第5讲 tidyverse

Author

Li Zongzhang

Published

April 10, 2024

“Tidy datasets are all alike, but every messy dataset is messy in its own way.” — Hadley Wickham

Ref: Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science.

需要安装和加载的包

# install.packages("nycflights13")
# install.packages("tidyverse")

library(nycflights13)
library(tidyverse)

1 什么是tidy data？

对比table1, table2, table3

table1

# A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583

table2

# A tibble: 12 × 4
   country      year type            count
   <chr>       <dbl> <chr>           <dbl>
 1 Afghanistan  1999 cases             745
 2 Afghanistan  1999 population   19987071
 3 Afghanistan  2000 cases            2666
 4 Afghanistan  2000 population   20595360
 5 Brazil       1999 cases           37737
 6 Brazil       1999 population  172006362
 7 Brazil       2000 cases           80488
 8 Brazil       2000 population  174504898
 9 China        1999 cases          212258
10 China        1999 population 1272915272
11 China        2000 cases          213766
12 China        2000 population 1280428583

table3

# A tibble: 6 × 3
  country      year rate             
  <chr>       <dbl> <chr>            
1 Afghanistan  1999 745/19987071     
2 Afghanistan  2000 2666/20595360    
3 Brazil       1999 37737/172006362  
4 Brazil       2000 80488/174504898  
5 China        1999 212258/1272915272
6 China        2000 213766/1280428583

tidydata的特征

每个变量是一列；每列是一个变量。
每个观察结果是一行；每行是一个观察结果。
每个值是一个单元格；每个单元格是一个单一值。

为什么要确保你的数据整洁？

如果你有一个一致的数据结构，学习与之配合的工具就更容易，因为它们具有底层的统一性。
将变量放在列中, 发挥R的向量化特性，大多数内置的R函数都是与向量一起工作的。

2 如何查看数据？

View()/glimpse()/head()/tail()

library(nycflights13)
data(flights)
flights #查看数据

# 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>

View(flights) #在View卡片下查看数据

glimpse(flights) #查看数据结构

Rows: 336,776
Columns: 19
$ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
$ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
$ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
$ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
$ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
$ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
$ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
$ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
$ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
$ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
$ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
$ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
$ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
$ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
$ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
$ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
$ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…

head(flights) #查看前6行

# A tibble: 6 × 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
# 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>

tail(flights) #查看后6行

# A tibble: 6 × 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     9    30       NA           1842        NA       NA           2019
2  2013     9    30       NA           1455        NA       NA           1634
3  2013     9    30       NA           2200        NA       NA           2312
4  2013     9    30       NA           1210        NA       NA           1330
5  2013     9    30       NA           1159        NA       NA           1344
6  2013     9    30       NA            840        NA       NA           1020
# 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>

int 整数 integer
dbl 双精度浮点数 bouble (实数)
chr 字符串
dttm 日期时间

3 dplyr函数

3.1 dplyr函数的共性

第1项参数是data frame.
第2项参数描述动作。
输出结果是一个新的data frame.

3.2 dplyr函数的分类

行的管理 Rows:
- filter() chooses rows based on column values.
- arrange() changes the order of the rows.
- distinct() removes duplicate rows.
- na.omit() removes rows with missing values.
- drop.na() removes rows with missing values.
- slice() chooses rows based on location.
列的管理 Columns:
- mutate() changes the values of columns and creates new columns.
- select() changes whether or not a column is included.
- rename() changes the name of columns.
- if_else() changes the values of columns based on a condition.
- case_when() changes the values of columns based on multiple conditions.
- recode() changes the values of columns based on a condition.
- relocate() changes the order of the columns.
行组的管理Groups of rows:
- summarise() collapses a group into a single row.
- count()
- top_n()

3.3 Rows

3.3.1 保留filter()

flights %>%  
  filter(dep_delay > 120) #保留dep_delay大于120的行

# 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>

>=（大于或等于）
<（小于）
<=（小于或等于）
==（等于）
!=（不等于）
& 条件组合，同时成立
| “或”，至少有一个条件成立

flights %>% 
  filter(month == 1 & day == 1) #筛选出1月1日的航班

# 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>

flights %>%
  filter(month == 1 | month == 2) #筛选出1月或2月的航班

# 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>

flights %>%
  filter(month %in% c(1, 2)) #筛选出1月或2月的航班

# 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>

注意：当你运行 filter() 时，dplyr 执行过滤操作，创建一个新的数据框，然后打印它。它不会修改现有的 flights 数据集，因为 dplyr 函数永远不会修改它们的输入。要保存结果，你需要使用赋值操作符，<-

#筛选出1月1日的航班,并保存到flights_jan1
flights_jan1 <- flights %>% 
  filter(month == 1 & day == 1)

常见错误

# flights %>% 
#   filter(month = 1) #错误，应该用==而不是=

# flights %>%
#    filter(month == 1 | 2)  #错误，应该用month == 1 | month == 2

3.3.2 排序arange()

flights %>% 
  arrange(year, month, day, dep_time) #按照year, month, day, dep_time排序

# 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>

flights %>% 
  arrange(desc(dep_delay)) #desc()按照dep_time降序排序

# 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>

3.3.3 删除有缺失值的行na.omit()/drop_na()

flights %>% 
  na.omit() #删除含有NA的行

# A tibble: 327,346 × 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 327,336 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>

flights %>%
  drop_na(dep_time, arr_time) #删除dep_time, arr_time为NA的行

# A tibble: 328,063 × 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 328,053 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>

