1.dplyr五个基础函数（数据使用内置数据集iris）
1.mutate(),新增列
mutate(test, new = Sepal.Length * Sepal.Width)
image.png
2.select(),按列筛选
select(test,1)
select(test,c(1,5))
select(test,Sepal.Length)
image.png
(2)按列名筛选
select(test, Petal.Length, Petal.Width)
vars <- c("Petal.Length", "Petal.Width")
select(test, one_of(vars))
image.png
image.png
3.filter()筛选行
filter(test, Species == "setosa")
image.png
filter(test, Species == "setosa"&Sepal.Length > 5 )
image.png
filter(test, Species %in% c("setosa","versicolor"))
image.png
4.arrange(),按某1列或某几列对整个表格进行排序
arrange(test, Sepal.Length)#默认从小到大排序
image.png
arrange(test, desc(Sepal.Length))#用desc从大到小
image.png
5.summarise()：汇总
summarise(test, mean(Sepal.Length), sd(Sepal.Length))# 计算Sepal.Length的平均值和标准差
image.png
group_by(test, Species)
image.png
summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
image.png
2.dplyr两个实用技能
1.管道操作 %>% (cmd/ctr + shift + M)
image.png
2：count统计某列的unique值
image.png
3.dplyr处理关系数据
1.內连inner_join,取交集
image.png
image.png
inner_join(test1, test2, by = "x")
image.png
2.左连left_join
left_join(test1, test2, by = 'x')
image.png
left_join(test1, test2, by = 'x')
image.png
3.全连full_join
image.png
4.半连接：返回能够与y表匹配的x表所有记录semi_join
semi_join(x = test1, y = test2, by = 'x')
image.png
5.反连接：返回无法与y表匹配的x表的所记录anti_join
anti_join(x = test2, y = test1, by = 'x')
image.png
6.简单合并
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)
