数据-加载 选择 过滤 缺失值 新变量 分组 计算
Install.packages(“tidyverse”)
> require(tidyverse)
载入需要的程辑包：tidyverse
-- Attaching packages ------------------------------------ tidyverse 1.3.0 --
√ ggplot2 3.3.2 √ purrr 0.3.4
√ tibble 3.0.1 √ dplyr 1.0.0
√ tidyr 1.1.0 √ stringr 1.4.0
√ readr 1.3.1 √ forcats 0.5.0
-- Conflicts --------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
> view(starwars)
> starwars %>%
+ select(gender,mass,height,species)
# A tibble: 87 x 4
gender mass height species
<chr> <dbl> <int> <chr>
1 masculine 77 172 Human
2 masculine 75 167 Droid
3 masculine 32 96 Droid
4 masculine 136 202 Human
5 feminine 49 150 Human
6 masculine 120 178 Human
7 feminine 75 165 Human
8 masculine 32 97 Droid
9 masculine 84 183 Human
10 masculine 77 182 Human
# ... with 77 more rows
> starwars %>%
+ select(gender,mass,height,species) %>%
+ filter(species == "Human")
# A tibble: 35 x 4
gender mass height species
<chr> <dbl> <int> <chr>
1 masculine 77 172 Human
2 masculine 136 202 Human
3 feminine 49 150 Human
4 masculine 120 178 Human
5 feminine 75 165 Human
6 masculine 84 183 Human
7 masculine 77 182 Human
8 masculine 84 188 Human
9 masculine NA 180 Human
10 masculine 80 180 Human
# ... with 25 more rows
> starwars %>%
+ select(gender,mass,height,species) %>%
+ filter(species == "Human") %>%
+ filter(species == "Human") %>%
+ na.omit()
# A tibble: 22 x 4
gender mass height species
<chr> <dbl> <int> <chr>
1 masculine 77 172 Human
2 masculine 136 202 Human
3 feminine 49 150 Human
4 masculine 120 178 Human
5 feminine 75 165 Human
6 masculine 84 183 Human
7 masculine 77 182 Human
8 masculine 84 188 Human
9 masculine 80 180 Human
10 masculine 77 170 Human
# ... with 12 more rows
> starwars %>%
+ select(gender,mass,height,species) %>%
+ filter(species == "Human") %>%
+ filter(species == "Human") %>%
+ na.omit() %>%
+ mutate(height=height/100)
# A tibble: 22 x 4
gender mass height species
<chr> <dbl> <dbl> <chr>
1 masculine 77 1.72 Human
2 masculine 136 2.02 Human
3 feminine 49 1.5 Human
4 masculine 120 1.78 Human
5 feminine 75 1.65 Human
6 masculine 84 1.83 Human
7 masculine 77 1.82 Human
8 masculine 84 1.88 Human
9 masculine 80 1.8 Human
10 masculine 77 1.7 Human
# ... with 12 more rows
> starwars %>%
+ select(gender,mass,height,species) %>%
+ filter(species == "Human") %>%
+ filter(species == "Human") %>%
+ na.omit() %>%
+ mutate(height=height/100) %>%
+ mutate(BMI=mass/height^2)
# A tibble: 22 x 5
gender mass height species BMI
<chr> <dbl> <dbl> <chr> <dbl>
1 masculine 77 1.72 Human 26.0
2 masculine 136 2.02 Human 33.3
3 feminine 49 1.5 Human 21.8
4 masculine 120 1.78 Human 37.9
5 feminine 75 1.65 Human 27.5
6 masculine 84 1.83 Human 25.1
7 masculine 77 1.82 Human 23.2
8 masculine 84 1.88 Human 23.8
9 masculine 80 1.8 Human 24.7
10 masculine 77 1.7 Human 26.6
# ... with 12 more rows
> starwars %>%
+ select(gender,mass,height,species) %>%
+ filter(species == "Human") %>%
+ filter(species == "Human") %>%
+ na.omit() %>%
+ mutate(height=height/100) %>%
+ mutate(BMI=mass/height^2) %>%
+ group_by(gender) %>%
+ summarise(Average_BMI=mean(BMI))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 2 x 2
gender Average_BMI
<chr> <dbl>
1 feminine 22.0
2 masculine 26.0