Day6-学习R包 (2).png
安装和加载R包
1.镜像设置
初级
# options函数就是设置R运行过程中的一些选项设置
> options()$repos
CRAN
"https://mirrors.tuna.tsinghua.edu.cn/CRAN/"
attr(,"RStudio")#对应清华源
[1] TRUE
> options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
> options()$repos
CRAN
"https://mirrors.tuna.tsinghua.edu.cn/CRAN/"
> options()$BioC_mirror
NULL
> options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/") #对应中科大源
> options()$BioC_mirror
[1] "https://mirrors.ustc.edu.cn/bioc/"
高级
file.edit('~/.Rprofile')
保存初级代码脚本至.Rprofile
# options函数就是设置R运行过程中的一些选项设置
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
# 当然可以换成其他地区的镜像
2.安装
install.packages(“包”)或者BiocManager::install(“包”)
3.加载
library(包)
require(包)
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")
WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:
https://cran.rstudio.com/bin/windows/Rtools/
also installing the dependency ‘lifecycle’
试开URL’https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/windows/contrib/4.0/lifecycle_1.0.0.zip'
Content type 'application/zip' length 111186 bytes (108 KB)
downloaded 108 KB
试开URL’https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/windows/contrib/4.0/dplyr_1.0.5.zip'
Content type 'application/zip' length 1334630 bytes (1.3 MB)
downloaded 1.3 MB
package ‘lifecycle’ successfully unpacked and MD5 sums checked
package ‘dplyr’ successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\Public\Documents\Wondershare\CreatorTemp\RtmpkZ853M\downloaded_packages
> library(dplyr)
载入程辑包：‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Warning message:
程辑包‘dplyr’是用R版本4.0.5 来建造的
示例数据：test <- iris[c(1:2,51:52,101:102),]
dplyr五个基础函数
1.mutate(),新增列
> test <- iris[c(1:2,51:52,101:102),]
> mutate(test, new = Sepal.Length * Sepal.Width)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
51 7.0 3.2 4.7 1.4
52 6.4 3.2 4.5 1.5
101 6.3 3.3 6.0 2.5
102 5.8 2.7 5.1 1.9
Species new
1 setosa 17.85
2 setosa 14.70
51 versicolor 22.40
52 versicolor 20.48
101 virginica 20.79
102 virginica 15.66
2.select(),按列筛选
(1)按列号筛选
> select(test,1)
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8
> select(test,c(1,5))
Sepal.Length Species
1 5.1 setosa
2 4.9 setosa
51 7.0 versicolor
52 6.4 versicolor
101 6.3 virginica
102 5.8 virginica
(2)按列名筛选
> select(test,Sepal.Length)
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8
> select(test, Petal.Length, Petal.Width)
Petal.Length Petal.Width
1 1.4 0.2
2 1.4 0.2
51 4.7 1.4
52 4.5 1.5
101 6.0 2.5
102 5.1 1.9
> vars <- c("Petal.Length", "Petal.Width")
> select(test, one_of(vars))
Petal.Length Petal.Width
1 1.4 0.2
2 1.4 0.2
51 4.7 1.4
52 4.5 1.5
101 6.0 2.5
102 5.1 1.9
3.filter()筛选行
> filter(test, Species == "setosa")
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
Species
1 setosa
2 setosa
> filter(test, Species == "setosa"&Sepal.Length > 5 )
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
Species
1 setosa
> filter(test, Species %in% c("setosa","versicolor"))
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 7.0 3.2 4.7 1.4
4 6.4 3.2 4.5 1.5
Species
1 setosa
2 setosa
3 versicolor
4 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