3.3.4 去重distinct()

flights %>% 
  distinct() #移除所有列都相同的行

# 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>

flights %>% 
  distinct(year, month, day) #提取year, month, day 的唯一组合

# A tibble: 365 × 3
    year month   day
   <int> <int> <int>
 1  2013     1     1
 2  2013     1     2
 3  2013     1     3
 4  2013     1     4
 5  2013     1     5
 6  2013     1     6
 7  2013     1     7
 8  2013     1     8
 9  2013     1     9
10  2013     1    10
# ℹ 355 more rows

flights %>% 
  distinct(origin, dest) #提取year, month, day 的唯一组合

# 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

flights %>% 
  distinct(origin, dest,    #找到year, month, day 的唯一组合
           .keep_all = TRUE) #保留所有的列。 .keep_all默认为FALSE，不保留其余的列

# 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>

3.3.5 缺失值is.na()

flights %>% 
  filter(is.na(dep_time)) #保留dep_time为NA的行

# A tibble: 8,255 × 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       NA           1630        NA       NA           1815
 2  2013     1     1       NA           1935        NA       NA           2240
 3  2013     1     1       NA           1500        NA       NA           1825
 4  2013     1     1       NA            600        NA       NA            901
 5  2013     1     2       NA           1540        NA       NA           1747
 6  2013     1     2       NA           1620        NA       NA           1746
 7  2013     1     2       NA           1355        NA       NA           1459
 8  2013     1     2       NA           1420        NA       NA           1644
 9  2013     1     2       NA           1321        NA       NA           1536
10  2013     1     2       NA           1545        NA       NA           1910
# ℹ 8,245 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>

flights %>% 
  filter(!is.na(dep_time)) #保留dep_time为NA的行

# A tibble: 328,521 × 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 328,511 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>

3.3.6 小练习

第1题: 将满足下列条件的航班筛选出来，保存为一个新的数据框christmas_jfk, 该数据框中一共有多少个航班？

航班出发日期为2013年12月24日.
出发地(origin)机场是”JFK”.

John F. Kennedy International Airport, JFK

LaGuardia Airport, LGA

Newark Liberty International Airport, EWR

航空公司(carrier)是”AA”,或者”UA”, 或者”US”.

AA, American Airlines.

UA, United Airlines

US, US Airways

将到达延误时长(arr_delay)中有缺失值的行剔除

第2题: 对数据框christmas_jfk, 按照到达延误时长(arr_delay)降序排列航班

第3题: 绘制数据框christmas_jfk中出发时间(dep_time)和到达延误时长(arr_delay)的散点图，散点的颜色用航空公司(carrier)来映射, 散点的大小用飞行距离来映射。

code

christmas_jfk <- flights %>% 
  filter(year == 2013 & month == 12 & day == 24 & origin == "JFK" ) %>%
  filter(carrier %in% c("AA", "UA", "US")) %>% 
  drop_na(arr_delay) %>% 
  arrange(desc(arr_delay))

christmas_jfk %>% print(width = Inf)

# A tibble: 52 × 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    24     2016           1530       286     2326           1915
 2  2013    12    24      816            750        26     1311           1255
 3  2013    12    24      612            615        -3      829            817
 4  2013    12    24      927            900        27     1237           1225
 5  2013    12    24      717            720        -3     1056           1045
 6  2013    12    24     1210           1200        10     1411           1400
 7  2013    12    24     1909           1900         9     2225           2215
 8  2013    12    24      804            800         4     1144           1135
 9  2013    12    24     1536           1510        26     1852           1845
10  2013    12    24     1700           1700         0     2046           2039
   arr_delay carrier flight tailnum origin dest  air_time distance  hour minute
       <dbl> <chr>    <int> <chr>   <chr>  <chr>    <dbl>    <dbl> <dbl>  <dbl>
 1       251 AA          85 N374AA  JFK    SFO        341     2586    15     30
 2        16 AA         936 N5EUAA  JFK    STT        206     1623     7     50
 3        12 US         840 N558UW  JFK    CLT        112      541     6     15
 4        12 AA           1 N336AA  JFK    LAX        343     2475     9      0
 5        11 AA        1345 N319AA  JFK    MIA        168     1089     7     20
 6        11 US        1817 N540UW  JFK    CLT         88      541    12      0
 7        10 AA          21 N339AA  JFK    LAX        343     2475    19      0
 8         9 AA          59 N377AA  JFK    SFO        364     2586     8      0
 9         7 AA         145 N5FHAA  JFK    MIA        173     1089    15     10
10         7 UA         512 N597UA  JFK    SFO        363     2586    17      0
   time_hour          
   <dttm>             
 1 2013-12-24 15:00:00
 2 2013-12-24 07:00:00
 3 2013-12-24 06:00:00
 4 2013-12-24 09:00:00
 5 2013-12-24 07:00:00
 6 2013-12-24 12:00:00
 7 2013-12-24 19:00:00
 8 2013-12-24 08:00:00
 9 2013-12-24 15:00:00
10 2013-12-24 17:00:00
# ℹ 42 more rows

nrow(christmas_jfk)

[1] 52

christmas_jfk %>% 
  ggplot(aes(dep_time, arr_delay, col = carrier, size = distance)) + 
  geom_point(alpha = 0.5)

flights %>%
  filter(year == 2013, month == 12, day == 24, 
         origin == "JFK", carrier %in% c("AA", "UA", "US")) %>%
  drop_na(arr_delay) %>% 
  arrange(desc(arr_delay)) %>% 
  ggplot(aes(dep_time, arr_delay, col = carrier, size = distance)) + 
  geom_point(alpha = 0.5)

3.4 Columns

3.4.1 追加变量mutate()

flights %>% 
  mutate(
    gain = arr_delay - dep_delay, 
    #gain = 到达延时 -出发延时。gain为正值,表示航班在飞行过程中增加了延迟，航班延迟增加。gain的负值，表示航班在飞行过程中减少了延迟，航班延迟减少，或者航班提前到达。
    speed = distance / air_time * 60, #计算飞行速度,
    .after = arr_delay  #将gain和speed放在arr_delay列后，默认是放在最后两列
)

