tidyverse初认识伍泉至221527139

1 第一题 编写代码

利用nycflights13包的flights数据集是2013年从纽约三大机场（JFK、LGA、EWR）起飞的所有航班的准点数据，共336776条记录。

• 计算纽约三大机场2013起飞航班数和平均延误时间（可使用group_by, summarise函数）

  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>

  flights %>%
  group_by(origin) %>%
  summarise(n=n(),depm=mean(dep_delay,na.rm=T))

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

• 计算不同航空公司2013从纽约起飞航班数和平均延误时间

  flights %>%
  group_by(carrier) %>%
  summarise(n=n(),depm=mean(dep_delay,na.rm=T)) %>%
  arrange(desc(n))

  # A tibble: 16 × 3
     carrier     n  depm
     <chr>   <int> <dbl>
   1 UA      58665 12.1 
   2 B6      54635 13.0 
   3 EV      54173 20.0 
   4 DL      48110  9.26
   5 AA      32729  8.59
   6 MQ      26397 10.6 
   7 US      20536  3.78
   8 9E      18460 16.7 
   9 WN      12275 17.7 
  10 VX       5162 12.9 
  11 FL       3260 18.7 
  12 AS        714  5.80
  13 F9        685 20.2 
  14 YV        601 19.0 
  15 HA        342  4.90
  16 OO         32 12.6 

• 计算纽约三大机场排名前三个目的地和平均飞行距离(可使用group_by, summarise, arrange, slice_max函数)

  flights %>%
  group_by(origin,dest) %>%
  summarise(n=n(),distm=mean(distance)) %>%
  slice_max(n,n=3)

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

  # A tibble: 9 × 4
  # Groups:   origin [3]
    origin dest      n distm
    <chr>  <chr> <int> <dbl>
  1 EWR    ORD    6100   719
  2 EWR    BOS    5327   200
  3 EWR    SFO    5127  2565
  4 JFK    LAX   11262  2475
  5 JFK    SFO    8204  2586
  6 JFK    BOS    5898   187
  7 LGA    ATL   10263   762
  8 LGA    ORD    8857   733
  9 LGA    CLT    6168   544

2 第二题 解释代码

1. 代码含义：用管道操作符 %>% 将数据框 iris 转换为 tibble 格式，arrange 的第一个参数是数据框（在这里是 tibble(iris)），后面的参数指定了排序的列和顺序，已品种为首，其次是sepal，降序排列

   tibble(iris) %>% 
     arrange(Species,across(starts_with("Sepal"), desc))

   # A tibble: 150 × 5
      Sepal.Length Sepal.Width Petal.Length Petal.Width Species
             <dbl>       <dbl>        <dbl>       <dbl> <fct>  
    1          5.8         4            1.2         0.2 setosa 
    2          5.7         4.4          1.5         0.4 setosa 
    3          5.7         3.8          1.7         0.3 setosa 
    4          5.5         4.2          1.4         0.2 setosa 
    5          5.5         3.5          1.3         0.2 setosa 
    6          5.4         3.9          1.7         0.4 setosa 
    7          5.4         3.9          1.3         0.4 setosa 
    8          5.4         3.7          1.5         0.2 setosa 
    9          5.4         3.4          1.7         0.2 setosa 
   10          5.4         3.4          1.5         0.4 setosa 
   # ℹ 140 more rows

2. 代码含义：将 starwars 数据集按照 gender 列进行分组。对每个性别组，计算该组中 mass 列的平均值（忽略缺失值）。筛选出每个组中 mass 大于组内平均值的角色

   starwars %>% 
     group_by(gender) %>% 
     filter(mass > mean(mass, na.rm = TRUE))

   # A tibble: 15 × 14
   # Groups:   gender [3]
      name    height   mass hair_color skin_color eye_color birth_year sex   gender
      <chr>    <int>  <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
    1 Darth …    202  136   none       white      yellow          41.9 male  mascu…
    2 Owen L…    178  120   brown, gr… light      blue            52   male  mascu…
    3 Beru W…    165   75   brown      light      blue            47   fema… femin…
    4 Chewba…    228  112   brown      unknown    blue           200   male  mascu…
    5 Jabba …    175 1358   <NA>       green-tan… orange         600   herm… mascu…
    6 Jek To…    180  110   brown      fair       blue            NA   <NA>  <NA>  
    7 IG-88      200  140   none       metal      red             15   none  mascu…
    8 Bossk      190  113   none       green      red             53   male  mascu…
    9 Ayla S…    178   55   none       blue       hazel           48   fema… femin…
   10 Gregar…    185   85   black      dark       brown           NA   <NA>  <NA>  
   11 Lumina…    170   56.2 black      yellow     blue            58   fema… femin…
   12 Zam We…    168   55   blonde     fair, gre… yellow          NA   fema… femin…
   13 Shaak …    178   57   none       red, blue… black           NA   fema… femin…
   14 Grievo…    216  159   none       brown, wh… green, y…       NA   male  mascu…
   15 Tarfful    234  136   brown      brown      blue            NA   male  mascu…
   # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
   #   vehicles <list>, starships <list>

