Day6-学习R包
一、安装和加载R包
1.镜像设置
参考你还在每次配置Rstudio的下载镜像吗?
编辑R配置文件 .Rprofile
file.edit('~/.Rprofile')
在Rprofile文件中添加清华源及中科大源,保存再重启RStudio。
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
运行options()BioC_mirror

安装加载三部曲
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)
dplyr的使用
示例数据直接使用内置数据集iris的简化版:
test <- iris[c(1:2,51:52,101:102),]

一. dplyr五个基础函数
1.mutate(),新增列
mutate(test, new = Sepal.Length * Sepal.Width)

2.select(),按列筛选
(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.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

5.summarise():汇总
对数据进行汇总操作,结合group_by使用实用性强,group_by的意思是根据by对数据按照哪个字段进行分组,或者是哪几个字段进行分组
# 计算Sepal.Length的平均值和标准差
summarise(test, 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
group_by(test, Species)
summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))

二、dplyr两个实用技能
1:管道操作 %>% (快捷键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)

三、dplyr处理关系数据
即将2个表进行连接,注意:不要引入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.內连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: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
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))
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)

网友评论