1.镜像设置
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
#对应清华源
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
# 对应中科大源
2.安装
install.packages("包")
或 BiocManager::install(“包”)
3.加载
library
或 require
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
install.packages("dplyr")
library(dplyr)
示例数据:
test <- iris
test <- iris[c(1:2,51:52,101:102),]
mutate()
,新增列mutate(test, new = Sepal.Length * Sepal.Width)
select()
,按列筛选select(test,1)
select(test,c(1,5))
select(test,Sepal.Length)
select(test, Petal.Length, Petal.Width)
vars <- c("Petal.Length", "Petal.Width")
select(test, one_of(vars))
filter()
,筛选行filter(test, Species == "setosa")
filter(test, Species == "setosa"&Sepal.Length > 5 )
filter(test, Species %in% c("setosa","versicolor"))
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
arrange(test, desc(Sepal.Length)) #用desc从大到小
```\
summarise()
,汇总summarise(test, mean(Sepal.Length), sd(Sepal.Length)) #计算Sepal.Length的平均值和标准差
group_by(test, Species) #先按照Species分组
summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length)) #计算每组Sepal.Length的平均值和标准差
%>%
或 (ctrl + shift + M)
count()
即将两个表连接
test1 <- data.frame(x = c('b','e','f','x'),
z = c("A","B","C",'D'))
test2 <- data.frame(x = c('a','b','c','d','e','f'),
y = c(1,2,3,4,5,6))
inner_join(test1, test2, by = "x")
left_join(test1, test2, by = 'x')
left_join(test2, test1, by = 'x')
full_join( test1, test2, by = 'x')
semi_join(x = test1, y = test2, by = 'x')
anti_join(x = test2, y = test1, by = 'x')
在相当于base包里的cbind()函数和rbind()函数;注意,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有相同的行数
test1 <- data.frame(x = c(1,2,3,4), y = c(10,20,30,40))
test2 <- data.frame(x = c(5,6), y = c(50,60))
test3 <- data.frame(z = c(100,200,300,400))
bind_rows(test1, test2)
bind_cols(test1, test3)
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原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。