# A tibble: 336,776 × 21
    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 13 more variables: arr_delay <dbl>, gain <dbl>, speed <dbl>, carrier <chr>,
#   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

flights %>% 
  mutate(
    gain = arr_delay - dep_delay,
    speed = distance / air_time * 60,
    .before = 1) #将gain和speed放在第1列,第2列

# A tibble: 336,776 × 21
    gain speed  year month   day dep_time sched_dep_time dep_delay arr_time
   <dbl> <dbl> <int> <int> <int>    <int>          <int>     <dbl>    <int>
 1     9  370.  2013     1     1      517            515         2      830
 2    16  374.  2013     1     1      533            529         4      850
 3    31  408.  2013     1     1      542            540         2      923
 4   -17  517.  2013     1     1      544            545        -1     1004
 5   -19  394.  2013     1     1      554            600        -6      812
 6    16  288.  2013     1     1      554            558        -4      740
 7    24  404.  2013     1     1      555            600        -5      913
 8   -11  259.  2013     1     1      557            600        -3      709
 9    -5  405.  2013     1     1      557            600        -3      838
10    10  319.  2013     1     1      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 12 more variables: sched_arr_time <int>, 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 %>% 
  mutate(
    gain = arr_delay - dep_delay,
    speed = distance / air_time * 60,
    .after = day) #将gain和speed放在day列后

# A tibble: 336,776 × 21
    year month   day  gain speed dep_time sched_dep_time dep_delay arr_time
   <int> <int> <int> <dbl> <dbl>    <int>          <int>     <dbl>    <int>
 1  2013     1     1     9  370.      517            515         2      830
 2  2013     1     1    16  374.      533            529         4      850
 3  2013     1     1    31  408.      542            540         2      923
 4  2013     1     1   -17  517.      544            545        -1     1004
 5  2013     1     1   -19  394.      554            600        -6      812
 6  2013     1     1    16  288.      554            558        -4      740
 7  2013     1     1    24  404.      555            600        -5      913
 8  2013     1     1   -11  259.      557            600        -3      709
 9  2013     1     1    -5  405.      557            600        -3      838
10  2013     1     1    10  319.      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 12 more variables: sched_arr_time <int>, 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>

注意：

当运行mutate() 时，追加变量，console中显示了追加的结果，但不会修改现有的数据框。

要保存这个新的数据框，需要将其赋值<-。

3.4.2 选择变量select()

flights %>% 
  select(year, month, day) #选择year, month, day列

# A tibble: 336,776 × 3
    year month   day
   <int> <int> <int>
 1  2013     1     1
 2  2013     1     1
 3  2013     1     1
 4  2013     1     1
 5  2013     1     1
 6  2013     1     1
 7  2013     1     1
 8  2013     1     1
 9  2013     1     1
10  2013     1     1
# ℹ 336,766 more rows

flights %>% 
  select(year:day) #选择year到day列

# A tibble: 336,776 × 3
    year month   day
   <int> <int> <int>
 1  2013     1     1
 2  2013     1     1
 3  2013     1     1
 4  2013     1     1
 5  2013     1     1
 6  2013     1     1
 7  2013     1     1
 8  2013     1     1
 9  2013     1     1
10  2013     1     1
# ℹ 336,766 more rows

flights %>%
  select(year:day, ends_with("delay")) #选择year到day列和以delay结尾的列

# A tibble: 336,776 × 5
    year month   day dep_delay arr_delay
   <int> <int> <int>     <dbl>     <dbl>
 1  2013     1     1         2        11
 2  2013     1     1         4        20
 3  2013     1     1         2        33
 4  2013     1     1        -1       -18
 5  2013     1     1        -6       -25
 6  2013     1     1        -4        12
 7  2013     1     1        -5        19
 8  2013     1     1        -3       -14
 9  2013     1     1        -3        -8
10  2013     1     1        -2         8
# ℹ 336,766 more rows

flights %>%
  select(!year:day) #选择除year到day列之外的列

# 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>

flights %>%
  select(where(is.character)) #选择类型是字符串的列

# 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

There are a number of helper functions you can use within select():

starts_with(“abc”): matches names that begin with “abc”.
ends_with(“xyz”): matches names that end with “xyz”.
contains(“ijk”): matches names that contain “ijk”.
num_range(“x”, 1:3): matches x1, x2 and x3.

3.4.3 重命名变量rename()

flights %>% 
  rename(tail_num = tailnum) # 新名称 = 旧名称

# 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>

# 保存重命名后的数据框
mydata <- flights %>% 
  rename(tail_num = tailnum) # 新名称 = 旧名称

3.4.4 二值转换if_else()

flights %>%
  mutate(season = if_else(month %in% c(6, 7, 8), "summer", "non-summer")) %>%
  relocate(season, .before = month) #将season列放在month列前

# A tibble: 336,776 × 20
    year season     month   day dep_time sched_dep_time dep_delay arr_time
   <int> <chr>      <int> <int>    <int>          <int>     <dbl>    <int>
 1  2013 non-summer     1     1      517            515         2      830
 2  2013 non-summer     1     1      533            529         4      850
 3  2013 non-summer     1     1      542            540         2      923
 4  2013 non-summer     1     1      544            545        -1     1004
 5  2013 non-summer     1     1      554            600        -6      812
 6  2013 non-summer     1     1      554            558        -4      740
 7  2013 non-summer     1     1      555            600        -5      913
 8  2013 non-summer     1     1      557            600        -3      709
 9  2013 non-summer     1     1      557            600        -3      838
10  2013 non-summer     1     1      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 12 more variables: sched_arr_time <int>, 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>

