[1] \"apple\" \"cherry\" \"date\" \n\n# 超界\nvector[100]\n\n[1] NA\n\n# 更新数据\nvector[7] <- \"New Data\"\nvector\n\n[1] \"apple\" \"banana\" \"cherry\" \"date\" NA NA \"New Data\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#索引-2", "href": "SD/1.2_R语言语法基础/index.html#索引-2", "title": "二、R语言语法基础", "section": "索引", "text": "索引\n\n(x <- 1:10)\n\n [1] 1 2 3 4 5 6 7 8 9 10\n\nx[x > 6]\n\n[1] 7 8 9 10\n\nx[x < 3] <- 99\nx\n\n [1] 99 99 3 4 5 6 7 8 9 10\n\n# which\nwhich(x > 10)\n\n[1] 1 2\n\nwhich.max(x)\n\n[1] 1\n\nwhich.min(x)\n\n[1] 3" }, { "objectID": "SD/1.2_R语言语法基础/index.html#索引-3", "href": "SD/1.2_R语言语法基础/index.html#索引-3", "title": "二、R语言语法基础", "section": "索引", "text": "索引\n列表\n\n# 创建一个列表\nmy_list <- list(\n fruit = c(\"apple\", \"banana\", \"cherry\"),\n numbers = c(1, 2, 3, 4, 5)\n)\n\n# 访问列表中的第二个元素\nmy_list[[2]]\n\n[1] 1 2 3 4 5" }, { "objectID": "SD/1.2_R语言语法基础/index.html#索引-4", "href": "SD/1.2_R语言语法基础/index.html#索引-4", "title": "二、R语言语法基础", "section": "索引", "text": "索引\n数据框\n\n# 创建一个数据框\ndf <- data.frame(fruit = c(\"apple\", \"banana\", \"cherry\"), quantity = c(5, 7, 3))\n\n# 访问数据框中的第一个元素\ndf[1, 1]\n\n[1] \"apple\"\n\n# 第2-3行\ndf[2:3, ]\n\n fruit quantity\n2 banana 7\n3 cherry 3" }, { "objectID": "SD/1.2_R语言语法基础/index.html#日期和时间", "href": "SD/1.2_R语言语法基础/index.html#日期和时间", "title": "二、R语言语法基础", "section": "日期和时间", "text": "日期和时间\nbase package\n\nas.Date(\"2024-01-01\")\n\n[1] \"2024-01-01\"\n\nas.POSIXct(1)\n\n[1] \"1970-01-01 08:00:01 CST\"\n\nas.Date(c(\"12/6/2022\", \"1/1/2023\"), format = \"%m/%d/%Y\")\n\n[1] \"2022-12-06\" \"2023-01-01\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#日期和时间-1", "href": "SD/1.2_R语言语法基础/index.html#日期和时间-1", "title": "二、R语言语法基础", "section": "日期和时间", "text": "日期和时间\nlubridate package\n\nlubridate::today()\n\n[1] \"2026-05-21\"\n\nrequire(lubridate)\nnow()\n\n[1] \"2026-05-21 15:10:08 CST\"\n\nymd(c(20200321, 240404, \"20181231\"))\n\n[1] \"2020-03-21\" \"2024-04-04\" \"2018-12-31\"\n\nmdy(c(\"3-10-1998\", \"01-17-2018\", \"Feb 3, 2024\"))\n\n[1] \"1998-03-10\" \"2018-01-17\" \"2024-02-03\"\n\nymd_hms(\"1998-03-16 13:15:45\", tz = \"Asia/Shanghai\")\n\n[1] \"1998-03-16 13:15:45 CST\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#日期和时间-2", "href": "SD/1.2_R语言语法基础/index.html#日期和时间-2", "title": "二、R语言语法基础", "section": "日期和时间", "text": "日期和时间\nlubridate package\n\nmake_date(2028, 1, 30)\n\n[1] \"2028-01-30\"\n\nas_date(\"2000-01-01\")\n\n[1] \"2000-01-01\"\n\nas_datetime(\"2000-01-01\", tz = \"Asia/Shanghai\")\n\n[1] \"2000-01-01 CST\"\n\nas_datetime(\"2024-02-01 8:00:00\", tz = \"Asia/Shanghai\")\n\n[1] \"2024-02-01 08:00:00 CST\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#日期和时间-3", "href": "SD/1.2_R语言语法基础/index.html#日期和时间-3", "title": "二、R语言语法基础", "section": "日期和时间", "text": "日期和时间\nlubridate package\n\nyear(today())\n\n[1] 2026\n\nwday(today())\n\n[1] 5\n\nhour(now())\n\n[1] 15" }, { "objectID": "SD/1.2_R语言语法基础/index.html#日期和时间-4", "href": "SD/1.2_R语言语法基础/index.html#日期和时间-4", "title": "二、R语言语法基础", "section": "日期和时间", "text": "日期和时间\nlubridate package\n\n(x <- now())\n\n[1] \"2026-05-21 15:10:08 CST\"\n\nfloor_date(x, unit = \"day\")\n\n[1] \"2026-05-21 CST\"\n\nfloor_date(x, unit = \"hour\")\n\n[1] \"2026-05-21 15:00:00 CST\"\n\nfloor_date(x, unit = \"10 minutes\")\n\n[1] \"2026-05-21 15:10:00 CST\"\n\nceiling_date(x, unit = \"10 minutes\")\n\n[1] \"2026-05-21 15:20:00 CST\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#因子factor", "href": "SD/1.2_R语言语法基础/index.html#因子factor", "title": "二、R语言语法基础", "section": "因子（factor）", "text": "因子（factor）\nFactor是什么？\n\n在R中，Factor是用来表示分类数据的特殊数据类型。\n它将数据分成不同的水平(levels)，每个水平代表了一个类别。" }, { "objectID": "SD/1.2_R语言语法基础/index.html#因子factor-1", "href": "SD/1.2_R语言语法基础/index.html#因子factor-1", "title": "二、R语言语法基础", "section": "因子（factor）", "text": "因子（factor）\n创建Factor\n\n# 创建一个Factor\ngender <- factor(c(\"Male\", \"Female\", \"Female\", \"Male\"))\n# 查看Factor的水平\nlevels(gender)\n\n[1] \"Female\" \"Male\" \n\n# 改变Factor的水平顺序\ngender <- factor(gender, levels = c(\"Female\", \"Male\"))\nsummary(gender) # 使用Factor进行分组\n\nFemale Male \n 2 2 \n\nas.numeric(gender) # 因子转换为纯粹的整数值\n\n[1] 2 1 1 2\n\nas.character(gender) # 转为字符\n\n[1] \"Male\" \"Female\" \"Female\" \"Male\"" }, { "objectID": "SD/1.2_R语言语法基础/index.html#因子factor-2", "href": "SD/1.2_R语言语法基础/index.html#因子factor-2", "title": "二、R语言语法基础", "section": "因子（factor）", "text": "因子（factor）\nLabel of Factor\n\n(x <- factor(1:12, label = month.abb))\n\n [1] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\nLevels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\n\nfactor(x, levels = month.abb[c(2:12, 1)])\n\n [1] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\nLevels: Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan\n\n\n分组\n\ncut(1:20, breaks = c(0, 5, 10, 15, 18, 20))\n\n [1] (0,5] (0,5] (0,5] (0,5] (0,5] (5,10] (5,10] (5,10] (5,10] \n[10] (5,10] (10,15] (10,15] (10,15] (10,15] (10,15] (15,18] (15,18] (15,18]\n[19] (18,20] (18,20]\nLevels: (0,5] (5,10] (10,15] (15,18] (18,20]" }, { "objectID": "SD/1.2_R语言语法基础/index.html#矩阵", "href": "SD/1.2_R语言语法基础/index.html#矩阵", "title": "二、R语言语法基础", "section": "矩阵", "text": "矩阵\n\n1:20\n\n [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n\n(A <- matrix(1:20, nrow = 4, byrow = TRUE))\n\n [,1] [,2] [,3] [,4] [,5]\n[1,] 1 2 3 4 5\n[2,] 6 7 8 9 10\n[3,] 11 12 13 14 15\n[4,] 16 17 18 19 20\n\n(B <- matrix(1:20, nrow = 4, byrow = FALSE))\n\n [,1] [,2] [,3] [,4] [,5]\n[1,] 1 5 9 13 17\n[2,] 2 6 10 14 18\n[3,] 3 7 11 15 19\n[4,] 4 8 12 16 20\n\nnrow(A)\n\n[1] 4\n\nncol(B)\n\n[1] 5" }, { "objectID": "SD/1.2_R语言语法基础/index.html#矩阵-1", "href": "SD/1.2_R语言语法基础/index.html#矩阵-1", "title": "二、R语言语法基础", "section": "矩阵", "text": "矩阵\n高维矩阵\n\nX <- array(1:12, dim = c(3, 2, 2))\ndim(C)\n\nNULL\n\nX[1, , ]\n\n [,1] [,2]\n[1,] 1 7\n[2,] 4 10\n\nX[1, , 1]\n\n[1] 1 4" }, { "objectID": "SD/1.2_R语言语法基础/index.html#矩阵-2", "href": "SD/1.2_R语言语法基础/index.html#矩阵-2", "title": "二、R语言语法基础", "section": "矩阵", "text": "矩阵\ncbind、rbind\n\ncbind(X[1, , ], X[2, , ], X[3, , ])\n\n [,1] [,2] [,3] [,4] [,5] [,6]\n[1,] 1 7 2 8 3 9\n[2,] 4 10 5 11 6 12\n\nrbind(X[1, , ], X[2, , ], X[3, , ])\n\n [,1] [,2]\n[1,] 1 7\n[2,] 4 10\n[3,] 2 8\n[4,] 5 11\n[5,] 3 9\n[6,] 6 12\n\ncbind(c(1, 2), c(3, 4), c(5, 6))\n\n [,1] [,2] [,3]\n[1,] 1 3 5\n[2,] 2 4 6" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据框data-frame", "href": "SD/1.2_R语言语法基础/index.html#数据框data-frame", "title": "二、R语言语法基础", "section": "数据框（data frame）", "text": "数据框（data frame）\n最主要的数据形式。\n\n# 创建数据框\n(df <- data.frame(\n Name = c(\"Alice\", \"Bob\", \"Charlie\"),\n Age = c(25, 30, 35),\n Married = c(TRUE, FALSE, TRUE)\n))\n\n Name Age Married\n1 Alice 25 TRUE\n2 Bob 30 FALSE\n3 Charlie 35 TRUE\n\nnames(df)\n\n[1] \"Name\" \"Age\" \"Married\"\n\ncolnames(df)\n\n[1] \"Name\" \"Age\" \"Married\"\n\nncol(df)\n\n[1] 3\n\nnrow(df)\n\n[1] 3" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据框data-frame-1", "href": "SD/1.2_R语言语法基础/index.html#数据框data-frame-1", "title": "二、R语言语法基础", "section": "数据框（data frame）", "text": "数据框（data frame）\n\ndf[1, 1]\n\n[1] \"Alice\"\n\ndf[2, ]\n\n Name Age Married\n2 Bob 30 FALSE\n\ndf[, 1]\n\n[1] \"Alice\" \"Bob\" \"Charlie\"\n\ndf$Age\n\n[1] 25 30 35\n\ndf[[\"Age\"]]\n\n[1] 25 30 35\n\ndf[, \"Age\"]\n\n[1] 25 30 35" }, { "objectID": "SD/1.2_R语言语法基础/index.html#数据框data-frame-2", "href": "SD/1.2_R语言语法基础/index.html#数据框data-frame-2", "title": "二、R语言语法基础", "section": "数据框（data frame）", "text": "数据框（data frame）\n\nX <- matrix(1:9, nrow = 3)\nclass(X)\n\n[1] \"matrix\" \"array\" \n\n(Y <- as.data.frame(X))\n\n V1 V2 V3\n1 1 4 7\n2 2 5 8\n3 3 6 9\n\nnames(Y)\n\n[1] \"V1\" \"V2\" \"V3\"\n\nnames(Y) <- c(\"colA\", \"colB\", \"colC\")" }, { "objectID": "SD/1.2_R语言语法基础/index.html#欢迎讨论", "href": "SD/1.2_R语言语法基础/index.html#欢迎讨论", "title": "二、R语言语法基础", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/1.9_课后作业6/index.html#第6次课后作业", "href": "SD/1.9_课后作业6/index.html#第6次课后作业", "title": "课后作业6", "section": "第6次课后作业", "text": "第6次课后作业\n\n如何在 R 中将数字 10 赋值给变量 x？\n在 R 中，如何创建一个包含数字 1 到 5 的向量？\n编写一个条件语句，如果变量 age 大于等于 18，则打印 “成年人”，否则打印 “未成年人”。\n使用 for 循环打印从 1 到 10 的整数。\n编写一个名为 addition 的函数，接受两个参数 a 和 b，返回它们的和。\n创建一个列表，包含三个元素：一个数字向量、一个字符向量和一个逻辑向量。\n使用 read.csv() 函数读取名为 data.csv 的 CSV 文件，并将数据存储在一个名为 data 的数据框中。\n从数据框中选择前五行，并将结果存储在一个新的数据框中。\n将字符串 “hello world” 转换为大写。\n从数据框中选择 score 列大于等于 90 的行。" }, { "objectID": "SD/1.9_课后作业6/index.html#data.csv内容", "href": "SD/1.9_课后作业6/index.html#data.csv内容", "title": "课后作业6", "section": "data.csv内容", "text": "data.csv内容\nname,age,score\nAlice,25,85\nBob,30,92\nCharlie,28,89\nDavid,22,95\nEva,35,87\nFrank,27,91\nGrace,29,88\nHelen,26,93\nIvan,31,86\nJack,24,94\nKelly,32,89\nLily,28,90\nMike,33,85\nNancy,27,92\nOlivia,34,88\nPeter,29,93\nQueen,25,89\nRyan,30,94\nSamantha,26,91\nTom,31,87" }, { "objectID": "SD/1.9_课后作业6/index.html#欢迎讨论", "href": "SD/1.9_课后作业6/index.html#欢迎讨论", "title": "课后作业6", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/2.2_dataimport/index.html#tidyverse风格数据分析总体流程", "href": "SD/2.2_dataimport/index.html#tidyverse风格数据分析总体流程", "title": "Data import", "section": "tidyverse风格数据分析总体流程", "text": "tidyverse风格数据分析总体流程" }, { "objectID": "SD/2.2_dataimport/index.html#导入csv数据", "href": "SD/2.2_dataimport/index.html#导入csv数据", "title": "Data import", "section": "导入csv数据", "text": "导入csv数据\n\nread_lines(\"../../data/students.csv\") |> cat(sep = \"\\n\")\n\nStudent ID,Full Name,favourite.food,mealPlan,AGE\n1,Sunil Huffmann,Strawberry yoghurt,Lunch only,4\n2,Barclay Lynn,French fries,Lunch only,5\n3,Jayendra Lyne,N/A,Breakfast and lunch,7\n4,Leon Rossini,Anchovies,Lunch only,\n5,Chidiegwu Dunkel,Pizza,Breakfast and lunch,five\n6,Güvenç Attila,Ice cream,Lunch only,6" }, { "objectID": "SD/2.2_dataimport/index.html#导入csv数据-1", "href": "SD/2.2_dataimport/index.html#导入csv数据-1", "title": "Data import", "section": "导入csv数据", "text": "导入csv数据\n\nread_csv(\"../../data/students.csv\") |>\n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\nStudent ID\nFull Name\nfavourite.food\nmealPlan\nAGE\n\n\n\n\n1\nSunil Huffmann\nStrawberry yoghurt\nLunch only\n4\n\n\n2\nBarclay Lynn\nFrench fries\nLunch only\n5\n\n\n3\nJayendra Lyne\nN/A\nBreakfast and lunch\n7\n\n\n4\nLeon Rossini\nAnchovies\nLunch only\nNA\n\n\n5\nChidiegwu Dunkel\nPizza\nBreakfast and lunch\nfive\n\n\n6\nGüvenç Attila\nIce cream\nLunch only\n6" }, { "objectID": "SD/2.2_dataimport/index.html#读取数据", "href": "SD/2.2_dataimport/index.html#读取数据", "title": "Data import", "section": "读取数据", "text": "读取数据\n\n(students <- read_csv(\"../../data/students.csv\"))\n\n# A tibble: 6 × 5\n `Student ID` `Full Name` favourite.food mealPlan AGE \n <dbl> <chr> <chr> <chr> <chr>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4 \n2 2 Barclay Lynn French fries Lunch only 5 \n3 3 Jayendra Lyne N/A Breakfast and lunch 7 \n4 4 Leon Rossini Anchovies Lunch only <NA> \n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five \n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#读取数据-1", "href": "SD/2.2_dataimport/index.html#读取数据-1", "title": "Data import", "section": "读取数据", "text": "读取数据\n\n(students <- read_csv(\"../../data/students.csv\", na = c(\"N/A\", \"\")))\n\n# A tibble: 6 × 5\n `Student ID` `Full Name` favourite.food mealPlan AGE \n <dbl> <chr> <chr> <chr> <chr>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4 \n2 2 Barclay Lynn French fries Lunch only 5 \n3 3 Jayendra Lyne <NA> Breakfast and lunch 7 \n4 4 Leon Rossini Anchovies Lunch only <NA> \n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five \n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#列名不要有空格", "href": "SD/2.2_dataimport/index.html#列名不要有空格", "title": "Data import", "section": "列名不要有空格", "text": "列名不要有空格\n\nstudents |>\n rename(\n student_id = `Student ID`,\n full_name = `Full Name`\n )\n\n# A tibble: 6 × 5\n student_id full_name favourite.food mealPlan AGE \n <dbl> <chr> <chr> <chr> <chr>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4 \n2 2 Barclay Lynn French fries Lunch only 5 \n3 3 Jayendra Lyne <NA> Breakfast and lunch 7 \n4 4 Leon Rossini Anchovies Lunch only <NA> \n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five \n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#janitor处理空格", "href": "SD/2.2_dataimport/index.html#janitor处理空格", "title": "Data import", "section": "janitor处理空格", "text": "janitor处理空格\n\nstudents |> janitor::clean_names()\n\n# A tibble: 6 × 5\n student_id full_name favourite_food meal_plan age \n <dbl> <chr> <chr> <chr> <chr>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4 \n2 2 Barclay Lynn French fries Lunch only 5 \n3 3 Jayendra Lyne <NA> Breakfast and lunch 7 \n4 4 Leon Rossini Anchovies Lunch only <NA> \n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five \n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#janitor处理空格-1", "href": "SD/2.2_dataimport/index.html#janitor处理空格-1", "title": "Data import", "section": "janitor处理空格", "text": "janitor处理空格\n\nstudents |>\n janitor::clean_names() |>\n mutate(meal_plan = factor(meal_plan))\n\n# A tibble: 6 × 5\n student_id full_name favourite_food meal_plan age \n <dbl> <chr> <chr> <fct> <chr>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4 \n2 2 Barclay Lynn French fries Lunch only 5 \n3 3 Jayendra Lyne <NA> Breakfast and lunch 7 \n4 4 Leon Rossini Anchovies Lunch only <NA> \n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five \n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#janitor处理空格-2", "href": "SD/2.2_dataimport/index.html#janitor处理空格-2", "title": "Data import", "section": "janitor处理空格", "text": "janitor处理空格\n\nstudents <- students |>\n janitor::clean_names() |>\n mutate(\n meal_plan = factor(meal_plan),\n age = parse_number(if_else(age == \"five\", \"5\", age))\n )\nstudents\n\n# A tibble: 6 × 5\n student_id full_name favourite_food meal_plan age\n <dbl> <chr> <chr> <fct> <dbl>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4\n2 2 Barclay Lynn French fries Lunch only 5\n3 3 Jayendra Lyne <NA> Breakfast and lunch 7\n4 4 Leon Rossini Anchovies Lunch only NA\n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch 5\n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#直接录入", "href": "SD/2.2_dataimport/index.html#直接录入", "title": "Data import", "section": "直接录入", "text": "直接录入\n\nread_csv(\n \"The first line of metadata\n The second line of metadata\n x,y,z\n 1,2,3\",\n skip = 2\n)\n\n# A tibble: 1 × 3\n x y z\n <dbl> <dbl> <dbl>\n1 1 2 3" }, { "objectID": "SD/2.2_dataimport/index.html#直接录入-1", "href": "SD/2.2_dataimport/index.html#直接录入-1", "title": "Data import", "section": "直接录入", "text": "直接录入\n\nread_csv(\n \"# A comment I want to skip\n x,y,z\n 1,2,3\",\n comment = \"#\"\n)\n\n# A tibble: 1 × 3\n x y z\n <dbl> <dbl> <dbl>\n1 1 2 3" }, { "objectID": "SD/2.2_dataimport/index.html#指定列名", "href": "SD/2.2_dataimport/index.html#指定列名", "title": "Data import", "section": "指定列名", "text": "指定列名\n\nread_csv(\n \"1,2,3\n 4,5,6\",\n col_names = c(\"x\", \"y\", \"z\")\n)\n\n# A tibble: 2 × 3\n x y z\n <dbl> <dbl> <dbl>\n1 1 2 3\n2 4 5 6" }, { "objectID": "SD/2.2_dataimport/index.html#指定列的类型", "href": "SD/2.2_dataimport/index.html#指定列的类型", "title": "Data import", "section": "指定列的类型", "text": "指定列的类型\n\nanother_csv <- \"\nx,y,z\n1,2,3\"\n\nread_csv(\n another_csv,\n col_types = cols(.default = col_character())\n)\n\n# A tibble: 1 × 3\n x y z \n <chr> <chr> <chr>\n1 1 2 3 \n\nread_csv(\n another_csv,\n col_types = cols_only(x = col_character())\n)\n\n# A tibble: 1 × 1\n x \n <chr>\n1 1" }, { "objectID": "SD/2.2_dataimport/index.html#练习", "href": "SD/2.2_dataimport/index.html#练习", "title": "Data import", "section": "练习", "text": "练习\n\nread_csv(\"a,b\\n1,2,3\\n4,5,6\")\nread_csv(\"a,b,c\\n1,2\\n1,2,3,4\")\nread_csv(\"a,b\\n\\\"1\")\nread_csv(\"a,b\\n1,2\\na,b\")\nread_csv(\"a;b\\n1;3\")" }, { "objectID": "SD/2.2_dataimport/index.html#练习-1", "href": "SD/2.2_dataimport/index.html#练习-1", "title": "Data import", "section": "练习", "text": "练习\n\nannoying <- tibble(\n `1` = 1:10,\n `2` = `1` * 2 + rnorm(length(`1`))\n)" }, { "objectID": "SD/2.2_dataimport/index.html#批量读取", "href": "SD/2.2_dataimport/index.html#批量读取", "title": "Data import", "section": "批量读取", "text": "批量读取\n\nsales_files <- c(\n \"../../data/01-sales.csv\",\n \"../../data/02-sales.csv\",\n \"../../data/03-sales.csv\"\n)\nread_csv(sales_files, id = \"file\")\n\n# A tibble: 19 × 6\n file month year brand item n\n <chr> <chr> <dbl> <dbl> <dbl> <dbl>\n 1 ../../data/01-sales.csv January 2019 1 1234 3\n 2 ../../data/01-sales.csv January 2019 1 8721 9\n 3 ../../data/01-sales.csv January 2019 1 1822 2\n 4 ../../data/01-sales.csv January 2019 2 3333 1\n 5 ../../data/01-sales.csv January 2019 2 2156 9\n 6 ../../data/01-sales.csv January 2019 2 3987 6\n 7 ../../data/01-sales.csv January 2019 2 3827 6\n 8 ../../data/02-sales.csv February 2019 1 1234 8\n 9 ../../data/02-sales.csv February 2019 1 8721 2\n10 ../../data/02-sales.csv February 2019 1 1822 3\n11 ../../data/02-sales.csv February 2019 2 3333 1\n12 ../../data/02-sales.csv February 2019 2 2156 3\n13 ../../data/02-sales.csv February 2019 2 3987 6\n14 ../../data/03-sales.csv March 2019 1 1234 3\n15 ../../data/03-sales.csv March 2019 1 3627 1\n16 ../../data/03-sales.csv March 2019 1 8820 3\n17 ../../data/03-sales.csv March 2019 2 7253 1\n18 ../../data/03-sales.csv March 2019 2 8766 3\n19 ../../data/03-sales.csv March 2019 2 8288 6" }, { "objectID": "SD/2.2_dataimport/index.html#读取excel建议用readxl包", "href": "SD/2.2_dataimport/index.html#读取excel建议用readxl包", "title": "Data import", "section": "读取Excel，建议用readxl包", "text": "读取Excel，建议用readxl包\n\n(surveydf <- readxl::read_xlsx(\"../../data/survey.xlsx\"))\n\n# A tibble: 6 × 2\n survey_id n_pets\n <dbl> <chr> \n1 1 0 \n2 2 1 \n3 3 N/A \n4 4 two \n5 5 2 \n6 6 <NA>" }, { "objectID": "SD/2.2_dataimport/index.html#读取excel", "href": "SD/2.2_dataimport/index.html#读取excel", "title": "Data import", "section": "读取Excel", "text": "读取Excel\n\n(airqualitydf <- readxl::read_xlsx(\"../../data/airquality.xlsx\", sheet = 2))\n\n# A tibble: 20,088 × 20\n datetime site `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`\n <dttm> <chr> <dbl> <dbl> <dbl>\n 1 2024-03-19 01:00:00 1001A 0.1 0.4 5\n 2 2024-03-19 01:00:00 1003A 0.2 0.4 9\n 3 2024-03-19 01:00:00 1004A 0.2 0.4 4\n 4 2024-03-19 01:00:00 1005A 0.1 0.3 6\n 5 2024-03-19 01:00:00 1006A 0.1 0.4 5\n 6 2024-03-19 01:00:00 1007A 0.3 0.5 6\n 7 2024-03-19 01:00:00 1008A 0.2 0.4 2\n 8 2024-03-19 01:00:00 1009A 0.2 0.4 2\n 9 2024-03-19 01:00:00 1010A 0.1 0.3 2\n10 2024-03-19 01:00:00 1011A 0.2 0.4 12\n# ℹ 20,078 more rows\n# ℹ 15 more variables: `NO2_24h_μg/m3` <dbl>, `O3_μg/m3` <dbl>,\n# `O3_24h_μg/m3` <lgl>, `O3_8h_μg/m3` <lgl>, `O3_8h_24h_μg/m3` <lgl>,\n# `PM10_μg/m3` <dbl>, `PM10_24h_μg/m3` <dbl>, `PM2.5_μg/m3` <dbl>,\n# `PM2.5_24h_μg/m3` <dbl>, `SO2_μg/m3` <dbl>, `SO2_24h_μg/m3` <dbl>,\n# AQI <dbl>, PrimaryPollutant <chr>, Quality <chr>, Unheathful <chr>" }, { "objectID": "SD/2.2_dataimport/index.html#批量读取-1", "href": "SD/2.2_dataimport/index.html#批量读取-1", "title": "Data import", "section": "批量读取", "text": "批量读取\n\nsales_files <- list.files(\n \"../../data\",\n pattern = \"sales\\\\.csv$\",\n full.names = TRUE\n)\nsales_files\n\n[1] \"../../data/01-sales.csv\" \"../../data/02-sales.csv\"\n[3] \"../../data/03-sales.csv\"" }, { "objectID": "SD/2.2_dataimport/index.html#写入csv", "href": "SD/2.2_dataimport/index.html#写入csv", "title": "Data import", "section": "写入csv", "text": "写入csv\n\nstudents\n\n# A tibble: 6 × 5\n student_id full_name favourite_food meal_plan age\n <dbl> <chr> <chr> <fct> <dbl>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4\n2 2 Barclay Lynn French fries Lunch only 5\n3 3 Jayendra Lyne <NA> Breakfast and lunch 7\n4 4 Leon Rossini Anchovies Lunch only NA\n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch 5\n6 6 Güvenç Attila Ice cream Lunch only 6\n\nwrite_csv(students, \"students-2.csv\")\nread_csv(\"students-2.csv\")\n\n# A tibble: 6 × 5\n student_id full_name favourite_food meal_plan age\n <dbl> <chr> <chr> <chr> <dbl>\n1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4\n2 2 Barclay Lynn French fries Lunch only 5\n3 3 Jayendra Lyne <NA> Breakfast and lunch 7\n4 4 Leon Rossini Anchovies Lunch only NA\n5 5 Chidiegwu Dunkel Pizza Breakfast and lunch 5\n6 6 Güvenç Attila Ice cream Lunch only 6" }, { "objectID": "SD/2.2_dataimport/index.html#写入excel", "href": "SD/2.2_dataimport/index.html#写入excel", "title": "Data import", "section": "写入Excel", "text": "写入Excel\n\nwritexl::write_xlsx(students, \"../../data/writexldemo.xlsx\")" }, { "objectID": "SD/2.2_dataimport/index.html#读取数据库以mysql为例", "href": "SD/2.2_dataimport/index.html#读取数据库以mysql为例", "title": "Data import", "section": "读取数据库，以MySQL为例", "text": "读取数据库，以MySQL为例\n\nif (FALSE) {\n conn <- cctdb::get_dbconn(\"nationalairquality\")\n DBI::dbListTables(conn)\n}" }, { "objectID": "SD/2.2_dataimport/index.html#读取数据库以mysql为例-1", "href": "SD/2.2_dataimport/index.html#读取数据库以mysql为例-1", "title": "Data import", "section": "读取数据库，以MySQL为例", "text": "读取数据库，以MySQL为例\n\nif (FALSE) {\n conn <- cctdb::get_dbconn(\"nationalairquality\")\n metadf <- tbl(conn, \"metadf\") |>\n head(100) |>\n collect()\n DBI::dbDisconnect(conn)\n saveRDS(metadf, file = \"../../data/metadfdemo.RDS\")\n}\nmetadf <- readRDS(file = \"../../data/metadfdemo.RDS\")\nlang <- \"cn\"\nmetadf |>\n ggplot(aes(lon, lat)) +\n geom_point(aes(fill = Area)) +\n dwfun::theme_sci()" }, { "objectID": "SD/2.2_dataimport/index.html#练习-2", "href": "SD/2.2_dataimport/index.html#练习-2", "title": "Data import", "section": "练习", "text": "练习\n\nmetadf <- readxl::read_xlsx(\"../../data/airquality.xlsx\")\ndir.create(\"../../data/metacity2/\")\nmetadf |>\n nest(sitedf = -site) |>\n mutate(\n flag = purrr::map2(\n site,\n sitedf,\n ~ writexl::write_xlsx(.y, paste0(\"../../data/metacity2/\", .x, \".xlsx\"))\n )\n )" }, { "objectID": "SD/2.2_dataimport/index.html#练习-3", "href": "SD/2.2_dataimport/index.html#练习-3", "title": "Data import", "section": "练习", "text": "练习\n\n从“../../data/sales.xlsx”读取第9到13行的数据\n从“../../data/meta_city.xlsx”读取所有的数据，并保存至“../../data/meta_city_onetable1.xlsx”\n从“../../data/metacity/”读取所有的数据，并保存至“../../data/meta_city_onetable2.xlsx”" }, { "objectID": "SD/2.2_dataimport/index.html#欢迎讨论", "href": "SD/2.2_dataimport/index.html#欢迎讨论", "title": "Data import", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/3.1_datatransform/index.html#计数", "href": "SD/3.1_datatransform/index.html#计数", "title": "Data Transform", "section": "计数", "text": "计数\n\nflights |>\n count(origin, dest, sort = TRUE)\n\n# A tibble: 224 × 3\n origin dest n\n <chr> <chr> <int>\n 1 JFK LAX 11262\n 2 LGA ATL 10263\n 3 LGA ORD 8857\n 4 JFK SFO 8204\n 5 LGA CLT 6168\n 6 EWR ORD 6100\n 7 JFK BOS 5898\n 8 LGA MIA 5781\n 9 JFK MCO 5464\n10 EWR BOS 5327\n# ℹ 214 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#计数-练习", "href": "SD/3.1_datatransform/index.html#计数-练习", "title": "Data Transform", "section": "计数-练习", "text": "计数-练习\n统计每月的航班数量。\n\n\n# A tibble: 12 × 3\n year month n\n <int> <int> <int>\n 1 2013 7 29425\n 2 2013 8 29327\n 3 2013 10 28889\n 4 2013 3 28834\n 5 2013 5 28796\n 6 2013 4 28330\n 7 2013 6 28243\n 8 2013 12 28135\n 9 2013 9 27574\n10 2013 11 27268\n11 2013 1 27004\n12 2013 2 24951" }, { "objectID": "SD/3.1_datatransform/index.html#计算新变量", "href": "SD/3.1_datatransform/index.html#计算新变量", "title": "Data Transform", "section": "计算新变量", "text": "计算新变量\n\nflights |>\n mutate(\n gain = dep_delay - arr_delay,\n speed = distance / air_time * 60\n )\n\n# A tibble: 336,776 × 21\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 13 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>, gain <dbl>, speed <dbl>" }, { "objectID": "SD/3.1_datatransform/index.html#计算新变量-1", "href": "SD/3.1_datatransform/index.html#计算新变量-1", "title": "Data Transform", "section": "计算新变量", "text": "计算新变量\n\nflights |>\n mutate(\n gain = dep_delay - arr_delay,\n speed = distance / air_time * 60,\n .before = 1\n )\n\n# A tibble: 336,776 × 21\n gain speed year month day dep_time sched_dep_time dep_delay arr_time\n <dbl> <dbl> <int> <int> <int> <int> <int> <dbl> <int>\n 1 -9 370. 2013 1 1 517 515 2 830\n 2 -16 374. 2013 1 1 533 529 4 850\n 3 -31 408. 2013 1 1 542 540 2 923\n 4 17 517. 2013 1 1 544 545 -1 1004\n 5 19 394. 2013 1 1 554 600 -6 812\n 6 -16 288. 2013 1 1 554 558 -4 740\n 7 -24 404. 2013 1 1 555 600 -5 913\n 8 11 259. 2013 1 1 557 600 -3 709\n 9 5 405. 2013 1 1 557 600 -3 838\n10 -10 319. 2013 1 1 558 600 -2 753\n# ℹ 336,766 more rows\n# ℹ 12 more variables: sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,\n# flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,\n# distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#计算新变量-2", "href": "SD/3.1_datatransform/index.html#计算新变量-2", "title": "Data Transform", "section": "计算新变量", "text": "计算新变量\n\nflights |>\n mutate(\n gain = dep_delay - arr_delay,\n speed = distance / air_time * 60,\n .after = day\n )\n\n# A tibble: 336,776 × 21\n year month day gain speed dep_time sched_dep_time dep_delay arr_time\n <int> <int> <int> <dbl> <dbl> <int> <int> <dbl> <int>\n 1 2013 1 1 -9 370. 517 515 2 830\n 2 2013 1 1 -16 374. 533 529 4 850\n 3 2013 1 1 -31 408. 542 540 2 923\n 4 2013 1 1 17 517. 544 545 -1 1004\n 5 2013 1 1 19 394. 554 600 -6 812\n 6 2013 1 1 -16 288. 554 558 -4 740\n 7 2013 1 1 -24 404. 555 600 -5 913\n 8 2013 1 1 11 259. 557 600 -3 709\n 9 2013 1 1 5 405. 557 600 -3 838\n10 2013 1 1 -10 319. 