Data Transform

《区域水环境污染数据分析实践》
Data analysis practice of regional water environment pollution

苏命

mingsu@rcees.ac.cn

中国科学院大学资源与环境学院
中国科学院生态环境研究中心

2026-05-21

tidyverse风格数据分析总体流程

dplyr cheatsheet

查看数据

flights

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

选择列

flights |>
  select(year, month, day)

选择列

flights |>
  select(year:day)

选择列

flights |>
  select(3:5)

# A tibble: 336,776 × 3
     day dep_time sched_dep_time
   <int>    <int>          <int>
 1     1      517            515
 2     1      533            529
 3     1      542            540
 4     1      544            545
 5     1      554            600
 6     1      554            558
 7     1      555            600
 8     1      557            600
 9     1      557            600
10     1      558            600
# ℹ 336,766 more rows

选择列

flights |>
  select(!year:day)

# A tibble: 336,776 × 16
   dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
      <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
 1      517            515         2      830            819        11 UA     
 2      533            529         4      850            830        20 UA     
 3      542            540         2      923            850        33 AA     
 4      544            545        -1     1004           1022       -18 B6     
 5      554            600        -6      812            837       -25 DL     
 6      554            558        -4      740            728        12 UA     
 7      555            600        -5      913            854        19 B6     
 8      557            600        -3      709            723       -14 EV     
 9      557            600        -3      838            846        -8 B6     
10      558            600        -2      753            745         8 AA     
# ℹ 336,766 more rows
# ℹ 9 more variables: flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

选择列

flights |>
  select(-(year:day))

# A tibble: 336,776 × 16
   dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
      <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
 1      517            515         2      830            819        11 UA     
 2      533            529         4      850            830        20 UA     
 3      542            540         2      923            850        33 AA     
 4      544            545        -1     1004           1022       -18 B6     
 5      554            600        -6      812            837       -25 DL     
 6      554            558        -4      740            728        12 UA     
 7      555            600        -5      913            854        19 B6     
 8      557            600        -3      709            723       -14 EV     
 9      557            600        -3      838            846        -8 B6     
10      558            600        -2      753            745         8 AA     
# ℹ 336,766 more rows
# ℹ 9 more variables: flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

选择列

flights |>
  select(where(is.character))

# A tibble: 336,776 × 4
   carrier tailnum origin dest 
   <chr>   <chr>   <chr>  <chr>
 1 UA      N14228  EWR    IAH  
 2 UA      N24211  LGA    IAH  
 3 AA      N619AA  JFK    MIA  
 4 B6      N804JB  JFK    BQN  
 5 DL      N668DN  LGA    ATL  
 6 UA      N39463  EWR    ORD  
 7 B6      N516JB  EWR    FLL  
 8 EV      N829AS  LGA    IAD  
 9 B6      N593JB  JFK    MCO  
10 AA      N3ALAA  LGA    ORD  
# ℹ 336,766 more rows

选择列

flights |>
  select(!where(is.character)) |>
  select(contains("_"))

# A tibble: 336,776 × 8
   dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay air_time
      <int>          <int>     <dbl>    <int>          <int>     <dbl>    <dbl>
 1      517            515         2      830            819        11      227
 2      533            529         4      850            830        20      227
 3      542            540         2      923            850        33      160
 4      544            545        -1     1004           1022       -18      183
 5      554            600        -6      812            837       -25      116
 6      554            558        -4      740            728        12      150
 7      555            600        -5      913            854        19      158
 8      557            600        -3      709            723       -14       53
 9      557            600        -3      838            846        -8      140
10      558            600        -2      753            745         8      138
# ℹ 336,766 more rows
# ℹ 1 more variable: time_hour <dttm>

选择列

flights |>
  select(tail_num = tailnum)

# A tibble: 336,776 × 1
   tail_num
   <chr>   
 1 N14228  
 2 N24211  
 3 N619AA  
 4 N804JB  
 5 N668DN  
 6 N39463  
 7 N516JB  
 8 N829AS  
 9 N593JB  
10 N3ALAA  
# ℹ 336,766 more rows

选择列

flights |>
  select(air_time, everything())

# A tibble: 336,776 × 19
   air_time  year month   day dep_time sched_dep_time dep_delay arr_time
      <dbl> <int> <int> <int>    <int>          <int>     <dbl>    <int>
 1      227  2013     1     1      517            515         2      830
 2      227  2013     1     1      533            529         4      850
 3      160  2013     1     1      542            540         2      923
 4      183  2013     1     1      544            545        -1     1004
 5      116  2013     1     1      554            600        -6      812
 6      150  2013     1     1      554            558        -4      740
 7      158  2013     1     1      555            600        -5      913
 8       53  2013     1     1      557            600        -3      709
 9      140  2013     1     1      557            600        -3      838
10      138  2013     1     1      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 11 more variables: sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
#   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

