221527136邹伟亮 tidyverse初认识

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

  # A tibble: 3 × 3
    origin      n  depm
    <chr>   <int> <dbl>
  1 EWR    120835 1337.
  2 JFK    111279 1399.
  3 LGA    104662 1310.

• 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：

   tibble(iris)：将鸢尾花数据及iris转换为tibble格式，

   arrange(Species,across(starts_with(“Sepal”), desc))：按照Species列进行升序排序，再对以"Sepal"开头的列（即Sepal.Length和Sepal.Width）进行降序排序。

   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：
   group_by(gender)：按照gender列（性别）对starwars数据集进行分组。

   filter(mass > mean(mass, na.rm = TRUE))：在每个性别组中， 分别计算每个性别组（gender）的平均体重，然后筛选出每个组中体重高于该组平均值的角色。

   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>

3. 代码含义：

   select(name, homeworld, species) ：从starwars中选择name, homeworld, species这三列数据
   mutate(across(!name, as.factor))：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

4. 代码含义：

   tibble(mtcars)：将mtcars数据集转换为tibble格式。

   group_by(vs)：按照vs列（发动机类型）对数据进行分组。

   mutate(hp_cut = cut(hp, 3))：在每组中，将hp列（马力）分成3个区间，并将分箱结果存储在新列hp_cut中。

   group_by(hp_cut)：最后，按照hp_cut列（马力分箱结果）对数据进行分组。

   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() ：
   返回两个数据框中键完全匹配的行，相当于取两个数据集的交集。

   library(dplyr)
   
   # 学生表（包含重复的id）
   students <- tibble(
     id = c(1, 2, 2, 3, 4),
     name = c("Alice", "Bob", "Bob", "Charlie", "David"),
     age = c(20, 21, 21, 22, 23)
   )
   
   # 成绩表（包含多个匹配和缺失）
   scores <- tibble(
     id = c(1, 2, 2, 4, 5),
     subject = c("Math", "Science", "Art", "Physics", "Music"),
     score = c(90, 85, 88, 92, 75)
   )
   
   # 内连接（基于id）
   result <- inner_join(students, scores, by = "id")

   Warning in inner_join(students, scores, by = "id"): Detected an unexpected many-to-many relationship between `x` and `y`.
   ℹ Row 2 of `x` matches multiple rows in `y`.
   ℹ Row 2 of `y` matches multiple rows in `x`.
   ℹ If a many-to-many relationship is expected, set `relationship =
     "many-to-many"` to silence this warning.

   print(result)

   # A tibble: 6 × 5
        id name    age subject score
     <dbl> <chr> <dbl> <chr>   <dbl>
   1     1 Alice    20 Math       90
   2     2 Bob      21 Science    85
   3     2 Bob      21 Art        88
   4     2 Bob      21 Science    85
   5     2 Bob      21 Art        88
   6     4 David    23 Physics    92

2. left_join() ：
   保留左表所有行，右表中无匹配的行用NA填充。

   # 左连接
   result <- left_join(students, scores, by = "id")

   Warning in left_join(students, scores, by = "id"): Detected an unexpected many-to-many relationship between `x` and `y`.
   ℹ Row 2 of `x` matches multiple rows in `y`.
   ℹ Row 2 of `y` matches multiple rows in `x`.
   ℹ If a many-to-many relationship is expected, set `relationship =
     "many-to-many"` to silence this warning.

   print(result)

   # A tibble: 7 × 5
        id name      age subject score
     <dbl> <chr>   <dbl> <chr>   <dbl>
   1     1 Alice      20 Math       90
   2     2 Bob        21 Science    85
   3     2 Bob        21 Art        88
   4     2 Bob        21 Science    85
   5     2 Bob        21 Art        88
   6     3 Charlie    22 <NA>       NA
   7     4 David      23 Physics    92

3. right_join() ：

   保留右表所有行，左表中无匹配的行用NA填充。

   # 右连接
   result <- right_join(students, scores, by = "id")

   Warning in right_join(students, scores, by = "id"): Detected an unexpected many-to-many relationship between `x` and `y`.
   ℹ Row 2 of `x` matches multiple rows in `y`.
   ℹ Row 2 of `y` matches multiple rows in `x`.
   ℹ If a many-to-many relationship is expected, set `relationship =
     "many-to-many"` to silence this warning.

   print(result)

   # A tibble: 7 × 5
        id name    age subject score
     <dbl> <chr> <dbl> <chr>   <dbl>
   1     1 Alice    20 Math       90
   2     2 Bob      21 Science    85
   3     2 Bob      21 Art        88
   4     2 Bob      21 Science    85
   5     2 Bob      21 Art        88
   6     4 David    23 Physics    92
   7     5 <NA>     NA Music      75

4. full_join() ：
   保留左右两表所有行，无匹配的行用NA填充，相当于取两个数据集的并集。

   # 全连接
   result <- full_join(students, scores, by = "id")

   Warning in full_join(students, scores, by = "id"): Detected an unexpected many-to-many relationship between `x` and `y`.
   ℹ Row 2 of `x` matches multiple rows in `y`.
   ℹ Row 2 of `y` matches multiple rows in `x`.
   ℹ If a many-to-many relationship is expected, set `relationship =
     "many-to-many"` to silence this warning.

   print(result)

   # A tibble: 8 × 5
        id name      age subject score
     <dbl> <chr>   <dbl> <chr>   <dbl>
   1     1 Alice      20 Math       90
   2     2 Bob        21 Science    85
   3     2 Bob        21 Art        88
   4     2 Bob        21 Science    85
   5     2 Bob        21 Art        88
   6     3 Charlie    22 <NA>       NA
   7     4 David      23 Physics    92
   8     5 <NA>       NA Music      75