558 600 -2 753\n# ℹ 336,766 more rows\n# ℹ 12 more variables: sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,\n# flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,\n# distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#计算新变量-3", "href": "SD/3.1_datatransform/index.html#计算新变量-3", "title": "Data Transform", "section": "计算新变量", "text": "计算新变量\n\nflights |>\n mutate(\n gain = dep_delay - arr_delay,\n hours = air_time / 60,\n gain_per_hour = gain / hours,\n .keep = \"used\"\n )\n\n# A tibble: 336,776 × 6\n dep_delay arr_delay air_time gain hours gain_per_hour\n <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 2 11 227 -9 3.78 -2.38\n 2 4 20 227 -16 3.78 -4.23\n 3 2 33 160 -31 2.67 -11.6 \n 4 -1 -18 183 17 3.05 5.57\n 5 -6 -25 116 19 1.93 9.83\n 6 -4 12 150 -16 2.5 -6.4 \n 7 -5 19 158 -24 2.63 -9.11\n 8 -3 -14 53 11 0.883 12.5 \n 9 -3 -8 140 5 2.33 2.14\n10 -2 8 138 -10 2.3 -4.35\n# ℹ 336,766 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#列排序", "href": "SD/3.1_datatransform/index.html#列排序", "title": "Data Transform", "section": "列排序", "text": "列排序\n\nflights |>\n relocate(time_hour, air_time)\n\n# A tibble: 336,776 × 19\n time_hour air_time year month day dep_time sched_dep_time\n <dttm> <dbl> <int> <int> <int> <int> <int>\n 1 2013-01-01 05:00:00 227 2013 1 1 517 515\n 2 2013-01-01 05:00:00 227 2013 1 1 533 529\n 3 2013-01-01 05:00:00 160 2013 1 1 542 540\n 4 2013-01-01 05:00:00 183 2013 1 1 544 545\n 5 2013-01-01 06:00:00 116 2013 1 1 554 600\n 6 2013-01-01 05:00:00 150 2013 1 1 554 558\n 7 2013-01-01 06:00:00 158 2013 1 1 555 600\n 8 2013-01-01 06:00:00 53 2013 1 1 557 600\n 9 2013-01-01 06:00:00 140 2013 1 1 557 600\n10 2013-01-01 06:00:00 138 2013 1 1 558 600\n# ℹ 336,766 more rows\n# ℹ 12 more variables: dep_delay <dbl>, arr_time <int>, sched_arr_time <int>,\n# arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, origin <chr>,\n# dest <chr>, distance <dbl>, hour <dbl>, minute <dbl>" }, { "objectID": "SD/3.1_datatransform/index.html#列排序-1", "href": "SD/3.1_datatransform/index.html#列排序-1", "title": "Data Transform", "section": "列排序", "text": "列排序\n\nflights |>\n relocate(year:dep_time, .after = time_hour)\nflights |>\n relocate(starts_with(\"arr\"), .before = dep_time)\nflights |>\n select(starts_with(\"arr\"), everything())" }, { "objectID": "SD/3.1_datatransform/index.html#练习", "href": "SD/3.1_datatransform/index.html#练习", "title": "Data Transform", "section": "练习", "text": "练习\n计算目的地为IAH，按飞行速度排序的表格，保留year:day, dep_time, carrier, flight与speed列。\n\nflights |>\n filter(dest == \"IAH\") |>\n mutate(speed = distance / air_time * 60) |>\n select(year:day, dep_time, carrier, flight, speed) |>\n arrange(desc(speed))\n\n# A tibble: 7,198 × 7\n year month day dep_time carrier flight speed\n <int> <int> <int> <int> <chr> <int> <dbl>\n 1 2013 7 9 707 UA 226 522.\n 2 2013 8 27 1850 UA 1128 521.\n 3 2013 8 28 902 UA 1711 519.\n 4 2013 8 28 2122 UA 1022 519.\n 5 2013 6 11 1628 UA 1178 515.\n 6 2013 8 27 1017 UA 333 515.\n 7 2013 8 27 1205 UA 1421 515.\n 8 2013 8 27 1758 UA 302 515.\n 9 2013 9 27 521 UA 252 515.\n10 2013 8 28 625 UA 559 515.\n# ℹ 7,188 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#练习-1", "href": "SD/3.1_datatransform/index.html#练习-1", "title": "Data Transform", "section": "练习", "text": "练习\n计算目的地为IAH，按飞行速度排序的表格，保留year:day, dep_time, carrier, flight与speed列。\n\nflights1 <- filter(flights, dest == \"IAH\")\nflights2 <- mutate(flights1, speed = distance / air_time * 60)\nflights3 <- select(flights2, year:day, dep_time, carrier, flight, speed)\narrange(flights3, desc(speed))" }, { "objectID": "SD/3.1_datatransform/index.html#练习-2", "href": "SD/3.1_datatransform/index.html#练习-2", "title": "Data Transform", "section": "练习", "text": "练习\n计算目的地为IAH，按飞行速度排序的表格，保留year:day, dep_time, carrier, flight与speed列。\n\nflights |>\n filter(dest == \"IAH\") |>\n mutate(speed = distance / air_time * 60) |>\n select(year:day, dep_time, carrier, flight, speed) |>\n arrange(desc(speed))\n\n# A tibble: 7,198 × 7\n year month day dep_time carrier flight speed\n <int> <int> <int> <int> <chr> <int> <dbl>\n 1 2013 7 9 707 UA 226 522.\n 2 2013 8 27 1850 UA 1128 521.\n 3 2013 8 28 902 UA 1711 519.\n 4 2013 8 28 2122 UA 1022 519.\n 5 2013 6 11 1628 UA 1178 515.\n 6 2013 8 27 1017 UA 333 515.\n 7 2013 8 27 1205 UA 1421 515.\n 8 2013 8 27 1758 UA 302 515.\n 9 2013 9 27 521 UA 252 515.\n10 2013 8 28 625 UA 559 515.\n# ℹ 7,188 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计", "href": "SD/3.1_datatransform/index.html#分组统计", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nlibrary(tidyverse)\n\nmtcars %>%\n group_by(cyl) %>%\n summarize(n = n())\n\n# A tibble: 3 × 2\n cyl n\n <dbl> <int>\n1 4 11\n2 6 7\n3 8 14" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-1", "href": "SD/3.1_datatransform/index.html#分组统计-1", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n group_by(month)\n\n# A tibble: 336,776 × 19\n# Groups: month [12]\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-2", "href": "SD/3.1_datatransform/index.html#分组统计-2", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n group_by(month) |>\n summarize(\n avg_delay = mean(dep_delay)\n )\n\n# A tibble: 12 × 2\n month avg_delay\n <int> <dbl>\n 1 1 NA\n 2 2 NA\n 3 3 NA\n 4 4 NA\n 5 5 NA\n 6 6 NA\n 7 7 NA\n 8 8 NA\n 9 9 NA\n10 10 NA\n11 11 NA\n12 12 NA" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-3", "href": "SD/3.1_datatransform/index.html#分组统计-3", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n group_by(month) |>\n summarize(\n avg_delay = mean(dep_delay, na.rm = TRUE)\n )\n\n# A tibble: 12 × 2\n month avg_delay\n <int> <dbl>\n 1 1 10.0 \n 2 2 10.8 \n 3 3 13.2 \n 4 4 13.9 \n 5 5 13.0 \n 6 6 20.8 \n 7 7 21.7 \n 8 8 12.6 \n 9 9 6.72\n10 10 6.24\n11 11 5.44\n12 12 16.6" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-4", "href": "SD/3.1_datatransform/index.html#分组统计-4", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n group_by(month) |>\n summarize(\n avg_delay = mean(dep_delay, na.rm = TRUE),\n n = n()\n )\n\n# A tibble: 12 × 3\n month avg_delay n\n <int> <dbl> <int>\n 1 1 10.0 27004\n 2 2 10.8 24951\n 3 3 13.2 28834\n 4 4 13.9 28330\n 5 5 13.0 28796\n 6 6 20.8 28243\n 7 7 21.7 29425\n 8 8 12.6 29327\n 9 9 6.72 27574\n10 10 6.24 28889\n11 11 5.44 27268\n12 12 16.6 28135" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-5", "href": "SD/3.1_datatransform/index.html#分组统计-5", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n group_by(dest) |>\n slice_max(arr_delay, n = 1) |>\n relocate(dest)\n\n# A tibble: 108 × 19\n# Groups: dest [105]\n dest year month day dep_time sched_dep_time dep_delay arr_time\n <chr> <int> <int> <int> <int> <int> <dbl> <int>\n 1 ABQ 2013 7 22 2145 2007 98 132\n 2 ACK 2013 7 23 1139 800 219 1250\n 3 ALB 2013 1 25 123 2000 323 229\n 4 ANC 2013 8 17 1740 1625 75 2042\n 5 ATL 2013 7 22 2257 759 898 121\n 6 AUS 2013 7 10 2056 1505 351 2347\n 7 AVL 2013 8 13 1156 832 204 1417\n 8 BDL 2013 2 21 1728 1316 252 1839\n 9 BGR 2013 12 1 1504 1056 248 1628\n10 BHM 2013 4 10 25 1900 325 136\n# ℹ 98 more rows\n# ℹ 11 more variables: sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,\n# flight <int>, tailnum <chr>, origin <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-6", "href": "SD/3.1_datatransform/index.html#分组统计-6", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n filter(dest == \"IAH\") |>\n group_by(year, month, day) |>\n summarize(\n arr_delay = mean(arr_delay, na.rm = TRUE)\n )\n\n# A tibble: 365 × 4\n# Groups: year, month [12]\n year month day arr_delay\n <int> <int> <int> <dbl>\n 1 2013 1 1 17.8 \n 2 2013 1 2 7 \n 3 2013 1 3 18.3 \n 4 2013 1 4 -3.2 \n 5 2013 1 5 20.2 \n 6 2013 1 6 9.28\n 7 2013 1 7 -7.74\n 8 2013 1 8 7.79\n 9 2013 1 9 18.1 \n10 2013 1 10 6.68\n# ℹ 355 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#分组", "href": "SD/3.1_datatransform/index.html#分组", "title": "Data Transform", "section": "分组", "text": "分组\n\ndaily <- flights |>\n group_by(year, month, day)\ndaily\n\n# A tibble: 336,776 × 19\n# Groups: year, month, day [365]\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-7", "href": "SD/3.1_datatransform/index.html#分组统计-7", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\ndaily_flights <- daily |>\n summarize(n = n())" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-8", "href": "SD/3.1_datatransform/index.html#分组统计-8", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\ndaily_flights <- daily |>\n summarize(\n n = n(),\n .groups = \"drop_last\"\n )" }, { "objectID": "SD/3.1_datatransform/index.html#删除分组", "href": "SD/3.1_datatransform/index.html#删除分组", "title": "Data Transform", "section": "删除分组", "text": "删除分组\n\ndaily |> ungroup()\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#删除分组-1", "href": "SD/3.1_datatransform/index.html#删除分组-1", "title": "Data Transform", "section": "删除分组", "text": "删除分组\n\ndaily |>\n ungroup() |>\n summarize(\n avg_delay = mean(dep_delay, na.rm = TRUE),\n flights = n()\n )\n\n# A tibble: 1 × 2\n avg_delay flights\n <dbl> <int>\n1 12.6 336776" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-9", "href": "SD/3.1_datatransform/index.html#分组统计-9", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n summarize(\n delay = mean(dep_delay, na.rm = TRUE),\n n = n(),\n .by = month\n )\n\n# A tibble: 12 × 3\n month delay n\n <int> <dbl> <int>\n 1 1 10.0 27004\n 2 10 6.24 28889\n 3 11 5.44 27268\n 4 12 16.6 28135\n 5 2 10.8 24951\n 6 3 13.2 28834\n 7 4 13.9 28330\n 8 5 13.0 28796\n 9 6 20.8 28243\n10 7 21.7 29425\n11 8 12.6 29327\n12 9 6.72 27574" }, { "objectID": "SD/3.1_datatransform/index.html#分组统计-10", "href": "SD/3.1_datatransform/index.html#分组统计-10", "title": "Data Transform", "section": "分组统计", "text": "分组统计\n\nflights |>\n summarize(\n delay = mean(dep_delay, na.rm = TRUE),\n n = n(),\n .by = c(origin, dest)\n )\n\n# A tibble: 224 × 4\n origin dest delay n\n <chr> <chr> <dbl> <int>\n 1 EWR IAH 11.8 3973\n 2 LGA IAH 9.06 2951\n 3 JFK MIA 9.34 3314\n 4 JFK BQN 6.67 599\n 5 LGA ATL 11.4 10263\n 6 EWR ORD 14.6 6100\n 7 EWR FLL 13.5 3793\n 8 LGA IAD 16.7 1803\n 9 JFK MCO 10.6 5464\n10 LGA ORD 10.7 8857\n# ℹ 214 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#练习-3", "href": "SD/3.1_datatransform/index.html#练习-3", "title": "Data Transform", "section": "练习", "text": "练习\n\ndf <- tibble(\n x = 1:5,\n y = c(\"a\", \"b\", \"a\", \"a\", \"b\"),\n z = c(\"K\", \"K\", \"L\", \"L\", \"K\")\n)\ndf\n\n# A tibble: 5 × 3\n x y z \n <int> <chr> <chr>\n1 1 a K \n2 2 b K \n3 3 a L \n4 4 a L \n5 5 b K \n\n\n\ndf |> arrange(y)" }, { "objectID": "SD/3.1_datatransform/index.html#练习-4", "href": "SD/3.1_datatransform/index.html#练习-4", "title": "Data Transform", "section": "练习", "text": "练习\n\n\n# A tibble: 5 × 3\n x y z \n <int> <chr> <chr>\n1 1 a K \n2 2 b K \n3 3 a L \n4 4 a L \n5 5 b K \n\n\n\ndf |>\n group_by(y) |>\n summarize(mean_x = mean(x))" }, { "objectID": "SD/3.1_datatransform/index.html#练习-5", "href": "SD/3.1_datatransform/index.html#练习-5", "title": "Data Transform", "section": "练习", "text": "练习\n\n\n# A tibble: 5 × 3\n x y z \n <int> <chr> <chr>\n1 1 a K \n2 2 b K \n3 3 a L \n4 4 a L \n5 5 b K \n\n\n\ndf |>\n group_by(y, z) |>\n summarize(mean_x = mean(x))" }, { "objectID": "SD/3.1_datatransform/index.html#练习-6", "href": "SD/3.1_datatransform/index.html#练习-6", "title": "Data Transform", "section": "练习", "text": "练习\n\n\n# A tibble: 5 × 3\n x y z \n <int> <chr> <chr>\n1 1 a K \n2 2 b K \n3 3 a L \n4 4 a L \n5 5 b K \n\n\n\ndf |>\n group_by(y, z) |>\n summarize(mean_x = mean(x), .groups = \"drop\")" }, { "objectID": "SD/3.1_datatransform/index.html#练习-7", "href": "SD/3.1_datatransform/index.html#练习-7", "title": "Data Transform", "section": "练习", "text": "练习\n\n\n# A tibble: 5 × 3\n x y z \n <int> <chr> <chr>\n1 1 a K \n2 2 b K \n3 3 a L \n4 4 a L \n5 5 b K \n\n\n\ndf |>\n group_by(y, z) |>\n summarize(mean_x = mean(x))\n\ndf |>\n group_by(y, z) |>\n mutate(mean_x = mean(x))" }, { "objectID": "SD/3.1_datatransform/index.html#练习-8", "href": "SD/3.1_datatransform/index.html#练习-8", "title": "Data Transform", "section": "练习", "text": "练习\n\n计算不同采样点的平均CO浓度、最大CO浓度、最小CO浓度、中位数CO浓度（CO_mg/m3）。\n计算各小时全国的平均CO浓度、最大CO浓度、最小CO浓度、中位数CO浓度（CO_mg/m3）。\n计算不同采样点各小时的平均CO浓度、最大CO浓度、最小CO浓度、中位数CO浓度（CO_mg/m3）。\n计算各采样点中CO浓度小于全国平均CO浓度的占比。\n找出全国各采样点中CO浓度小于全国平均CO浓度的占比最高的10个采样点。\n\n\nairqualitydf <- readxl::read_xlsx(\"../../data/airquality.xlsx\", sheet = 2)" }, { "objectID": "SD/3.1_datatransform/index.html#练习-9", "href": "SD/3.1_datatransform/index.html#练习-9", "title": "Data Transform", "section": "练习", "text": "练习\n按月统计dep_delay最大的3个航班的航班号（flight），用逗号连接。\n\n\n\n\n\nyear\nmonth\nflight\n\n\n\n\n2013\n1\nHA51, MQ3695, MQ3944\n\n\n2013\n2\nF9835, DL2319, DL575\n\n\n2013\n3\nDL2119, DL2363, FL361\n\n\n2013\n4\nDL2391, DL1435, AA257\n\n\n2013\n5\nMQ3744, AA257, AA753\n\n\n2013\n6\nMQ3535, DL2007, AA2019\n\n\n2013\n7\nMQ3075, DL2047, VX187\n\n\n2013\n8\nEV4978, DL843, DL1373\n\n\n2013\n9\nAA177, DL2131, FL350\n\n\n2013\n10\nDL502, DL2347, YV2693\n\n\n2013\n11\nDL2042, AA1697, AA1139\n\n\n2013\n12\nAA172, DL1223, AA172" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形示意图", "href": "SD/3.1_datatransform/index.html#数据变形示意图", "title": "Data Transform", "section": "数据变形示意图", "text": "数据变形示意图\n\nbillboard\n\n# A tibble: 317 × 79\n artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8\n <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA\n 2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA NA\n 3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54 53\n 4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55 59\n 5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36 49\n 6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2\n 7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA NA\n 8 Aaliyah I Do… 2000-01-29 84 62 51 41 38 35 35 38\n 9 Aaliyah Try … 2000-03-18 59 53 38 28 21 18 16 14\n10 Adams, Yo… Open… 2000-08-26 76 76 74 69 68 67 61 58\n# ℹ 307 more rows\n# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,\n# wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,\n# wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,\n# wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,\n# wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,\n# wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …\n\nknitr::include_graphics(\"../../image/tidy-data/variables.png\", dpi = 270)" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形", "href": "SD/3.1_datatransform/index.html#数据变形", "title": "Data Transform", "section": "数据变形", "text": "数据变形\n\nbillboard |>\n pivot_longer(\n cols = starts_with(\"wk\"),\n names_to = \"week\",\n values_to = \"rank\",\n values_drop_na = TRUE\n )\n\n# A tibble: 5,307 × 5\n artist track date.entered week rank\n <chr> <chr> <date> <chr> <dbl>\n 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87\n 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82\n 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72\n 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77\n 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87\n 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94\n 7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99\n 8 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91\n 9 2Ge+her The Hardest Part Of ... 2000-09-02 wk2 87\n10 2Ge+her The Hardest Part Of ... 2000-09-02 wk3 92\n# ℹ 5,297 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形-1", "href": "SD/3.1_datatransform/index.html#数据变形-1", "title": "Data Transform", "section": "数据变形", "text": "数据变形\n\nbillboard_longer <- billboard |>\n pivot_longer(\n cols = starts_with(\"wk\"),\n names_to = \"week\",\n values_to = \"rank\",\n values_drop_na = TRUE\n ) |>\n mutate(\n week = parse_number(week)\n )\nbillboard_longer\n\n# A tibble: 5,307 × 5\n artist track date.entered week rank\n <chr> <chr> <date> <dbl> <dbl>\n 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87\n 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82\n 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72\n 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77\n 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87\n 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94\n 7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99\n 8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91\n 9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87\n10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92\n# ℹ 5,297 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#练习-10", "href": "SD/3.1_datatransform/index.html#练习-10", "title": "Data Transform", "section": "练习", "text": "练习\n\n\n# A tibble: 3 × 3\n id bp1 bp2\n <chr> <dbl> <dbl>\n1 A 100 120\n2 B 140 115\n3 C 120 125\n\n\n将以上数据(df)转换为如下形式。\n\n\n# A tibble: 6 × 3\n id measurement value\n <chr> <chr> <dbl>\n1 A bp1 100\n2 A bp2 120\n3 B bp1 140\n4 B bp2 115\n5 C bp1 120\n6 C bp2 125" }, { "objectID": "SD/3.1_datatransform/index.html#练习-11", "href": "SD/3.1_datatransform/index.html#练习-11", "title": "Data Transform", "section": "练习", "text": "练习\n请转换如下iris数据。\n\n\n# A tibble: 3 × 5\n Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n <dbl> <dbl> <dbl> <dbl> <fct> \n1 5.1 3.5 1.4 0.2 setosa \n2 4.9 3 1.4 0.2 setosa \n3 4.7 3.2 1.3 0.2 setosa \n\n\n转为如下形式：\n\n\n# A tibble: 6 × 3\n Species flower_attr attr_value\n <fct> <chr> <dbl>\n1 setosa Sepal.Length 5.1\n2 setosa Sepal.Width 3.5\n3 setosa Petal.Length 1.4\n4 setosa Petal.Width 0.2\n5 setosa Sepal.Length 4.9\n6 setosa Sepal.Width 3" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形示意图2", "href": "SD/3.1_datatransform/index.html#数据变形示意图2", "title": "Data Transform", "section": "数据变形示意图2", "text": "数据变形示意图2\n\nwho2\n\n# A tibble: 7,240 × 58\n country year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564\n <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 Afghanistan 1980 NA NA NA NA NA NA\n 2 Afghanistan 1981 NA NA NA NA NA NA\n 3 Afghanistan 1982 NA NA NA NA NA NA\n 4 Afghanistan 1983 NA NA NA NA NA NA\n 5 Afghanistan 1984 NA NA NA NA NA NA\n 6 Afghanistan 1985 NA NA NA NA NA NA\n 7 Afghanistan 1986 NA NA NA NA NA NA\n 8 Afghanistan 1987 NA NA NA NA NA NA\n 9 Afghanistan 1988 NA NA NA NA NA NA\n10 Afghanistan 1989 NA NA NA NA NA NA\n# ℹ 7,230 more rows\n# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,\n# sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,\n# sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,\n# sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,\n# sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,\n# sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …\n\nknitr::include_graphics(\"../../image/tidy-data/multiple-names.png\", dpi = 270)" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形-2", "href": "SD/3.1_datatransform/index.html#数据变形-2", "title": "Data Transform", "section": "数据变形", "text": "数据变形\n\nwho2 |>\n pivot_longer(\n cols = !(country:year),\n names_to = c(\"diagnosis\", \"gender\", \"age\"),\n names_sep = \"_\",\n values_to = \"count\"\n )\n\n# A tibble: 405,440 × 6\n country year diagnosis gender age count\n <chr> <dbl> <chr> <chr> <chr> <dbl>\n 1 Afghanistan 1980 sp m 014 NA\n 2 Afghanistan 1980 sp m 1524 NA\n 3 Afghanistan 1980 sp m 2534 NA\n 4 Afghanistan 1980 sp m 3544 NA\n 5 Afghanistan 1980 sp m 4554 NA\n 6 Afghanistan 1980 sp m 5564 NA\n 7 Afghanistan 1980 sp m 65 NA\n 8 Afghanistan 1980 sp f 014 NA\n 9 Afghanistan 1980 sp f 1524 NA\n10 Afghanistan 1980 sp f 2534 NA\n# ℹ 405,430 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形示意图-1", "href": "SD/3.1_datatransform/index.html#数据变形示意图-1", "title": "Data Transform", "section": "数据变形示意图", "text": "数据变形示意图\n\nhousehold\n\n# A tibble: 5 × 5\n family dob_child1 dob_child2 name_child1 name_child2\n <int> <date> <date> <chr> <chr> \n1 1 1998-11-26 2000-01-29 Susan Jose \n2 2 1996-06-22 NA Mark <NA> \n3 3 2002-07-11 2004-04-05 Sam Seth \n4 4 2004-10-10 2009-08-27 Craig Khai \n5 5 2000-12-05 2005-02-28 Parker Gracie \n\nknitr::include_graphics(\"../../image/tidy-data/names-and-values.png\", dpi = 270)" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形-3", "href": "SD/3.1_datatransform/index.html#数据变形-3", "title": "Data Transform", "section": "数据变形", "text": "数据变形\n\nhousehold |>\n pivot_longer(\n cols = !family,\n names_to = c(\".value\", \"child\"),\n names_sep = \"_\",\n values_drop_na = TRUE\n )\n\n# A tibble: 9 × 4\n family child dob name \n <int> <chr> <date> <chr> \n1 1 child1 1998-11-26 Susan \n2 1 child2 2000-01-29 Jose \n3 2 child1 1996-06-22 Mark \n4 3 child1 2002-07-11 Sam \n5 3 child2 2004-04-05 Seth \n6 4 child1 2004-10-10 Craig \n7 4 child2 2009-08-27 Khai \n8 5 child1 2000-12-05 Parker\n9 5 child2 2005-02-28 Gracie" }, { "objectID": "SD/3.1_datatransform/index.html#查看数据", "href": "SD/3.1_datatransform/index.html#查看数据", "title": "Data Transform", "section": "查看数据", "text": "查看数据\n\ncms_patient_experience\n\n# A tibble: 500 × 5\n org_pac_id org_nm measure_cd measure_title prf_rate\n <chr> <chr> <chr> <chr> <dbl>\n 1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 63\n 2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 87\n 3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 86\n 4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 57\n 5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 85\n 6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 24\n 7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 59\n 8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 85\n 9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 83\n10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 63\n# ℹ 490 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#查看数据-1", "href": "SD/3.1_datatransform/index.html#查看数据-1", "title": "Data Transform", "section": "查看数据", "text": "查看数据\n\ncms_patient_experience |>\n distinct(measure_cd, measure_title)\n\n# A tibble: 6 × 2\n measure_cd measure_title \n <chr> <chr> \n1 CAHPS_GRP_1 CAHPS for MIPS SSM: Getting Timely Care, Appointments, and Infor…\n2 CAHPS_GRP_2 CAHPS for MIPS SSM: How Well Providers Communicate \n3 CAHPS_GRP_3 CAHPS for MIPS SSM: Patient's Rating of Provider \n4 CAHPS_GRP_5 CAHPS for MIPS SSM: Health Promotion and Education \n5 CAHPS_GRP_8 CAHPS for MIPS SSM: Courteous and Helpful Office Staff \n6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形变宽", "href": "SD/3.1_datatransform/index.html#数据变形变宽", "title": "Data Transform", "section": "数据变形（变宽）", "text": "数据变形（变宽）\n\ncms_patient_experience |>\n pivot_wider(\n names_from = measure_cd,\n values_from = prf_rate\n )\n\n# A tibble: 500 × 9\n org_pac_id org_nm measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3\n <chr> <chr> <chr> <dbl> <dbl> <dbl>\n 1 0446157747 USC CARE MEDICA… CAHPS for MI… 63 NA NA\n 2 0446157747 USC CARE MEDICA… CAHPS for MI… NA 87 NA\n 3 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA 86\n 4 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA\n 5 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA\n 6 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA\n 7 0446162697 ASSOCIATION OF … CAHPS for MI… 59 NA NA\n 8 0446162697 ASSOCIATION OF … CAHPS for MI… NA 85 NA\n 9 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA 83\n10 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA NA\n# ℹ 490 more rows\n# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>" }, { "objectID": "SD/3.1_datatransform/index.html#数据变形变宽-1", "href": "SD/3.1_datatransform/index.html#数据变形变宽-1", "title": "Data Transform", "section": "数据变形（变宽）", "text": "数据变形（变宽）\n\ncms_patient_experience |>\n pivot_wider(\n id_cols = starts_with(\"org\"),\n names_from = measure_cd,\n values_from = prf_rate\n )\n\n# A tibble: 95 × 8\n org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8\n <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>\n 1 0446157747 USC C… 63 87 86 57 85\n 2 0446162697 ASSOC… 59 85 83 63 88\n 3 0547164295 BEAVE… 49 NA 75 44 73\n 4 0749333730 CAPE … 67 84 85 65 82\n 5 0840104360 ALLIA… 66 87 87 64 87\n 6 0840109864 REX H… 73 87 84 67 91\n 7 0840513552 SCL H… 58 83 76 58 78\n 8 0941545784 GRITM… 46 86 81 54 NA\n 9 1052612785 COMMU… 65 84 80 58 87\n10 1254237779 OUR L… 61 NA NA 65 NA\n# ℹ 85 more rows\n# ℹ 1 more variable: CAHPS_GRP_12 <dbl>" }, { "objectID": "SD/3.1_datatransform/index.html#练习-12", "href": "SD/3.1_datatransform/index.html#练习-12", "title": "Data Transform", "section": "练习", "text": "练习\n\ndf <- tribble(\n ~id,\n ~measurement,\n ~value,\n \"A\",\n \"bp1\",\n 100,\n \"B\",\n \"bp1\",\n 140,\n \"B\",\n \"bp2\",\n 115,\n \"A\",\n \"bp2\",\n 120,\n \"A\",\n \"bp3\",\n 105\n)\n\n变形成如下形式：\n\n\n# A tibble: 2 × 4\n id bp1 bp2 bp3\n <chr> <dbl> <dbl> <dbl>\n1 A 100 120 105\n2 B 140 115 NA" }, { "objectID": "SD/3.1_datatransform/index.html#练习变宽", "href": "SD/3.1_datatransform/index.html#练习变宽", "title": "Data Transform", "section": "练习：变宽", "text": "练习：变宽\n\ndf <- tribble(\n ~id,\n ~measurement,\n ~value,\n \"A\",\n \"bp1\",\n 100,\n \"A\",\n \"bp1\",\n 102,\n \"A\",\n \"bp2\",\n 120,\n \"B\",\n \"bp1\",\n 140,\n \"B\",\n \"bp2\",\n 115\n)" }, { "objectID": "SD/3.1_datatransform/index.html#练习-13", "href": "SD/3.1_datatransform/index.html#练习-13", "title": "Data Transform", "section": "练习", "text": "练习\n\ndf |>\n pivot_wider(\n names_from = measurement,\n values_from = value\n )\n\n# A tibble: 2 × 3\n id bp1 bp2 \n <chr> <list> <list> \n1 A <dbl [2]> <dbl [1]>\n2 B <dbl [1]> <dbl [1]>" }, { "objectID": "SD/3.1_datatransform/index.html#练习-14", "href": "SD/3.1_datatransform/index.html#练习-14", "title": "Data Transform", "section": "练习", "text": "练习\n\ndf |>\n group_by(id, measurement) |>\n summarize(n = n(), .groups = \"drop\") |>\n filter(n > 1)\n\n# A tibble: 1 × 3\n id measurement n\n <chr> <chr> <int>\n1 A bp1 2" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\n\n\n# A tibble: 6 × 3\n x y z\n <dbl> <int> <int>\n1 1 1 6\n2 1 2 5\n3 1 3 4\n4 2 4 3\n5 2 5 2\n6 3 6 1\n\n\n\ndf %>% nest(data = c(y, z))\n\n# A tibble: 3 × 2\n x data \n <dbl> <list> \n1 1 <tibble [3 × 2]>\n2 2 <tibble [2 × 2]>\n3 3 <tibble [1 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-1", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-1", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nSpecify variables to nest by (rather than variables to nest) using .by\n\ndf %>% nest(.by = x)\n\n# A tibble: 3 × 2\n x data \n <dbl> <list> \n1 1 <tibble [3 × 2]>\n2 2 <tibble [2 × 2]>\n3 3 <tibble [1 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-2", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-2", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nIn this case, since ... isn’t used you can specify the resulting column name with .key\n\ndf %>% nest(.by = x, .key = \"cols\")\n\n# A tibble: 3 × 2\n x cols \n <dbl> <list> \n1 1 <tibble [3 × 2]>\n2 2 <tibble [2 × 2]>\n3 3 <tibble [1 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-3", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-3", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nUse tidyselect syntax and helpers, just like in dplyr::select()\n\ndf %>% nest(data = any_of(c(\"y\", \"z\")))\n\n# A tibble: 3 × 2\n x data \n <dbl> <list> \n1 1 <tibble [3 × 2]>\n2 2 <tibble [2 × 2]>\n3 3 <tibble [1 