重命名

flights |>
  rename(tail_num = tailnum)

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tail_num <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

重命名

flights |>
  rename(年份 = 1) |>
  rename(月份 = 2)

# A tibble: 336,776 × 19
    年份  月份   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

重命名

flights |> select(1:4) |> head(n = 3)

# A tibble: 3 × 4
   year month   day dep_time
  <int> <int> <int>    <int>
1  2013     1     1      517
2  2013     1     1      533
3  2013     1     1      542

# 重命名
flights |> select(1:4) |> head(n = 3) |> rename_all(~ c("c1", "c2", "c3", "c4"))

# A tibble: 3 × 4
     c1    c2    c3    c4
  <int> <int> <int> <int>
1  2013     1     1   517
2  2013     1     1   533
3  2013     1     1   542

重命名

flights |> select(1:4) |> head(n = 3)

# A tibble: 3 × 4
   year month   day dep_time
  <int> <int> <int>    <int>
1  2013     1     1      517
2  2013     1     1      533
3  2013     1     1      542

# 重命名
flights |> select(1:4) |> head(n = 3) |> rename_all(toupper)

# A tibble: 3 × 4
   YEAR MONTH   DAY DEP_TIME
  <int> <int> <int>    <int>
1  2013     1     1      517
2  2013     1     1      533
3  2013     1     1      542

重命名

flights |> select(1:4) |> head(n = 3)

# A tibble: 3 × 4
   year month   day dep_time
  <int> <int> <int>    <int>
1  2013     1     1      517
2  2013     1     1      533
3  2013     1     1      542

# 重命名
flights |>
  select(1:4) |>
  head(n = 3) |>
  rename_all(~ paste0(toupper(.), "_NEW"))

# A tibble: 3 × 4
  YEAR_NEW MONTH_NEW DAY_NEW DEP_TIME_NEW
     <int>     <int>   <int>        <int>
1     2013         1       1          517
2     2013         1       1          533
3     2013         1       1          542

练习

将含有下划线的列名中的下划线去掉。

flights |> select(1:4) |> head(n = 3)

# A tibble: 3 × 4
   year month   day dep_time
  <int> <int> <int>    <int>
1  2013     1     1      517
2  2013     1     1      533
3  2013     1     1      542

练习

将airqualitydf中列名的单位信息去除（前5列）。

airqualitydf <- readxl::read_xlsx("../../data/airquality.xlsx", sheet = 2)
airqualitydf |> select(1:5)

# A tibble: 20,088 × 5
   datetime            site  `CO_mg/m3` `CO_24h_mg/m3` `NO2_μg/m3`
   <dttm>              <chr>      <dbl>          <dbl>       <dbl>
 1 2024-03-19 01:00:00 1001A        0.1            0.4           5
 2 2024-03-19 01:00:00 1003A        0.2            0.4           9
 3 2024-03-19 01:00:00 1004A        0.2            0.4           4
 4 2024-03-19 01:00:00 1005A        0.1            0.3           6
 5 2024-03-19 01:00:00 1006A        0.1            0.4           5
 6 2024-03-19 01:00:00 1007A        0.3            0.5           6
 7 2024-03-19 01:00:00 1008A        0.2            0.4           2
 8 2024-03-19 01:00:00 1009A        0.2            0.4           2
 9 2024-03-19 01:00:00 1010A        0.1            0.3           2
10 2024-03-19 01:00:00 1011A        0.2            0.4          12
# ℹ 20,078 more rows

filter

flights |>
  filter(dep_delay > 120)

# A tibble: 9,723 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      848           1835       853     1001           1950
 2  2013     1     1      957            733       144     1056            853
 3  2013     1     1     1114            900       134     1447           1222
 4  2013     1     1     1540           1338       122     2020           1825
 5  2013     1     1     1815           1325       290     2120           1542
 6  2013     1     1     1842           1422       260     1958           1535
 7  2013     1     1     1856           1645       131     2212           2005
 8  2013     1     1     1934           1725       129     2126           1855
 9  2013     1     1     1938           1703       155     2109           1823
10  2013     1     1     1942           1705       157     2124           1830
# ℹ 9,713 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

filter 练习

Flights that departed on January 1.