3.4.5 多值转换case_when()

flights  %>%  
  mutate(
    status = case_when(
      is.na(arr_delay)      ~ "cancelled", #如果arr_delay为NA,则status为cancelled
      arr_delay < -30       ~ "very early", #如果航班提前超过30分钟到达，status为"very early"
      arr_delay < -15       ~ "early", #如果航班提前15到30分钟到达，status为"early"
      abs(arr_delay) <= 15  ~ "on time", #如果航班提前或延迟在15分钟以内，status为"on time"
      arr_delay < 60        ~ "late", #如果航班延迟小于60分钟，status为"late"
      arr_delay < Inf       ~ "very late", #如果航班延迟大于等于60分钟，status为"late"
    ),
    .after = arr_delay) #将status列放在arr_delay列后

# A tibble: 336,776 × 20
    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 12 more variables: arr_delay <dbl>, status <chr>, carrier <chr>,
#   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

3.4.6 重新编码recode()

# recode()函数 旧值 = 新值
# case_when 适合于复杂条件的转换

flights %>%
  mutate(quarter = recode(month,
                         "1" = "Winter", "2" = "Winter", "12" = "Winter", 
                         "3" = "Spring", "4" = "Spring", "5" = "Spring",
                         "6" = "Summer", "7" = "Summer", "8" = "Summer",
                         "9" = "Fall", "10" = "Fall", "11" = "Fall"),
         .before = month)

# A tibble: 336,776 × 20
    year quarter month   day dep_time sched_dep_time dep_delay arr_time
   <int> <chr>   <int> <int>    <int>          <int>     <dbl>    <int>
 1  2013 Winter      1     1      517            515         2      830
 2  2013 Winter      1     1      533            529         4      850
 3  2013 Winter      1     1      542            540         2      923
 4  2013 Winter      1     1      544            545        -1     1004
 5  2013 Winter      1     1      554            600        -6      812
 6  2013 Winter      1     1      554            558        -4      740
 7  2013 Winter      1     1      555            600        -5      913
 8  2013 Winter      1     1      557            600        -3      709
 9  2013 Winter      1     1      557            600        -3      838
10  2013 Winter      1     1      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 12 more variables: sched_arr_time <int>, 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>

# 用case_when()更简洁
flights %>%
  mutate(quarter = case_when(
    month %in% c(12, 1, 2) ~ "Winter",
    month %in% c(3, 4, 5) ~ "Spring",
    month %in% c(6, 7, 8) ~ "Summer",
    month %in% c(9, 10, 11) ~ "Fall"
  ), 
  .before = month)

# A tibble: 336,776 × 20
    year quarter month   day dep_time sched_dep_time dep_delay arr_time
   <int> <chr>   <int> <int>    <int>          <int>     <dbl>    <int>
 1  2013 Winter      1     1      517            515         2      830
 2  2013 Winter      1     1      533            529         4      850
 3  2013 Winter      1     1      542            540         2      923
 4  2013 Winter      1     1      544            545        -1     1004
 5  2013 Winter      1     1      554            600        -6      812
 6  2013 Winter      1     1      554            558        -4      740
 7  2013 Winter      1     1      555            600        -5      913
 8  2013 Winter      1     1      557            600        -3      709
 9  2013 Winter      1     1      557            600        -3      838
10  2013 Winter      1     1      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 12 more variables: sched_arr_time <int>, 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 %>%
  mutate(company = recode(carrier,
                          "9E" = "Endeavor Air",
                          "AA" = "American Airlines",
                          "AS" = "Alaska Airlines",
                          "B6" = "JetBlue Airways",
                          "DL" = "Delta Air Lines",
                          "EV" = "ExpressJet Airlines",
                          "F9" = "Frontier Airlines",
                          "FL" = "AirTran Airways",
                          "HA" = "Hawaiian Airlines",
                          "MQ" = "Envoy Air",
                          "OO" = "SkyWest Airlines",
                          "UA" = "United Airlines",
                          "US" = "US Airways",
                          "VX" = "Virgin America",
                          "WN" = "Southwest Airlines",
                          "YV" = "Mesa Airlines"),
         .after = carrier)

# A tibble: 336,776 × 20
    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 12 more variables: arr_delay <dbl>, carrier <chr>, company <chr>,
#   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

3.4.7 前置变量relocate()

flights %>% 
  relocate(time_hour, air_time) #将time_hour, air_time放到最前面

# A tibble: 336,776 × 19
   time_hour           air_time  year month   day dep_time sched_dep_time
   <dttm>                 <dbl> <int> <int> <int>    <int>          <int>
 1 2013-01-01 05:00:00      227  2013     1     1      517            515
 2 2013-01-01 05:00:00      227  2013     1     1      533            529
 3 2013-01-01 05:00:00      160  2013     1     1      542            540
 4 2013-01-01 05:00:00      183  2013     1     1      544            545
 5 2013-01-01 06:00:00      116  2013     1     1      554            600
 6 2013-01-01 05:00:00      150  2013     1     1      554            558
 7 2013-01-01 06:00:00      158  2013     1     1      555            600
 8 2013-01-01 06:00:00       53  2013     1     1      557            600
 9 2013-01-01 06:00:00      140  2013     1     1      557            600
10 2013-01-01 06:00:00      138  2013     1     1      558            600
# ℹ 336,766 more rows
# ℹ 12 more variables: dep_delay <dbl>, arr_time <int>, sched_arr_time <int>,
#   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, origin <chr>,
#   dest <chr>, distance <dbl>, hour <dbl>, minute <dbl>