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-4", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-4", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\n... and .by can be used together to drop columns you no longer need, or to include the columns you are nesting by in the inner data frame too. This drops z:\n\ndf %>% nest(data = y, .by = x)\n\n# A tibble: 3 × 2\n x data \n <dbl> <list> \n1 1 <tibble [3 × 1]>\n2 2 <tibble [2 × 1]>\n3 3 <tibble [1 × 1]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-5", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-5", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nThis includes x in the inner data frame:\n\ndf %>% nest(data = everything(), .by = x)\n\n# A tibble: 3 × 2\n x data \n <dbl> <list> \n1 1 <tibble [3 × 3]>\n2 2 <tibble [2 × 3]>\n3 3 <tibble [1 × 3]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-6", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-6", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nMultiple nesting structures can be specified at once\n\niris %>%\n nest(petal = starts_with(\"Petal\"), sepal = starts_with(\"Sepal\"))\n\n# A tibble: 3 × 3\n Species petal sepal \n <fct> <list> <list> \n1 setosa <tibble [50 × 2]> <tibble [50 × 2]>\n2 versicolor <tibble [50 × 2]> <tibble [50 × 2]>\n3 virginica <tibble [50 × 2]> <tibble [50 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-7", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-7", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\n\niris %>%\n nest(width = contains(\"Width\"), length = contains(\"Length\"))\n\n# A tibble: 3 × 3\n Species width length \n <fct> <list> <list> \n1 setosa <tibble [50 × 2]> <tibble [50 × 2]>\n2 versicolor <tibble [50 × 2]> <tibble [50 × 2]>\n3 virginica <tibble [50 × 2]> <tibble [50 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-8", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-8", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nNesting a grouped data frame nests all variables apart from the group vars\n\nfish_encounters\n\n# A tibble: 114 × 3\n fish station seen\n <fct> <fct> <int>\n 1 4842 Release 1\n 2 4842 I80_1 1\n 3 4842 Lisbon 1\n 4 4842 Rstr 1\n 5 4842 Base_TD 1\n 6 4842 BCE 1\n 7 4842 BCW 1\n 8 4842 BCE2 1\n 9 4842 BCW2 1\n10 4842 MAE 1\n# ℹ 104 more rows\n\nfish_encounters %>%\n dplyr::group_by(fish) %>%\n nest()\n\n# A tibble: 19 × 2\n# Groups: fish [19]\n fish data \n <fct> <list> \n 1 4842 <tibble [11 × 2]>\n 2 4843 <tibble [11 × 2]>\n 3 4844 <tibble [11 × 2]>\n 4 4845 <tibble [5 × 2]> \n 5 4847 <tibble [3 × 2]> \n 6 4848 <tibble [4 × 2]> \n 7 4849 <tibble [2 × 2]> \n 8 4850 <tibble [6 × 2]> \n 9 4851 <tibble [2 × 2]> \n10 4854 <tibble [2 × 2]> \n11 4855 <tibble [5 × 2]> \n12 4857 <tibble [9 × 2]> \n13 4858 <tibble [11 × 2]>\n14 4859 <tibble [5 × 2]> \n15 4861 <tibble [11 × 2]>\n16 4862 <tibble [9 × 2]> \n17 4863 <tibble [2 × 2]> \n18 4864 <tibble [2 × 2]> \n19 4865 <tibble [3 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-9", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-9", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nThat is similar to nest(.by = ), except here the result isn’t grouped\n\nfish_encounters %>%\n nest(.by = fish)\n\n# A tibble: 19 × 2\n fish data \n <fct> <list> \n 1 4842 <tibble [11 × 2]>\n 2 4843 <tibble [11 × 2]>\n 3 4844 <tibble [11 × 2]>\n 4 4845 <tibble [5 × 2]> \n 5 4847 <tibble [3 × 2]> \n 6 4848 <tibble [4 × 2]> \n 7 4849 <tibble [2 × 2]> \n 8 4850 <tibble [6 × 2]> \n 9 4851 <tibble [2 × 2]> \n10 4854 <tibble [2 × 2]> \n11 4855 <tibble [5 × 2]> \n12 4857 <tibble [9 × 2]> \n13 4858 <tibble [11 × 2]>\n14 4859 <tibble [5 × 2]> \n15 4861 <tibble [11 × 2]>\n16 4862 <tibble [9 × 2]> \n17 4863 <tibble [2 × 2]> \n18 4864 <tibble [2 × 2]> \n19 4865 <tibble [3 × 2]>" }, { "objectID": "SD/3.1_datatransform/index.html#nest套嵌数据框-10", "href": "SD/3.1_datatransform/index.html#nest套嵌数据框-10", "title": "Data Transform", "section": "nest，套嵌数据框", "text": "nest，套嵌数据框\nNesting is often useful for creating per group models\n\nmtcars %>%\n nest(.by = cyl) %>%\n dplyr::mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df)))\n\n# A tibble: 3 × 3\n cyl data models\n <dbl> <list> <list>\n1 6 <tibble [7 × 10]> <lm> \n2 4 <tibble [11 × 10]> <lm> \n3 8 <tibble [14 × 10]> <lm>" }, { "objectID": "SD/3.1_datatransform/index.html#练习-15", "href": "SD/3.1_datatransform/index.html#练习-15", "title": "Data Transform", "section": "练习", "text": "练习\n\n\n# A tibble: 20,088 × 20\n datetime site `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`\n <dttm> <chr> <dbl> <dbl> <dbl>\n 1 2024-03-19 01:00:00 1001A 0.1 0.4 5\n 2 2024-03-19 01:00:00 1003A 0.2 0.4 9\n 3 2024-03-19 01:00:00 1004A 0.2 0.4 4\n 4 2024-03-19 01:00:00 1005A 0.1 0.3 6\n 5 2024-03-19 01:00:00 1006A 0.1 0.4 5\n 6 2024-03-19 01:00:00 1007A 0.3 0.5 6\n 7 2024-03-19 01:00:00 1008A 0.2 0.4 2\n 8 2024-03-19 01:00:00 1009A 0.2 0.4 2\n 9 2024-03-19 01:00:00 1010A 0.1 0.3 2\n10 2024-03-19 01:00:00 1011A 0.2 0.4 12\n# ℹ 20,078 more rows\n# ℹ 15 more variables: `NO2_24h_μg/m3` <dbl>, `O3_μg/m3` <dbl>,\n# `O3_24h_μg/m3` <lgl>, `O3_8h_μg/m3` <lgl>, `O3_8h_24h_μg/m3` <lgl>,\n# `PM10_μg/m3` <dbl>, `PM10_24h_μg/m3` <dbl>, `PM2.5_μg/m3` <dbl>,\n# `PM2.5_24h_μg/m3` <dbl>, `SO2_μg/m3` <dbl>, `SO2_24h_μg/m3` <dbl>,\n# AQI <dbl>, PrimaryPollutant <chr>, Quality <chr>, Unheathful <chr>\n\n\n\nairqualitydf\n\n# A tibble: 20,088 × 20\n datetime site `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`\n <dttm> <chr> <dbl> <dbl> <dbl>\n 1 2024-03-19 01:00:00 1001A 0.1 0.4 5\n 2 2024-03-19 01:00:00 1003A 0.2 0.4 9\n 3 2024-03-19 01:00:00 1004A 0.2 0.4 4\n 4 2024-03-19 01:00:00 1005A 0.1 0.3 6\n 5 2024-03-19 01:00:00 1006A 0.1 0.4 5\n 6 2024-03-19 01:00:00 1007A 0.3 0.5 6\n 7 2024-03-19 01:00:00 1008A 0.2 0.4 2\n 8 2024-03-19 01:00:00 1009A 0.2 0.4 2\n 9 2024-03-19 01:00:00 1010A 0.1 0.3 2\n10 2024-03-19 01:00:00 1011A 0.2 0.4 12\n# ℹ 20,078 more rows\n# ℹ 15 more variables: `NO2_24h_μg/m3` <dbl>, `O3_μg/m3` <dbl>,\n# `O3_24h_μg/m3` <lgl>, `O3_8h_μg/m3` <lgl>, `O3_8h_24h_μg/m3` <lgl>,\n# `PM10_μg/m3` <dbl>, `PM10_24h_μg/m3` <dbl>, `PM2.5_μg/m3` <dbl>,\n# `PM2.5_24h_μg/m3` <dbl>, `SO2_μg/m3` <dbl>, `SO2_24h_μg/m3` <dbl>,\n# AQI <dbl>, PrimaryPollutant <chr>, Quality <chr>, Unheathful <chr>\n\nairqualitynestdf <- airqualitydf |>\n nest(sitedf = -site)" }, { "objectID": "SD/3.1_datatransform/index.html#nest与group_by联用", "href": "SD/3.1_datatransform/index.html#nest与group_by联用", "title": "Data Transform", "section": "nest与group_by联用", "text": "nest与group_by联用\n\n\n# A tibble: 3 × 2\n# Groups: Species [3]\n Species spdf \n <fct> <list> \n1 setosa <tibble [50 × 4]>\n2 versicolor <tibble [50 × 4]>\n3 virginica <tibble [50 × 4]>" }, { "objectID": "SD/3.1_datatransform/index.html#unnest", "href": "SD/3.1_datatransform/index.html#unnest", "title": "Data Transform", "section": "unnest", "text": "unnest\n\nairqualitynestdf |> unnest(sitedf)\n\n# A tibble: 20,088 × 20\n site datetime `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`\n <chr> <dttm> <dbl> <dbl> <dbl>\n 1 1001A 2024-03-19 01:00:00 0.1 0.4 5\n 2 1001A 2024-03-19 03:00:00 0.1 0.3 6\n 3 1001A 2024-03-19 05:00:00 0.2 0.3 8\n 4 1001A 2024-03-19 07:00:00 0.2 0.3 12\n 5 1001A 2024-03-19 09:00:00 0.3 0.3 19\n 6 1001A 2024-03-19 11:00:00 0.2 0.2 9\n 7 1001A 2024-03-19 13:00:00 0.2 0.2 7\n 8 1001A 2024-03-19 15:00:00 0.1 0.2 5\n 9 1001A 2024-03-19 17:00:00 0.2 0.2 3\n10 1001A 2024-03-19 19:00:00 0.2 0.2 5\n# ℹ 20,078 more rows\n# ℹ 15 more variables: `NO2_24h_μg/m3` <dbl>, `O3_μg/m3` <dbl>,\n# `O3_24h_μg/m3` <lgl>, `O3_8h_μg/m3` <lgl>, `O3_8h_24h_μg/m3` <lgl>,\n# `PM10_μg/m3` <dbl>, `PM10_24h_μg/m3` <dbl>, `PM2.5_μg/m3` <dbl>,\n# `PM2.5_24h_μg/m3` <dbl>, `SO2_μg/m3` <dbl>, `SO2_24h_μg/m3` <dbl>,\n# AQI <dbl>, PrimaryPollutant <chr>, Quality <chr>, Unheathful <chr>" }, { "objectID": "SD/3.1_datatransform/index.html#purrr包", "href": "SD/3.1_datatransform/index.html#purrr包", "title": "Data Transform", "section": "purrr包", "text": "purrr包\n\nmap()：依次应用一元函数到一个序列的每个元素上，基本等同 lapply()\nmap2()：依次应用二元函数到两个序列的每对元素上\npmap()：应用多元函数到多个序列的每组元素上，可以实现对数据框逐行迭代\nmap 系列默认返回列表型，可根据想要的返回类型添加后缀：_int, _dbl, _lgl, _chr, _df, 甚至可以接着对返回的数据框df做行/列合并：_dfr, _dfc\n如果只想要函数依次作用的过程，而不需要返回结果，改用 walk 系列即可\n所应用的函数，有 purrr公式风格简写（匿名函数），支持一元，二元，多元函数\npurrr 包中的其它有用函数" }, { "objectID": "SD/3.1_datatransform/index.html#purrr包-1", "href": "SD/3.1_datatransform/index.html#purrr包-1", "title": "Data Transform", "section": "purrr包", "text": "purrr包\n\nmap_chr(.x, .f): 返回字符型向量\nmap_lgl(.x, .f): 返回逻辑型向量\nmap_dbl(.x, .f): 返回实数型向量\nmap_int(.x, .f): 返回整数型向量\nmap_dfr(.x, .f): 返回数据框列表，再 bind_rows 按行合并为一个数据框\nmap_dfc(.x, .f): 返回数据框列表，再 bind_cols 按列合并为一个数据框" }, { "objectID": "SD/3.1_datatransform/index.html#purrr包-cheatsheet", "href": "SD/3.1_datatransform/index.html#purrr包-cheatsheet", "title": "Data Transform", "section": "purrr包-cheatsheet", "text": "purrr包-cheatsheet\n\ndwfun::ggsavep(\"../../image/cheatsheet/purrr.svg\", loadit = TRUE)\n\n\n\n\n\n\n\n\npurrr" }, { "objectID": "SD/3.1_datatransform/index.html#purrr包-2", "href": "SD/3.1_datatransform/index.html#purrr包-2", "title": "Data Transform", "section": "purrr包", "text": "purrr包\n生成从1到10的10组随机数，每组随机数个数为100，均值依次为1到10，标准差为1，并存储在数据框中。\n\nres <- list()\nfor (i in 1:10) {\n res[[i]] <- tibble(随机数 = rnorm(n = 100, mean = i, sd = 1))\n}\nres\n\n[[1]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 -0.400\n 2 1.26 \n 3 -1.44 \n 4 0.994\n 5 1.62 \n 6 2.15 \n 7 -0.822\n 8 0.753\n 9 0.756\n10 0.717\n# ℹ 90 more rows\n\n[[2]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 1.61 \n 2 1.21 \n 3 0.943\n 4 1.20 \n 5 0.244\n 6 1.31 \n 7 1.44 \n 8 1.46 \n 9 2.23 \n10 2.98 \n# ℹ 90 more rows\n\n[[3]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 2.57 \n 2 4.36 \n 3 2.93 \n 4 2.73 \n 5 0.553\n 6 3.07 \n 7 1.90 \n 8 2.37 \n 9 0.936\n10 5.65 \n# ℹ 90 more rows\n\n[[4]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 3.64\n 2 2.94\n 3 5.08\n 4 5.18\n 5 4.20\n 6 3.60\n 7 4.62\n 8 5.97\n 9 5.88\n10 2.41\n# ℹ 90 more rows\n\n[[5]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 6.74\n 2 4.15\n 3 4.04\n 4 6.02\n 5 3.50\n 6 3.82\n 7 5.63\n 8 7.10\n 9 4.39\n10 3.37\n# ℹ 90 more rows\n\n[[6]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 5.29\n 2 6.66\n 3 6.29\n 4 6.20\n 5 4.80\n 6 5.96\n 7 6.69\n 8 6.71\n 9 6.99\n10 7.14\n# ℹ 90 more rows\n\n[[7]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 7.24\n 2 7.63\n 3 7.42\n 4 8.98\n 5 6.49\n 6 5.89\n 7 6.05\n 8 7.48\n 9 6.20\n10 7.23\n# ℹ 90 more rows\n\n[[8]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 8.89\n 2 7.62\n 3 8.61\n 4 8.00\n 5 7.48\n 6 7.36\n 7 7.36\n 8 8.11\n 9 9.18\n10 8.45\n# ℹ 90 more rows\n\n[[9]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 10.5 \n 2 8.58\n 3 8.48\n 4 9.85\n 5 9.33\n 6 8.17\n 7 8.78\n 8 7.45\n 9 9.23\n10 9.03\n# ℹ 90 more rows\n\n[[10]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 11.4 \n 2 11.7 \n 3 11.2 \n 4 9.85\n 5 9.82\n 6 10.8 \n 7 9.58\n 8 11.2 \n 9 11.2 \n10 10.7 \n# ℹ 90 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#purrr包-3", "href": "SD/3.1_datatransform/index.html#purrr包-3", "title": "Data Transform", "section": "purrr包", "text": "purrr包\n生成从1到10的10组随机数，每组随机数个数为100，均值依次为1到10，标准差为1，并存储在数据框中。\n\n1:10 |>\n purrr::map(~ tibble(随机数 = rnorm(n = 100, mean = .x, sd = 1)))\n\n[[1]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 0.663\n 2 0.784\n 3 1.62 \n 4 -0.284\n 5 -0.300\n 6 0.623\n 7 1.10 \n 8 0.296\n 9 2.50 \n10 0.697\n# ℹ 90 more rows\n\n[[2]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 0.294 \n 2 1.14 \n 3 1.86 \n 4 1.68 \n 5 1.83 \n 6 0.764 \n 7 0.0977\n 8 1.91 \n 9 2.03 \n10 2.46 \n# ℹ 90 more rows\n\n[[3]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 2.09\n 2 6.11\n 3 1.93\n 4 2.30\n 5 2.80\n 6 3.74\n 7 2.95\n 8 4.31\n 9 3.09\n10 2.37\n# ℹ 90 more rows\n\n[[4]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 2.85\n 2 3.73\n 3 4.46\n 4 3.98\n 5 3.46\n 6 4.88\n 7 4.74\n 8 3.84\n 9 3.25\n10 2.75\n# ℹ 90 more rows\n\n[[5]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 3.86\n 2 4.72\n 3 4.11\n 4 5.14\n 5 4.25\n 6 5.52\n 7 4.81\n 8 5.03\n 9 5.36\n10 4.97\n# ℹ 90 more rows\n\n[[6]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 5.42\n 2 5.83\n 3 4.08\n 4 4.47\n 5 4.89\n 6 7.60\n 7 5.36\n 8 7.57\n 9 4.55\n10 5.21\n# ℹ 90 more rows\n\n[[7]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 6.29\n 2 8.48\n 3 7.85\n 4 8.29\n 5 7.30\n 6 6.59\n 7 6.86\n 8 6.78\n 9 8.75\n10 6.92\n# ℹ 90 more rows\n\n[[8]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 8.48\n 2 9.67\n 3 7.96\n 4 7.56\n 5 8.73\n 6 7.42\n 7 8.07\n 8 8.06\n 9 8.85\n10 10.6 \n# ℹ 90 more rows\n\n[[9]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 10.0 \n 2 9.53\n 3 10.2 \n 4 9.17\n 5 9.58\n 6 8.88\n 7 9.90\n 8 8.98\n 9 8.08\n10 7.94\n# ℹ 90 more rows\n\n[[10]]\n# A tibble: 100 × 1\n 随机数\n <dbl>\n 1 9.29\n 2 8.64\n 3 9.50\n 4 11.4 \n 5 9.49\n 6 10.00\n 7 11.8 \n 8 11.0 \n 9 9.42\n10 8.41\n# ℹ 90 more rows" }, { "objectID": "SD/3.1_datatransform/index.html#purrr", "href": "SD/3.1_datatransform/index.html#purrr", "title": "Data Transform", "section": "purrr", "text": "purrr\n\nlibrary(purrr)\nmtcars |>\n split(mtcars$cyl) |> # from base R\n map(\\(df) lm(mpg ~ wt, data = df)) |>\n map(summary) %>%\n map_dbl(\"r.squared\")\n\n 4 6 8 \n0.5086326 0.4645102 0.4229655" }, { "objectID": "SD/3.1_datatransform/index.html#练习-16", "href": "SD/3.1_datatransform/index.html#练习-16", "title": "Data Transform", "section": "练习：", "text": "练习：\n计算每月最后一个周六的航班数：\n\nflights\n\n# A tibble: 336,776 × 19\n year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time\n <int> <int> <int> <int> <int> <dbl> <int> <int>\n 1 2013 1 1 517 515 2 830 819\n 2 2013 1 1 533 529 4 850 830\n 3 2013 1 1 542 540 2 923 850\n 4 2013 1 1 544 545 -1 1004 1022\n 5 2013 1 1 554 600 -6 812 837\n 6 2013 1 1 554 558 -4 740 728\n 7 2013 1 1 555 600 -5 913 854\n 8 2013 1 1 557 600 -3 709 723\n 9 2013 1 1 557 600 -3 838 846\n10 2013 1 1 558 600 -2 753 745\n# ℹ 336,766 more rows\n# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,\n# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,\n# hour <dbl>, minute <dbl>, time_hour <dttm>" }, { "objectID": "SD/3.1_datatransform/index.html#tidyr-purrr包", "href": "SD/3.1_datatransform/index.html#tidyr-purrr包", "title": "Data Transform", "section": "tidyr + purrr包", "text": "tidyr + purrr包\n任务：展示不同城市间的大气指标散点图\n\n(airqualitydf <- readxl::read_xlsx(\"../../data/airquality.xlsx\", sheet = 2))\n\n# A tibble: 20,088 × 20\n datetime site `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`\n <dttm> <chr> <dbl> <dbl> <dbl>\n 1 2024-03-19 01:00:00 1001A 0.1 0.4 5\n 2 2024-03-19 01:00:00 1003A 0.2 0.4 9\n 3 2024-03-19 01:00:00 1004A 0.2 0.4 4\n 4 2024-03-19 01:00:00 1005A 0.1 0.3 6\n 5 2024-03-19 01:00:00 1006A 0.1 0.4 5\n 6 2024-03-19 01:00:00 1007A 0.3 0.5 6\n 7 2024-03-19 01:00:00 1008A 0.2 0.4 2\n 8 2024-03-19 01:00:00 1009A 0.2 0.4 2\n 9 2024-03-19 01:00:00 1010A 0.1 0.3 2\n10 2024-03-19 01:00:00 1011A 0.2 0.4 12\n# ℹ 20,078 more rows\n# ℹ 15 more variables: `NO2_24h_μg/m3` <dbl>, `O3_μg/m3` <dbl>,\n# `O3_24h_μg/m3` <lgl>, `O3_8h_μg/m3` <lgl>, `O3_8h_24h_μg/m3` <lgl>,\n# `PM10_μg/m3` <dbl>, `PM10_24h_μg/m3` <dbl>, `PM2.5_μg/m3` <dbl>,\n# `PM2.5_24h_μg/m3` <dbl>, `SO2_μg/m3` <dbl>, `SO2_24h_μg/m3` <dbl>,\n# AQI <dbl>, PrimaryPollutant <chr>, Quality <chr>, Unheathful <chr>" }, { "objectID": "SD/3.1_datatransform/index.html#join", "href": "SD/3.1_datatransform/index.html#join", "title": "Data Transform", "section": "join", "text": "join\nPerform left join\n\n(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))\n\n id value1\n1 1 a\n2 2 b\n3 3 c\n4 4 d\n5 5 e\n\n(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))\n\n id value2\n1 2 A\n2 4 B\n3 6 C\n\nleft_join(df1, df2, by = \"id\")\n\n id value1 value2\n1 1 a <NA>\n2 2 b A\n3 3 c <NA>\n4 4 d B\n5 5 e <NA>" }, { "objectID": "SD/3.1_datatransform/index.html#left-join", "href": "SD/3.1_datatransform/index.html#left-join", "title": "Data Transform", "section": "left join", "text": "left join\nCreate sample data frames with non-matching rows\n\n(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))\n\n id value1\n1 1 a\n2 2 b\n3 3 c\n4 4 d\n5 5 e\n\n(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))\n\n id value2\n1 2 A\n2 4 B\n3 6 C\n\nleft_join(df2, df1, by = \"id\")\n\n id value2 value1\n1 2 A b\n2 4 B d\n3 6 C <NA>" }, { "objectID": "SD/3.1_datatransform/index.html#left-join-1", "href": "SD/3.1_datatransform/index.html#left-join-1", "title": "Data Transform", "section": "left join", "text": "left join\nCreate sample data frames with multiple columns\n\ndf1 <- data.frame(\n id1 = c(1, 2, 3),\n id2 = c(\"A\", \"B\", \"C\"),\n value1 = letters[1:3]\n)\ndf2 <- data.frame(\n id1 = c(2, 3, 4),\n id2 = c(\"B\", \"C\", \"D\"),\n value2 = LETTERS[1:3]\n)\n# Perform left join\nleft_join(df1, df2, by = c(\"id1\", \"id2\"))\n\n id1 id2 value1 value2\n1 1 A a <NA>\n2 2 B b A\n3 3 C c B" }, { "objectID": "SD/3.1_datatransform/index.html#right-join", "href": "SD/3.1_datatransform/index.html#right-join", "title": "Data Transform", "section": "right join", "text": "right join\n\n(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))\n\n id value1\n1 1 a\n2 2 b\n3 3 c\n4 4 d\n5 5 e\n\n(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))\n\n id value2\n1 2 A\n2 4 B\n3 6 C\n\n# Perform right join\nright_join(df1, df2, by = \"id\")\n\n id value1 value2\n1 2 b A\n2 4 d B\n3 6 <NA> C" }, { "objectID": "SD/3.1_datatransform/index.html#inner-join", "href": "SD/3.1_datatransform/index.html#inner-join", "title": "Data Transform", "section": "inner join", "text": "inner join\n\n# Create sample data frames\n(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))\n\n id value1\n1 1 a\n2 2 b\n3 3 c\n4 4 d\n5 5 e\n\n(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))\n\n id value2\n1 2 A\n2 4 B\n3 6 C\n\n# Perform inner join\ninner_join(df1, df2, by = \"id\")\n\n id value1 value2\n1 2 b A\n2 4 d B" }, { "objectID": "SD/3.1_datatransform/index.html#full-join", "href": "SD/3.1_datatransform/index.html#full-join", "title": "Data Transform", "section": "full join", "text": "full join\n\n# Create sample data frames\n(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))\n\n id value1\n1 1 a\n2 2 b\n3 3 c\n4 4 d\n5 5 e\n\n(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))\n\n id value2\n1 2 A\n2 4 B\n3 6 C\n\n# Perform inner join\nfull_join(df1, df2, by = \"id\")\n\n id value1 value2\n1 1 a <NA>\n2 2 b A\n3 3 c <NA>\n4 4 d B\n5 5 e <NA>\n6 6 <NA> C" }, { "objectID": "SD/3.1_datatransform/index.html#semi-join", "href": "SD/3.1_datatransform/index.html#semi-join", "title": "Data Transform", "section": "semi join", "text": "semi join\nCreate sample data frames\n\n(df1 <- data.frame(id = 1:5, value1 = letters[1:5]))\n\n id value1\n1 1 a\n2 2 b\n3 3 c\n4 4 d\n5 5 e\n\n(df2 <- data.frame(id = c(2, 4, 6), value2 = LETTERS[1:3]))\n\n id value2\n1 2 A\n2 4 B\n3 6 C\n\n# Perform semi join\nsemi_join(df1, df2, by = \"id\")\n\n id value1\n1 2 b\n2 4 d" }, { "objectID": "SD/3.1_datatransform/index.html#练习-17", "href": "SD/3.1_datatransform/index.html#练习-17", "title": "Data Transform", "section": "练习", "text": "练习\n合并airquality.xlsx中的数据。" }, { "objectID": "SD/3.1_datatransform/index.html#练习-18", "href": "SD/3.1_datatransform/index.html#练习-18", "title": "Data Transform", "section": "练习", "text": "练习\n统计各城市白天与晚上的大气质量差异，计算不同指标差异最大的10个城市。" }, { "objectID": "SD/3.1_datatransform/index.html#欢迎讨论", "href": "SD/3.1_datatransform/index.html#欢迎讨论", "title": "Data Transform", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/4.1_datavisualize/index.html#the-ggplot2-package", "href": "SD/4.1_datavisualize/index.html#the-ggplot2-package", "title": "数据可视化", "section": "The ggplot2 Package", "text": "The ggplot2 Package\n\n… is an R package to visualize data created by Hadley Wickham in 2005\n\n# install.packages(\"ggplot2\")\nlibrary(ggplot2)\n\n\n\n… is part of the {tidyverse}\n\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)" }, { "objectID": "SD/4.1_datavisualize/index.html#the-grammar-of-ggplot2-1", "href": "SD/4.1_datavisualize/index.html#the-grammar-of-ggplot2-1", "title": "数据可视化", "section": "The Grammar of {ggplot2}", "text": "The Grammar of {ggplot2}\n\n\n\n\nComponent\n\n\nFunction\n\n\nExplanation\n\n\n\n\nData\n\n\nggplot(data) \n\n\nThe raw data that you want to visualise.\n\n\n\n\nAesthetics \n\n\naes()\n\n\nAesthetic mappings between variables and visual properties.\n\n\n\nGeometries\n\n\ngeom_*()\n\n\nThe geometric shapes representing the data." }, { "objectID": "SD/4.1_datavisualize/index.html#the-grammar-of-ggplot2-2", "href": "SD/4.1_datavisualize/index.html#the-grammar-of-ggplot2-2", "title": "数据可视化", "section": "The Grammar of {ggplot2}", "text": "The Grammar of {ggplot2}\n\n\n\n\nComponent\n\n\nFunction\n\n\nExplanation\n\n\n\n\nData\n\n\nggplot(data) \n\n\nThe raw data that you want to visualise.\n\n\n\n\nAesthetics \n\n\naes()\n\n\nAesthetic mappings between variables and visual properties.\n\n\n\nGeometries\n\n\ngeom_*()\n\n\nThe geometric shapes representing the data.\n\n\n\n\nStatistics\n\n\nstat_*()\n\n\nThe statistical transformations applied to the data.\n\n\n\n\nScales\n\n\nscale_*()\n\n\nMaps between the data and the aesthetic dimensions.\n\n\n\n\nCoordinate System\n\n\ncoord_*()\n\n\nMaps data into the plane of the data rectangle.\n\n\n\n\nFacets\n\n\nfacet_*()\n\n\nThe arrangement of the data into a grid of plots.\n\n\n\n\nVisual Themes\n\n\ntheme() / theme_*()\n\n\nThe overall visual defaults of a plot." }, { "objectID": "SD/4.1_datavisualize/index.html#the-data", "href": "SD/4.1_datavisualize/index.html#the-data", "title": "数据可视化", "section": "The Data", "text": "The Data\nBike sharing counts in London, UK, powered by TfL Open Data\n\ncovers the years 2015 and 2016\nincl. weather data acquired from freemeteo.com\nprepared by Hristo Mavrodiev for Kaggle\n\n\n\n\nbikes <- readr::read_csv(\n \"../../data/ggplot2/london-bikes-custom.csv\",\n ## or: \"https://raw.githubusercontent.com/z3tt/graphic-design-ggplot2/main/data/london-bikes-custom.csv\"\n col_types = \"Dcfffilllddddc\"\n)\n\nbikes$season <- forcats::fct_inorder(bikes$season)" }, { "objectID": "SD/4.1_datavisualize/index.html#ggplot2ggplot", "href": "SD/4.1_datavisualize/index.html#ggplot2ggplot", "title": "数据可视化", "section": "ggplot2::ggplot()", "text": "ggplot2::ggplot()" }, { "objectID": "SD/4.1_datavisualize/index.html#data", "href": "SD/4.1_datavisualize/index.html#data", "title": "数据可视化", "section": "Data", "text": "Data\n\n\nggplot(data = bikes)" }, { "objectID": "SD/4.1_datavisualize/index.html#aesthetic-mapping视觉映射aes.", "href": "SD/4.1_datavisualize/index.html#aesthetic-mapping视觉映射aes.", "title": "数据可视化", "section": "Aesthetic Mapping（视觉映射）：aes(.)", "text": "Aesthetic Mapping（视觉映射）：aes(.)\n\n= link variables to graphical properties\n\npositions (x, y)\ncolors (color, fill)\nshapes (shape, linetype)\nsize (size)\ntransparency (alpha)\ngroupings (group)" }, { "objectID": "SD/4.1_datavisualize/index.html#aesthetic-mapping视觉映射aes.-1", "href": "SD/4.1_datavisualize/index.html#aesthetic-mapping视觉映射aes.-1", "title": "数据可视化", "section": "Aesthetic Mapping（视觉映射）：aes(.)", "text": "Aesthetic Mapping（视觉映射）：aes(.)\n\n\nggplot(data = bikes) +\n aes(x = temp_feel, y = count)" }, { "objectID": "SD/4.1_datavisualize/index.html#aesthetics", "href": "SD/4.1_datavisualize/index.html#aesthetics", "title": "数据可视化", "section": "aesthetics", "text": "aesthetics\naes() outside as component\n\nggplot(data = bikes) +\n aes(x = temp_feel, y = count)\n\n\n\naes() inside, explicit matching\n\nggplot(data = bikes, mapping = aes(x = temp_feel, y = count))\n\n\n\n\naes() inside, implicit matching\n\nggplot(bikes, aes(temp_feel, count))\n\n\n\n\naes() inside, mixed matching\n\nggplot(bikes, aes(x = temp_feel, y = count))" }, { "objectID": "SD/4.1_datavisualize/index.html#geometries几何图层geom_", "href": "SD/4.1_datavisualize/index.html#geometries几何图层geom_", "title": "数据可视化", "section": "Geometries（几何图层）：geom_*", "text": "Geometries（几何图层）：geom_*\n\n= interpret aesthetics as graphical representations\n\npoints\nlines\npolygons\ntext labels\n…" }, { "objectID": "SD/4.1_datavisualize/index.html#geometries几何图层geom_-1", "href": "SD/4.1_datavisualize/index.html#geometries几何图层geom_-1", "title": "数据可视化", "section": "Geometries（几何图层）：geom_*", "text": "Geometries（几何图层）：geom_*\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point()" }, { "objectID": "SD/4.1_datavisualize/index.html#visual-properties-of-layers图层属性", "href": "SD/4.1_datavisualize/index.html#visual-properties-of-layers图层属性", "title": "数据可视化", "section": "Visual Properties of Layers（图层属性）", "text": "Visual Properties of Layers（图层属性）\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n color = \"#28a87d\",\n alpha = .5,\n shape = \"X\",\n stroke = 1,\n size = 4\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#setting-vs-mapping-of-visual-properties", "href": "SD/4.1_datavisualize/index.html#setting-vs-mapping-of-visual-properties", "title": "数据可视化", "section": "Setting vs Mapping of Visual Properties", "text": "Setting vs Mapping of Visual Properties\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n color = \"#28a87d\",\n alpha = .5\n )\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n aes(color = season),\n alpha = .5\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#mapping-expressions", "href": "SD/4.1_datavisualize/index.html#mapping-expressions", "title": "数据可视化", "section": "Mapping Expressions", "text": "Mapping Expressions\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n aes(color = temp_feel > 20),\n alpha = .5\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#filter-data", "href": "SD/4.1_datavisualize/index.html#filter-data", "title": "数据可视化", "section": "Filter Data", "text": "Filter Data\n\n\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = temp, y = temp_feel)\n) +\n geom_point(\n aes(color = weather_type == \"clear\", size = count),\n shape = 18,\n alpha = .5\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#filter-data-1", "href": "SD/4.1_datavisualize/index.html#filter-data-1", "title": "数据可视化", "section": "Filter Data", "text": "Filter Data\n\n\nggplot(\n bikes %>% filter(!is.na(weather_type)),\n aes(x = temp, y = temp_feel)\n) +\n geom_point(\n aes(color = weather_type == \"clear\", size = count),\n shape = 18,\n alpha = .5\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#local-vs.