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
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)
第二题 解释代码
代码含义：
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：
%>% 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：
%>% 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：
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