# A tibble: 842 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 832 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

filter 练习

Select flights that departed in January or February

# A tibble: 51,955 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 51,945 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

filter 练习

jan1 <- flights |>
  filter(month == 1 & day == 1)

filter

flights |>
  filter(month = 1)

filter

flights |>
  filter(month == 1 | 2)

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

排序

flights |>
  arrange(year, month, day, dep_time)

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

排序

flights |>
  arrange(desc(dep_delay))

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     9      641            900      1301     1242           1530
 2  2013     6    15     1432           1935      1137     1607           2120
 3  2013     1    10     1121           1635      1126     1239           1810
 4  2013     9    20     1139           1845      1014     1457           2210
 5  2013     7    22      845           1600      1005     1044           1815
 6  2013     4    10     1100           1900       960     1342           2211
 7  2013     3    17     2321            810       911      135           1020
 8  2013     6    27      959           1900       899     1236           2226
 9  2013     7    22     2257            759       898      121           1026
10  2013    12     5      756           1700       896     1058           2020
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

slice

flights |> head(n = 5)

# A tibble: 5 × 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     1     1      517            515         2      830            819
2  2013     1     1      533            529         4      850            830
3  2013     1     1      542            540         2      923            850
4  2013     1     1      544            545        -1     1004           1022
5  2013     1     1      554            600        -6      812            837
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

flights |> slice(1:5)

# A tibble: 5 × 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     1     1      517            515         2      830            819
2  2013     1     1      533            529         4      850            830
3  2013     1     1      542            540         2      923            850
4  2013     1     1      544            545        -1     1004           1022
5  2013     1     1      554            600        -6      812            837
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

slice

flights |>
  slice_max(dep_delay, n = 5)

# A tibble: 5 × 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     1     9      641            900      1301     1242           1530
2  2013     6    15     1432           1935      1137     1607           2120
3  2013     1    10     1121           1635      1126     1239           1810
4  2013     9    20     1139           1845      1014     1457           2210
5  2013     7    22      845           1600      1005     1044           1815
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

slice

flights |>
  slice_min(dep_delay, prop = 0.005)

# A tibble: 2,257 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013    12     7     2040           2123       -43       40           2352
 2  2013     2     3     2022           2055       -33     2240           2338
 3  2013    11    10     1408           1440       -32     1549           1559
 4  2013     1    11     1900           1930       -30     2233           2243
 5  2013     1    29     1703           1730       -27     1947           1957
 6  2013     8     9      729            755       -26     1002            955
 7  2013    10    23     1907           1932       -25     2143           2143
 8  2013     3    30     2030           2055       -25     2213           2250
 9  2013     3     2     1431           1455       -24     1601           1631
10  2013     5     5      934            958       -24     1225           1309
# ℹ 2,247 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

排序练习

根据origin、dest、air_time倒序排序。

# A tibble: 336,776 × 19
   origin dest  air_time  year month   day dep_time sched_dep_time dep_delay
   <chr>  <chr>    <dbl> <int> <int> <int>    <int>          <int>     <dbl>
 1 EWR    ALB         50  2013     5     5     1950           2000       -10
 2 EWR    ALB         45  2013     1    13     1721           1619        62
 3 EWR    ALB         43  2013     1    20     1623           1619         4
 4 EWR    ALB         42  2013     4     1     1439           1340        59
 5 EWR    ALB         41  2013    12     4     1316           1310         6
 6 EWR    ALB         41  2013     2     1     2034           2000        34
 7 EWR    ALB         41  2013     5     7     1956           2000        -4
 8 EWR    ALB         38  2013     1    18     1824           1619       125
 9 EWR    ALB         38  2013     1    28     1636           1619        17
10 EWR    ALB         38  2013    11    10     2149           2159       -10
# ℹ 336,766 more rows
# ℹ 10 more variables: arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,
#   carrier <chr>, flight <int>, tailnum <chr>, distance <dbl>, hour <dbl>,
#   minute <dbl>, time_hour <dttm>

去重

# Remove duplicate rows, if any
flights |>
  distinct()

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

去重

# Find all unique origin and destination pairs
flights |>
  distinct(origin, dest)

# A tibble: 224 × 2
   origin dest 
   <chr>  <chr>
 1 EWR    IAH  
 2 LGA    IAH  
 3 JFK    MIA  
 4 JFK    BQN  
 5 LGA    ATL  
 6 EWR    ORD  
 7 EWR    FLL  
 8 LGA    IAD  
 9 JFK    MCO  
10 LGA    ORD  
# ℹ 214 more rows

去重

flights |>
  distinct(origin, dest, .keep_all = TRUE)

# A tibble: 224 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
 1  2013     1     1      517            515         2      830            819
 2  2013     1     1      533            529         4      850            830
 3  2013     1     1      542            540         2      923            850
 4  2013     1     1      544            545        -1     1004           1022
 5  2013     1     1      554            600        -6      812            837
 6  2013     1     1      554            558        -4      740            728
 7  2013     1     1      555            600        -5      913            854
 8  2013     1     1      557            600        -3      709            723
 9  2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ℹ 214 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

欢迎讨论！

苏命|https://drwater.net; https://drwater.net/team/ming-su/; Slides

mingsu@rcees.ac.cn;