-global应用至当前图层或所有图层", "href": "SD/4.1_datavisualize/index.html#local-vs.-global应用至当前图层或所有图层", "title": "数据可视化", "section": "Local vs. Global（应用至当前图层或所有图层）", "text": "Local vs. Global（应用至当前图层或所有图层）\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n aes(color = season),\n alpha = .5\n )\nggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season)\n) +\n geom_point(\n alpha = .5\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#adding-more-layers", "href": "SD/4.1_datavisualize/index.html#adding-more-layers", "title": "数据可视化", "section": "Adding More Layers", "text": "Adding More Layers\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season)\n) +\n geom_point(\n alpha = .5\n ) +\n geom_smooth(\n method = \"lm\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#global-color-encoding", "href": "SD/4.1_datavisualize/index.html#global-color-encoding", "title": "数据可视化", "section": "Global Color Encoding", "text": "Global Color Encoding\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season)\n) +\n geom_point(\n alpha = .5\n ) +\n geom_smooth(\n method = \"lm\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#local-color-encoding", "href": "SD/4.1_datavisualize/index.html#local-color-encoding", "title": "数据可视化", "section": "Local Color Encoding", "text": "Local Color Encoding\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n aes(color = season),\n alpha = .5\n ) +\n geom_smooth(\n method = \"lm\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#the-group-aesthetic", "href": "SD/4.1_datavisualize/index.html#the-group-aesthetic", "title": "数据可视化", "section": "The `group` Aesthetic", "text": "The `group` Aesthetic\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point(\n aes(color = season),\n alpha = .5\n ) +\n geom_smooth(\n aes(group = day_night),\n method = \"lm\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#set-both-as-global-aesthetics", "href": "SD/4.1_datavisualize/index.html#set-both-as-global-aesthetics", "title": "数据可视化", "section": "Set Both as Global Aesthetics", "text": "Set Both as Global Aesthetics\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season, group = day_night)\n) +\n geom_point(\n alpha = .5\n ) +\n geom_smooth(\n method = \"lm\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-global-aesthetics", "href": "SD/4.1_datavisualize/index.html#overwrite-global-aesthetics", "title": "数据可视化", "section": "Overwrite Global Aesthetics", "text": "Overwrite Global Aesthetics\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season, group = day_night)\n) +\n geom_point(\n alpha = .5\n ) +\n geom_smooth(\n method = \"lm\",\n color = \"black\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#stat_-and-geom_", "href": "SD/4.1_datavisualize/index.html#stat_-and-geom_", "title": "数据可视化", "section": "`stat_*()` and `geom_*()`", "text": "`stat_*()` and `geom_*()`\nggplot(bikes, aes(x = temp_feel, y = count)) +\n stat_smooth(geom = \"smooth\")\nggplot(bikes, aes(x = temp_feel, y = count)) +\n geom_smooth(stat = \"smooth\")" }, { "objectID": "SD/4.1_datavisualize/index.html#stat_-and-geom_-1", "href": "SD/4.1_datavisualize/index.html#stat_-and-geom_-1", "title": "数据可视化", "section": "`stat_*()` and `geom_*()`", "text": "`stat_*()` and `geom_*()`\nggplot(bikes, aes(x = season)) +\n stat_count(geom = \"bar\")\nggplot(bikes, aes(x = season)) +\n geom_bar(stat = \"count\")" }, { "objectID": "SD/4.1_datavisualize/index.html#stat_-and-geom_-2", "href": "SD/4.1_datavisualize/index.html#stat_-and-geom_-2", "title": "数据可视化", "section": "`stat_*()` and `geom_*()`", "text": "`stat_*()` and `geom_*()`\nggplot(bikes, aes(x = date, y = temp_feel)) +\n stat_identity(geom = \"point\")\nggplot(bikes, aes(x = date, y = temp_feel)) +\n geom_point(stat = \"identity\")" }, { "objectID": "SD/4.1_datavisualize/index.html#statistical-summaries", "href": "SD/4.1_datavisualize/index.html#statistical-summaries", "title": "数据可视化", "section": "Statistical Summaries", "text": "Statistical Summaries\n\n\nggplot(\n bikes,\n aes(x = season, y = temp_feel)\n) +\n stat_summary()" }, { "objectID": "SD/4.1_datavisualize/index.html#statistical-summaries-1", "href": "SD/4.1_datavisualize/index.html#statistical-summaries-1", "title": "数据可视化", "section": "Statistical Summaries", "text": "Statistical Summaries\n\n\nggplot(\n bikes,\n aes(x = season, y = temp_feel)\n) +\n stat_summary(\n fun.data = mean_se, ## the default\n geom = \"pointrange\" ## the default\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#statistical-summaries-2", "href": "SD/4.1_datavisualize/index.html#statistical-summaries-2", "title": "数据可视化", "section": "Statistical Summaries", "text": "Statistical Summaries\n\n\nggplot(\n bikes,\n aes(x = season, y = temp_feel)\n) +\n geom_boxplot() +\n stat_summary(\n fun = mean,\n geom = \"point\",\n color = \"#28a87d\",\n size = 3\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#statistical-summaries-3", "href": "SD/4.1_datavisualize/index.html#statistical-summaries-3", "title": "数据可视化", "section": "Statistical Summaries", "text": "Statistical Summaries\n\n\nggplot(\n bikes,\n aes(x = season, y = temp_feel)\n) +\n stat_summary(\n fun = mean,\n fun.max = function(y) mean(y) + sd(y),\n fun.min = function(y) mean(y) - sd(y)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#store-a-ggplot-as-object", "href": "SD/4.1_datavisualize/index.html#store-a-ggplot-as-object", "title": "数据可视化", "section": "Store a ggplot as Object", "text": "Store a ggplot as Object\n\ng <-\n ggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season, group = day_night)\n ) +\n geom_point(\n alpha = .5\n ) +\n geom_smooth(\n method = \"lm\",\n color = \"black\"\n )\n\nclass(g)\n\n[1] \"ggplot2::ggplot\" \"ggplot\" \"ggplot2::gg\" \"S7_object\" \n[5] \"gg\"" }, { "objectID": "SD/4.1_datavisualize/index.html#inspect-a-ggplot-object", "href": "SD/4.1_datavisualize/index.html#inspect-a-ggplot-object", "title": "数据可视化", "section": "Inspect a ggplot Object", "text": "Inspect a ggplot Object\n\ng$data\n\n# A tibble: 1,454 × 14\n date day_night year month season count is_workday is_weekend\n <date> <chr> <fct> <fct> <fct> <int> <lgl> <lgl> \n 1 2015-01-04 day 2015 1 winter 6830 FALSE TRUE \n 2 2015-01-04 night 2015 1 winter 2404 FALSE TRUE \n 3 2015-01-05 day 2015 1 winter 14763 TRUE FALSE \n 4 2015-01-05 night 2015 1 winter 5609 TRUE FALSE \n 5 2015-01-06 day 2015 1 winter 14501 TRUE FALSE \n 6 2015-01-06 night 2015 1 winter 6112 TRUE FALSE \n 7 2015-01-07 day 2015 1 winter 16358 TRUE FALSE \n 8 2015-01-07 night 2015 1 winter 4706 TRUE FALSE \n 9 2015-01-08 day 2015 1 winter 9971 TRUE FALSE \n10 2015-01-08 night 2015 1 winter 5630 TRUE FALSE \n# ℹ 1,444 more rows\n# ℹ 6 more variables: is_holiday <lgl>, temp <dbl>, temp_feel <dbl>,\n# humidity <dbl>, wind_speed <dbl>, weather_type <chr>" }, { "objectID": "SD/4.1_datavisualize/index.html#inspect-a-ggplot-object-1", "href": "SD/4.1_datavisualize/index.html#inspect-a-ggplot-object-1", "title": "数据可视化", "section": "Inspect a ggplot Object", "text": "Inspect a ggplot Object\n\ng$mapping\n\nAesthetic mapping: \n* `x` -> `temp_feel`\n* `y` -> `count`\n* `colour` -> `season`\n* `group` -> `day_night`" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-layers", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-layers", "title": "数据可视化", "section": "Extend a ggplot Object: Add Layers", "text": "Extend a ggplot Object: Add Layers\n\n\ng +\n geom_rug(\n alpha = .2\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#remove-a-layer-from-the-legend", "href": "SD/4.1_datavisualize/index.html#remove-a-layer-from-the-legend", "title": "数据可视化", "section": "Remove a Layer from the Legend", "text": "Remove a Layer from the Legend\n\n\ng +\n geom_rug(\n alpha = .2,\n show.legend = FALSE\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels", "title": "数据可视化", "section": "Extend a ggplot Object: Add Labels", "text": "Extend a ggplot Object: Add Labels\n\n\ng +\n xlab(\"Feels-like temperature (°F)\") +\n ylab(\"Reported bike shares\") +\n ggtitle(\"TfL bike sharing trends\")" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-1", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-1", "title": "数据可视化", "section": "Extend a ggplot Object: Add Labels", "text": "Extend a ggplot Object: Add Labels\n\n\ng +\n labs(\n x = \"Feels-like temperature (°F)\",\n y = \"Reported bike shares\",\n title = \"TfL bike sharing trends\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-2", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-2", "title": "数据可视化", "section": "Extend a ggplot Object: Add Labels", "text": "Extend a ggplot Object: Add Labels\n\n\ng <- g +\n labs(\n x = \"Feels-like temperature (°F)\",\n y = \"Reported bike shares\",\n title = \"TfL bike sharing trends\",\n color = \"Season:\"\n )\n\ng" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-3", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-3", "title": "数据可视化", "section": "Extend a ggplot Object: Add Labels", "text": "Extend a ggplot Object: Add Labels\n\n\ng +\n labs(\n x = \"Feels-like temperature (°F)\",\n y = \"Reported bike shares\",\n title = \"TfL bike sharing trends\",\n subtitle = \"Reported bike rents versus feels-like temperature in London\",\n caption = \"Data: TfL\",\n color = \"Season:\",\n tag = \"Fig. 1\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-4", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-add-labels-4", "title": "数据可视化", "section": "Extend a ggplot Object: Add Labels", "text": "Extend a ggplot Object: Add Labels\ng +\n labs(\n x = \"\",\n caption = \"Data: TfL\"\n )\ng +\n labs(\n x = NULL,\n caption = \"Data: TfL\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-themes", "href": "SD/4.1_datavisualize/index.html#extend-a-ggplot-object-themes", "title": "数据可视化", "section": "Extend a ggplot Object: Themes", "text": "Extend a ggplot Object: Themes\ng + theme_light()\ng + theme_minimal()" }, { "objectID": "SD/4.1_datavisualize/index.html#change-the-theme-base-settings", "href": "SD/4.1_datavisualize/index.html#change-the-theme-base-settings", "title": "数据可视化", "section": "Change the Theme Base Settings", "text": "Change the Theme Base Settings\n\n\ng +\n theme_light(\n base_size = 14\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#set-a-theme-globally", "href": "SD/4.1_datavisualize/index.html#set-a-theme-globally", "title": "数据可视化", "section": "Set a Theme Globally", "text": "Set a Theme Globally\n\n\ntheme_set(theme_light())\n\ng" }, { "objectID": "SD/4.1_datavisualize/index.html#change-the-theme-base-settings-1", "href": "SD/4.1_datavisualize/index.html#change-the-theme-base-settings-1", "title": "数据可视化", "section": "Change the Theme Base Settings", "text": "Change the Theme Base Settings\n\n\ntheme_set(theme_light(\n base_size = 14\n))\n\ng" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings", "href": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings", "title": "数据可视化", "section": "Overwrite Specific Theme Settings", "text": "Overwrite Specific Theme Settings\n\n\ng +\n theme(\n panel.grid.minor = element_blank()\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-1", "href": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-1", "title": "数据可视化", "section": "Overwrite Specific Theme Settings", "text": "Overwrite Specific Theme Settings\n\n\ng +\n theme(\n panel.grid.minor = element_blank(),\n plot.title = element_text(face = \"bold\")\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-2", "href": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-2", "title": "数据可视化", "section": "Overwrite Specific Theme Settings", "text": "Overwrite Specific Theme Settings\n\n\ng +\n theme(\n panel.grid.minor = element_blank(),\n plot.title = element_text(face = \"bold\"),\n legend.position = \"top\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-3", "href": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-3", "title": "数据可视化", "section": "Overwrite Specific Theme Settings", "text": "Overwrite Specific Theme Settings\n\n\ng +\n theme(\n panel.grid.minor = element_blank(),\n plot.title = element_text(face = \"bold\"),\n legend.position = \"none\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-4", "href": "SD/4.1_datavisualize/index.html#overwrite-specific-theme-settings-4", "title": "数据可视化", "section": "Overwrite Specific Theme Settings", "text": "Overwrite Specific Theme Settings\n\n\ng +\n theme(\n panel.grid.minor = element_blank(),\n plot.title = element_text(face = \"bold\"),\n legend.position = \"top\",\n plot.title.position = \"plot\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#overwrite-theme-settings-globally", "href": "SD/4.1_datavisualize/index.html#overwrite-theme-settings-globally", "title": "数据可视化", "section": "Overwrite Theme Settings Globally", "text": "Overwrite Theme Settings Globally\n\n\ntheme_update(\n panel.grid.minor = element_blank(),\n plot.title = element_text(face = \"bold\"),\n legend.position = \"top\",\n plot.title.position = \"plot\"\n)\n\ng" }, { "objectID": "SD/4.1_datavisualize/index.html#save-the-graphic", "href": "SD/4.1_datavisualize/index.html#save-the-graphic", "title": "数据可视化", "section": "Save the Graphic", "text": "Save the Graphic\n\nggsave(g, filename = \"my_plot.png\")\n\n\n\nggsave(\"my_plot.png\")\n\n\n\n\nggsave(\"my_plot.png\", width = 8, height = 5, dpi = 600)\n\n\n\n\nggsave(\"my_plot.pdf\", width = 20, height = 12, unit = \"cm\", device = cairo_pdf)\n\n\n\n\ngrDevices::cairo_pdf(\"my_plot.pdf\", width = 10, height = 7)\ng\ndev.off()" }, { "objectID": "SD/4.1_datavisualize/index.html#facets面-1", "href": "SD/4.1_datavisualize/index.html#facets面-1", "title": "数据可视化", "section": "Facets（面）", "text": "Facets（面）\n\n= split variables to multiple panels\n\nFacets are also known as:\n\nsmall multiples\ntrellis graphs\nlattice plots\nconditioning" }, { "objectID": "SD/4.1_datavisualize/index.html#setup", "href": "SD/4.1_datavisualize/index.html#setup", "title": "数据可视化", "section": "Setup", "text": "Setup\n\n\ng <-\n ggplot(\n bikes,\n aes(x = temp_feel, y = count, color = season)\n ) +\n geom_point(\n alpha = .3,\n guide = \"none\"\n )\n\ng" }, { "objectID": "SD/4.1_datavisualize/index.html#wrapped-facets", "href": "SD/4.1_datavisualize/index.html#wrapped-facets", "title": "数据可视化", "section": "Wrapped Facets", "text": "Wrapped Facets\n\n\ng +\n facet_wrap(\n vars(day_night)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#wrapped-facets-1", "href": "SD/4.1_datavisualize/index.html#wrapped-facets-1", "title": "数据可视化", "section": "Wrapped Facets", "text": "Wrapped Facets\n\n\ng +\n facet_wrap(\n ~day_night\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-multiple-variables", "href": "SD/4.1_datavisualize/index.html#facet-multiple-variables", "title": "数据可视化", "section": "Facet Multiple Variables", "text": "Facet Multiple Variables\n\n\ng +\n facet_wrap(\n ~ is_workday + day_night\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-cols-rows", "href": "SD/4.1_datavisualize/index.html#facet-options-cols-rows", "title": "数据可视化", "section": "Facet Options: Cols + Rows", "text": "Facet Options: Cols + Rows\n\n\ng +\n facet_wrap(\n ~day_night,\n ncol = 1\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-free-scaling", "href": "SD/4.1_datavisualize/index.html#facet-options-free-scaling", "title": "数据可视化", "section": "Facet Options: Free Scaling", "text": "Facet Options: Free Scaling\n\n\ng +\n facet_wrap(\n ~day_night,\n ncol = 1,\n scales = \"free\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-free-scaling-1", "href": "SD/4.1_datavisualize/index.html#facet-options-free-scaling-1", "title": "数据可视化", "section": "Facet Options: Free Scaling", "text": "Facet Options: Free Scaling\n\n\ng +\n facet_wrap(\n ~day_night,\n ncol = 1,\n scales = \"free_y\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-switch-labels", "href": "SD/4.1_datavisualize/index.html#facet-options-switch-labels", "title": "数据可视化", "section": "Facet Options: Switch Labels", "text": "Facet Options: Switch Labels\n\n\ng +\n facet_wrap(\n ~day_night,\n ncol = 1\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#gridded-facets", "href": "SD/4.1_datavisualize/index.html#gridded-facets", "title": "数据可视化", "section": "Gridded Facets", "text": "Gridded Facets\n\n\ng +\n facet_grid(\n rows = vars(day_night),\n cols = vars(is_workday)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#gridded-facets-1", "href": "SD/4.1_datavisualize/index.html#gridded-facets-1", "title": "数据可视化", "section": "Gridded Facets", "text": "Gridded Facets\n\n\ng +\n facet_grid(\n day_night ~ is_workday\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-multiple-variables-1", "href": "SD/4.1_datavisualize/index.html#facet-multiple-variables-1", "title": "数据可视化", "section": "Facet Multiple Variables", "text": "Facet Multiple Variables\n\n\ng +\n facet_grid(\n day_night ~ is_workday + season\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-free-scaling-2", "href": "SD/4.1_datavisualize/index.html#facet-options-free-scaling-2", "title": "数据可视化", "section": "Facet Options: Free Scaling", "text": "Facet Options: Free Scaling\n\n\ng +\n facet_grid(\n day_night ~ is_workday,\n scales = \"free\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-switch-labels-1", "href": "SD/4.1_datavisualize/index.html#facet-options-switch-labels-1", "title": "数据可视化", "section": "Facet Options: Switch Labels", "text": "Facet Options: Switch Labels\n\n\ng +\n facet_grid(\n day_night ~ is_workday,\n scales = \"free\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-proportional-spacing", "href": "SD/4.1_datavisualize/index.html#facet-options-proportional-spacing", "title": "数据可视化", "section": "Facet Options: Proportional Spacing", "text": "Facet Options: Proportional Spacing\n\n\ng +\n facet_grid(\n day_night ~ is_workday,\n scales = \"free\",\n space = \"free\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#facet-options-proportional-spacing-1", "href": "SD/4.1_datavisualize/index.html#facet-options-proportional-spacing-1", "title": "数据可视化", "section": "Facet Options: Proportional Spacing", "text": "Facet Options: Proportional Spacing\n\n\ng +\n facet_grid(\n day_night ~ is_workday,\n scales = \"free_y\",\n space = \"free_y\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet", "href": "SD/4.1_datavisualize/index.html#diamonds-facet", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nggplot(\n diamonds,\n aes(x = carat, y = price)\n) +\n geom_point(\n alpha = .3\n ) +\n geom_smooth(\n method = \"lm\",\n se = FALSE,\n color = \"dodgerblue\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-1", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-1", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nggplot(\n diamonds,\n aes(x = carat, y = price)\n) +\n geom_point(\n alpha = .3\n ) +\n geom_smooth(\n method = \"lm\",\n se = FALSE,\n color = \"dodgerblue\"\n ) +\n facet_grid(\n cut ~ clarity,\n space = \"free_x\",\n scales = \"free_x\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-dark-theme-bonus", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-dark-theme-bonus", "title": "数据可视化", "section": "Diamonds Facet (Dark Theme Bonus)", "text": "Diamonds Facet (Dark Theme Bonus)\n\n\nggplot(\n diamonds,\n aes(x = carat, y = price)\n) +\n geom_point(\n alpha = .3,\n color = \"white\"\n ) +\n geom_smooth(\n method = \"lm\",\n se = FALSE,\n color = \"dodgerblue\"\n ) +\n facet_grid(\n cut ~ clarity,\n space = \"free_x\",\n scales = \"free_x\"\n ) +\n theme_dark(\n base_size = 14\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scales", "href": "SD/4.1_datavisualize/index.html#scales", "title": "数据可视化", "section": "Scales", "text": "Scales\n\n= translate between variable ranges and property ranges\n\nfeels-like temperature ⇄ x\nreported bike shares ⇄ y\nseason ⇄ color\nyear ⇄ shape\n…" }, { "objectID": "SD/4.1_datavisualize/index.html#scales-1", "href": "SD/4.1_datavisualize/index.html#scales-1", "title": "数据可视化", "section": "Scales", "text": "Scales\nThe scale_*() components control the properties of all theaesthetic dimensions mapped to the data.\nConsequently, there are scale_*() functions for all aesthetics such as:\n\npositions via scale_x_*() and scale_y_*()\ncolors via scale_color_*() and scale_fill_*()\nsizes via scale_size_*() and scale_radius_*()\nshapes via scale_shape_*() and scale_linetype_*()\ntransparency via scale_alpha_*()" }, { "objectID": "SD/4.1_datavisualize/index.html#scales-2", "href": "SD/4.1_datavisualize/index.html#scales-2", "title": "数据可视化", "section": "Scales", "text": "Scales\nThe scale_*() components control the properties of all theaesthetic dimensions mapped to the data.\nThe extensions (*) can be filled by e.g.:\n\ncontinuous(), discrete(), reverse(), log10(), sqrt(), date() for positions\ncontinuous(), discrete(), manual(), gradient(), gradient2(), brewer() for colors\ncontinuous(), discrete(), manual(), ordinal(), area(), date() for sizes\ncontinuous(), discrete(), manual(), ordinal() for shapes\ncontinuous(), discrete(), manual(), ordinal(), date() for transparency" }, { "objectID": "SD/4.1_datavisualize/index.html#continuous-vs.-discrete-in-ggplot2", "href": "SD/4.1_datavisualize/index.html#continuous-vs.-discrete-in-ggplot2", "title": "数据可视化", "section": "Continuous vs. Discrete in {ggplot2}", "text": "Continuous vs. Discrete in {ggplot2}\n\n\n\nContinuous:quantitative or numerical data\n\nheight\nweight\nage\ncounts\n\n\n\nDiscrete:qualitative or categorical data\n\nspecies\nsex\nstudy sites\nage group" }, { "objectID": "SD/4.1_datavisualize/index.html#continuous-vs.-discrete-in-ggplot2-1", "href": "SD/4.1_datavisualize/index.html#continuous-vs.-discrete-in-ggplot2-1", "title": "数据可视化", "section": "Continuous vs. Discrete in {ggplot2}", "text": "Continuous vs. Discrete in {ggplot2}\n\n\n\nContinuous:quantitative or numerical data\n\nheight (continuous)\nweight (continuous)\nage (continuous or discrete)\ncounts (discrete)\n\n\n\nDiscrete:qualitative or categorical data\n\nspecies (nominal)\nsex (nominal)\nstudy site (nominal or ordinal)\nage group (ordinal)" }, { "objectID": "SD/4.1_datavisualize/index.html#aesthetics-scales", "href": "SD/4.1_datavisualize/index.html#aesthetics-scales", "title": "数据可视化", "section": "Aesthetics + Scales", "text": "Aesthetics + Scales\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point()" }, { "objectID": "SD/4.1_datavisualize/index.html#aesthetics-scales-1", "href": "SD/4.1_datavisualize/index.html#aesthetics-scales-1", "title": "数据可视化", "section": "Aesthetics + Scales", "text": "Aesthetics + Scales\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_date() +\n scale_y_continuous() +\n scale_color_discrete()" }, { "objectID": "SD/4.1_datavisualize/index.html#scales-3", "href": "SD/4.1_datavisualize/index.html#scales-3", "title": "数据可视化", "section": "Scales", "text": "Scales\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_continuous() +\n scale_y_continuous() +\n scale_color_discrete()" }, { "objectID": "SD/4.1_datavisualize/index.html#scales-4", "href": "SD/4.1_datavisualize/index.html#scales-4", "title": "数据可视化", "section": "Scales", "text": "Scales\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_continuous() +\n scale_y_log10() +\n scale_color_discrete()" }, { "objectID": "SD/4.1_datavisualize/index.html#scales-5", "href": "SD/4.1_datavisualize/index.html#scales-5", "title": "数据可视化", "section": "Scales", "text": "Scales\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_continuous() +\n scale_y_log10() +\n scale_color_viridis_d()" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n trans = \"log10\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-1", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-1", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-2", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-2", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = seq(0, 60000, by = 15000)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-3", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-3", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = 0:4 * 15000\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-4", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-4", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = c(0, 2:12 * 2500, 40000, 50000)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-5", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-5", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares in thousands\",\n breaks = 0:4 * 15000,\n labels = 0:4 * 15\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-6", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-6", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares in thousands\",\n breaks = 0:4 * 15000,\n labels = paste(0:4 * 15000, \"bikes\")\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-7", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-7", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = 0:4 * 15000,\n limits = c(NA, 60000)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-8", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-8", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = 0:4 * 15000,\n expand = c(0, 0)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-9", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-9", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = -1:5 * 15000,\n expand = c(.5, .5) ## c(add, mult)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-10", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-10", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = -1:5 * 15000,\n expand = expansion(add = 2000)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_continuous-11", "href": "SD/4.1_datavisualize/index.html#scale_xy_continuous-11", "title": "数据可视化", "section": "`scale_x|y_continuous`", "text": "`scale_x|y_continuous`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_y_continuous(\n name = \"Reported bike shares\",\n breaks = 0:4 * 15000,\n guide = \"none\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_date", "href": "SD/4.1_datavisualize/index.html#scale_xy_date", "title": "数据可视化", "section": "`scale_x|y_date`", "text": "`scale_x|y_date`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_date(\n name = NULL,\n date_breaks = \"4 months\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_date-1", "href": "SD/4.1_datavisualize/index.html#scale_xy_date-1", "title": "数据可视化", "section": "`scale_x|y_date`", "text": "`scale_x|y_date`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_date(\n name = NULL,\n date_breaks = \"20 weeks\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_date-with-strftime", "href": "SD/4.1_datavisualize/index.html#scale_xy_date-with-strftime", "title": "数据可视化", "section": "`scale_x|y_date` with `strftime()`", "text": "`scale_x|y_date` with `strftime()`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_date(\n name = NULL,\n date_breaks = \"6 months\",\n date_labels = \"%Y/%m/%d\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_date-with-strftime-1", "href": "SD/4.1_datavisualize/index.html#scale_xy_date-with-strftime-1", "title": "数据可视化", "section": "`scale_x|y_date` with `strftime()`", "text": "`scale_x|y_date` with `strftime()`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_x_date(\n name = NULL,\n date_breaks = \"6 months\",\n date_labels = \"%b '%y\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_discrete", "href": "SD/4.1_datavisualize/index.html#scale_xy_discrete", "title": "数据可视化", "section": "`scale_x|y_discrete`", "text": "`scale_x|y_discrete`\n\n\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n scale_x_discrete(\n name = \"Period\",\n labels = c(\"Dec-Feb\", \"Mar-May\", \"Jun-Aug\", \"Sep-Nov\")\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_xy_discrete-1", "href": "SD/4.1_datavisualize/index.html#scale_xy_discrete-1", "title": "数据可视化", "section": "`scale_x|y_discrete`", "text": "`scale_x|y_discrete`\n\n\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n scale_x_discrete(\n name = \"Season\",\n expand = c(.5, 0) ## add, mult\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#discrete-or-continuous", "href": "SD/4.1_datavisualize/index.html#discrete-or-continuous", "title": "数据可视化", "section": "Discrete or Continuous?", "text": "Discrete or Continuous?