3.4.8 小练习

第1题：将出发地(origin)是”JFK”机场的航班筛选出来, 创建数据框jfk_flights, 在该数据框中追加如下变量：

追加新变量distance_group。根据distance(单位：英里)的数值，将航班分为短途、中途和长途三类。 distance_group值为”short”, “medium”, “long”。distance_group与distance的对应关系如下：

短途飞行（Short-haul flight）：飞行距离少于500英里的航班。
中途飞行（Medium-haul flight）：飞行距离在500到1,550英里之间的航班。
长途飞行（Long-haul flight）：飞行距离超过1,550英里的航班

追加新变量status。若arr_delay的值为缺失值，则status等于cancelled，若arr_delay的值不是缺失值，则status等于normal。
追加新变量month_name，将month的值转换为月份的英文名称，例如1转换为January，2转换为February，以此类推。

第2题：绘制出第1题中得到的数据框jfk_flights中航班数量的条形图，横坐标为月份的英文名称，纵坐标为航班数量。

第3题：绘制出第1题中得到的数据框jfk_flights中每个月份的取消/正常航班数量的条形图，横坐标为月份的英文名称，纵坐标为航班数量。

code

# 筛选出发地为"JFK"的航班
jfk_flights <- flights %>% 
  filter(origin == "JFK")

# 追加新变量distance_group, status和month_name
jfk_flights <- jfk_flights %>%
  mutate(
    distance_group = case_when(
      distance < 500 ~ "short",
      distance >= 500 & distance <= 1550 ~ "medium",
      distance > 1550 ~ "long"
    ),
    status = if_else(is.na(arr_delay), "cancelled", "normal"),
    month_name = month.abb[month],  # 使用内置的月份缩写向量month.abb
    .after = day  # 将新变量放在day列后
  )

jfk_flights

# A tibble: 111,279 × 22
    year month   day distance_group status month_name dep_time sched_dep_time
   <int> <int> <int> <chr>          <chr>  <chr>         <int>          <int>
 1  2013     1     1 medium         normal Jan             542            540
 2  2013     1     1 long           normal Jan             544            545
 3  2013     1     1 medium         normal Jan             557            600
 4  2013     1     1 medium         normal Jan             558            600
 5  2013     1     1 medium         normal Jan             558            600
 6  2013     1     1 long           normal Jan             558            600
 7  2013     1     1 short          normal Jan             559            559
 8  2013     1     1 medium         normal Jan             606            610
 9  2013     1     1 long           normal Jan             611            600
10  2013     1     1 medium         normal Jan             613            610
# ℹ 111,269 more rows
# ℹ 14 more variables: dep_delay <dbl>, arr_time <int>, sched_arr_time <int>,
#   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>

# 将month_name转换为有序因子，确保月份按自然顺序排序
jfk_flights$month_name <- factor(jfk_flights$month_name, 
                                    levels = month.abb, 
                                    ordered = TRUE)

# 绘制柱状图
ggplot(jfk_flights, aes(x = month_name, fill = month_name)) +
  geom_bar() +
  xlab("Month") +
  ylab("Number of Flights") +
  ggtitle("Number of Flights from JFK by Month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) # 优化x轴标签显示

# 绘制帕累托图
ggplot(jfk_flights, aes(x = fct_infreq(month_name), fill = month_name)) +
  geom_bar() +
  xlab("Month") +
  ylab("Number of Flights") +
  ggtitle("Number of Flights from JFK by Month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) # 优化x轴标签显示

# 绘制柱状图
ggplot(jfk_flights, aes(x = month_name, fill = month_name)) +
  geom_bar() +
  facet_wrap( ~ status, scales = "free_y") +  # 按status分面，y轴自由缩放
  xlab("Month") +
  ylab("Number of Flights") +
  ggtitle("Number of Flights from JFK by Month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) # 优化x轴标签显示

3.5 The pipe

合并追加变量的代码,并保存到flights_new数据框中

Show the code

#追加gain, status, quarter和company

flights_new <- flights %>% 
  mutate(
    gain = arr_delay - dep_delay,
    speed = distance / air_time * 60,
    .after = arr_delay) %>%
    mutate(
    status = case_when(
      is.na(arr_delay)      ~ "cancelled", 
      arr_delay < -30       ~ "very early", 
      arr_delay < -15       ~ "early", 
      abs(arr_delay) <= 15  ~ "on time", 
      arr_delay < 60        ~ "late", 
      arr_delay < Inf       ~ "very late", 
    ),
    .after = arr_delay) %>% 
  mutate(quarter = case_when(
    month %in% c(12, 1, 2) ~ "Winter",
    month %in% c(3, 4, 5) ~ "Spring",
    month %in% c(6, 7, 8) ~ "Summer",
    month %in% c(9, 10, 11) ~ "Fall"
  ), 
  .before = month) %>%
  mutate(company = recode(carrier,
                          "9E" = "Endeavor Air",
                          "AA" = "American Airlines",
                          "AS" = "Alaska Airlines",
                          "B6" = "JetBlue Airways",
                          "DL" = "Delta Air Lines",
                          "EV" = "ExpressJet Airlines",
                          "F9" = "Frontier Airlines",
                          "FL" = "AirTran Airways",
                          "HA" = "Hawaiian Airlines",
                          "MQ" = "Envoy Air",
                          "OO" = "SkyWest Airlines",
                          "UA" = "United Airlines",
                          "US" = "US Airways",
                          "VX" = "Virgin America",
                          "WN" = "Southwest Airlines",
                          "YV" = "Mesa Airlines"),
         .after = carrier) 

flights_new

# A tibble: 336,776 × 24
    year quarter month   day dep_time sched_dep_time dep_delay arr_time
   <int> <chr>   <int> <int>    <int>          <int>     <dbl>    <int>
 1  2013 Winter      1     1      517            515         2      830
 2  2013 Winter      1     1      533            529         4      850
 3  2013 Winter      1     1      542            540         2      923
 4  2013 Winter      1     1      544            545        -1     1004
 5  2013 Winter      1     1      554            600        -6      812
 6  2013 Winter      1     1      554            558        -4      740
 7  2013 Winter      1     1      555            600        -5      913
 8  2013 Winter      1     1      557            600        -3      709
 9  2013 Winter      1     1      557            600        -3      838
10  2013 Winter      1     1      558            600        -2      753
# ℹ 336,766 more rows
# ℹ 16 more variables: sched_arr_time <int>, arr_delay <dbl>, status <chr>,
#   gain <dbl>, speed <dbl>, carrier <chr>, company <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