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 4.9 3.0 1.4 0.2
2 5.1 3.5 1.4 0.2
3 5.8 2.7 5.1 1.9
4 6.3 3.3 6.0 2.5
5 6.4 3.2 4.5 1.5
6 7.0 3.2 4.7 1.4
Species
1 setosa
2 setosa
3 virginica
4 virginica
5 versicolor
6 versicolor
> arrange(test, desc(Sepal.Length))#用desc从大到小
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 7.0 3.2 4.7 1.4
2 6.4 3.2 4.5 1.5
3 6.3 3.3 6.0 2.5
4 5.8 2.7 5.1 1.9
5 5.1 3.5 1.4 0.2
6 4.9 3.0 1.4 0.2
Species
1 versicolor
2 versicolor
3 virginica
4 virginica
5 setosa
6 setosa
5.summarise()：汇总
> summarise(test, mean(Sepal.Length), sd(Sepal.Length)) #计算Sepal.Length的平均值和标准差
mean(Sepal.Length) sd(Sepal.Length)
1 5.916667 0.8084965
> group_by(test, Species) # 先按照Species分组
# A tibble: 6 x 5
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width
<dbl> <dbl> <dbl> <dbl>
1 5.1 3.5 1.4 0.2
2 4.9 3 1.4 0.2
3 7 3.2 4.7 1.4
4 6.4 3.2 4.5 1.5
5 6.3 3.3 6 2.5
6 5.8 2.7 5.1 1.9
# ... with 1 more variable: Species <fct>
> 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
# A tibble: 3 x 3
Species `mean(Sepal.Length)` `sd(Sepal.Length)`
<fct> <dbl> <dbl>
1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354
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))
# A tibble: 3 x 3
Species `mean(Sepal.Length)` `sd(Sepal.Length)`
<fct> <dbl> <dbl>
1 setosa 5 0.141
2 versicolor 6.7 0.424
3 virginica 6.05 0.354
2：count统计某列的unique值
> count(test,Species)
Species n
1 setosa 2
2 versicolor 2
3 virginica 2
dplyr处理关系数据
将2个表进行连接，注意：不要引入factor
options(stringsAsFactors = F)
> options(stringsAsFactors = F)
>
> test1 <- data.frame(x = c('b','e','f','x'),
+ z = c("A","B","C",'D'),
+ stringsAsFactors = F)
> test1
x z
1 b A
2 e B
3 f C
4 x D
> test2 <- data.frame(x = c('a','b','c','d','e','f'),
+ y = c(1,2,3,4,5,6),
+ stringsAsFactors = F)
> test2
x y
1 a 1
2 b 2
3 c 3
4 d 4
5 e 5
6 f 6
1.內连inner_join,取交集
> inner_join(test1, test2, by = "x")
x z y
1 b A 2
2 e B 5
3 f C 6
2.左连left_join
> left_join(test1, test2, by = 'x')
x z y
1 b A 2
2 e B 5
3 f C 6
4 x D NA
> left_join(test2, test1, by = 'x')
x y z
1 a 1 <NA>
2 b 2 A
3 c 3 <NA>
4 d 4 <NA>
5 e 5 B
6 f 6 C
3.全连full_join，取并集
> full_join( test1, test2, by = 'x')
x z y
1 b A 2
2 e B 5
3 f C 6
4 x D NA
5 a <NA> 1
6 c <NA> 3
7 d <NA> 4
> full_join( test2, test1, by = 'x')
x y z
1 a 1 <NA>
2 b 2 A
3 c 3 <NA>
4 d 4 <NA>
5 e 5 B
6 f 6 C
7 x NA D
4.半连接semi_join：返回能够与y表匹配的x表所有记录
> semi_join(x = test1, y = test2, by = 'x')
x z
1 b A
2 e B
3 f C
> semi_join(x = test2, y = test1, by = 'x')
x y
1 b 2
2 e 5
3 f 6
5.反连接anti_join：返回无法与y表匹配的x表的所记录
> anti_join(x = test2, y = test1, by = 'x')
x y
1 a 1
2 c 3
3 d 4
> anti_join(x = test1, y = test2, by = 'x')
x z
1 x D
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
#建立数据框
> 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))
> test1
x y
1 1 10
2 2 20
3 3 30
4 4 40
> test2
x y
1 5 50
2 6 60
> test3
z
1 100
2 200
3 300
4 400
> bind_rows(test1, test2)
x y
1 1 10
2 2 20
3 3 30
4 4 40
5 5 50
6 6 60
> bind_rows(test2, test1)
x y
1 5 50
2 6 60
3 1 10
4 2 20
5 3 30
6 4 40
> bind_cols(test1, test3)
x y z
1 1 10 100
2 2 20 200
3 3 30 300
4 4 40 400
> bind_cols(test3, test1)
z x y
1 100 1 10
2 200 2 20
3 300 3 30
4 400 4 40