\n\n\nggplot(\n bikes,\n aes(x = as.numeric(season), y = count)\n) +\n geom_boxplot(\n aes(group = season)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#discrete-or-continuous-1", "href": "SD/4.1_datavisualize/index.html#discrete-or-continuous-1", "title": "数据可视化", "section": "Discrete or Continuous?", "text": "Discrete or Continuous?\n\n\nggplot(\n bikes,\n aes(x = as.numeric(season), y = count)\n) +\n geom_boxplot(\n aes(group = season)\n ) +\n scale_x_continuous(\n name = \"Season\",\n breaks = 1:4,\n labels = levels(bikes$season)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#discrete-or-continuous-2", "href": "SD/4.1_datavisualize/index.html#discrete-or-continuous-2", "title": "数据可视化", "section": "Discrete or Continuous?", "text": "Discrete or Continuous?\n\n\nggplot(\n bikes,\n aes(\n x = as.numeric(season) +\n as.numeric(season) / 8,\n y = count\n )\n) +\n geom_boxplot(\n aes(group = season)\n ) +\n scale_x_continuous(\n name = \"Season\",\n breaks = 1:4,\n labels = levels(bikes$season)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete", "href": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete", "title": "数据可视化", "section": "`scale_color|fill_discrete`", "text": "`scale_color|fill_discrete`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_color_discrete(\n name = \"Season:\",\n type = c(\"#69b0d4\", \"#00CB79\", \"#F7B01B\", \"#a78f5f\")\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#inspect-assigned-colors", "href": "SD/4.1_datavisualize/index.html#inspect-assigned-colors", "title": "数据可视化", "section": "Inspect Assigned Colors", "text": "Inspect Assigned Colors\n\n\ng <- ggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_color_discrete(\n name = \"Season:\",\n type = c(\"#3ca7d9\", \"#1ec99b\", \"#F7B01B\", \"#bb7e8f\")\n )\n\ngb <- ggplot_build(g)\n\ngb$data[[1]][c(1:5, 200:205, 400:405), 1:5]\n\n\n x y colour PANEL group\n1 16439 6830 #3ca7d9 1 1\n2 16439 2404 #3ca7d9 1 1\n3 16440 14763 #3ca7d9 1 1\n4 16440 5609 #3ca7d9 1 1\n5 16441 14501 #3ca7d9 1 1\n200 16538 8830 #1ec99b 1 2\n201 16539 24019 #1ec99b 1 2\n202 16539 10500 #1ec99b 1 2\n203 16540 25640 #1ec99b 1 2\n204 16540 11830 #1ec99b 1 2\n205 16541 22216 #1ec99b 1 2\n400 16638 12079 #F7B01B 1 3\n401 16639 26646 #F7B01B 1 3\n402 16639 12446 #F7B01B 1 3\n403 16640 11312 #F7B01B 1 3\n404 16640 4722 #F7B01B 1 3\n405 16641 22748 #F7B01B 1 3" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete-1", "href": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete-1", "title": "数据可视化", "section": "`scale_color|fill_discrete`", "text": "`scale_color|fill_discrete`\n\n\nmy_colors <- c(\n `winter` = \"#3c89d9\",\n `spring` = \"#1ec99b\",\n `summer` = \"#F7B01B\",\n `autumn` = \"#a26e7c\"\n)\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_color_discrete(\n name = \"Season:\",\n type = my_colors\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete-2", "href": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete-2", "title": "数据可视化", "section": "`scale_color|fill_discrete`", "text": "`scale_color|fill_discrete`\n\n\nmy_colors_alphabetical <- c(\n `autumn` = \"#a26e7c\",\n `spring` = \"#1ec99b\",\n `summer` = \"#F7B01B\",\n `winter` = \"#3c89d9\"\n)\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_color_discrete(\n name = \"Season:\",\n type = my_colors_alphabetical\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete-3", "href": "SD/4.1_datavisualize/index.html#scale_colorfill_discrete-3", "title": "数据可视化", "section": "`scale_color|fill_discrete`", "text": "`scale_color|fill_discrete`\n\n\nlibrary(RColorBrewer)\n\nggplot(\n bikes,\n aes(x = date, y = count, color = season)\n) +\n geom_point() +\n scale_color_discrete(\n name = \"Season:\",\n type = brewer.pal(\n n = 4,\n name = \"Dark2\"\n )\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#scale_colorfill_manual", "href": "SD/4.1_datavisualize/index.html#scale_colorfill_manual", "title": "数据可视化", "section": "`scale_color|fill_manual`", "text": "`scale_color|fill_manual`\n\n\nggplot(\n bikes,\n aes(x = date, y = count, color = weather_type)\n) +\n geom_point() +\n scale_color_manual(\n name = \"Season:\",\n values = brewer.pal(n = 6, name = \"Pastel1\"),\n na.value = \"black\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-2", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-2", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet <-\n ggplot(\n diamonds,\n aes(x = carat, y = price)\n ) +\n geom_point(\n alpha = .3\n ) +\n geom_smooth(\n aes(color = cut),\n method = \"lm\",\n se = FALSE\n ) +\n facet_grid(\n cut ~ clarity,\n space = \"free_x\",\n scales = \"free_x\"\n )\n\nfacet" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-3", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-3", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet +\n scale_x_continuous(\n breaks = 0:5\n ) +\n scale_y_continuous(\n limits = c(0, 30000),\n breaks = 0:3 * 10000,\n labels = c(\"$0\", \"$10,000\", \"$20,000\", \"$30,000\")\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-4", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-4", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet +\n scale_x_continuous(\n breaks = 0:5\n ) +\n scale_y_continuous(\n limits = c(0, 30000),\n breaks = 0:3 * 10000,\n labels = paste0(\n \"$\",\n format(\n 0:3 * 10000,\n big.mark = \",\",\n trim = TRUE\n )\n )\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-5", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-5", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet +\n scale_x_continuous(\n breaks = 0:5\n ) +\n scale_y_continuous(\n limits = c(0, 30000),\n breaks = 0:3 * 10000,\n labels = function(y)\n paste0(\n \"$\",\n format(\n y,\n big.mark = \",\",\n trim = TRUE\n )\n )\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-6", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-6", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet +\n scale_x_continuous(\n breaks = 0:5\n ) +\n scale_y_continuous(\n limits = c(0, 30000),\n breaks = 0:3 * 10000,\n labels = scales::dollar_format()\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-7", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-7", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet +\n scale_x_continuous(\n breaks = 0:5\n ) +\n scale_y_continuous(\n limits = c(0, 30000),\n breaks = 0:3 * 10000,\n labels = scales::dollar_format()\n ) +\n scale_color_brewer(\n palette = \"Set2\",\n guide = \"none\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#diamonds-facet-8", "href": "SD/4.1_datavisualize/index.html#diamonds-facet-8", "title": "数据可视化", "section": "Diamonds Facet", "text": "Diamonds Facet\n\n\nfacet +\n scale_x_continuous(\n breaks = 0:5\n ) +\n scale_y_continuous(\n limits = c(0, 30000),\n breaks = 0:3 * 10000,\n labels = scales::dollar_format()\n ) +\n scale_color_brewer(\n palette = \"Set2\"\n ) +\n theme(\n legend.position = \"none\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#coordinate-systems", "href": "SD/4.1_datavisualize/index.html#coordinate-systems", "title": "数据可视化", "section": "Coordinate Systems", "text": "Coordinate Systems\n\n= interpret the position aesthetics\n\nlinear coordinate systems: preserve the geometrical shapes\n\ncoord_cartesian()\ncoord_fixed()\ncoord_flip()\n\nnon-linear coordinate systems: likely change the geometrical shapes\n\ncoord_polar()\ncoord_map() and coord_sf()\ncoord_trans()" }, { "objectID": "SD/4.1_datavisualize/index.html#cartesian-coordinate-system", "href": "SD/4.1_datavisualize/index.html#cartesian-coordinate-system", "title": "数据可视化", "section": "Cartesian Coordinate System", "text": "Cartesian Coordinate System\n\n\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n coord_cartesian()" }, { "objectID": "SD/4.1_datavisualize/index.html#cartesian-coordinate-system-1", "href": "SD/4.1_datavisualize/index.html#cartesian-coordinate-system-1", "title": "数据可视化", "section": "Cartesian Coordinate System", "text": "Cartesian Coordinate System\n\n\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n coord_cartesian(\n ylim = c(NA, 15000)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#changing-limits", "href": "SD/4.1_datavisualize/index.html#changing-limits", "title": "数据可视化", "section": "Changing Limits", "text": "Changing Limits\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n coord_cartesian(\n ylim = c(NA, 15000)\n )\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n scale_y_continuous(\n limits = c(NA, 15000)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#clipping", "href": "SD/4.1_datavisualize/index.html#clipping", "title": "数据可视化", "section": "Clipping", "text": "Clipping\n\n\nggplot(\n bikes,\n aes(x = season, y = count)\n) +\n geom_boxplot() +\n coord_cartesian(\n ylim = c(NA, 15000),\n clip = \"off\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#clipping-1", "href": "SD/4.1_datavisualize/index.html#clipping-1", "title": "数据可视化", "section": "Clipping", "text": "Clipping\n\n\nggplot(\n filter(bikes, is_holiday == TRUE),\n aes(x = temp_feel, y = count)\n) +\n geom_point() +\n geom_text(\n aes(label = season),\n nudge_x = .3,\n hjust = 0\n ) +\n coord_cartesian(\n clip = \"off\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#or-better-use-ggrepel", "href": "SD/4.1_datavisualize/index.html#or-better-use-ggrepel", "title": "数据可视化", "section": "… or better use {ggrepel}", "text": "… or better use {ggrepel}\n\n\nggplot(\n filter(bikes, is_holiday == TRUE),\n aes(x = temp_feel, y = count)\n) +\n geom_point() +\n ggrepel::geom_text_repel(\n aes(label = season),\n nudge_x = .3,\n hjust = 0\n ) +\n coord_cartesian(\n clip = \"off\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#remove-all-padding", "href": "SD/4.1_datavisualize/index.html#remove-all-padding", "title": "数据可视化", "section": "Remove All Padding", "text": "Remove All Padding\n\n\nggplot(\n bikes,\n aes(x = temp_feel, y = count)\n) +\n geom_point() +\n coord_cartesian(\n expand = FALSE,\n clip = \"off\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#fixed-coordinate-system", "href": "SD/4.1_datavisualize/index.html#fixed-coordinate-system", "title": "数据可视化", "section": "Fixed Coordinate System", "text": "Fixed Coordinate System\nggplot(\n bikes,\n aes(x = temp_feel, y = temp)\n) +\n geom_point() +\n coord_fixed()\nggplot(\n bikes,\n aes(x = temp_feel, y = temp)\n) +\n geom_point() +\n coord_fixed(ratio = 4)" }, { "objectID": "SD/4.1_datavisualize/index.html#flipped-coordinate-system", "href": "SD/4.1_datavisualize/index.html#flipped-coordinate-system", "title": "数据可视化", "section": "Flipped Coordinate System", "text": "Flipped Coordinate System\nggplot(\n bikes,\n aes(x = weather_type)\n) +\n geom_bar() +\n coord_cartesian()\nggplot(\n bikes,\n aes(x = weather_type)\n) +\n geom_bar() +\n coord_flip()" }, { "objectID": "SD/4.1_datavisualize/index.html#flipped-coordinate-system-1", "href": "SD/4.1_datavisualize/index.html#flipped-coordinate-system-1", "title": "数据可视化", "section": "Flipped Coordinate System", "text": "Flipped Coordinate System\nggplot(\n bikes,\n aes(y = weather_type)\n) +\n geom_bar() +\n coord_cartesian()\nggplot(\n bikes,\n aes(x = weather_type)\n) +\n geom_bar() +\n coord_flip()" }, { "objectID": "SD/4.1_datavisualize/index.html#reminder-sort-your-bars", "href": "SD/4.1_datavisualize/index.html#reminder-sort-your-bars", "title": "数据可视化", "section": "Reminder: Sort Your Bars!", "text": "Reminder: Sort Your Bars!\n\n\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(y = fct_infreq(weather_type))\n) +\n geom_bar()" }, { "objectID": "SD/4.1_datavisualize/index.html#reminder-sort-your-bars-1", "href": "SD/4.1_datavisualize/index.html#reminder-sort-your-bars-1", "title": "数据可视化", "section": "Reminder: Sort Your Bars!", "text": "Reminder: Sort Your Bars!\n\n\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(\n y = fct_rev(\n fct_infreq(weather_type)\n )\n )\n) +\n geom_bar()" }, { "objectID": "SD/4.1_datavisualize/index.html#circular-corrdinate-system", "href": "SD/4.1_datavisualize/index.html#circular-corrdinate-system", "title": "数据可视化", "section": "Circular Corrdinate System", "text": "Circular Corrdinate System\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = weather_type, fill = weather_type)\n) +\n geom_bar() +\n coord_polar()\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = weather_type, fill = weather_type)\n) +\n geom_bar() +\n coord_cartesian()" }, { "objectID": "SD/4.1_datavisualize/index.html#circular-cordinate-system", "href": "SD/4.1_datavisualize/index.html#circular-cordinate-system", "title": "数据可视化", "section": "Circular Cordinate System", "text": "Circular Cordinate System\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = fct_infreq(weather_type), fill = weather_type)\n) +\n geom_bar(width = 1) +\n coord_polar()\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = fct_infreq(weather_type), fill = weather_type)\n) +\n geom_bar(width = 1) +\n coord_cartesian()" }, { "objectID": "SD/4.1_datavisualize/index.html#circular-corrdinate-system-1", "href": "SD/4.1_datavisualize/index.html#circular-corrdinate-system-1", "title": "数据可视化", "section": "Circular Corrdinate System", "text": "Circular Corrdinate System\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = fct_infreq(weather_type), fill = weather_type)\n) +\n geom_bar() +\n coord_polar(theta = \"x\")\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = fct_infreq(weather_type), fill = weather_type)\n) +\n geom_bar() +\n coord_polar(theta = \"y\")" }, { "objectID": "SD/4.1_datavisualize/index.html#circular-corrdinate-system-2", "href": "SD/4.1_datavisualize/index.html#circular-corrdinate-system-2", "title": "数据可视化", "section": "Circular Corrdinate System", "text": "Circular Corrdinate System\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = 1, fill = weather_type)\n) +\n geom_bar(position = \"stack\") +\n coord_polar(theta = \"y\")\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = 1, fill = weather_type)\n) +\n geom_bar(position = \"stack\") +\n coord_cartesian()" }, { "objectID": "SD/4.1_datavisualize/index.html#circular-corrdinate-system-3", "href": "SD/4.1_datavisualize/index.html#circular-corrdinate-system-3", "title": "数据可视化", "section": "Circular Corrdinate System", "text": "Circular Corrdinate System\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = 1, fill = fct_rev(fct_infreq(weather_type)))\n) +\n geom_bar(position = \"stack\") +\n coord_polar(theta = \"y\") +\n scale_fill_discrete(name = NULL)\nggplot(\n filter(bikes, !is.na(weather_type)),\n aes(x = 1, fill = fct_rev(fct_infreq(weather_type)))\n) +\n geom_bar(position = \"stack\") +\n coord_cartesian() +\n scale_fill_discrete(name = NULL)" }, { "objectID": "SD/4.1_datavisualize/index.html#transform-a-coordinate-system", "href": "SD/4.1_datavisualize/index.html#transform-a-coordinate-system", "title": "数据可视化", "section": "Transform a Coordinate System", "text": "Transform a Coordinate System\n\n\nggplot(\n bikes,\n aes(x = temp, y = count)\n) +\n geom_point() +\n coord_trans(y = \"log10\")" }, { "objectID": "SD/4.1_datavisualize/index.html#transform-a-coordinate-system-1", "href": "SD/4.1_datavisualize/index.html#transform-a-coordinate-system-1", "title": "数据可视化", "section": "Transform a Coordinate System", "text": "Transform a Coordinate System\nggplot(\n bikes,\n aes(x = temp, y = count, group = day_night)\n) +\n geom_point() +\n geom_smooth(method = \"lm\") +\n coord_trans(y = \"log10\")\nggplot(\n bikes,\n aes(x = temp, y = count, group = day_night)\n) +\n geom_point() +\n geom_smooth(method = \"lm\") +\n scale_y_log10()" }, { "objectID": "SD/4.1_datavisualize/index.html#patchwork", "href": "SD/4.1_datavisualize/index.html#patchwork", "title": "数据可视化", "section": "{patchwork}", "text": "{patchwork}\n\n# install.packages(\"patchwork\")\nrequire(patchwork)\n(p1 + p2) / p3" }, { "objectID": "SD/4.1_datavisualize/index.html#collect-guides", "href": "SD/4.1_datavisualize/index.html#collect-guides", "title": "数据可视化", "section": "“Collect Guides”", "text": "“Collect Guides”\n\n(p1 + p2) / p3 + plot_layout(guides = \"collect\")" }, { "objectID": "SD/4.1_datavisualize/index.html#apply-theming", "href": "SD/4.1_datavisualize/index.html#apply-theming", "title": "数据可视化", "section": "Apply Theming", "text": "Apply Theming\n\n((p1 + p2) / p3 & theme(legend.justification = \"top\")) +\n plot_layout(guides = \"collect\")" }, { "objectID": "SD/4.1_datavisualize/index.html#apply-theming-1", "href": "SD/4.1_datavisualize/index.html#apply-theming-1", "title": "数据可视化", "section": "Apply Theming", "text": "Apply Theming\n\n(p1 + p2) /\n p3 &\n theme(\n legend.position = \"none\",\n plot.background = element_rect(color = \"black\", size = 3)\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#adjust-widths-and-heights", "href": "SD/4.1_datavisualize/index.html#adjust-widths-and-heights", "title": "数据可视化", "section": "Adjust Widths and Heights", "text": "Adjust Widths and Heights\n\n((p1 + p2) / p3 & theme(legend.position = \"none\")) +\n plot_layout(heights = c(.2, .1), widths = c(2, 1))" }, { "objectID": "SD/4.1_datavisualize/index.html#use-a-custom-layout", "href": "SD/4.1_datavisualize/index.html#use-a-custom-layout", "title": "数据可视化", "section": "Use A Custom Layout", "text": "Use A Custom Layout\n\npicasso <- \"\nAAAAAA#BBBB\nCCCCCCCCC##\nCCCCCCCCC##\"\n(p1 + p2 + p3 & theme(legend.position = \"none\")) +\n plot_layout(design = picasso)" }, { "objectID": "SD/4.1_datavisualize/index.html#add-labels", "href": "SD/4.1_datavisualize/index.html#add-labels", "title": "数据可视化", "section": "Add Labels", "text": "Add Labels\n\npl1 <- p1 +\n labs(tag = NULL, title = \"Plot One\") +\n theme(legend.position = \"none\")\npl2 <- p2 +\n labs(tag = NULL, title = \"Plot Two\") +\n theme(legend.position = \"none\")\npl3 <- p3 +\n labs(tag = NULL, title = \"Plot Three\") +\n theme(legend.position = \"none\")" }, { "objectID": "SD/4.1_datavisualize/index.html#add-labels-1", "href": "SD/4.1_datavisualize/index.html#add-labels-1", "title": "数据可视化", "section": "Add Labels", "text": "Add Labels\n\n(pl1 + pl2) /\n pl3 +\n plot_annotation(\n tag_levels = \"1\",\n tag_prefix = \"P\",\n title = \"An overarching title for all 3 plots, placed on the very top while all other titles are sitting below the tags.\"\n )" }, { "objectID": "SD/4.1_datavisualize/index.html#add-text", "href": "SD/4.1_datavisualize/index.html#add-text", "title": "数据可视化", "section": "Add Text", "text": "Add Text\n\nGraphicCode\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ntext <- tibble::tibble(\n x = 0,\n y = 0,\n label = \"Lorem ipsum dolor sit amet, **consectetur adipiscing elit**, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation <b style='color:#000080;'>ullamco laboris nisi</b> ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat <b style='color:#cc0000;'>cupidatat non proident</b>, sunt in culpa qui officia deserunt mollit anim id est laborum.\"\n)\npt <- ggplot(text, aes(x = x, y = y)) +\n ggtext::geom_textbox(\n aes(label = label),\n box.color = NA,\n width = unit(23, \"lines\"),\n color = \"grey40\",\n size = 6.5,\n lineheight = 1.4\n ) +\n coord_cartesian(expand = FALSE, clip = \"off\") +\n theme_void()\npt" }, { "objectID": "SD/4.1_datavisualize/index.html#add-text-1", "href": "SD/4.1_datavisualize/index.html#add-text-1", "title": "数据可视化", "section": "Add Text", "text": "Add Text\n\n(p1 + pt) / p3" }, { "objectID": "SD/4.1_datavisualize/index.html#add-inset-plots", "href": "SD/4.1_datavisualize/index.html#add-inset-plots", "title": "数据可视化", "section": "Add Inset Plots", "text": "Add Inset Plots\n\npl1 + inset_element(pl2, l = .6, b = .1, r = 1, t = .6)" }, { "objectID": "SD/4.1_datavisualize/index.html#add-inset-plots-1", "href": "SD/4.1_datavisualize/index.html#add-inset-plots-1", "title": "数据可视化", "section": "Add Inset Plots", "text": "Add Inset Plots\n\npl1 + inset_element(pl2, l = .6, b = 0, r = 1, t = .5, align_to = 'full')" }, { "objectID": "SD/4.1_datavisualize/index.html#add-inset-plots-2", "href": "SD/4.1_datavisualize/index.html#add-inset-plots-2", "title": "数据可视化", "section": "Add Inset Plots", "text": "Add Inset Plots\n\n(pl1 + inset_element(pl2, l = .6, b = .1, r = 1, t = .6) + pt) / pl3" }, { "objectID": "SD/4.1_datavisualize/index.html#练习", "href": "SD/4.1_datavisualize/index.html#练习", "title": "数据可视化", "section": "练习", "text": "练习\n\nlibrary(palmerpenguins)\nlibrary(ggthemes)\npenguins\n\n# A tibble: 344 × 8\n species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g\n <fct> <fct> <dbl> <dbl> <int> <int>\n 1 Adelie Torgersen 39.1 18.7 181 3750\n 2 Adelie Torgersen 39.5 17.4 186 3800\n 3 Adelie Torgersen 40.3 18 195 3250\n 4 Adelie Torgersen NA NA NA NA\n 5 Adelie Torgersen 36.7 19.3 193 3450\n 6 Adelie Torgersen 39.3 20.6 190 3650\n 7 Adelie Torgersen 38.9 17.8 181 3625\n 8 Adelie Torgersen 39.2 19.6 195 4675\n 9 Adelie Torgersen 34.1 18.1 193 3475\n10 Adelie Torgersen 42 20.2 190 4250\n# ℹ 334 more rows\n# ℹ 2 more variables: sex <fct>, year <int>" }, { "objectID": "SD/4.1_datavisualize/index.html#效果", "href": "SD/4.1_datavisualize/index.html#效果", "title": "数据可视化", "section": "效果", "text": "效果" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-1", "href": "SD/4.1_datavisualize/index.html#练习-1", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(\n data = penguins,\n mapping = aes(x = flipper_length_mm, y = body_mass_g)\n) +\n geom_point(aes(color = species, shape = species)) +\n geom_smooth(method = \"lm\") +\n labs(\n title = \"Body mass and flipper length\",\n subtitle = \"Dimensions for Adelie, Chinstrap, and Gentoo Penguins\",\n x = \"Flipper length (mm)\",\n y = \"Body mass (g)\",\n color = \"Species\",\n shape = \"Species\"\n ) +\n scale_color_colorblind()" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-2", "href": "SD/4.1_datavisualize/index.html#练习-2", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(\n data = penguins,\n mapping = aes(x = flipper_length_mm, y = body_mass_g)\n) +\n geom_point(aes(color = bill_depth_mm)) +\n geom_smooth()" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-3", "href": "SD/4.1_datavisualize/index.html#练习-3", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(\n data = penguins,\n mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)\n) +\n geom_point() +\n geom_smooth(se = FALSE)" }, { "objectID": "SD/4.1_datavisualize/index.html#分层展示", "href": "SD/4.1_datavisualize/index.html#分层展示", "title": "数据可视化", "section": "分层展示", "text": "分层展示\n\nCodeGraphic\n\n\n\np <- ggplot(\n data = penguins,\n mapping = aes(x = flipper_length_mm, y = body_mass_g)\n) +\n geom_point() +\n geom_smooth()" }, { "objectID": "SD/4.1_datavisualize/index.html#柱状图", "href": "SD/4.1_datavisualize/index.html#柱状图", "title": "数据可视化", "section": "柱状图", "text": "柱状图\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = species)) +\n geom_bar()" }, { "objectID": "SD/4.1_datavisualize/index.html#柱状图-1", "href": "SD/4.1_datavisualize/index.html#柱状图-1", "title": "数据可视化", "section": "柱状图", "text": "柱状图\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = fct_infreq(species))) +\n geom_bar()" }, { "objectID": "SD/4.1_datavisualize/index.html#直方图", "href": "SD/4.1_datavisualize/index.html#直方图", "title": "数据可视化", "section": "直方图", "text": "直方图\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = body_mass_g)) +\n geom_histogram(binwidth = 200)" }, { "objectID": "SD/4.1_datavisualize/index.html#直方图-1", "href": "SD/4.1_datavisualize/index.html#直方图-1", "title": "数据可视化", "section": "直方图", "text": "直方图\n\nCodeGraphic\n\n\np1 <- ggplot(penguins, aes(x = body_mass_g)) +\n geom_histogram(binwidth = 20)\np2 <- ggplot(penguins, aes(x = body_mass_g)) +\n geom_histogram(binwidth = 2000)\np <- p1 + p2" }, { "objectID": "SD/4.1_datavisualize/index.html#密度图", "href": "SD/4.1_datavisualize/index.html#密度图", "title": "数据可视化", "section": "密度图", "text": "密度图\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = body_mass_g)) +\n geom_density()" }, { "objectID": "SD/4.1_datavisualize/index.html#箱图", "href": "SD/4.1_datavisualize/index.html#箱图", "title": "数据可视化", "section": "箱图", "text": "箱图\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = species, y = body_mass_g)) +\n geom_boxplot()" }, { "objectID": "SD/4.1_datavisualize/index.html#分组", "href": "SD/4.1_datavisualize/index.html#分组", "title": "数据可视化", "section": "分组", "text": "分组\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = body_mass_g, color = species)) +\n geom_density(linewidth = 0.75)" }, { "objectID": "SD/4.1_datavisualize/index.html#分组-1", "href": "SD/4.1_datavisualize/index.html#分组-1", "title": "数据可视化", "section": "分组", "text": "分组\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = body_mass_g, color = species, fill = species)) +\n geom_density(alpha = 0.5)" }, { "objectID": "SD/4.1_datavisualize/index.html#分组-2", "href": "SD/4.1_datavisualize/index.html#分组-2", "title": "数据可视化", "section": "分组", "text": "分组\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = island, fill = species)) +\n geom_bar()" }, { "objectID": "SD/4.1_datavisualize/index.html#分组-3", "href": "SD/4.1_datavisualize/index.html#分组-3", "title": "数据可视化", "section": "分组", "text": "分组\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"fill\")" }, { "objectID": "SD/4.1_datavisualize/index.html#分组-4", "href": "SD/4.1_datavisualize/index.html#分组-4", "title": "数据可视化", "section": "分组", "text": "分组\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +\n geom_point()" }, { "objectID": "SD/4.1_datavisualize/index.html#分组-5", "href": "SD/4.1_datavisualize/index.html#分组-5", "title": "数据可视化", "section": "分组", "text": "分组\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +\n geom_point(aes(color = species, shape = island))" }, { "objectID": "SD/4.1_datavisualize/index.html#分面", "href": "SD/4.1_datavisualize/index.html#分面", "title": "数据可视化", "section": "分面", "text": "分面\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +\n geom_point(aes(color = species, shape = species)) +\n facet_wrap(~island)" }, { "objectID": "SD/4.1_datavisualize/index.html#分面-1", "href": "SD/4.1_datavisualize/index.html#分面-1", "title": "数据可视化", "section": "分面", "text": "分面\n\nCodeGraphic\n\n\n\np <- ggplot(\n data = penguins,\n mapping = aes(\n x = bill_length_mm,\n y = bill_depth_mm,\n color = species,\n shape = species\n )\n) +\n geom_point() +\n labs(color = \"Species\")" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-4", "href": "SD/4.1_datavisualize/index.html#练习-4", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\np1 <- ggplot(penguins, aes(x = island, fill = species)) +\n geom_bar(position = \"fill\")\np2 <- ggplot(penguins, aes(x = species, fill = island)) +\n geom_bar(position = \"fill\")\np <- p1 + p2" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-5", "href": "SD/4.1_datavisualize/index.html#练习-5", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +\n geom_point()" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-6", "href": "SD/4.1_datavisualize/index.html#练习-6", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(mpg, aes(x = class)) +\n geom_bar()" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-7", "href": "SD/4.1_datavisualize/index.html#练习-7", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(mpg, aes(x = cty, y = hwy)) +\n geom_point()" }, { "objectID": "SD/4.1_datavisualize/index.html#练习-8", "href": "SD/4.1_datavisualize/index.html#练习-8", "title": "数据可视化", "section": "练习", "text": "练习\n\nCodeGraphic\n\n\n\np <- ggplot(data = mpg) +\n geom_point(mapping = aes(x = displ, y = hwy))" }, { "objectID": "SD/4.1_datavisualize/index.html#欢迎讨论", "href": "SD/4.1_datavisualize/index.html#欢迎讨论", "title": "数据可视化", "section": "欢迎讨论！", "text": "欢迎讨论！