安装和加载R包

1.镜像设置

你还在每次配置Rstudio的下载镜像吗？

2.安装

install.packages(“包”)
BiocManager::install(“包”)

取决于你要安装的包存在于CRAN网站还是Biocductor

3.加载

library(包)
require(包)

dplyr五个基础函数

iris
| 花萼长度 花萼宽度 花瓣长度 花瓣宽度
物种 |
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
test <- iris[c(1:2,51:52,101:102),]为例

1.新增列

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

2.按列筛选

select()

(1)按列号筛选

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

(2)按列名筛选

select(test, Petal.Length, Petal.Width)

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

3.筛选行

.filter()

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

4.按某1列或某几列对整个表格进行排序

arrange()

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

5.汇总

summarise()

summarise(test, mean(Sepal.Length), sd(Sepal.Length))# 计算Sepal.Length的平均值和标准差
# 先按照Species分组，计算每组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))

dplyr两个实用技能

1：管道操作

%>% (cmd/ctr + shift + M)

（加载任意一个tidyverse包即可用管道符号）

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

• 准备工作：即将2个表进行连接

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.內连,取交集

inner_join

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：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')

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')

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)

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