flights |>
group_by(origin) |>
summarise(n=n(),dly=mean(dep_delay,na.rm=T))# A tibble: 3 × 3
origin n dly
<chr> <int> <dbl>
1 EWR 120835 15.1
2 JFK 111279 12.1
3 LGA 104662 10.3tidyverse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）
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(),dly=mean(dep_delay,na.rm=T)) |> arrange(desc(n))# A tibble: 16 × 3 carrier n dly <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(),dly=mean(dep_delay,na.rm=T)) |> 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 dly <chr> <chr> <int> <dbl> 1 EWR ORD 6100 14.6 2 EWR BOS 5327 12.5 3 EWR SFO 5127 14.3 4 JFK LAX 11262 8.52 5 JFK SFO 8204 12.0 6 JFK BOS 5898 11.7 7 LGA ATL 10263 11.4 8 LGA ORD 8857 10.7 9 LGA CLT 6168 8.97
2 第二题 解释代码
代码含义：按Species排序，然后在每个物种内，按 Sepal开头的两个变量降序排序
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代码含义： 按gender对数据进行分组后筛选出mass大于mass的平均值的数据，随后去除空行
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：选择出name，homeworld和species三个变量，将除了name变量的其他两个变量转换为因子变量
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代码含义：将 mtcars数据转换为数据框，按照vs数据进行分组，利用cut函数对VS不同的hp值分为三个等宽的区间，然后再对其进行分组
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。
inner_join()：X1 <- data.frame(id = c(1, 2, 3), value1 = c("尹", "杨", "张")) X2 <- data.frame(id = c(2, 3, 4), value2 = c("优", "中", "良")) inner_join_result <- inner_join(X1, X2, by = "id") inner_join_resultid value1 value2 1 2 杨 优 2 3 张 中left_join()：left_join_result <- left_join(X1, X2, by = "id") left_join_resultid value1 value2 1 1 尹 <NA> 2 2 杨 优 3 3 张 中right_join()：right_join_result <- right_join(X1, X2, by = "id") right_join_resultid value1 value2 1 2 杨 优 2 3 张 中 3 4 <NA> 良full_join()：full_join_result <- full_join(X1, X2, by = "id") full_join_resultid value1 value2 1 1 尹 <NA> 2 2 杨 优 3 3 张 中 4 4 <NA> 良