\n#| results: ‘asis’ #| echo: false rmdify::slideend( wechat = FALSE, type = “public”, tel = FALSE, thislink = “../” ) ` ``" }, { "objectID": "SD/5.1_model/index.html#何为tidymodels", "href": "SD/5.1_model/index.html#何为tidymodels", "title": "模型构建", "section": "何为tidymodels?", "text": "何为tidymodels?\n\nlibrary(tidymodels)\n\n#> ── Attaching packages ──────────────────────────── tidymodels 1.3.0 ──\n\n\n#> ✔ broom 1.0.9 ✔ rsample 1.3.1\n#> ✔ dials 1.4.1 ✔ tune 1.3.0\n#> ✔ infer 1.0.9 ✔ workflows 1.2.0\n#> ✔ modeldata 1.5.0 ✔ workflowsets 1.1.1\n#> ✔ parsnip 1.3.2 ✔ yardstick 1.3.2\n#> ✔ recipes 1.3.1\n\n\n#> ── Conflicts ─────────────────────────────── tidymodels_conflicts() ──\n#> ✖ scales::discard() masks purrr::discard()\n#> ✖ dplyr::filter() masks stats::filter()\n#> ✖ recipes::fixed() masks stringr::fixed()\n#> ✖ dplyr::lag() masks stats::lag()\n#> ✖ yardstick::spec() masks readr::spec()\n#> ✖ recipes::step() masks stats::step()" }, { "objectID": "SD/5.1_model/index.html#整体思路", "href": "SD/5.1_model/index.html#整体思路", "title": "模型构建", "section": "整体思路", "text": "整体思路" }, { "objectID": "SD/5.1_model/index.html#整体思路-1", "href": "SD/5.1_model/index.html#整体思路-1", "title": "模型构建", "section": "整体思路", "text": "整体思路\n\n\n\n\n\n\n\n\n\n\nStress that we are not fitting a model on the entire training set other than for illustrative purposes in deck 2." }, { "objectID": "SD/5.1_model/index.html#整体思路-2", "href": "SD/5.1_model/index.html#整体思路-2", "title": "模型构建", "section": "整体思路", "text": "整体思路" }, { "objectID": "SD/5.1_model/index.html#整体思路-3", "href": "SD/5.1_model/index.html#整体思路-3", "title": "模型构建", "section": "整体思路", "text": "整体思路" }, { "objectID": "SD/5.1_model/index.html#整体思路-4", "href": "SD/5.1_model/index.html#整体思路-4", "title": "模型构建", "section": "整体思路", "text": "整体思路" }, { "objectID": "SD/5.1_model/index.html#整体思路-5", "href": "SD/5.1_model/index.html#整体思路-5", "title": "模型构建", "section": "整体思路", "text": "整体思路" }, { "objectID": "SD/5.1_model/index.html#整体思路-6", "href": "SD/5.1_model/index.html#整体思路-6", "title": "模型构建", "section": "整体思路", "text": "整体思路" }, { "objectID": "SD/5.1_model/index.html#相关包的安装", "href": "SD/5.1_model/index.html#相关包的安装", "title": "模型构建", "section": "相关包的安装", "text": "相关包的安装\n\n# Install the packages for the workshop\npkgs <-\n c(\n \"bonsai\",\n \"doParallel\",\n \"embed\",\n \"finetune\",\n \"lightgbm\",\n \"lme4\",\n \"plumber\",\n \"probably\",\n \"ranger\",\n \"rpart\",\n \"rpart.plot\",\n \"rules\",\n \"splines2\",\n \"stacks\",\n \"text2vec\",\n \"textrecipes\",\n \"tidymodels\",\n \"vetiver\",\n \"remotes\"\n )\n\ninstall.packages(pkgs)" }, { "objectID": "SD/5.1_model/index.html#data-on-chicago-taxi-trips", "href": "SD/5.1_model/index.html#data-on-chicago-taxi-trips", "title": "模型构建", "section": "Data on Chicago taxi trips", "text": "Data on Chicago taxi trips\n\nlibrary(tidymodels)\ntaxi\n\n#> # A tibble: 10,000 × 7\n#> tip distance company local dow month hour\n#> <fct> <dbl> <fct> <fct> <fct> <fct> <int>\n#> 1 yes 17.2 Chicago Independents no Thu Feb 16\n#> 2 yes 0.88 City Service yes Thu Mar 8\n#> 3 yes 18.1 other no Mon Feb 18\n#> 4 yes 20.7 Chicago Independents no Mon Apr 8\n#> 5 yes 12.2 Chicago Independents no Sun Mar 21\n#> 6 yes 0.94 Sun Taxi yes Sat Apr 23\n#> 7 yes 17.5 Flash Cab no Fri Mar 12\n#> 8 yes 17.7 other no Sun Jan 6\n#> 9 yes 1.85 Taxicab Insurance Agency Llc no Fri Apr 12\n#> 10 yes 1.47 City Service no Tue Mar 14\n#> # ℹ 9,990 more rows" }, { "objectID": "SD/5.1_model/index.html#数据分割与使用", "href": "SD/5.1_model/index.html#数据分割与使用", "title": "模型构建", "section": "数据分割与使用", "text": "数据分割与使用\n对于机器学习，我们通常将数据分成训练集和测试集：\n\n\n训练集用于估计模型参数。\n测试集用于独立评估模型性能。\n\n\n\n在训练过程中不要使用测试集。" }, { "objectID": "SD/5.1_model/index.html#the-initial-split", "href": "SD/5.1_model/index.html#the-initial-split", "title": "模型构建", "section": "The initial split", "text": "The initial split\n\nset.seed(123)\ntaxi_split <- initial_split(taxi)\ntaxi_split\n\n#> <Training/Testing/Total>\n#> <7500/2500/10000>" }, { "objectID": "SD/5.1_model/index.html#accessing-the-data", "href": "SD/5.1_model/index.html#accessing-the-data", "title": "模型构建", "section": "Accessing the data", "text": "Accessing the data\n\ntaxi_train <- training(taxi_split)\ntaxi_test <- testing(taxi_split)" }, { "objectID": "SD/5.1_model/index.html#the-training-set", "href": "SD/5.1_model/index.html#the-training-set", "title": "模型构建", "section": "The training set", "text": "The training set\n\ntaxi_train\n\n#> # A tibble: 7,500 × 7\n#> tip distance company local dow month hour\n#> <fct> <dbl> <fct> <fct> <fct> <fct> <int>\n#> 1 yes 0.7 Taxi Affiliation Services yes Tue Mar 18\n#> 2 yes 0.99 Sun Taxi yes Tue Jan 8\n#> 3 yes 1.78 other no Sat Mar 22\n#> 4 yes 0 Taxi Affiliation Services yes Wed Apr 15\n#> 5 yes 0 Taxi Affiliation Services no Sun Jan 21\n#> 6 yes 2.3 other no Sat Apr 21\n#> 7 yes 6.35 Sun Taxi no Wed Mar 16\n#> 8 yes 2.79 other no Sun Feb 14\n#> 9 yes 16.6 other no Sun Apr 18\n#> 10 yes 0.02 Chicago Independents yes Sun Apr 15\n#> # ℹ 7,490 more rows" }, { "objectID": "SD/5.1_model/index.html#练习", "href": "SD/5.1_model/index.html#练习", "title": "模型构建", "section": "练习", "text": "练习\n\nset.seed(123)\ntaxi_split <- initial_split(taxi, prop = 0.8)\ntaxi_train <- training(taxi_split)\ntaxi_test <- testing(taxi_split)\n\nnrow(taxi_train)\n\n#> [1] 8000\n\nnrow(taxi_test)\n\n#> [1] 2000" }, { "objectID": "SD/5.1_model/index.html#stratification", "href": "SD/5.1_model/index.html#stratification", "title": "模型构建", "section": "Stratification", "text": "Stratification\nUse strata = tip\n\nset.seed(123)\ntaxi_split <- initial_split(taxi, prop = 0.8, strata = tip)\ntaxi_split\n\n#> <Training/Testing/Total>\n#> <8000/2000/10000>" }, { "objectID": "SD/5.1_model/index.html#stratification-1", "href": "SD/5.1_model/index.html#stratification-1", "title": "模型构建", "section": "Stratification", "text": "Stratification\nStratification often helps, with very little downside" }, { "objectID": "SD/5.1_model/index.html#模型类型", "href": "SD/5.1_model/index.html#模型类型", "title": "模型构建", "section": "模型类型", "text": "模型类型\n模型多种多样\n\nlm for linear model\nglm for generalized linear model (e.g. logistic regression)\nglmnet for regularized regression\nkeras for regression using TensorFlow\nstan for Bayesian regression\nspark for large data sets" }, { "objectID": "SD/5.1_model/index.html#指定模型", "href": "SD/5.1_model/index.html#指定模型", "title": "模型构建", "section": "指定模型", "text": "指定模型\n\nlogistic_reg()\n\n#> Logistic Regression Model Specification (classification)\n#> \n#> Computational engine: glm\n\n\n\nModels have default engines" }, { "objectID": "SD/5.1_model/index.html#to-specify-a-model", "href": "SD/5.1_model/index.html#to-specify-a-model", "title": "模型构建", "section": "To specify a model", "text": "To specify a model\n\nlogistic_reg() %>%\n set_engine(\"glmnet\")\n\n#> Logistic Regression Model Specification (classification)\n#> \n#> Computational engine: glmnet\n\n\n\n\nlogistic_reg() %>%\n set_engine(\"stan\")\n\n#> Logistic Regression Model Specification (classification)\n#> \n#> Computational engine: stan\n\n\n\n\n\nChoose a model\nSpecify an engine\nSet the mode" }, { "objectID": "SD/5.1_model/index.html#to-specify-a-model-1", "href": "SD/5.1_model/index.html#to-specify-a-model-1", "title": "模型构建", "section": "To specify a model", "text": "To specify a model\n\ndecision_tree()\n\n#> Decision Tree Model Specification (unknown mode)\n#> \n#> Computational engine: rpart\n\n\n\nSome models have a default mode" }, { "objectID": "SD/5.1_model/index.html#to-specify-a-model-2", "href": "SD/5.1_model/index.html#to-specify-a-model-2", "title": "模型构建", "section": "To specify a model", "text": "To specify a model\n\ndecision_tree() %>%\n set_mode(\"classification\")\n\n#> Decision Tree Model Specification (classification)\n#> \n#> Computational engine: rpart\n\n\n\n\n\nAll available models are listed at https://www.tidymodels.org/find/parsnip/" }, { "objectID": "SD/5.1_model/index.html#workflows", "href": "SD/5.1_model/index.html#workflows", "title": "模型构建", "section": "Workflows", "text": "Workflows" }, { "objectID": "SD/5.1_model/index.html#为什么要使用-workflow", "href": "SD/5.1_model/index.html#为什么要使用-workflow", "title": "模型构建", "section": "为什么要使用 workflow()?", "text": "为什么要使用 workflow()?\n\n与基本的 R 工具相比，工作流能更好地处理新的因子水平\n\n\n\n除了公式之外，还可以使用其他的预处理器（更多关于高级 tidymodels 中的特征工程！）\n\n\n\n\n在使用多个模型时，它们可以帮助组织工作\n\n\n\n\n最重要的是，工作流涵盖了整个建模过程：fit() 和 predict() 不仅适用于实际的模型拟合，还适用于预处理步骤\n\n\n工作流比基本的 R 处理水平更好的两种方式：\n\n强制要求在预测时不允许出现新的水平（这是一个可选的检查，可以关闭）\n恢复在拟合时存在但在预测时缺失的水平（例如，“新”数据中没有该水平的实例）" }, { "objectID": "SD/5.1_model/index.html#a-model-workflow", "href": "SD/5.1_model/index.html#a-model-workflow", "title": "模型构建", "section": "A model workflow", "text": "A model workflow\n\ntree_spec <-\n decision_tree(cost_complexity = 0.002) %>%\n set_mode(\"classification\")\n\ntree_spec %>%\n fit(tip ~ ., data = taxi_train)\n\n#> parsnip model object\n#> \n#> n= 8000 \n#> \n#> node), split, n, loss, yval, (yprob)\n#> * denotes terminal node\n#> \n#> 1) root 8000 616 yes (0.92300000 0.07700000) \n#> 2) distance>=14.12 2041 68 yes (0.96668300 0.03331700) *\n#> 3) distance< 14.12 5959 548 yes (0.90803826 0.09196174) \n#> 6) distance< 5.275 5419 450 yes (0.91695885 0.08304115) *\n#> 7) distance>=5.275 540 98 yes (0.81851852 0.18148148) \n#> 14) company=Chicago Independents,City Service,Sun Taxi,Taxi Affiliation Services,Taxicab Insurance Agency Llc,other 478 68 yes (0.85774059 0.14225941) *\n#> 15) company=Flash Cab 62 30 yes (0.51612903 0.48387097) \n#> 30) dow=Thu 12 2 yes (0.83333333 0.16666667) *\n#> 31) dow=Sun,Mon,Tue,Wed,Fri,Sat 50 22 no (0.44000000 0.56000000) \n#> 62) distance>=11.77 14 4 yes (0.71428571 0.28571429) *\n#> 63) distance< 11.77 36 12 no (0.33333333 0.66666667) *" }, { "objectID": "SD/5.1_model/index.html#a-model-workflow-1", "href": "SD/5.1_model/index.html#a-model-workflow-1", "title": "模型构建", "section": "A model workflow", "text": "A model workflow\n\ntree_spec <-\n decision_tree(cost_complexity = 0.002) %>%\n set_mode(\"classification\")\n\nworkflow() %>%\n add_formula(tip ~ .) %>%\n add_model(tree_spec) %>%\n fit(data = taxi_train)\n\n#> ══ Workflow [trained] ════════════════════════════════════════════════\n#> Preprocessor: Formula\n#> Model: decision_tree()\n#> \n#> ── Preprocessor ──────────────────────────────────────────────────────\n#> tip ~ .\n#> \n#> ── Model ─────────────────────────────────────────────────────────────\n#> n= 8000 \n#> \n#> node), split, n, loss, yval, (yprob)\n#> * denotes terminal node\n#> \n#> 1) root 8000 616 yes (0.92300000 0.07700000) \n#> 2) distance>=14.12 2041 68 yes (0.96668300 0.03331700) *\n#> 3) distance< 14.12 5959 548 yes (0.90803826 0.09196174) \n#> 6) distance< 5.275 5419 450 yes (0.91695885 0.08304115) *\n#> 7) distance>=5.275 540 98 yes (0.81851852 0.18148148) \n#> 14) company=Chicago Independents,City Service,Sun Taxi,Taxi Affiliation Services,Taxicab Insurance Agency Llc,other 478 68 yes (0.85774059 0.14225941) *\n#> 15) company=Flash Cab 62 30 yes (0.51612903 0.48387097) \n#> 30) dow=Thu 12 2 yes (0.83333333 0.16666667) *\n#> 31) dow=Sun,Mon,Tue,Wed,Fri,Sat 50 22 no (0.44000000 0.56000000) \n#> 62) distance>=11.77 14 4 yes (0.71428571 0.28571429) *\n#> 63) distance< 11.77 36 12 no (0.33333333 0.66666667) *" }, { "objectID": "SD/5.1_model/index.html#a-model-workflow-2", "href": "SD/5.1_model/index.html#a-model-workflow-2", "title": "模型构建", "section": "A model workflow", "text": "A model workflow\n\ntree_spec <-\n decision_tree(cost_complexity = 0.002) %>%\n set_mode(\"classification\")\n\nworkflow(tip ~ ., tree_spec) %>%\n fit(data = taxi_train)\n\n#> ══ Workflow [trained] ════════════════════════════════════════════════\n#> Preprocessor: Formula\n#> Model: decision_tree()\n#> \n#> ── Preprocessor ──────────────────────────────────────────────────────\n#> tip ~ .\n#> \n#> ── Model ─────────────────────────────────────────────────────────────\n#> n= 8000 \n#> \n#> node), split, n, loss, yval, (yprob)\n#> * denotes terminal node\n#> \n#> 1) root 8000 616 yes (0.92300000 0.07700000) \n#> 2) distance>=14.12 2041 68 yes (0.96668300 0.03331700) *\n#> 3) distance< 14.12 5959 548 yes (0.90803826 0.09196174) \n#> 6) distance< 5.275 5419 450 yes (0.91695885 0.08304115) *\n#> 7) distance>=5.275 540 98 yes (0.81851852 0.18148148) \n#> 14) company=Chicago Independents,City Service,Sun Taxi,Taxi Affiliation Services,Taxicab Insurance Agency Llc,other 478 68 yes (0.85774059 0.14225941) *\n#> 15) company=Flash Cab 62 30 yes (0.51612903 0.48387097) \n#> 30) dow=Thu 12 2 yes (0.83333333 0.16666667) *\n#> 31) dow=Sun,Mon,Tue,Wed,Fri,Sat 50 22 no (0.44000000 0.56000000) \n#> 62) distance>=11.77 14 4 yes (0.71428571 0.28571429) *\n#> 63) distance< 11.77 36 12 no (0.33333333 0.66666667) *" }, { "objectID": "SD/5.1_model/index.html#预测", "href": "SD/5.1_model/index.html#预测", "title": "模型构建", "section": "预测", "text": "预测\nHow do you use your new tree_fit model?\n\ntree_spec <-\n decision_tree(cost_complexity = 0.002) %>%\n set_mode(\"classification\")\n\ntree_fit <-\n workflow(tip ~ ., tree_spec) %>%\n fit(data = taxi_train)" }, { "objectID": "SD/5.1_model/index.html#练习-1", "href": "SD/5.1_model/index.html#练习-1", "title": "模型构建", "section": "练习", "text": "练习\nRun:\npredict(tree_fit, new_data = taxi_test)\n\nRun:\naugment(tree_fit, new_data = taxi_test)\nWhat do you get?" }, { "objectID": "SD/5.1_model/index.html#tidymodels-的预测", "href": "SD/5.1_model/index.html#tidymodels-的预测", "title": "模型构建", "section": "tidymodels 的预测", "text": "tidymodels 的预测\n\n预测结果始终在一个 tibble 内\n列名和类型可读性强\nnew_data 中的行数和输出中的行数相同" }, { "objectID": "SD/5.1_model/index.html#理解模型", "href": "SD/5.1_model/index.html#理解模型", "title": "模型构建", "section": "理解模型", "text": "理解模型\n如何理解tree_fit 模型？" }, { "objectID": "SD/5.1_model/index.html#evaluating-models-预测值", "href": "SD/5.1_model/index.html#evaluating-models-预测值", "title": "模型构建", "section": "Evaluating models: 预测值", "text": "Evaluating models: 预测值\n\naugment(taxi_fit, new_data = taxi_train) %>%\n relocate(tip, .pred_class, .pred_yes, .pred_no)\n\n#> # A tibble: 8,000 × 10\n#> tip .pred_class .pred_yes .pred_no distance company local dow month hour\n#> <fct> <fct> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <int>\n#> 1 yes yes 0.967 0.0333 17.2 Chicag… no Thu Feb 16\n#> 2 yes yes 0.935 0.0646 0.88 City S… yes Thu Mar 8\n#> 3 yes yes 0.967 0.0333 18.1 other no Mon Feb 18\n#> 4 yes yes 0.949 0.0507 12.2 Chicag… no Sun Mar 21\n#> 5 yes yes 0.821 0.179 0.94 Sun Ta… yes Sat Apr 23\n#> 6 yes yes 0.967 0.0333 17.5 Flash … no Fri Mar 12\n#> 7 yes yes 0.967 0.0333 17.7 other no Sun Jan 6\n#> 8 yes yes 0.938 0.0616 1.85 Taxica… no Fri Apr 12\n#> 9 yes yes 0.938 0.0616 0.53 Sun Ta… no Tue Mar 18\n#> 10 yes yes 0.931 0.0694 6.65 Taxica… no Sun Apr 11\n#> # ℹ 7,990 more rows" }, { "objectID": "SD/5.1_model/index.html#confusion-matrix", "href": "SD/5.1_model/index.html#confusion-matrix", "title": "模型构建", "section": "Confusion matrix", "text": "Confusion matrix" }, { "objectID": "SD/5.1_model/index.html#confusion-matrix-1", "href": "SD/5.1_model/index.html#confusion-matrix-1", "title": "模型构建", "section": "Confusion matrix", "text": "Confusion matrix\n\naugment(taxi_fit, new_data = taxi_train) %>%\n conf_mat(truth = tip, estimate = .pred_class)\n\n#> Truth\n#> Prediction yes no\n#> yes 7341 536\n#> no 43 80" }, { "objectID": "SD/5.1_model/index.html#confusion-matrix-2", "href": "SD/5.1_model/index.html#confusion-matrix-2", "title": "模型构建", "section": "Confusion matrix", "text": "Confusion matrix\n\naugment(taxi_fit, new_data = taxi_train) %>%\n conf_mat(truth = tip, estimate = .pred_class) %>%\n autoplot(type = \"heatmap\")" }, { "objectID": "SD/5.1_model/index.html#metrics-for-model-performance", "href": "SD/5.1_model/index.html#metrics-for-model-performance", "title": "模型构建", "section": "Metrics for model performance", "text": "Metrics for model performance\n\n\n\naugment(taxi_fit, new_data = taxi_train) %>%\n accuracy(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 1 × 3\n#> .metric .estimator .estimate\n#> <chr> <chr> <dbl>\n#> 1 accuracy binary 0.928" }, { "objectID": "SD/5.1_model/index.html#二分类模型评估", "href": "SD/5.1_model/index.html#二分类模型评估", "title": "模型构建", "section": "二分类模型评估", "text": "二分类模型评估\n模型的敏感性（Sensitivity）和特异性（Specificity）是评估二分类模型性能的重要指标：\n\n敏感性（Sensitivity），也称为真阳性率，衡量了模型正确识别正类别样本的能力。公式为真阳性数除以真阳性数加上假阴性数：\n\n\\[\n\\text{Sensitivity} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}\n\\]\n\n特异性（Specificity），也称为真阴性率，衡量了模型正确识别负类别样本的能力。公式为真阴性数除以真阴性数加上假阳性数：\n\n\\[\n\\text{Specificity} = \\frac{\\text{True Negatives}}{\\text{True Negatives} + \\text{False Positives}}\n\\]\n在评估模型时，我们希望敏感性和特异性都很高。高敏感性表示模型能够捕获真正的正类别样本，高特异性表示模型能够准确排除负类别样本。" }, { "objectID": "SD/5.1_model/index.html#metrics-for-model-performance-1", "href": "SD/5.1_model/index.html#metrics-for-model-performance-1", "title": "模型构建", "section": "Metrics for model performance", "text": "Metrics for model performance\n\n\n\naugment(taxi_fit, new_data = taxi_train) %>%\n sensitivity(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 1 × 3\n#> .metric .estimator .estimate\n#> <chr> <chr> <dbl>\n#> 1 sensitivity binary 0.994" }, { "objectID": "SD/5.1_model/index.html#metrics-for-model-performance-2", "href": "SD/5.1_model/index.html#metrics-for-model-performance-2", "title": "模型构建", "section": "Metrics for model performance", "text": "Metrics for model performance\n\n\n\naugment(taxi_fit, new_data = taxi_train) %>%\n sensitivity(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 1 × 3\n#> .metric .estimator .estimate\n#> <chr> <chr> <dbl>\n#> 1 sensitivity binary 0.994\n\n\n\n\naugment(taxi_fit, new_data = taxi_train) %>%\n specificity(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 1 × 3\n#> .metric .estimator .estimate\n#> <chr> <chr> <dbl>\n#> 1 specificity binary 0.130" }, { "objectID": "SD/5.1_model/index.html#metrics-for-model-performance-3", "href": "SD/5.1_model/index.html#metrics-for-model-performance-3", "title": "模型构建", "section": "Metrics for model performance", "text": "Metrics for model performance\nWe can use metric_set() to combine multiple calculations into one\n\ntaxi_metrics <- metric_set(accuracy, specificity, sensitivity)\n\naugment(taxi_fit, new_data = taxi_train) %>%\n taxi_metrics(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 3 × 3\n#> .metric .estimator .estimate\n#> <chr> <chr> <dbl>\n#> 1 accuracy binary 0.928\n#> 2 specificity binary 0.130\n#> 3 sensitivity binary 0.994" }, { "objectID": "SD/5.1_model/index.html#metrics-for-model-performance-4", "href": "SD/5.1_model/index.html#metrics-for-model-performance-4", "title": "模型构建", "section": "Metrics for model performance", "text": "Metrics for model performance\n\ntaxi_metrics <- metric_set(accuracy, specificity, sensitivity)\n\naugment(taxi_fit, new_data = taxi_train) %>%\n group_by(local) %>%\n taxi_metrics(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 6 × 4\n#> local .metric .estimator .estimate\n#> <fct> <chr> <chr> <dbl>\n#> 1 yes accuracy binary 0.898\n#> 2 no accuracy binary 0.935\n#> 3 yes specificity binary 0.169\n#> 4 no specificity binary 0.116\n#> 5 yes sensitivity binary 0.987\n#> 6 no sensitivity binary 0.996" }, { "objectID": "SD/5.1_model/index.html#varying-the-threshold", "href": "SD/5.1_model/index.html#varying-the-threshold", "title": "模型构建", "section": "Varying the threshold", "text": "Varying the threshold" }, { "objectID": "SD/5.1_model/index.html#roc-曲线", "href": "SD/5.1_model/index.html#roc-曲线", "title": "模型构建", "section": "ROC 曲线", "text": "ROC 曲线\n\nROC（Receiver Operating Characteristic）曲线用于评估二分类模型的性能，特别是在不同的阈值下比较模型的敏感性和特异性。\nROC曲线的横轴是假阳性率（False Positive Rate，FPR），纵轴是真阳性率（True Positive Rate，TPR）。在ROC曲线上，每个点对应于一个特定的阈值。通过改变阈值，我们可以观察到模型在不同条件下的表现。\nROC曲线越接近左上角（0,1）点，说明模型的性能越好，因为这表示在较低的假阳性率下，模型能够获得较高的真阳性率。ROC曲线下面积（Area Under the ROC Curve，AUC）也是评估模型性能的一种指标，AUC值越大表示模型性能越好。" }, { "objectID": "SD/5.1_model/index.html#roc-curve-plot", "href": "SD/5.1_model/index.html#roc-curve-plot", "title": "模型构建", "section": "ROC curve plot", "text": "ROC curve plot\n\n\naugment(taxi_fit, new_data = taxi_train) %>%\n roc_curve(truth = tip, .pred_yes) %>%\n autoplot()" }, { "objectID": "SD/5.1_model/index.html#过度拟合", "href": "SD/5.1_model/index.html#过度拟合", "title": "模型构建", "section": "过度拟合", "text": "过度拟合" }, { "objectID": "SD/5.1_model/index.html#过度拟合-1", "href": "SD/5.1_model/index.html#过度拟合-1", "title": "模型构建", "section": "过度拟合", "text": "过度拟合" }, { "objectID": "SD/5.1_model/index.html#cross-validation", "href": "SD/5.1_model/index.html#cross-validation", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation" }, { "objectID": "SD/5.1_model/index.html#cross-validation-1", "href": "SD/5.1_model/index.html#cross-validation-1", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation" }, { "objectID": "SD/5.1_model/index.html#cross-validation-2", "href": "SD/5.1_model/index.html#cross-validation-2", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation" }, { "objectID": "SD/5.1_model/index.html#cross-validation-3", "href": "SD/5.1_model/index.html#cross-validation-3", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation\n\nvfold_cv(taxi_train) # v = 10 is default\n\n#> # 10-fold cross-validation \n#> # A tibble: 10 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [7200/800]> Fold01\n#> 2 <split [7200/800]> Fold02\n#> 3 <split [7200/800]> Fold03\n#> 4 <split [7200/800]> Fold04\n#> 5 <split [7200/800]> Fold05\n#> 6 <split [7200/800]> Fold06\n#> 7 <split [7200/800]> Fold07\n#> 8 <split [7200/800]> Fold08\n#> 9 <split [7200/800]> Fold09\n#> 10 <split [7200/800]> Fold10" }, { "objectID": "SD/5.1_model/index.html#cross-validation-4", "href": "SD/5.1_model/index.html#cross-validation-4", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation\nWhat is in this?