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
联网
install.packages(" ")
<包在CRAN网站
BiocManager::install(" ")
<包在Biocductor
可以搜索包在哪
library()
require()
mutate(test, new = Sepal.Length \* Sepal.Width)
select(test,1)
select(test,c(1,5))
select(test, Petal.Length, Petal.Width)
vars <- c("Petal.Length", "Petal.Width")
select(test, one\_of(vars))
filter(test, Species == "setosa")
filter(test, Species == "setosa"&Sepal.Length > 5 )
filter(test, Species %in% c("setosa","versicolor"))
arrange(test, Sepal.Length)
#默认从小到大排序
arrange(test, desc(Sepal.Length))
#用desc从大到小
summarise(test, mean(Sepal.Length), sd(Sepal.Length))
# 计算Sepal.Length的平均值和标准差
group\_by(test, Species)
summarise(group\_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
#先按照Species分组,计算每组Sepal.Length的平均值和标准差
先给test1和test2赋值
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')
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
先给a,b,c赋值
bind\_rows(a, b)
bind\_cols(a, c)
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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。