第6天的学习内容，简单罗列如下：
Day 6. 学习R包.png
以dplyr为例，学习和使用R包——多个函数的集合。
安装和加载
# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just dplyr:
install.packages("dplyr")
library(dplyr) # or require(dplyr)
test <- iris[c(1:2,51:52,101:102),] #This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.
R包dplyr的基础函数
1. mutate() 可以在现有变量的基础上添加新变量
> mutate(test, new = Sepal.Length * Sepal.Width) # mutate() adds new variables that are functions of existing variables.
Sepal.Length Sepal.Width Petal.Length Petal.Width Species new
1 5.1 3.5 1.4 0.2 setosa 17.85
2 4.9 3.0 1.4 0.2 setosa 14.70
3 7.0 3.2 4.7 1.4 versicolor 22.40
4 6.4 3.2 4.5 1.5 versicolor 20.48
5 6.3 3.3 6.0 2.5 virginica 20.79
6 5.8 2.7 5.1 1.9 virginica 15.66
2. select() 按列号或列名筛选变量
>select(test,1) # or select(test,Sepal.Length). # select() picks variables based on their names.
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8
> select(test, c(1,5)) # or select(test, Petal.Length, Petal.Species) or select(test, one_of("Petal.Length", "Petal.Species"))
Sepal.Length Species
1 5.1 setosa
2 4.9 setosa
51 7.0 versicolor
52 6.4 versicolor
101 6.3 virginica
102 5.8 virginica
3. filter() 按行名筛选
> filter(test, Species == "setosa") # filter() picks cases based on their values.
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
> filter(test, Species == "setosa"&Sepal.Length > 5 )
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
> filter(test, Species %in% c("setosa","versicolor"))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 7.0 3.2 4.7 1.4 versicolor
4 6.4 3.2 4.5 1.5 versicolor
4. summarise() 对多个数值汇总
# summarise() reduces multiple values down to a single summary.
> summarise(test, mean(Sepal.Length), sd(Sepal.Length)) # Calculate the mean and standard deviation of Sepal.Length.
mean(Sepal.Length) sd(Sepal.Length)
1 5.916667 0.8084965
> group_by(test, Species) # Group test by Species
# A tibble: 6 x 5
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
* <dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 7 3.2 4.7 1.4 versicolor
4 6.4 3.2 4.5 1.5 versicolor
5 6.3 3.3 6 2.5 virginica
6 5.8 2.7 5.1 1.9 virginica
> summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
# A tibble: 3 x 3
Species `mean(Sepal.Length)` `sd(Sepal.Length)`
<fct> <dbl> <dbl>
1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354
5. arrange() 按某1列或某几列对整个表格进行排序
> arrange(test, Sepal.Length) # The default order is from small to large. # arrange() changes the ordering of the rows.
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3.0 1.4 0.2 setosa
2 5.1 3.5 1.4 0.2 setosa
3 5.8 2.7 5.1 1.9 virginica
4 6.3 3.3 6.0 2.5 virginica
5 6.4 3.2 4.5 1.5 versicolor
6 7.0 3.2 4.7 1.4 versicolor
> arrange(test, desc(Sepal.Length)) # Reverse the order using desc()
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.0 3.2 4.7 1.4 versicolor
2 6.4 3.2 4.5 1.5 versicolor
3 6.3 3.3 6.0 2.5 virginica
4 5.8 2.7 5.1 1.9 virginica
5 5.1 3.5 1.4 0.2 setosa
6 4.9 3.0 1.4 0.2 setosa
R包dplyr的实用技能
1. 管道
> test %>% # Use %>% to emphasise a sequence of actions, rather than the object that the actions are being performed on.
+ group_by(Species) %>% # %>% should always have a space before it, and should usually be followed by a new line.
+ summarise(mean(Sepal.Length), sd(Sepal.Length))
# A tibble: 3 x 3
Species `mean(Sepal.Length)` `sd(Sepal.Length)`
<fct> <dbl> <dbl>
1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354
2. 统计某列中的记录并计数
> count(test,Species)
# A tibble: 3 x 2
Species n
<fct> <int>
1 setosa 2
2 versicolor 2
3 virginica 2