3.6 Groups

3.6.1 group_by()

flights %>% 
  group_by(month) #按照month分组

# A tibble: 336,776 × 19
# Groups:   month [12]
    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>

group_by() 不会改变数据，但输出显示它是按”月份”分组的。这意味着随后的操作现在将”按月”进行。group_by() 为数据框添加了这个分组特征，这改变了随后应用于数据的动作。

3.6.2 summarize()

flights %>% 
  group_by(month) %>% 
  summarise(mean_delay = mean(dep_delay)) #按照month分组，计算dep_delay的均值

# A tibble: 12 × 2
   month mean_delay
   <int>      <dbl>
 1     1         NA
 2     2         NA
 3     3         NA
 4     4         NA
 5     5         NA
 6     6         NA
 7     7         NA
 8     8         NA
 9     9         NA
10    10         NA
11    11         NA
12    12         NA

NA: missing value

当向量中有NA，mean()函数默认返回NA，如果想忽略NA值，可以使用na.rm = TRUE

flights %>% 
  group_by(month) %>% 
  summarise(mean_delay = mean(dep_delay, na.rm = TRUE), #na.rm = TRUE表示忽略NA值
            n = n()
  ) #按照month分组，计算dep_delay的均值和n

# A tibble: 12 × 3
   month mean_delay     n
   <int>      <dbl> <int>
 1     1      10.0  27004
 2     2      10.8  24951
 3     3      13.2  28834
 4     4      13.9  28330
 5     5      13.0  28796
 6     6      20.8  28243
 7     7      21.7  29425
 8     8      12.6  29327
 9     9       6.72 27574
10    10       6.24 28889
11    11       5.44 27268
12    12      16.6  28135

# 统计每天的航班数量
flights %>% 
  group_by(year, month, day) %>% 
  summarise(n = n()) #生成的数据框是一个有分组结构的数据框

`summarise()` has grouped output by 'year', 'month'. You can override using the
`.groups` argument.

# A tibble: 365 × 4
# Groups:   year, month [12]
    year month   day     n
   <int> <int> <int> <int>
 1  2013     1     1   842
 2  2013     1     2   943
 3  2013     1     3   914
 4  2013     1     4   915
 5  2013     1     5   720
 6  2013     1     6   832
 7  2013     1     7   933
 8  2013     1     8   899
 9  2013     1     9   902
10  2013     1    10   932
# ℹ 355 more rows

flights %>% 
  group_by(year, month, day) %>% 
  summarise(n = n(),           #按照year, month, day分组，计算n
            .groups = "drop"   #生成的数据框是没有分组结构
            )

# A tibble: 365 × 4
    year month   day     n
   <int> <int> <int> <int>
 1  2013     1     1   842
 2  2013     1     2   943
 3  2013     1     3   914
 4  2013     1     4   915
 5  2013     1     5   720
 6  2013     1     6   832
 7  2013     1     7   933
 8  2013     1     8   899
 9  2013     1     9   902
10  2013     1    10   932
# ℹ 355 more rows

3.6.3 .by

flights %>% 
  summarize(mean_delay = mean(dep_delay, na.rm = TRUE) %>% round(3),
            n = n(),
            .by = month 
  ) %>% #按月份分组，计算dep_delay的均值和n
  arrange(desc(mean_delay)) #按mean_delay降序

# A tibble: 12 × 3
   month mean_delay     n
   <int>      <dbl> <int>
 1     7      21.7  29425
 2     6      20.8  28243
 3    12      16.6  28135
 4     4      13.9  28330
 5     3      13.2  28834
 6     5      13.0  28796
 7     8      12.6  29327
 8     2      10.8  24951
 9     1      10.0  27004
10     9       6.72 27574
11    10       6.24 28889
12    11       5.44 27268

flights %>% 
  summarize(mean_delay = mean(dep_delay, na.rm = TRUE) %>% round(3),
            n = n(),
            .by = c(origin)  #按origin分组，计算dep_delay的均值和n
            ) %>% 
  arrange(desc(mean_delay)) #按mean_delay降序

# A tibble: 3 × 3
  origin mean_delay      n
  <chr>       <dbl>  <int>
1 EWR          15.1 120835
2 JFK          12.1 111279
3 LGA          10.3 104662

flights %>% 
  summarize(mean_delay = mean(dep_delay, na.rm = TRUE)%>% round(3),
            n = n(),
            .by = c(dest) #按dest分组，计算dep_delay的均值和n
            ) %>%
  arrange(desc(mean_delay)) #按mean_delay降序

# A tibble: 105 × 3
   dest  mean_delay     n
   <chr>      <dbl> <int>
 1 CAE         35.6   116
 2 TUL         34.9   315
 3 OKC         30.6   346
 4 BHM         29.7   297
 5 TYS         28.5   631
 6 JAC         26.5    25
 7 DSM         26.2   569
 8 RIC         23.6  2454
 9 ALB         23.6   439
10 MSN         23.6   572
# ℹ 95 more rows

flights %>% 
  summarize(mean_delay = mean(dep_delay, na.rm = TRUE)%>% round(3),
            n = n(),
            .by = c(origin, dest) #按origin, dest分组，计算dep_delay的均值和n
            ) %>%
  arrange(desc(mean_delay)) #按mean_delay降序