\n\ntaxi_folds <- vfold_cv(taxi_train)\ntaxi_folds$splits[1:3]\n\n#> [[1]]\n#> <Analysis/Assess/Total>\n#> <7200/800/8000>\n#> \n#> [[2]]\n#> <Analysis/Assess/Total>\n#> <7200/800/8000>\n#> \n#> [[3]]\n#> <Analysis/Assess/Total>\n#> <7200/800/8000>\n\n\n\nTalk about a list column, storing non-atomic types in dataframe" }, { "objectID": "SD/5.1_model/index.html#cross-validation-5", "href": "SD/5.1_model/index.html#cross-validation-5", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation\n\nvfold_cv(taxi_train, v = 5)\n\n#> # 5-fold cross-validation \n#> # A tibble: 5 × 2\n#> splits id \n#> <list> <chr>\n#> 1 <split [6400/1600]> Fold1\n#> 2 <split [6400/1600]> Fold2\n#> 3 <split [6400/1600]> Fold3\n#> 4 <split [6400/1600]> Fold4\n#> 5 <split [6400/1600]> Fold5" }, { "objectID": "SD/5.1_model/index.html#cross-validation-6", "href": "SD/5.1_model/index.html#cross-validation-6", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation\n\nvfold_cv(taxi_train, strata = tip)\n\n#> # 10-fold cross-validation using stratification \n#> # A tibble: 10 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [7200/800]> Fold01\n#> 2 <split [7200/800]> Fold02\n#> 3 <split [7200/800]> Fold03\n#> 4 <split [7200/800]> Fold04\n#> 5 <split [7200/800]> Fold05\n#> 6 <split [7200/800]> Fold06\n#> 7 <split [7200/800]> Fold07\n#> 8 <split [7200/800]> Fold08\n#> 9 <split [7200/800]> Fold09\n#> 10 <split [7200/800]> Fold10\n\n\n\nStratification often helps, with very little downside" }, { "objectID": "SD/5.1_model/index.html#cross-validation-7", "href": "SD/5.1_model/index.html#cross-validation-7", "title": "模型构建", "section": "Cross-validation", "text": "Cross-validation\nWe’ll use this setup:\n\nset.seed(123)\ntaxi_folds <- vfold_cv(taxi_train, v = 10, strata = tip)\ntaxi_folds\n\n#> # 10-fold cross-validation using stratification \n#> # A tibble: 10 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [7200/800]> Fold01\n#> 2 <split [7200/800]> Fold02\n#> 3 <split [7200/800]> Fold03\n#> 4 <split [7200/800]> Fold04\n#> 5 <split [7200/800]> Fold05\n#> 6 <split [7200/800]> Fold06\n#> 7 <split [7200/800]> Fold07\n#> 8 <split [7200/800]> Fold08\n#> 9 <split [7200/800]> Fold09\n#> 10 <split [7200/800]> Fold10\n\n\n\nSet the seed when creating resamples" }, { "objectID": "SD/5.1_model/index.html#fit-our-model-to-the-resamples", "href": "SD/5.1_model/index.html#fit-our-model-to-the-resamples", "title": "模型构建", "section": "Fit our model to the resamples", "text": "Fit our model to the resamples\n\ntaxi_res <- fit_resamples(taxi_wflow, taxi_folds)\ntaxi_res\n\n#> # Resampling results\n#> # 10-fold cross-validation using stratification \n#> # A tibble: 10 × 4\n#> splits id .metrics .notes \n#> <list> <chr> <list> <list> \n#> 1 <split [7200/800]> Fold01 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 2 <split [7200/800]> Fold02 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 3 <split [7200/800]> Fold03 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 4 <split [7200/800]> Fold04 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 5 <split [7200/800]> Fold05 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 6 <split [7200/800]> Fold06 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 7 <split [7200/800]> Fold07 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 8 <split [7200/800]> Fold08 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 9 <split [7200/800]> Fold09 <tibble [3 × 4]> <tibble [0 × 3]>\n#> 10 <split [7200/800]> Fold10 <tibble [3 × 4]> <tibble [0 × 3]>" }, { "objectID": "SD/5.1_model/index.html#evaluating-model-performance", "href": "SD/5.1_model/index.html#evaluating-model-performance", "title": "模型构建", "section": "Evaluating model performance", "text": "Evaluating model performance\n\ntaxi_res %>%\n collect_metrics()\n\n#> # A tibble: 3 × 6\n#> .metric .estimator mean n std_err .config \n#> <chr> <chr> <dbl> <int> <dbl> <chr> \n#> 1 accuracy binary 0.915 10 0.00309 Preprocessor1_Model1\n#> 2 brier_class binary 0.0721 10 0.00245 Preprocessor1_Model1\n#> 3 roc_auc binary 0.624 10 0.0105 Preprocessor1_Model1\n\n\n\ncollect_metrics() 是一套 collect_() 函数之一，可用于处理调参结果的列。调参结果中以 . 为前缀的大多数列都有对应的 collect_() 函数，可以进行常见摘要选项的汇总。\n\n\nWe can reliably measure performance using only the training data 🎉" }, { "objectID": "SD/5.1_model/index.html#comparing-metrics", "href": "SD/5.1_model/index.html#comparing-metrics", "title": "模型构建", "section": "Comparing metrics", "text": "Comparing metrics\nHow do the metrics from resampling compare to the metrics from training and testing?\n\n\n\ntaxi_res %>%\n collect_metrics() %>%\n select(.metric, mean, n)\n\n#> # A tibble: 3 × 3\n#> .metric mean n\n#> <chr> <dbl> <int>\n#> 1 accuracy 0.915 10\n#> 2 brier_class 0.0721 10\n#> 3 roc_auc 0.624 10\n\n\n\nThe ROC AUC previously was\n\n0.69 for the training set\n0.64 for test set\n\n\n\nRemember that:\n⚠️ the training set gives you overly optimistic metrics\n⚠️ the test set is precious" }, { "objectID": "SD/5.1_model/index.html#evaluating-model-performance-1", "href": "SD/5.1_model/index.html#evaluating-model-performance-1", "title": "模型构建", "section": "Evaluating model performance", "text": "Evaluating model performance\n\n# Save the assessment set results\nctrl_taxi <- control_resamples(save_pred = TRUE)\ntaxi_res <- fit_resamples(taxi_wflow, taxi_folds, control = ctrl_taxi)\n\ntaxi_res\n\n#> # Resampling results\n#> # 10-fold cross-validation using stratification \n#> # A tibble: 10 × 5\n#> splits id .metrics .notes .predictions\n#> <list> <chr> <list> <list> <list> \n#> 1 <split [7200/800]> Fold01 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 2 <split [7200/800]> Fold02 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 3 <split [7200/800]> Fold03 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 4 <split [7200/800]> Fold04 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 5 <split [7200/800]> Fold05 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 6 <split [7200/800]> Fold06 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 7 <split [7200/800]> Fold07 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 8 <split [7200/800]> Fold08 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 9 <split [7200/800]> Fold09 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 10 <split [7200/800]> Fold10 <tibble [3 × 4]> <tibble [0 × 3]> <tibble>" }, { "objectID": "SD/5.1_model/index.html#evaluating-model-performance-2", "href": "SD/5.1_model/index.html#evaluating-model-performance-2", "title": "模型构建", "section": "Evaluating model performance", "text": "Evaluating model performance\n\n# Save the assessment set results\ntaxi_preds <- collect_predictions(taxi_res)\ntaxi_preds\n\n#> # A tibble: 8,000 × 7\n#> .pred_class .pred_yes .pred_no id .row tip .config \n#> <fct> <dbl> <dbl> <chr> <int> <fct> <chr> \n#> 1 yes 0.938 0.0615 Fold01 14 yes Preprocessor1_Model1\n#> 2 yes 0.946 0.0544 Fold01 19 yes Preprocessor1_Model1\n#> 3 yes 0.973 0.0269 Fold01 33 yes Preprocessor1_Model1\n#> 4 yes 0.903 0.0971 Fold01 43 yes Preprocessor1_Model1\n#> 5 yes 0.973 0.0269 Fold01 74 yes Preprocessor1_Model1\n#> 6 yes 0.903 0.0971 Fold01 103 yes Preprocessor1_Model1\n#> 7 yes 0.915 0.0851 Fold01 104 no Preprocessor1_Model1\n#> 8 yes 0.903 0.0971 Fold01 124 yes Preprocessor1_Model1\n#> 9 yes 0.667 0.333 Fold01 126 yes Preprocessor1_Model1\n#> 10 yes 0.949 0.0510 Fold01 128 yes Preprocessor1_Model1\n#> # ℹ 7,990 more rows" }, { "objectID": "SD/5.1_model/index.html#evaluating-model-performance-3", "href": "SD/5.1_model/index.html#evaluating-model-performance-3", "title": "模型构建", "section": "Evaluating model performance", "text": "Evaluating model performance\n\ntaxi_preds %>%\n group_by(id) %>%\n taxi_metrics(truth = tip, estimate = .pred_class)\n\n#> # A tibble: 30 × 4\n#> id .metric .estimator .estimate\n#> <chr> <chr> <chr> <dbl>\n#> 1 Fold01 accuracy binary 0.905\n#> 2 Fold02 accuracy binary 0.925\n#> 3 Fold03 accuracy binary 0.926\n#> 4 Fold04 accuracy binary 0.915\n#> 5 Fold05 accuracy binary 0.902\n#> 6 Fold06 accuracy binary 0.912\n#> 7 Fold07 accuracy binary 0.906\n#> 8 Fold08 accuracy binary 0.91 \n#> 9 Fold09 accuracy binary 0.918\n#> 10 Fold10 accuracy binary 0.931\n#> # ℹ 20 more rows" }, { "objectID": "SD/5.1_model/index.html#where-are-the-fitted-models", "href": "SD/5.1_model/index.html#where-are-the-fitted-models", "title": "模型构建", "section": "Where are the fitted models?", "text": "Where are the fitted models?\n\ntaxi_res\n\n#> # Resampling results\n#> # 10-fold cross-validation using stratification \n#> # A tibble: 10 × 5\n#> splits id .metrics .notes .predictions\n#> <list> <chr> <list> <list> <list> \n#> 1 <split [7200/800]> Fold01 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 2 <split [7200/800]> Fold02 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 3 <split [7200/800]> Fold03 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 4 <split [7200/800]> Fold04 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 5 <split [7200/800]> Fold05 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 6 <split [7200/800]> Fold06 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 7 <split [7200/800]> Fold07 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 8 <split [7200/800]> Fold08 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 9 <split [7200/800]> Fold09 <tibble [3 × 4]> <tibble [0 × 3]> <tibble> \n#> 10 <split [7200/800]> Fold10 <tibble [3 × 4]> <tibble [0 × 3]> <tibble>" }, { "objectID": "SD/5.1_model/index.html#bootstrapping", "href": "SD/5.1_model/index.html#bootstrapping", "title": "模型构建", "section": "Bootstrapping", "text": "Bootstrapping" }, { "objectID": "SD/5.1_model/index.html#bootstrapping-1", "href": "SD/5.1_model/index.html#bootstrapping-1", "title": "模型构建", "section": "Bootstrapping", "text": "Bootstrapping\n\nset.seed(3214)\nbootstraps(taxi_train)\n\n#> # Bootstrap sampling \n#> # A tibble: 25 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [8000/2902]> Bootstrap01\n#> 2 <split [8000/2916]> Bootstrap02\n#> 3 <split [8000/3004]> Bootstrap03\n#> 4 <split [8000/2979]> Bootstrap04\n#> 5 <split [8000/2961]> Bootstrap05\n#> 6 <split [8000/2962]> Bootstrap06\n#> 7 <split [8000/3026]> Bootstrap07\n#> 8 <split [8000/2926]> Bootstrap08\n#> 9 <split [8000/2972]> Bootstrap09\n#> 10 <split [8000/2972]> Bootstrap10\n#> # ℹ 15 more rows" }, { "objectID": "SD/5.1_model/index.html#monte-carlo-cross-validation", "href": "SD/5.1_model/index.html#monte-carlo-cross-validation", "title": "模型构建", "section": "Monte Carlo Cross-Validation", "text": "Monte Carlo Cross-Validation\n\nset.seed(322)\nmc_cv(taxi_train, times = 10)\n\n#> # Monte Carlo cross-validation (0.75/0.25) with 10 resamples \n#> # A tibble: 10 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [6000/2000]> Resample01\n#> 2 <split [6000/2000]> Resample02\n#> 3 <split [6000/2000]> Resample03\n#> 4 <split [6000/2000]> Resample04\n#> 5 <split [6000/2000]> Resample05\n#> 6 <split [6000/2000]> Resample06\n#> 7 <split [6000/2000]> Resample07\n#> 8 <split [6000/2000]> Resample08\n#> 9 <split [6000/2000]> Resample09\n#> 10 <split [6000/2000]> Resample10" }, { "objectID": "SD/5.1_model/index.html#validation-set", "href": "SD/5.1_model/index.html#validation-set", "title": "模型构建", "section": "Validation set", "text": "Validation set\n\nset.seed(853)\ntaxi_val_split <- initial_validation_split(taxi, strata = tip)\nvalidation_set(taxi_val_split)\n\n#> # A tibble: 1 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [6000/2000]> validation" }, { "objectID": "SD/5.1_model/index.html#create-a-random-forest-model", "href": "SD/5.1_model/index.html#create-a-random-forest-model", "title": "模型构建", "section": "Create a random forest model", "text": "Create a random forest model\n\nrf_spec <- rand_forest(trees = 1000, mode = \"classification\")\nrf_spec\n\n#> Random Forest Model Specification (classification)\n#> \n#> Main Arguments:\n#> trees = 1000\n#> \n#> Computational engine: ranger" }, { "objectID": "SD/5.1_model/index.html#create-a-random-forest-model-1", "href": "SD/5.1_model/index.html#create-a-random-forest-model-1", "title": "模型构建", "section": "Create a random forest model", "text": "Create a random forest model\n\nrf_wflow <- workflow(tip ~ ., rf_spec)\nrf_wflow\n\n#> ══ Workflow ══════════════════════════════════════════════════════════\n#> Preprocessor: Formula\n#> Model: rand_forest()\n#> \n#> ── Preprocessor ──────────────────────────────────────────────────────\n#> tip ~ .\n#> \n#> ── Model ─────────────────────────────────────────────────────────────\n#> Random Forest Model Specification (classification)\n#> \n#> Main Arguments:\n#> trees = 1000\n#> \n#> Computational engine: ranger" }, { "objectID": "SD/5.1_model/index.html#evaluating-model-performance-4", "href": "SD/5.1_model/index.html#evaluating-model-performance-4", "title": "模型构建", "section": "Evaluating model performance", "text": "Evaluating model performance\n\nctrl_taxi <- control_resamples(save_pred = TRUE)\n\n# Random forest uses random numbers so set the seed first\n\nset.seed(2)\nrf_res <- fit_resamples(rf_wflow, taxi_folds, control = ctrl_taxi)\ncollect_metrics(rf_res)\n\n#> # A tibble: 3 × 6\n#> .metric .estimator mean n std_err .config \n#> <chr> <chr> <dbl> <int> <dbl> <chr> \n#> 1 accuracy binary 0.923 10 0.00317 Preprocessor1_Model1\n#> 2 brier_class binary 0.0706 10 0.00243 Preprocessor1_Model1\n#> 3 roc_auc binary 0.616 10 0.0147 Preprocessor1_Model1" }, { "objectID": "SD/5.1_model/index.html#the-whole-game---status-update", "href": "SD/5.1_model/index.html#the-whole-game---status-update", "title": "模型构建", "section": "The whole game - status update", "text": "The whole game - status update" }, { "objectID": "SD/5.1_model/index.html#the-final-fit", "href": "SD/5.1_model/index.html#the-final-fit", "title": "模型构建", "section": "The final fit", "text": "The final fit\n\n# taxi_split has train + test info\nfinal_fit <- last_fit(rf_wflow, taxi_split)\n\nfinal_fit\n\n#> # Resampling results\n#> # Manual resampling \n#> # A tibble: 1 × 6\n#> splits id .metrics .notes .predictions .workflow \n#> <list> <chr> <list> <list> <list> <list> \n#> 1 <split [8000/2000]> train/test split <tibble> <tibble> <tibble> <workflow>" }, { "objectID": "SD/5.1_model/index.html#何为final_fit", "href": "SD/5.1_model/index.html#何为final_fit", "title": "模型构建", "section": "何为final_fit?", "text": "何为final_fit?\n\ncollect_metrics(final_fit)\n\n#> # A tibble: 3 × 4\n#> .metric .estimator .estimate .config \n#> <chr> <chr> <dbl> <chr> \n#> 1 accuracy binary 0.914 Preprocessor1_Model1\n#> 2 roc_auc binary 0.638 Preprocessor1_Model1\n#> 3 brier_class binary 0.0772 Preprocessor1_Model1\n\n\n\nThese are metrics computed with the test set" }, { "objectID": "SD/5.1_model/index.html#何为final_fit-1", "href": "SD/5.1_model/index.html#何为final_fit-1", "title": "模型构建", "section": "何为final_fit?", "text": "何为final_fit?\n\ncollect_predictions(final_fit)\n\n#> # A tibble: 2,000 × 7\n#> .pred_class .pred_yes .pred_no id .row tip .config \n#> <fct> <dbl> <dbl> <chr> <int> <fct> <chr> \n#> 1 yes 0.957 0.0426 train/test split 4 yes Preprocessor1_Mo…\n#> 2 yes 0.938 0.0621 train/test split 10 yes Preprocessor1_Mo…\n#> 3 yes 0.958 0.0416 train/test split 19 yes Preprocessor1_Mo…\n#> 4 yes 0.894 0.106 train/test split 23 yes Preprocessor1_Mo…\n#> 5 yes 0.943 0.0573 train/test split 28 yes Preprocessor1_Mo…\n#> 6 yes 0.979 0.0213 train/test split 34 yes Preprocessor1_Mo…\n#> 7 yes 0.954 0.0463 train/test split 35 yes Preprocessor1_Mo…\n#> 8 yes 0.928 0.0722 train/test split 38 yes Preprocessor1_Mo…\n#> 9 yes 0.985 0.0147 train/test split 40 yes Preprocessor1_Mo…\n#> 10 yes 0.948 0.0523 train/test split 42 no Preprocessor1_Mo…\n#> # ℹ 1,990 more rows" }, { "objectID": "SD/5.1_model/index.html#何为final_fit-2", "href": "SD/5.1_model/index.html#何为final_fit-2", "title": "模型构建", "section": "何为final_fit?", "text": "何为final_fit?\n\nextract_workflow(final_fit)\n\n#> ══ Workflow [trained] ════════════════════════════════════════════════\n#> Preprocessor: Formula\n#> Model: rand_forest()\n#> \n#> ── Preprocessor ──────────────────────────────────────────────────────\n#> tip ~ .\n#> \n#> ── Model ─────────────────────────────────────────────────────────────\n#> Ranger result\n#> \n#> Call:\n#> ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~1000, num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE) \n#> \n#> Type: Probability estimation \n#> Number of trees: 1000 \n#> Sample size: 8000 \n#> Number of independent variables: 6 \n#> Mtry: 2 \n#> Target node size: 10 \n#> Variable importance mode: none \n#> Splitrule: gini \n#> OOB prediction error (Brier s.): 0.07069778\n\n\n\nUse this for prediction on new data, like for deploying" }, { "objectID": "SD/5.1_model/index.html#tuning-models---specifying-tuning-parameters", "href": "SD/5.1_model/index.html#tuning-models---specifying-tuning-parameters", "title": "模型构建", "section": "Tuning models - Specifying tuning parameters", "text": "Tuning models - Specifying tuning parameters\n\nrf_spec <- rand_forest(min_n = tune()) %>%\n set_mode(\"classification\")\n\nrf_wflow <- workflow(tip ~ ., rf_spec)\nrf_wflow\n\n#> ══ Workflow ══════════════════════════════════════════════════════════\n#> Preprocessor: Formula\n#> Model: rand_forest()\n#> \n#> ── Preprocessor ──────────────────────────────────────────────────────\n#> tip ~ .\n#> \n#> ── Model ─────────────────────────────────────────────────────────────\n#> Random Forest Model Specification (classification)\n#> \n#> Main Arguments:\n#> min_n = tune()\n#> \n#> Computational engine: ranger" }, { "objectID": "SD/5.1_model/index.html#try-out-multiple-values", "href": "SD/5.1_model/index.html#try-out-multiple-values", "title": "模型构建", "section": "Try out multiple values", "text": "Try out multiple values\ntune_grid() works similar to fit_resamples() but covers multiple parameter values:\n\nset.seed(22)\nrf_res <- tune_grid(\n rf_wflow,\n taxi_folds,\n grid = 5\n)" }, { "objectID": "SD/5.1_model/index.html#compare-results", "href": "SD/5.1_model/index.html#compare-results", "title": "模型构建", "section": "Compare results", "text": "Compare results\nInspecting results and selecting the best-performing hyperparameter(s):\n\nshow_best(rf_res)\n\n#> # A tibble: 5 × 7\n#> min_n .metric .estimator mean n std_err .config \n#> <int> <chr> <chr> <dbl> <int> <dbl> <chr> \n#> 1 40 roc_auc binary 0.623 10 0.0163 Preprocessor1_Model1\n#> 2 22 roc_auc binary 0.622 10 0.0151 Preprocessor1_Model5\n#> 3 31 roc_auc binary 0.621 10 0.0152 Preprocessor1_Model2\n#> 4 12 roc_auc binary 0.617 10 0.0149 Preprocessor1_Model4\n#> 5 3 roc_auc binary 0.609 10 0.0144 Preprocessor1_Model3\n\nbest_parameter <- select_best(rf_res)\nbest_parameter\n\n#> # A tibble: 1 × 2\n#> min_n .config \n#> <int> <chr> \n#> 1 40 Preprocessor1_Model1\n\n\ncollect_metrics() and autoplot() are also available." }, { "objectID": "SD/5.1_model/index.html#the-final-fit-1", "href": "SD/5.1_model/index.html#the-final-fit-1", "title": "模型构建", "section": "The final fit", "text": "The final fit\n\nrf_wflow <- finalize_workflow(rf_wflow, best_parameter)\n\nfinal_fit <- last_fit(rf_wflow, taxi_split)\n\ncollect_metrics(final_fit)\n\n#> # A tibble: 3 × 4\n#> .metric .estimator .estimate .config \n#> <chr> <chr> <dbl> <chr> \n#> 1 accuracy binary 0.913 Preprocessor1_Model1\n#> 2 roc_auc binary 0.647 Preprocessor1_Model1\n#> 3 brier_class binary 0.0762 Preprocessor1_Model1" }, { "objectID": "SD/5.1_model/index.html#数据", "href": "SD/5.1_model/index.html#数据", "title": "模型构建", "section": "数据", "text": "数据\n\nrequire(tidyverse)\nsitedf <- readr::read_csv(\n \"https://www.epa.gov/sites/default/files/2014-01/nla2007_sampledlakeinformation_20091113.csv\"\n) |>\n select(\n SITE_ID,\n lon = LON_DD,\n lat = LAT_DD,\n name = LAKENAME,\n area = LAKEAREA,\n zmax = DEPTHMAX\n ) |>\n group_by(SITE_ID) |>\n summarize(\n lon = mean(lon, na.rm = TRUE),\n lat = mean(lat, na.rm = TRUE),\n name = unique(name),\n area = mean(area, na.rm = TRUE),\n zmax = mean(zmax, na.rm = TRUE)\n )\n\n\nvisitdf <- readr::read_csv(\n \"https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv\"\n) |>\n select(SITE_ID, date = DATE_PROFILE, year = YEAR, visit = VISIT_NO) |>\n distinct()\n\n\nwaterchemdf <- readr::read_csv(\n \"https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv\"\n) |>\n select(\n SITE_ID,\n date = DATE_PROFILE,\n depth = DEPTH,\n temp = TEMP_FIELD,\n do = DO_FIELD,\n ph = PH_FIELD,\n cond = COND_FIELD,\n )\n\nsddf <- readr::read_csv(\n \"https://www.epa.gov/sites/default/files/2014-10/nla2007_secchi_20091008.csv\"\n) |>\n select(\n SITE_ID,\n date = DATE_SECCHI,\n sd = SECMEAN,\n clear_to_bottom = CLEAR_TO_BOTTOM\n )\n\ntrophicdf <- readr::read_csv(\n \"https://www.epa.gov/sites/default/files/2014-10/nla2007_trophic_conditionestimate_20091123.csv\"\n) |>\n select(SITE_ID, visit = VISIT_NO, tp = PTL, tn = NTL, chla = CHLA) |>\n left_join(visitdf, by = c(\"SITE_ID\", \"visit\")) |>\n select(-year, -visit) |>\n group_by(SITE_ID, date) |>\n summarize(\n tp = mean(tp, na.rm = TRUE),\n tn = mean(tn, na.rm = TRUE),\n chla = mean(chla, na.rm = TRUE)\n )\n\n\nphytodf <- readr::read_csv(\n \"https://www.epa.gov/sites/default/files/2014-10/nla2007_phytoplankton_softalgaecount_20091023.csv\"\n) |>\n select(\n SITE_ID,\n date = DATEPHYT,\n depth = SAMPLE_DEPTH,\n phyta = DIVISION,\n genus = GENUS,\n species = SPECIES,\n tax = TAXANAME,\n abund = ABUND\n ) |>\n mutate(phyta = gsub(\" .*$\", \"\", phyta)) |>\n filter(!is.na(genus)) |>\n group_by(SITE_ID, date, depth, phyta, genus) |>\n summarize(abund = sum(abund, na.rm = TRUE)) |>\n nest(phytodf = -c(SITE_ID, date))\n\nenvdf <- waterchemdf |>\n filter(depth < 2) |>\n select(-depth) |>\n group_by(SITE_ID, date) |>\n summarise_all(~ mean(., na.rm = TRUE)) |>\n ungroup() |>\n left_join(sddf, by = c(\"SITE_ID\", \"date\")) |>\n left_join(trophicdf, by = c(\"SITE_ID\", \"date\"))\n\nnla <- envdf |>\n left_join(phytodf) |>\n left_join(sitedf, by = \"SITE_ID\") |>\n filter(!purrr::map_lgl(phytodf, is.null)) |>\n mutate(\n cyanophyta = purrr::map(\n phytodf,\n ~ .x |>\n dplyr::filter(phyta == \"Cyanophyta\") |>\n summarize(cyanophyta = sum(abund, na.rm = TRUE))\n )\n ) |>\n unnest(cyanophyta) |>\n select(-phyta) |>\n mutate(clear_to_bottom = ifelse(is.na(clear_to_bottom), TRUE, FALSE))\n\n# library(rmdify)\n# library(dwfun)\n# dwfun::init()" }, { "objectID": "SD/5.1_model/index.html#数据-1", "href": "SD/5.1_model/index.html#数据-1", "title": "模型构建", "section": "数据", "text": "数据\n\nskimr::skim(nla)\n\n\nData summary\n\n\nName\nnla\n\n\nNumber of rows\n1208\n\n\nNumber of columns\n19\n\n\n_______________________\n\n\n\nColumn type frequency:\n\n\n\ncharacter\n3\n\n\nlist\n1\n\n\nlogical\n1\n\n\nnumeric\n14\n\n\n________________________\n\n\n\nGroup variables\nNone\n\n\n\nVariable type: character\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmin\nmax\nempty\nn_unique\nwhitespace\n\n\n\n\nSITE_ID\n0\n1.00\n12\n24\n0\n1114\n0\n\n\ndate\n0\n1.00\n8\n10\n0\n116\n0\n\n\nname\n44\n0.96\n5\n48\n0\n990\n0\n\n\n\nVariable type: list\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nn_unique\nmin_length\nmax_length\n\n\n\n\nphytodf\n0\n1\n1207\n4\n4\n\n\n\nVariable type: logical\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmean\ncount\n\n\n\n\nclear_to_bottom\n0\n1\n0.96\nTRU: 1154, FAL: 54\n\n\n\nVariable type: numeric\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmean\nsd\np0\np25\np50\np75\np100\nhist\n\n\n\n\ntemp\n1\n1.00\n24.13\n4.06\n10.95\n21.43\n24.53\n27.20\n37.73\n▁▅▇▅▁\n\n\ndo\n54\n0.96\n7.84\n1.97\n0.77\n6.85\n7.88\n8.70\n21.00\n▁▇▂▁▁\n\n\nph\n3\n1.00\n8.08\n0.88\n4.10\n7.55\n8.25\n8.64\n10.33\n▁▁▅▇▂\n\n\ncond\n247\n0.80\n714.37\n2499.57\n3.00\n86.93\n219.40\n471.00\n42487.75\n▇▁▁▁▁\n\n\nsd\n62\n0.95\n2.15\n2.49\n0.04\n0.60\n1.35\n2.75\n36.71\n▇▁▁▁▁\n\n\ntp\n0\n1.00\n112.77\n301.34\n1.00\n10.00\n25.00\n90.00\n4865.00\n▇▁▁▁▁\n\n\ntn\n0\n1.00\n1174.39\n2061.71\n5.00\n317.75\n584.00\n1174.25\n26100.00\n▇▁▁▁▁\n\n\nchla\n5\n1.00\n29.91\n69.27\n0.07\n2.96\n7.79\n26.08\n936.00\n▇▁▁▁▁\n\n\nlon\n0\n1.00\n-94.34\n14.08\n-124.25\n-103.12\n-94.48\n-84.32\n-67.70\n▃▃▇▆▃\n\n\nlat\n0\n1.00\n40.55\n5.02\n26.94\n37.12\n41.31\n44.64\n48.98\n▁▃▆▇▆\n\n\narea\n0\n1.00\n12.01\n78.53\n0.04\n0.24\n0.70\n2.87\n1674.90\n▇▁▁▁▁\n\n\nzmax\n0\n1.00\n9.41\n10.13\n0.50\n2.90\n5.90\n12.00\n97.00\n▇▁▁▁▁\n\n\ndepth\n9\n0.99\n1.58\n0.59\n0.08\n1.13\n2.00\n2.00\n2.00\n▁▂▂▁▇\n\n\ncyanophyta\n0\n1.00\n38382.63\n191373.91\n0.66\n1200.61\n5483.81\n23504.81\n4982222.22\n▇▁▁▁▁" }, { "objectID": "SD/5.1_model/index.html#简单模型", "href": "SD/5.1_model/index.html#简单模型", "title": "模型构建", "section": "简单模型", "text": "简单模型\n\nnla |>\n filter(tp > 1) |>\n ggplot(aes(tn, tp)) +\n geom_point() +\n geom_smooth(method = \"lm\") +\n scale_x_log10(\n breaks = scales::trans_breaks(\"log10\", function(x) 10^x),\n labels = scales::trans_format(\"log10\", scales::math_format(10^.x))\n ) +\n scale_y_log10(\n breaks = scales::trans_breaks(\"log10\", function(x) 10^x),\n labels = scales::trans_format(\"log10\", scales::math_format(10^.x))\n )\n\n\n\n\n\n\n\nm1 <- lm(log10(tp) ~ log10(tn), data = nla)\n\nsummary(m1)\n\n#> \n#> Call:\n#> lm(formula = log10(tp) ~ log10(tn), data = nla)\n#> \n#> Residuals:\n#> Min 1Q Median 3Q Max \n#> -1.8063 -0.2360 0.0125 0.2245 1.9140 \n#> \n#> Coefficients:\n#> Estimate Std. Error t value Pr(>|t|) \n#> (Intercept) -1.92315 0.07166 -26.84 <2e-16 ***\n#> log10(tn) 1.21700 0.02528 48.13 <2e-16 ***\n#> ---\n#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#> \n#> Residual standard error: 0.405 on 1206 degrees of freedom\n#> Multiple R-squared: 0.6576, Adjusted R-squared: 0.6574 \n#> F-statistic: 2317 on 1 and 1206 DF, p-value: < 2.2e-16" }, { "objectID": "SD/5.1_model/index.html#复杂指标", "href": "SD/5.1_model/index.html#复杂指标", "title": "模型构建", "section": "复杂指标", "text": "复杂指标\n\nnla |>\n filter(tp > 1) |>\n ggplot(aes(tp, cyanophyta)) +\n geom_point() +\n geom_smooth(method = \"lm\") +\n scale_x_log10(\n breaks = scales::trans_breaks(\"log10\", function(x) 10^x),\n labels = scales::trans_format(\"log10\", scales::math_format(10^.x))\n ) +\n scale_y_log10(\n breaks = scales::trans_breaks(\"log10\", function(x) 10^x),\n labels = scales::trans_format(\"log10\", scales::math_format(10^.x))\n )\n\n\n\n\n\n\n\nm2 <- lm(log10(cyanophyta) ~ log10(tp), data = nla)\n\nsummary(m2)\n\n#> \n#> Call:\n#> lm(formula = log10(cyanophyta) ~ log10(tp), data = nla)\n#> \n#> Residuals:\n#> Min 1Q Median 3Q Max \n#> -5.1551 -0.5128 0.1407 0.6546 3.1811 \n#> \n#> Coefficients:\n#> Estimate Std. Error t value Pr(>|t|) \n#> (Intercept) 2.82739 0.06181 45.74 <2e-16 ***\n#> log10(tp) 0.58577 0.03784 15.48 <2e-16 ***\n#> ---\n#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#> \n#> Residual standard error: 0.9095 on 1206 degrees of freedom\n#> Multiple R-squared: 0.1658, Adjusted R-squared: 0.1651 \n#> F-statistic: 239.7 on 1 and 1206 DF, p-value: < 2.2e-16" }, { "objectID": "SD/5.1_model/index.html#tidymodels---data-split", "href": "SD/5.1_model/index.html#tidymodels---data-split", "title": "模型构建", "section": "tidymodels - Data split", "text": "tidymodels - Data split\n\n(nla_split <- rsample::initial_split(nla, prop = 0.7, strata = zmax))\n\n#> <Training/Testing/Total>\n#> <844/364/1208>\n\n(nla_train <- training(nla_split))\n\n#> # A tibble: 844 × 19\n#> SITE_ID date temp do ph cond sd clear_to_bottom tp tn\n#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl>\n#> 1 NLA06608-… 9/6/… 30.0 9 8.4 1220. 