R包的使用(以dplyr为例)

加载包及数据

library(dplyr)
test <- iris[c(1:2,51:52,101:102),]

dplyr的5个基础函数

1.mutate() 新增列

mutate(test,new = Sepal.Length * Sepal.Width)

2.select() 按列筛选

2.1按列号

select(test,1)
select(test,c(1,5))
select(test,Sepal.Length)

2.2按列名筛选

select(test, Petal.Length, Petal.Width)
vars <- c("Petal.Length","Petal.Width")
select(test,one_of(vars))

3.filter()筛选行

filter(test, Species == "setosa")
filter(test, Species == "setosa" & Sepal.Length > 5)
filter(test, Species %in% c("setosa", "versicolor"))

4.arrange()按某一列或某几列对整个表格进行排序

arrange(test, Sepal.Length)#默认从小到大
arrange(test,desc(Sepal.Length))#用desc从大到小排序

5.summarise() 汇总

summarise(test,mean(Sepal.Length),sd(Sepal.Length))
group_by(test,Species)
summarise(group_by(test,Species),mean(Sepal.Length),sd(Sepal.Length))

dplyr 的两个实用技能

1.管道操作 %>% (cmd/ctr + shift + M)

test %>%
  group_by(Species) %>%
  summarise(mean(Sepal.Length),sd(Sepal.Length))

2.count统计某列的unique值

count(test,Species)

dplyr处理关系数据（将两个表格连接，但不引入factor）

options(stringsAsFactors = F)

test1 <- data.frame(x = c("b","e","f","x"),
                    z = c("A","B","C","D"),
                    stringsAsFactors = F)
test1
test2 <- data.frame(x = c("a","b","c","d","e","f"),
                    y = c(1,2,3,4,5,6),
                    stringsAsFactors = F)
test2

1.内连inner_join 取交集

inner_join(test1, test2, by = "x")

2.左连left_join

left_join(test1, test2, by = "x")
left_join(test2, test1, by = "x")

3.全连full_join

full_join( test1, test2, by = "x")

4.半连接semi_join（返回能够与y表匹配的x表所有记录）

semi_join(x = test1, y = test2, by = "x")

5.反连接anti_join (返回无法与x表匹配的y表的所有记录)

anti_join(x = test2, y = test1, by = "x")

6.简单的合并

相当于base包里的cbind()函数和rbind()函数。
Note：bind_rows()函数需要两个表格列数相同，而bind_cols()函数则需要两个数据框相同的行数

test1 <- data.frame(x = c(1,2,3,4), y = c(10,20,30,40))
test1

test2 <- data.frame(x = c(5,6), y = c(50,60))
test2

test3 <- data_frame(z = c(100,200,300,400))
test3

bind_rows(test1,test2)
bind_cols(test1,test3)
``