# A tibble: 224 × 4
   origin dest  mean_delay     n
   <chr>  <chr>      <dbl> <int>
 1 EWR    TYS         41.8   323
 2 EWR    CAE         36.3   104
 3 EWR    TUL         34.9   315
 4 LGA    SBN         31.3     6
 5 EWR    OKC         30.6   346
 6 LGA    BHM         29.8   296
 7 LGA    CAE         29.5    12
 8 EWR    DSM         29.3   411
 9 EWR    JAC         28.7    23
10 EWR    ROC         27.9   513
# ℹ 214 more rows

3.6.4 小练习

第1题：如果你想尽可能避免延误，你应该选择哪个时间段起飞的航班？

第2题：哪三家航空公司(carrier)的平均延误时间(arr_delay)最长？

第3题：哪个月份的平均延误时间(arr_delay)最长？

第4题：将延误时间(arr_delay)超过15分钟的定义为延误航班。计算各个月份的延误航班的比例，并按比例降序排列。

第5题：将延误时间(arr_delay)超过15分钟的定义为延误航班。计算各个航空公司(carrier)的延误航班的比例，并按各航空公司航班数量降序排列。

第6题：从JFK机场出发(origin)的航班中，航班数量最多的5家航空公司是哪些？并统计出其航班数量？

code

# 第1题
flights %>%
  group_by(hour) %>%
  summarise(avg_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  arrange(avg_arr_delay) %>% 
  print(n = Inf)

# A tibble: 20 × 2
    hour avg_arr_delay
   <dbl>         <dbl>
 1     7        -5.30 
 2     5        -4.80 
 3     6        -3.38 
 4     9        -1.45 
 5     8        -1.11 
 6    10         0.954
 7    11         1.48 
 8    12         3.49 
 9    13         6.54 
10    14         9.20 
11    23        11.8  
12    15        12.3  
13    16        12.6  
14    18        14.8  
15    22        16.0  
16    17        16.0  
17    19        16.7  
18    20        16.7  
19    21        18.4  
20     1       NaN

# 第2题
flights %>%
  group_by(carrier) %>%
  summarise(avg_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  arrange(desc(avg_arr_delay)) %>%
  top_n(3, avg_arr_delay)

# A tibble: 3 × 2
  carrier avg_arr_delay
  <chr>           <dbl>
1 F9               21.9
2 FL               20.1
3 EV               15.8

# 第3题
flights %>%
  group_by(month) %>%
  summarise(avg_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  arrange(desc(avg_arr_delay))

# A tibble: 12 × 2
   month avg_arr_delay
   <int>         <dbl>
 1     7        16.7  
 2     6        16.5  
 3    12        14.9  
 4     4        11.2  
 5     1         6.13 
 6     8         6.04 
 7     3         5.81 
 8     2         5.61 
 9     5         3.52 
10    11         0.461
11    10        -0.167
12     9        -4.02

# 第4题
flights %>%
  mutate(is_delayed = arr_delay > 15) %>%
  group_by(month) %>%
  summarise(
    total_flights = n(),
    delayed_flights = sum(is_delayed, na.rm = TRUE),
    delay_proportion = delayed_flights / total_flights
  ) %>%
  arrange(desc(delay_proportion))

# A tibble: 12 × 4
   month total_flights delayed_flights delay_proportion
   <int>         <int>           <int>            <dbl>
 1    12         28135            8812            0.313
 2     7         29425            8902            0.303
 3     6         28243            8340            0.295
 4     4         28330            7700            0.272
 5     8         29327            6852            0.234
 6     1         27004            6001            0.222
 7     3         28834            6374            0.221
 8     2         24951            5418            0.217
 9     5         28796            6178            0.215
10    11         27268            4680            0.172
11    10         28889            4798            0.166
12     9         27574            3575            0.130

# 第5题
flights %>%
  mutate(is_delayed = arr_delay > 15) %>%
  group_by(carrier) %>%
  summarise(
    total_flights = n(),
    delayed_flights = sum(is_delayed, na.rm = TRUE),
    delay_proportion = delayed_flights / total_flights
  ) %>%
  arrange(desc(total_flights))

# A tibble: 16 × 4
   carrier total_flights delayed_flights delay_proportion
   <chr>           <int>           <int>            <dbl>
 1 UA              58665           12592            0.215
 2 B6              54635           14111            0.258
 3 EV              54173           16028            0.296
 4 DL              48110            8690            0.181
 5 AA              32729            6004            0.183
 6 MQ              26397            6777            0.257
 7 US              20536            3559            0.173
 8 9E              18460            4309            0.233
 9 WN              12275            2994            0.244
10 VX               5162             924            0.179
11 FL               3260            1065            0.327
12 AS                714             102            0.143
13 F9                685             254            0.371
14 YV                601             171            0.285
15 HA                342              43            0.126
16 OO                 32               7            0.219

# 第6题
flights %>%
  filter(origin == "JFK") %>%
  group_by(carrier) %>%
  summarise(number_of_flights = n()) %>%
  arrange(desc(number_of_flights)) %>%
  top_n(5, number_of_flights)

# A tibble: 5 × 2
  carrier number_of_flights
  <chr>               <int>
1 B6                  42076
2 DL                  20701
3 9E                  14651
4 AA                  13783
5 MQ                   7193

4 forcats函数

library(tidyverse)
data(mpg)

count(mpg, drv)