   代码含义：从 starwars 数据集中选择 name、homeworld 和 species 三列。将 homeworld 和 species 列转换为因子类型。

   starwars %>%
     select(name, homeworld, species) %>%
     mutate(across(!name, as.factor))

   # A tibble: 87 × 3
      name               homeworld species
      <chr>              <fct>     <fct>  
    1 Luke Skywalker     Tatooine  Human  
    2 C-3PO              Tatooine  Droid  
    3 R2-D2              Naboo     Droid  
    4 Darth Vader        Tatooine  Human  
    5 Leia Organa        Alderaan  Human  
    6 Owen Lars          Tatooine  Human  
    7 Beru Whitesun Lars Tatooine  Human  
    8 R5-D4              Tatooine  Droid  
    9 Biggs Darklighter  Tatooine  Human  
   10 Obi-Wan Kenobi     Stewjon   Human  
   # ℹ 77 more rows

3. 代码含义：将 mtcars 数据集转换为 tibble 格式。按照 vs 列（发动机类型）进行分组。在每个 vs 组内，将 hp 列（马力）分成3个区间，并创建一个新列 hp_cut 来存储分箱结果。最后，按照 hp_cut 列（马力区间）重新分组

   tibble(mtcars) %>%
     group_by(vs) %>%
     mutate(hp_cut = cut(hp, 3)) %>%
     group_by(hp_cut)

   # A tibble: 32 × 12
   # Groups:   hp_cut [6]
        mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb hp_cut     
      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>      
    1  21       6  160    110  3.9   2.62  16.5     0     1     4     4 (90.8,172] 
    2  21       6  160    110  3.9   2.88  17.0     0     1     4     4 (90.8,172] 
    3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1 (75.7,99.3]
    4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1 (99.3,123] 
    5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 (172,254]  
    6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 (99.3,123] 
    7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 (172,254]  
    8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2 (51.9,75.7]
    9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2 (75.7,99.3]
   10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 (99.3,123] 
   # ℹ 22 more rows

3 第三题 查找帮助理解函数

阅读 https://dplyr.tidyverse.org/reference/mutate-joins.html 内容，说明4个数据集链接函数函数的作用。分别举一个实际例子演示并解释其输出结果。

1. inner_join() ：

   library(dplyr)
   
   students <- tibble(
     id = 1:3,
     name = c("Alice", "Bob", "Charlie")
   )
   
   scores <- tibble(
     id = c(2, 3, 4),
     score = c(90, 85, 70)
   )
   
   inner_join(students, scores, by = "id")

   # A tibble: 2 × 3
        id name    score
     <dbl> <chr>   <dbl>
   1     2 Bob        90
   2     3 Charlie    85

2. left_join() ：

   library(dplyr)
   
   students <- tibble(
     id = c(1, 2, 3),
     name = c("Alice", "Bob", "Charlie")
   )
   
   scores <- tibble(
     id = c(1, 2, 4),
     score = c(90, 85, 88)
   )
   
   result <- left_join(students, scores, by = "id")
   print(result)

   # A tibble: 3 × 3
        id name    score
     <dbl> <chr>   <dbl>
   1     1 Alice      90
   2     2 Bob        85
   3     3 Charlie    NA

3. right_join() ：

   result <- right_join(students, scores, by = "id")
   print(result)

   # A tibble: 3 × 3
        id name  score
     <dbl> <chr> <dbl>
   1     1 Alice    90
   2     2 Bob      85
   3     4 <NA>     88

4. full_join() ：

   result <- full_join(students, scores, by = "id")
   print(result)

   # A tibble: 4 × 3
        id name    score
     <dbl> <chr>   <dbl>
   1     1 Alice      90
   2     2 Bob        85
   3     3 Charlie    NA
   4     4 <NA>       88