0.49 TRUE 50 843\n#> 2 NLA06608-… 9/14… 22.7 NaN 5.8 44.5 1.05 TRUE 20 264\n#> 3 NLA06608-… 9/4/… 25.1 NaN 5.26 45.5 0.65 TRUE 28 384\n#> 4 NLA06608-… 8/6/… 21.6 7.6 9.4 9080 0.85 TRUE 175 4147\n#> 5 NLA06608-… 6/11… 22.9 4.75 8.38 3373 0.35 TRUE 801 7047\n#> 6 NLA06608-… 9/19… 27.6 8.18 8.4 182 1.3 TRUE 24 562\n#> 7 NLA06608-… 7/2/… 24.6 8.28 7.68 157. 0.27 TRUE 247 2106\n#> 8 NLA06608-… 9/11… 27.4 10 9.5 148 0.25 TRUE 162 2146\n#> 9 NLA06608-… 7/17… 20.4 0.767 6.6 110. 0.7 TRUE 67 835\n#> 10 NLA06608-… 7/30… 24.1 9.97 8.37 281. 0.1 TRUE 523 6741\n#> # ℹ 834 more rows\n#> # ℹ 9 more variables: chla <dbl>, phytodf <list>, lon <dbl>, lat <dbl>,\n#> # name <chr>, area <dbl>, zmax <dbl>, depth <dbl>, cyanophyta <dbl>\n\n(nla_test <- testing(nla_split))\n\n#> # A tibble: 364 × 19\n#> SITE_ID date temp do ph cond sd clear_to_bottom tp tn\n#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl>\n#> 1 NLA06608-0… 6/14… 22.2 NaN 5.52 62.1 0.55 TRUE 36 695\n#> 2 NLA06608-0… 7/23… 24.8 NaN 5.07 46.0 0.45 TRUE 22 469\n#> 3 NLA06608-0… 8/29… 30.1 7.5 8.35 1128. 0.71 TRUE 43 738\n#> 4 NLA06608-0… 7/10… 20.6 6.7 8.3 296 1.8 TRUE 18 344\n#> 5 NLA06608-0… 8/18… 24.7 8.85 8.5 612. 2.6 TRUE 10 485\n#> 6 NLA06608-0… 8/23… 18.1 8.4 9.4 9052. 0.63 TRUE 175 4456\n#> 7 NLA06608-0… 8/7/… 21.2 4.71 9.03 3125. 0.55 TRUE 1376 6578\n#> 8 NLA06608-0… 6/11… 25.2 8.6 8 214 0.69 TRUE 30 605\n#> 9 NLA06608-0… 7/27… 25.3 8.85 7.93 369 7.1 TRUE 7 390\n#> 10 NLA06608-0… 8/30… 23.5 8.2 7.9 NaN 4.75 TRUE 11 463\n#> # ℹ 354 more rows\n#> # ℹ 9 more variables: chla <dbl>, phytodf <list>, lon <dbl>, lat <dbl>,\n#> # name <chr>, area <dbl>, zmax <dbl>, depth <dbl>, cyanophyta <dbl>" }, { "objectID": "SD/5.1_model/index.html#tidymodels---recipe", "href": "SD/5.1_model/index.html#tidymodels---recipe", "title": "模型构建", "section": "tidymodels - recipe", "text": "tidymodels - recipe\n\nnla_formula <- as.formula(\n \"cyanophyta ~ temp + do + ph + cond + sd + tp + tn + chla + clear_to_bottom\"\n)\n# nla_formula <- as.formula(\"cyanophyta ~ temp + do + ph + cond + sd + tp + tn\")\nnla_recipe <- recipes::recipe(nla_formula, data = nla_train) |>\n recipes::step_string2factor(all_nominal()) |>\n recipes::step_nzv(all_nominal()) |>\n recipes::step_log(chla, cyanophyta, base = 10) |>\n recipes::step_normalize(all_numeric_predictors()) |>\n prep()\nnla_recipe" }, { "objectID": "SD/5.1_model/index.html#tidymodels---cross-validation", "href": "SD/5.1_model/index.html#tidymodels---cross-validation", "title": "模型构建", "section": "tidymodels - cross validation", "text": "tidymodels - cross validation\n\nnla_cv <- recipes::bake(\n nla_recipe,\n new_data = training(nla_split)\n) |>\n rsample::vfold_cv(v = 10)\nnla_cv\n\n#> # 10-fold cross-validation \n#> # A tibble: 10 × 2\n#> splits id \n#> <list> <chr> \n#> 1 <split [759/85]> Fold01\n#> 2 <split [759/85]> Fold02\n#> 3 <split [759/85]> Fold03\n#> 4 <split [759/85]> Fold04\n#> 5 <split [760/84]> Fold05\n#> 6 <split [760/84]> Fold06\n#> 7 <split [760/84]> Fold07\n#> 8 <split [760/84]> Fold08\n#> 9 <split [760/84]> Fold09\n#> 10 <split [760/84]> Fold10" }, { "objectID": "SD/5.1_model/index.html#tidymodels---model-specification", "href": "SD/5.1_model/index.html#tidymodels---model-specification", "title": "模型构建", "section": "tidymodels - Model specification", "text": "tidymodels - Model specification\n\nxgboost_model <- parsnip::boost_tree(\n mode = \"regression\",\n trees = 1000,\n min_n = tune(),\n tree_depth = tune(),\n learn_rate = tune(),\n loss_reduction = tune()\n) |>\n set_engine(\"xgboost\", objective = \"reg:squarederror\")\nxgboost_model\n\n#> Boosted Tree Model Specification (regression)\n#> \n#> Main Arguments:\n#> trees = 1000\n#> min_n = tune()\n#> tree_depth = tune()\n#> learn_rate = tune()\n#> loss_reduction = tune()\n#> \n#> Engine-Specific Arguments:\n#> objective = reg:squarederror\n#> \n#> Computational engine: xgboost" }, { "objectID": "SD/5.1_model/index.html#tidymodels---grid-specification", "href": "SD/5.1_model/index.html#tidymodels---grid-specification", "title": "模型构建", "section": "tidymodels - Grid specification", "text": "tidymodels - Grid specification\n\n# grid specification\nxgboost_params <- dials::parameters(\n min_n(),\n tree_depth(),\n learn_rate(),\n loss_reduction()\n)\nxgboost_params" }, { "objectID": "SD/5.1_model/index.html#tidymodels---grid-specification-1", "href": "SD/5.1_model/index.html#tidymodels---grid-specification-1", "title": "模型构建", "section": "tidymodels - Grid specification", "text": "tidymodels - Grid specification\n\nxgboost_grid <- dials::grid_max_entropy(\n xgboost_params,\n size = 60\n)\nknitr::kable(head(xgboost_grid))\n\n\n\n\nmin_n\ntree_depth\nlearn_rate\nloss_reduction\n\n\n\n\n11\n8\n0.0000000\n0.9340165\n\n\n19\n3\n0.0000004\n0.0000000\n\n\n10\n12\n0.0000000\n25.6175732\n\n\n23\n2\n0.0000000\n0.0000000\n\n\n8\n2\n0.0161978\n0.3890567\n\n\n39\n4\n0.0000068\n0.0461147" }, { "objectID": "SD/5.1_model/index.html#tidymodels---workflow", "href": "SD/5.1_model/index.html#tidymodels---workflow", "title": "模型构建", "section": "tidymodels - Workflow", "text": "tidymodels - Workflow\n\nxgboost_wf <- workflows::workflow() |>\n add_model(xgboost_model) |>\n add_formula(nla_formula)\nxgboost_wf\n\n#> ══ Workflow ══════════════════════════════════════════════════════════\n#> Preprocessor: Formula\n#> Model: boost_tree()\n#> \n#> ── Preprocessor ──────────────────────────────────────────────────────\n#> cyanophyta ~ temp + do + ph + cond + sd + tp + tn + chla + clear_to_bottom\n#> \n#> ── Model ─────────────────────────────────────────────────────────────\n#> Boosted Tree Model Specification (regression)\n#> \n#> Main Arguments:\n#> trees = 1000\n#> min_n = tune()\n#> tree_depth = tune()\n#> learn_rate = tune()\n#> loss_reduction = tune()\n#> \n#> Engine-Specific Arguments:\n#> objective = reg:squarederror\n#> \n#> Computational engine: xgboost" }, { "objectID": "SD/5.1_model/index.html#tidymodels---tune", "href": "SD/5.1_model/index.html#tidymodels---tune", "title": "模型构建", "section": "tidymodels - Tune", "text": "tidymodels - Tune\n\n# hyperparameter tuning\nif (FALSE) {\n xgboost_tuned <- tune::tune_grid(\n object = xgboost_wf,\n resamples = nla_cv,\n grid = xgboost_grid,\n metrics = yardstick::metric_set(rmse, rsq, mae),\n control = tune::control_grid(verbose = TRUE)\n )\n saveRDS(xgboost_tuned, \"./xgboost_tuned.RDS\")\n}\nxgboost_tuned <- readRDS(\"./xgboost_tuned.RDS\")" }, { "objectID": "SD/5.1_model/index.html#tidymodels---best-model", "href": "SD/5.1_model/index.html#tidymodels---best-model", "title": "模型构建", "section": "tidymodels - Best model", "text": "tidymodels - Best model\n\nxgboost_tuned |>\n tune::show_best(metric = \"rmse\") |>\n knitr::kable()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nmin_n\ntree_depth\nlearn_rate\nloss_reduction\n.metric\n.estimator\nmean\nn\nstd_err\n.config\n\n\n\n\n11\n1\n0.0120291\n0.0000001\nrmse\nstandard\n0.7946305\n10\n0.0091198\nPreprocessor1_Model28\n\n\n12\n1\n0.0247860\n0.0000000\nrmse\nstandard\n0.7976293\n10\n0.0083676\nPreprocessor1_Model56\n\n\n39\n1\n0.0194065\n0.0000000\nrmse\nstandard\n0.8022666\n10\n0.0078647\nPreprocessor1_Model08\n\n\n34\n6\n0.0969629\n8.1171897\nrmse\nstandard\n0.8060259\n10\n0.0117724\nPreprocessor1_Model48\n\n\n32\n14\n0.0087588\n0.0003154\nrmse\nstandard\n0.8240243\n10\n0.0100750\nPreprocessor1_Model57" }, { "objectID": "SD/5.1_model/index.html#tidymodels---best-model-1", "href": "SD/5.1_model/index.html#tidymodels---best-model-1", "title": "模型构建", "section": "tidymodels - Best model", "text": "tidymodels - Best model\n\nxgboost_tuned |>\n collect_metrics()\n\n#> # A tibble: 180 × 10\n#> min_n tree_depth learn_rate loss_reduction .metric .estimator mean n\n#> <int> <int> <dbl> <dbl> <chr> <chr> <dbl> <int>\n#> 1 31 3 9.20e- 5 3.09e-9 mae standard 2.93 10\n#> 2 31 3 9.20e- 5 3.09e-9 rmse standard 3.09 10\n#> 3 31 3 9.20e- 5 3.09e-9 rsq standard 0.325 10\n#> 4 36 14 2.61e-10 2.60e-8 mae standard 3.21 10\n#> 5 36 14 2.61e-10 2.60e-8 rmse standard 3.36 10\n#> 6 36 14 2.61e-10 2.60e-8 rsq standard NaN 0\n#> 7 32 4 1.55e- 4 1.18e+1 mae standard 2.76 10\n#> 8 32 4 1.55e- 4 1.18e+1 rmse standard 2.91 10\n#> 9 32 4 1.55e- 4 1.18e+1 rsq standard 0.323 10\n#> 10 35 6 1.11e-10 1.22e-8 mae standard 3.21 10\n#> # ℹ 170 more rows\n#> # ℹ 2 more variables: std_err <dbl>, .config <chr>" }, { "objectID": "SD/5.1_model/index.html#tidymodels---best-model-2", "href": "SD/5.1_model/index.html#tidymodels---best-model-2", "title": "模型构建", "section": "tidymodels - Best model", "text": "tidymodels - Best model\n\nxgboost_tuned |>\n autoplot()" }, { "objectID": "SD/5.1_model/index.html#tidymodels---best-model-3", "href": "SD/5.1_model/index.html#tidymodels---best-model-3", "title": "模型构建", "section": "tidymodels - Best model", "text": "tidymodels - Best model\n\nxgboost_best_params <- xgboost_tuned |>\n tune::select_best(metric = \"rmse\")\n\nknitr::kable(xgboost_best_params)\n\n\n\n\nmin_n\ntree_depth\nlearn_rate\nloss_reduction\n.config\n\n\n\n\n11\n1\n0.0120291\n1e-07\nPreprocessor1_Model28" }, { "objectID": "SD/5.1_model/index.html#tidymodels---final-model", "href": "SD/5.1_model/index.html#tidymodels---final-model", "title": "模型构建", "section": "tidymodels - Final model", "text": "tidymodels - Final model\n\nxgboost_model_final <- xgboost_model |>\n finalize_model(xgboost_best_params)\nxgboost_model_final\n\n#> Boosted Tree Model Specification (regression)\n#> \n#> Main Arguments:\n#> trees = 1000\n#> min_n = 11\n#> tree_depth = 1\n#> learn_rate = 0.0120291137490354\n#> loss_reduction = 6.99604840409217e-08\n#> \n#> Engine-Specific Arguments:\n#> objective = reg:squarederror\n#> \n#> Computational engine: xgboost" }, { "objectID": "SD/5.1_model/index.html#tidymodels---train-evaluation", "href": "SD/5.1_model/index.html#tidymodels---train-evaluation", "title": "模型构建", "section": "tidymodels - Train evaluation", "text": "tidymodels - Train evaluation\n\n(train_processed <- bake(nla_recipe, new_data = nla_train))\n\n#> # A tibble: 844 × 10\n#> temp do ph cond sd tp tn chla clear_to_bottom\n#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> \n#> 1 1.43 0.629 0.381 0.185 -0.657 -0.220 -0.145 0.244 TRUE \n#> 2 -0.379 NaN -2.64 -0.258 -0.435 -0.322 -0.437 -0.00415 TRUE \n#> 3 0.226 NaN -3.27 -0.258 -0.594 -0.294 -0.377 0.389 TRUE \n#> 4 -0.652 -0.103 1.54 3.15 -0.514 0.206 1.52 -0.00789 TRUE \n#> 5 -0.320 -1.59 0.358 0.996 -0.713 2.34 2.98 -0.516 TRUE \n#> 6 0.847 0.198 0.381 -0.206 -0.336 -0.308 -0.287 -0.749 TRUE \n#> 7 0.109 0.250 -0.461 -0.216 -0.745 0.451 0.490 1.72 TRUE \n#> 8 0.810 1.15 1.66 -0.219 -0.753 0.162 0.511 1.63 TRUE \n#> 9 -0.932 -3.68 -1.71 -0.233 -0.574 -0.162 -0.150 0.00472 TRUE \n#> 10 -0.0301 1.14 0.343 -0.169 -0.812 1.39 2.82 1.76 TRUE \n#> # ℹ 834 more rows\n#> # ℹ 1 more variable: cyanophyta <dbl>" }, { "objectID": "SD/5.1_model/index.html#tidymodels---train-data", "href": "SD/5.1_model/index.html#tidymodels---train-data", "title": "模型构建", "section": "tidymodels - Train data", "text": "tidymodels - Train data\n\ntrain_prediction <- xgboost_model_final |>\n # fit the model on all the training data\n fit(\n formula = nla_formula,\n data = train_processed\n ) |>\n # predict the sale prices for the training data\n predict(new_data = train_processed) |>\n bind_cols(\n nla_train |>\n mutate(.obs = log10(cyanophyta))\n )\nxgboost_score_train <-\n train_prediction |>\n yardstick::metrics(.obs, .pred) |>\n mutate(.estimate = format(round(.estimate, 2), big.mark = \",\"))\nknitr::kable(xgboost_score_train)\n\n\n\n\n.metric\n.estimator\n.estimate\n\n\n\n\nrmse\nstandard\n0.78\n\n\nrsq\nstandard\n0.40\n\n\nmae\nstandard\n0.62" }, { "objectID": "SD/5.1_model/index.html#tidymodels---train-evaluation-1", "href": "SD/5.1_model/index.html#tidymodels---train-evaluation-1", "title": "模型构建", "section": "tidymodels - train evaluation", "text": "tidymodels - train evaluation\n\ntrain_prediction |>\n ggplot(aes(.pred, .obs)) +\n geom_point() +\n geom_smooth(method = \"lm\")" }, { "objectID": "SD/5.1_model/index.html#tidymodels---test-data", "href": "SD/5.1_model/index.html#tidymodels---test-data", "title": "模型构建", "section": "tidymodels - test data", "text": "tidymodels - test data\n\ntest_processed <- bake(nla_recipe, new_data = nla_test)\n\ntest_prediction <- xgboost_model_final |>\n # fit the model on all the training data\n fit(\n formula = nla_formula,\n data = train_processed\n ) |>\n # use the training model fit to predict the test data\n predict(new_data = test_processed) |>\n bind_cols(\n nla_test |>\n mutate(.obs = log10(cyanophyta))\n )\n\n# measure the accuracy of our model using `yardstick`\nxgboost_score <- test_prediction |>\n yardstick::metrics(.obs, .pred) |>\n mutate(.estimate = format(round(.estimate, 2), big.mark = \",\"))\n\nknitr::kable(xgboost_score)\n\n\n\n\n.metric\n.estimator\n.estimate\n\n\n\n\nrmse\nstandard\n0.81\n\n\nrsq\nstandard\n0.33\n\n\nmae\nstandard\n0.64" }, { "objectID": "SD/5.1_model/index.html#tidymodels---evaluation", "href": "SD/5.1_model/index.html#tidymodels---evaluation", "title": "模型构建", "section": "tidymodels - evaluation", "text": "tidymodels - evaluation\n\ncyanophyta_prediction_residual <- test_prediction |>\n arrange(.pred) %>%\n mutate(residual_pct = (.obs - .pred) / .pred) |>\n select(.pred, residual_pct)\n\ncyanophyta_prediction_residual |>\n ggplot(aes(x = .pred, y = residual_pct)) +\n geom_point() +\n xlab(\"Predicted Cyanophyta\") +\n ylab(\"Residual (%)\")" }, { "objectID": "SD/5.1_model/index.html#tidymodels---test-evaluation", "href": "SD/5.1_model/index.html#tidymodels---test-evaluation", "title": "模型构建", "section": "tidymodels - test evaluation", "text": "tidymodels - test evaluation\n\ntest_prediction |>\n ggplot(aes(.pred, .obs)) +\n geom_point() +\n geom_smooth(method = \"lm\", colour = \"black\")" }, { "objectID": "SD/5.1_model/index.html#欢迎讨论", "href": "SD/5.1_model/index.html#欢迎讨论", "title": "模型构建", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" }, { "objectID": "SD/5.3_大数据分析工具/index.html#匹配数字", "href": "SD/5.3_大数据分析工具/index.html#匹配数字", "title": "大数据分析工具", "section": "匹配数字", "text": "匹配数字\n匹配数字：\n\n匹配任意数字字符。\n：匹配一个或多个数字字符。\n\n\n匹配字母：\n\n：匹配任意字母、数字或下划线字符。\n+：匹配一个或多个字母、数字或下划线字符。" }, { "objectID": "SD/5.3_大数据分析工具/index.html#匹配数字-2", "href": "SD/5.3_大数据分析工具/index.html#匹配数字-2", "title": "大数据分析工具", "section": "匹配数字", "text": "匹配数字\n匹配空白字符：\n\n：匹配任意空白字符，包括空格、制表符、换行符等。\n+：匹配一个或多个空白字符。\n\n匹配特定字符：\n\n[abc]：匹配字符 a、b 或 c 中的任意一个。\n[a-z]：匹配任意小写字母。\n[A-Z]：匹配任意大写字母。\n0-9：匹配任意数字。" }, { "objectID": "SD/5.3_大数据分析工具/index.html#匹配数字-3", "href": "SD/5.3_大数据分析工具/index.html#匹配数字-3", "title": "大数据分析工具", "section": "匹配数字", "text": "匹配数字\n匹配重复次数：\n\n{n}：匹配前一个字符恰好 n 次。\n{n,}：匹配前一个字符至少 n 次。\n{n,m}：匹配前一个字符至少 n 次，但不超过 m 次。\n\n匹配边界：\n\n^：匹配字符串的开头。\n$：匹配字符串的结尾。" }, { "objectID": "SD/5.3_大数据分析工具/index.html#匹配数字-4", "href": "SD/5.3_大数据分析工具/index.html#匹配数字-4", "title": "大数据分析工具", "section": "匹配数字", "text": "匹配数字\n匹配特殊字符：\n\n：转义特殊字符，使其按字面意义匹配。\n.：匹配任意单个字符。\n|：表示“或”关系，匹配两个或多个表达式之一。\n\n匹配次数：\n\n*：匹配前一个字符零次或多次。\n+：匹配前一个字符一次或多次。\n?：匹配前一个字符零次或一次。" }, { "objectID": "SD/5.3_大数据分析工具/index.html#匹配数字-5", "href": "SD/5.3_大数据分析工具/index.html#匹配数字-5", "title": "大数据分析工具", "section": "匹配数字", "text": "匹配数字\n分组和捕获：\n\n()：将一系列模式组合成一个单元，可与特殊字符一起使用。\n\n预定义字符集：\n\n任意数字，相当于 0-9。\n：任意字母、数字或下划线字符，相当于 [a-zA-Z0-9_]。\n：任意空白字符，相当于 [ 。" }, { "objectID": "SD/5.3_大数据分析工具/index.html#实例", "href": "SD/5.3_大数据分析工具/index.html#实例", "title": "大数据分析工具", "section": "实例", "text": "实例\n\nlibrary(babynames)\n(x <- c(\"apple\", \"apppple\", \"abc123def\"))\n\n[1] \"apple\" \"apppple\" \"abc123def\"\n\nx[str_detect(x, \"[0-9]\")]\n\n[1] \"abc123def\"\n\nx[str_detect(x, \"abc[0-9]+\")]\n\n[1] \"abc123def\"\n\nx[str_detect(x, \"pp\")]\n\n[1] \"apple\" \"apppple\"\n\nx[str_detect(x, \"p{4}\")]\n\n[1] \"apppple\"\n\nx[str_detect(x, \"p{4}\")]\n\n[1] \"apppple\"\n\nx[str_detect(\"apple\", \"ap*\")]\n\n[1] \"apple\" \"apppple\" \"abc123def\"\n\nx[str_detect(\"apple\", \"app*\")]\n\n[1] \"apple\" \"apppple\" \"abc123def\"\n\nx[str_detect(\"apple\", \"a..le\")]\n\n[1] \"apple\" \"apppple\" \"abc123def\"" }, { "objectID": "SD/5.3_大数据分析工具/index.html#练习", "href": "SD/5.3_大数据分析工具/index.html#练习", "title": "大数据分析工具", "section": "练习", "text": "练习\n找出babyname中名字含有ar的行\n\n\n# A tibble: 199,504 × 5\n year sex name n prop\n <dbl> <chr> <chr> <int> <dbl>\n 1 1880 F Mary 7065 0.0724 \n 2 1880 F Margaret 1578 0.0162 \n 3 1880 F Sarah 1288 0.0132 \n 4 1880 F Clara 1226 0.0126 \n 5 1880 F Martha 1040 0.0107 \n 6 1880 F Carrie 949 0.00972\n 7 1880 F Pearl 569 0.00583\n 8 1880 F Marie 471 0.00483\n 9 1880 F Harriet 319 0.00327\n10 1880 F Caroline 306 0.00314\n# ℹ 199,494 more rows" }, { "objectID": "SD/5.3_大数据分析工具/index.html#练习-1", "href": "SD/5.3_大数据分析工具/index.html#练习-1", "title": "大数据分析工具", "section": "练习", "text": "练习\n找出babyname中名字含有ar或者以ry结尾的行。\n\n\n# A tibble: 199,504 × 5\n year sex name n prop\n <dbl> <chr> <chr> <int> <dbl>\n 1 1880 F Mary 7065 0.0724 \n 2 1880 F Margaret 1578 0.0162 \n 3 1880 F Sarah 1288 0.0132 \n 4 1880 F Clara 1226 0.0126 \n 5 1880 F Martha 1040 0.0107 \n 6 1880 F Carrie 949 0.00972\n 7 1880 F Pearl 569 0.00583\n 8 1880 F Marie 471 0.00483\n 9 1880 F Harriet 319 0.00327\n10 1880 F Caroline 306 0.00314\n# ℹ 199,494 more rows" }, { "objectID": "SD/5.3_大数据分析工具/index.html#ssh---安全远程连接", "href": "SD/5.3_大数据分析工具/index.html#ssh---安全远程连接", "title": "大数据分析工具", "section": "SSH - 安全远程连接", "text": "SSH - 安全远程连接\n# 连接到远程服务器\nssh username@remote.server.com\n\n# 使用特定端口连接\nssh -p 2222 username@remote.server.com\n\n# 执行远程命令\nssh username@server \"ls -l /tmp\"\n案例：\n- 远程管理云服务器\n- 自动化脚本执行" }, { "objectID": "SD/5.3_大数据分析工具/index.html#windows下的ssh工具---putty", "href": "SD/5.3_大数据分析工具/index.html#windows下的ssh工具---putty", "title": "大数据分析工具", "section": "Windows下的SSH工具 - PuTTY", "text": "Windows下的SSH工具 - PuTTY\n\n\n\nPuTTY界面\n\n\n# 主要功能：\n1. 保存会话配置（IP/端口/认证信息）\n2. 支持SSH/Telnet/Serial连接\n3. 公钥认证（配合Pageant使用）\n4. 端口转发（SSH隧道）\n案例：\n- 连接Linux服务器进行管理\n- 建立SSH隧道访问内网资源" }, { "objectID": "SD/5.3_大数据分析工具/index.html#scp---安全文件传输", "href": "SD/5.3_大数据分析工具/index.html#scp---安全文件传输", "title": "大数据分析工具", "section": "SCP - 安全文件传输", "text": "SCP - 安全文件传输\n# 复制本地文件到远程\nscp file.txt username@remote:/path/to/dest\n\n# 从远程复制到本地\nscp username@remote:/path/file.txt .\n\n# 递归复制目录\nscp -r dir/ username@remote:/path/\n案例：\n- 部署网站文件到生产环境\n- 备份远程日志文件" }, { "objectID": "SD/5.3_大数据分析工具/index.html#windows下的scp工具---winscp", "href": "SD/5.3_大数据分析工具/index.html#windows下的scp工具---winscp", "title": "大数据分析工具", "section": "Windows下的SCP工具 - WinSCP", "text": "Windows下的SCP工具 - WinSCP\n\n\n\nWinSCP界面 w:400\n\n\n# 主要特性：\n- 图形化SFTP/SCP客户端\n- 与PuTTY集成\n- 支持拖拽操作\n- 可保存常用连接\n- 批处理脚本功能\n案例：\n- 可视化管理服务器文件\n- 本地与服务器间同步代码" }, { "objectID": "SD/5.3_大数据分析工具/index.html#windows替代bash的工具", "href": "SD/5.3_大数据分析工具/index.html#windows替代bash的工具", "title": "大数据分析工具", "section": "Windows替代bash的工具", "text": "Windows替代bash的工具\n1. Git Bash\n# 包含常用Linux命令\nls, grep, ssh, scp, awk等\n2. WSL (Windows Subsystem for Linux)\n# 完整Linux环境\nsudo apt install python3\n3. Cygwin\n# POSIX兼容环境\ncygwin.com/setup-x86_64.exe" }, { "objectID": "SD/5.3_大数据分析工具/index.html#windows终端工具推荐", "href": "SD/5.3_大数据分析工具/index.html#windows终端工具推荐", "title": "大数据分析工具", "section": "Windows终端工具推荐", "text": "Windows终端工具推荐\n\n\n\n工具\n特点\n适用场景\n\n\n\n\nWindows Terminal\n多标签/色彩支持\n日常开发\n\n\nMobaXterm\n内置X11/插件\n远程开发\n\n\nTabby\n跨平台/主题丰富\n多平台用户\n\n\nConEmu\n高度可定制\n高级用户" }, { "objectID": "SD/5.3_大数据分析工具/index.html#开发工具跨平台方案", "href": "SD/5.3_大数据分析工具/index.html#开发工具跨平台方案", "title": "大数据分析工具", "section": "开发工具跨平台方案", "text": "开发工具跨平台方案\n最佳实践：\n1. 代码编辑器统一使用VS Code（全平台支持）\n- 配合Remote-SSH插件\n2. 数据库工具使用DBeaver/DataGrip\n3. 版本控制使用Git GUI（GitKraken/Fork）\n# 保持环境一致的建议：\n- 使用WSL2开发环境\n- 配置相同的.ssh/config文件\n- 共享相同的IDE配置" }, { "objectID": "SD/5.3_大数据分析工具/index.html#grep---文本搜索", "href": "SD/5.3_大数据分析工具/index.html#grep---文本搜索", "title": "大数据分析工具", "section": "grep - 文本搜索", "text": "grep - 文本搜索\n# 基本搜索\ngrep \"error\" logfile.txt\n\n# 递归搜索目录\ngrep -r \"function\" src/\n\n# 显示行号\ngrep -n \"TODO\" *.py\n\n# 反向匹配\ngrep -v \"success\" results.log\n案例：\n- 在日志中查找错误信息\n- 分析代码库中的特定模式" }, { "objectID": "SD/5.3_大数据分析工具/index.html#sed---流编辑器", "href": "SD/5.3_大数据分析工具/index.html#sed---流编辑器", "title": "大数据分析工具", "section": "sed - 流编辑器", "text": "sed - 流编辑器\n# 替换文本\nsed 's/old/new/g' file.txt\n\n# 删除空行\nsed '/^$/d' file.txt\n\n# 原地编辑文件\nsed -i 's/python/python3/g' script.sh\n案例：\n- 批量重命名文件中的字符串\n- 清理数据文件中的不规范格式" }, { "objectID": "SD/5.3_大数据分析工具/index.html#awk---文本处理", "href": "SD/5.3_大数据分析工具/index.html#awk---文本处理", "title": "大数据分析工具", "section": "awk - 文本处理", "text": "awk - 文本处理\n# 打印特定列\nawk '{print $1,$3}' data.csv\n\n# 条件过滤\nawk '$3 > 100 {print $0}' sales.txt\n\n# 使用分隔符\nawk -F',' '{print $2}' users.csv\n案例：\n- 分析服务器日志统计状态码\n- 处理CSV格式数据" }, { "objectID": "SD/5.3_大数据分析工具/index.html#find-xargs---文件查找处理", "href": "SD/5.3_大数据分析工具/index.html#find-xargs---文件查找处理", "title": "大数据分析工具", "section": "find & xargs - 文件查找处理", "text": "find & xargs - 文件查找处理\n# 查找并删除\nfind . -name \"*.tmp\" -delete\n\n# 查找并处理\nfind /var/log -name \"*.log\" | xargs ls -lh\n\n# 复杂组合\nfind src/ -type f -name \"*.js\" | xargs grep -l \"deprecated\"\n案例：\n- 清理旧临时文件\n- 批量处理项目文件" }, { "objectID": "SD/5.3_大数据分析工具/index.html#代码编辑器", "href": "SD/5.3_大数据分析工具/index.html#代码编辑器", "title": "大数据分析工具", "section": "代码编辑器", "text": "代码编辑器\n\nVS Code：现代轻量级编辑器\n\n丰富的插件生态\n内置Git支持\n\nVim：终端高效编辑器\nvim file.txt\nRStudio：R语言集成环境\nJupyterLab：交互式笔记本环境" }, { "objectID": "SD/5.3_大数据分析工具/index.html#git版本控制", "href": "SD/5.3_大数据分析工具/index.html#git版本控制", "title": "大数据分析工具", "section": "Git版本控制", "text": "Git版本控制\n# 基本工作流\ngit clone https://repo.url\ngit add .\ngit commit -m \"message\"\ngit push\n\n# 分支管理\ngit checkout -b new-feature\ngit merge main\n案例：\n- 团队协作开发\n- 版本回滚与问题追踪" }, { "objectID": "SD/5.3_大数据分析工具/index.html#mysql数据库", "href": "SD/5.3_大数据分析工具/index.html#mysql数据库", "title": "大数据分析工具", "section": "MySQL数据库", "text": "MySQL数据库\n-- 基本查询\nSELECT * FROM users WHERE age > 18;\n\n-- 创建表\nCREATE TABLE products (\n id INT PRIMARY KEY,\n name VARCHAR(100)\n);\n\n-- 数据操作\nINSERT INTO products VALUES (1, 'Laptop');\nUPDATE products SET price=999 WHERE id=1;\n案例：\n- Web应用数据存储\n- 数据分析与报表生成" }, { "objectID": "SD/5.3_大数据分析工具/index.html#案例全国气象数据", "href": "SD/5.3_大数据分析工具/index.html#案例全国气象数据", "title": "大数据分析工具", "section": "案例：全国气象数据", "text": "案例：全国气象数据\n\n#!/bin/bash\nlogfn=\"${HOME}/service/log/nationalairquality/nationalairquality.log\"\nworkdirfn=\"${HOME}/service/nationalairquality/\"\nmkdir -p \"$(dirname \"${logfn}\")\"\ntouch \"${logfn}\"\n\necho \"$(date '+%Y-%m-%d %H:%M:%S'): 下载大气质量数据\" >>\"${logfn}\"\n\ndeclare -a citynames\n\ncitynames=(北京市石家庄市秦皇岛市)\n\njsonfn=\"${workdirfn}/nationalairquality_$(date '+%Y%d%m%H').json\"\njsonfn=\"nationalairquality_$(date '+%Y%d%m%H').json\"\n# [[ -f \"${jsonfn}\" ]] && rm \"${jsonfn}\"\necho \"[\" >\"${jsonfn}\"\n\nfor cityname in \"${citynames[@]}\"; do\n echo \"下载${cityname}空气质量数据...\" >>\"${logfn}\"\n curl \"https://air.cnemc.cn:18007/CityData/GetAQIDataPublishLive?cityName=${cityname}\" \\\n -H 'Accept: */*' \\\n -H 'Accept-Language: en-US,en;q=0.9' \\\n -H 'Connection: keep-alive' \\\n -H 'Referer: https://air.cnemc.cn:18007/' \\\n -H 'Sec-Fetch-Dest: empty' \\\n -H 'Sec-Fetch-Mode: cors' \\\n -H 'Sec-Fetch-Site: same-origin' \\\n -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36' \\\n -H 'X-Requested-With: XMLHttpRequest' \\\n -H 'sec-ch-ua: \"Google Chrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"' \\\n -H 'sec-ch-ua-mobile: ?0' \\\n -H 'sec-ch-ua-platform: \"macOS\"' \\\n --compressed --silent | jq | sed -e '1d' -e '$d' -e 's/^\\( *}\\)$/\\1,/' >>\"${jsonfn}\"\ndone\nsed -i '' '$s/},/}/' \"${jsonfn}\"\necho \"]\" >>\"${jsonfn}\"\n\necho \"大气质量数据下载完成！\" >>\"${logfn}\"\necho \"开始上传数据库...\" >>\"${logfn}\"\n/home/ming/bin/getnationalairquality.R \"${jsonfn}\"\necho \"上传数据库完毕！\" >>\"${logfn}\"" }, { "objectID": "SD/5.3_大数据分析工具/index.html#r代码", "href": "SD/5.3_大数据分析工具/index.html#r代码", "title": "大数据分析工具", "section": "R代码", "text": "R代码\n\n#!/usr/bin/Rscript\n\njsonfn <- commandArgs(TRUE)[1]\n# jsonfn <- \"~/nationalairquality_2023191219.json\"\njsondf <- jsonlite::fromJSON(jsonfn, flatten = TRUE)\n# metadf <- tibble::as_tibble(jsondf) |>\n# dplyr::select(site = StationCode, name = PositionName, Area, lon = Longitude, lat = Latitude) |>\n# dplyr::mutate(lon = as.numeric(lon), lat = as.numeric(lat))\n# DBI::dbWriteTable(conn, \"metadf\", metadf, overwrite = TRUE, row.names = FALSE)\nairqualitydf <- tibble::as_tibble(jsondf) |>\n dplyr::select(\n datetime = TimePoint,\n site = StationCode,\n `CO_mg/m3` = CO,\n `CO_24h_mg/m3` = CO_24h,\n `NO2_μg/m3` = NO2,\n `NO2_24h_μg/m3` = NO2_24h,\n `O3_μg/m3` = O3,\n `O3_24h_μg/m3` = O3_24h,\n `O3_8h_μg/m3` = O3_8h,\n `O3_8h_24h_μg/m3` = O3_8h_24h,\n `PM10_μg/m3` = PM10,\n `PM10_24h_μg/m3` = PM10_24h,\n `PM2.5_μg/m3` = PM2_5,\n `PM2.5_24h_μg/m3` = PM2_5_24h,\n `SO2_μg/m3` = SO2,\n `SO2_24h_μg/m3` = SO2_24h,\n `NO_μg/m3` = NO,\n `NO_24h_μg/m3` = NO_24h,\n `NOx_μg/m3` = NOx,\n `NOx_24h_μg/m3` = NOx_24h,\n AQI,\n COLevel,\n NO2Level,\n O3Level,\n O3_8hLevel,\n PM10Level,\n PM2_5Level,\n SO2Level,\n PrimaryPollutant,\n Quality,\n Unheathful\n ) |>\n dplyr::mutate(dplyr::across(\n `CO_mg/m3`:AQI,\n ~ round(readr::parse_number(.x), 4)\n )) |>\n dplyr::mutate(datetime = lubridate::as_datetime(datetime))\nconn <- cctdb::get_dbconn(\"nationalairquality\", writepermission = TRUE)\nDBI::dbWriteTable(\n conn,\n \"airqualitydf\",\n airqualitydf,\n append = TRUE,\n row.names = FALSE\n)\nDBI::dbDisconnect(conn)" }, { "objectID": "SD/5.3_大数据分析工具/index.html#欢迎讨论", "href": "SD/5.3_大数据分析工具/index.html#欢迎讨论", "title": "大数据分析工具", "section": "欢迎讨论！", "text": "欢迎讨论！\n 苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides\n\n\n mingsu@rcees.ac.cn;" } ]