# A tibble: 3 × 2
  drv       n
  <chr> <int>
1 4       103
2 f       106
3 r        25

mpg %>% 
  ggplot(aes(drv, fill = drv)) +
  geom_bar()

4.1 fct_recode()

mpg <- mpg %>%
  mutate(drv = fct_recode(drv,
                          "front wheels" = "f",
                          "rear wheels" = "r",
                          "four wheels" = "4"))

mpg %>% 
  ggplot(aes(drv, fill = drv)) +
  geom_bar()

4.2 fct_infreq() 按频数降序

mpg %>% 
  ggplot(aes(fct_infreq(drv), fill = drv)) +
  geom_bar()

4.3 fct_rev()

mpg %>% 
  ggplot(aes(fct_rev(fct_infreq(drv)), fill = drv)) +
  geom_bar()

4.4 fct_reorder()

4.4.1 按中位数升序排列箱线图

mpg %>%
  ggplot(aes(cty, fct_reorder(class, cty),
             fill = class)) +
  geom_boxplot()

4.4.2 按中位数降序排列箱线图

mpg %>%
  ggplot(aes(cty, fct_reorder(class, -cty),
             fill = class)) +
  geom_boxplot()

4.4.3 按平均数排列箱线图

mpg %>% 
  ggplot(aes(fct_reorder(class, cty, .fun = mean), 
             cty, fill = class)) +
  geom_boxplot() +
  labs(fill = "Class", x = "Class")

4.4.4 柱形的排列

mpg %>% 
  group_by(drv) %>%
  summarise(mean_cty = mean(cty)) %>% 
  mutate(drv = fct_reorder(drv, mean_cty)) %>%
  ggplot(aes(drv, mean_cty, fill = drv)) +
  geom_col()

mpg %>% 
  group_by(drv) %>%
  summarise(mean_cty = mean(cty)) %>% 
  mutate(drv = fct_reorder(drv, -mean_cty)) %>%
  ggplot(aes(drv, mean_cty, fill = drv)) +
  geom_col()

4.5 fct_lump_()合并小类

library(tidyverse)
data(mpg)

count(mpg, class) %>% arrange(desc(n))

# A tibble: 7 × 2
  class          n
  <chr>      <int>
1 suv           62
2 compact       47
3 midsize       41
4 subcompact    35
5 pickup        33
6 minivan       11
7 2seater        5

fct_lump_n(mpg$class, 4) %>% fct_count()

# A tibble: 5 × 2
  f              n
  <fct>      <int>
1 compact       47
2 midsize       41
3 subcompact    35
4 suv           62
5 Other         49

fct_lump_min(mpg$class, 30) %>% fct_count()

# A tibble: 6 × 2
  f              n
  <fct>      <int>
1 compact       47
2 midsize       41
3 pickup        33
4 subcompact    35
5 suv           62
6 Other         16

fct_lump_lowfreq(mpg$class) %>% fct_count()

# A tibble: 6 × 2
  f              n
  <fct>      <int>
1 compact       47
2 midsize       41
3 pickup        33
4 subcompact    35
5 suv           62
6 Other         16

fct_lump_prop(mpg$class, 0.14) %>% fct_count()

# A tibble: 6 × 2
  f              n
  <fct>      <int>
1 compact       47
2 midsize       41
3 pickup        33
4 subcompact    35
5 suv           62
6 Other         16

4.6 fct_collaps()保留指定类别

mpg %>%
  group_by(class) %>%
  summarise(proportion = n() / nrow(mpg)) %>%
  arrange(desc(proportion))

# A tibble: 7 × 2
  class      proportion
  <chr>           <dbl>
1 suv            0.265 
2 compact        0.201 
3 midsize        0.175 
4 subcompact     0.150 
5 pickup         0.141 
6 minivan        0.0470
7 2seater        0.0214

fct_collapse(mpg$class,
             "compact" = c("compact", "subcompact"),
             "midsize" = c("midsize", "minivan", "pickup")) %>% 
  fct_count()

# A tibble: 4 × 2
  f           n
  <fct>   <int>
1 2seater     5
2 compact    82
3 midsize    85
4 suv        62

4.7 fct_other()

# c: CNG (Compressed Natural Gas)
# d: Diesel
# e: Ethanol blend, typically referring to gasoline blended with a certain proportion of ethanol
# p: Premium unleaded gasoline 辛烷值较高，通常在91到93之间（在美国），有的甚至更高
# r: Regular unleaded gasoline 辛烷值较低，通常是87（在美国）

mpg %>% 
  count(fl) %>% 
  arrange(desc(n))

# A tibble: 5 × 2
  fl        n
  <chr> <int>
1 r       168
2 p        52
3 e         8
4 d         5
5 c         1

fct_other(mpg$fl, keep = c("p", "r")) %>% fct_count()

# A tibble: 3 × 2
  f         n
  <fct> <int>
1 p        52
2 r       168
3 Other    14

fct_other(mpg$fl, drop = c("c", "d", "e")) %>% fct_count()

# A tibble: 3 × 2
  f         n
  <fct> <int>
1 p        52
2 r       168
3 Other    14

4.8 保存合并后的类别

# 保存合并后的类别

mpg <- mpg %>% 
  mutate(fl = fct_other(fl, keep = c("p", "r")),
         class = fct_collapse(class,
                               "compact" = c("compact", "subcompact"),
                               "midsize" = c("midsize", "minivan", "pickup"))
         )

mpg %>% count(fl)

# A tibble: 3 × 2
  fl        n
  <fct> <int>
1 p        52
2 r       168
3 Other    14

mpg %>% count(class)

# A tibble: 4 × 2
  class       n
  <fct>   <int>
1 2seater     5
2 compact    82
3 midsize    85
4 suv        62