Day 6 - Learning R package

LunaprimRose 2020.03.18

Day 6.png

Install and load R package

镜像设置

• CRAN 镜像
  • TUNA Team, Tsinghua University https://mirrors.tuna.tsinghua.edu.cn/CRAN/
  • University of Science and Technology of China https://mirrors.ustc.edu.cn/CRAN/
  • Lanzhou University Open Source Society https://mirror.lzu.edu.cn/CRAN/
  • Tencent https://mirrors.cloud.tencent.com/CRAN/
  • Aliyun https://mirrors.aliyun.com/CRAN/
• Bioconductor 镜像
  • TUNA Team, Tsinghua University https://mirrors.tuna.tsinghua.edu.cn/bioconductor/
  • University of Science and Technology of China https://mirrors.ustc.edu.cn/bioc/

1. 初始模式

RStudio - Tools - Global Options - Packegs - Package Managment

2. 升级模式

Tuna Team, Tsinghua University 为例

options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="https://mirrors.tuna.tsinghua.edu.cn/bioconductor")
options()$repos
                                        CRAN 
"https://mirrors.tuna.tsinghua.edu.cn/CRAN/" 
                              China(Tencent) 
    "http://mirrors.cloud.tencent.com/CRAN/" 
attr(,"RStudio")
[1] TRUE
options()$BioC_mirror
[1] "https://mirrors.tuna.tsinghua.edu.cn/bioconductor"

3. 高级模式

file.edit("~/.Rprofile")
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="https://mirrors.tuna.tsinghua.edu.cn/bioconductor")

Install Packages

1. 在线安装

install.packegs('ggplot2')
BiocManager::install('DEseq2')

2. 本地安装

install.packages('path_to_packages')

Load Packages

library('ggplot2')
require('ggplot2')

Basic function

1. mutate() 新增列

test <- iris[c(1:2,51:52,101:102),]
test
Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            5.1         3.5          1.4         0.2     setosa
2            4.9         3.0          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
mutate(test,new = Sepal.Length*Sepal.Width)
Sepal.Length Sepal.Width Petal.Length Petal.Width    Species   new
1          5.1         3.5          1.4         0.2     setosa 17.85
2          4.9         3.0          1.4         0.2     setosa 14.70
3          7.0         3.2          4.7         1.4 versicolor 22.40
4          6.4         3.2          4.5         1.5 versicolor 20.48
5          6.3         3.3          6.0         2.5  virginica 20.79
6          5.8         2.7          5.1         1.9  virginica 15.66
transmute(test,new = Sepal.Length*Sepal.Width)
    new
1 17.85
2 14.70
3 22.40
4 20.48
5 20.79
6 15.66

2. select() 按列筛选

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(1,3))
    Sepal.Length Petal.Length
1            5.1          1.4
2            4.9          1.4
51           7.0          4.7
52           6.4          4.5
101          6.3          6.0
102          5.8          5.1
select(test,1,Species)
    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

3. filter() 筛选行

filter(test,Species == "setosa")
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
filter(test,Species == "setosa"&Sepal.Length >5)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
filter(test,Species %in% c("setosa","versicolor"))
  Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1          5.1         3.5          1.4         0.2     setosa
2          4.9         3.0          1.4         0.2     setosa
3          7.0         3.2          4.7         1.4 versicolor
4          6.4         3.2          4.5         1.5 versicolor

4. arrange() 排序

arrange(test,Sepal.Length)
  Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1          4.9         3.0          1.4         0.2     setosa
2          5.1         3.5          1.4         0.2     setosa
3          5.8         2.7          5.1         1.9  virginica
4          6.3         3.3          6.0         2.5  virginica
5          6.4         3.2          4.5         1.5 versicolor
6          7.0         3.2          4.7         1.4 versicolor
arrange(test,desc(Sepal.Length))
  Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1          7.0         3.2          4.7         1.4 versicolor
2          6.4         3.2          4.5         1.5 versicolor
3          6.3         3.3          6.0         2.5  virginica
4          5.8         2.7          5.1         1.9  virginica
5          5.1         3.5          1.4         0.2     setosa
6          4.9         3.0          1.4         0.2     setosa

5. summarise() 汇总

summarise(test,mean(Sepal.Length),sd(Sepal.Length))
  mean(Sepal.Length) sd(Sepal.Length)
1           5.916667        0.8084965
group_by(test,Species)
# A tibble: 6 x 5
# Groups:   Species [3]
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
*        <dbl>       <dbl>        <dbl>       <dbl> <fct>     
1          5.1         3.5          1.4         0.2 setosa    
2          4.9         3            1.4         0.2 setosa    
3          7           3.2          4.7         1.4 versicolor
4          6.4         3.2          4.5         1.5 versicolor
5          6.3         3.3          6           2.5 virginica 
6          5.8         2.7          5.1         1.9 virginica 
summarise(group_by(test,Species),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

Practical skills

1. 管道操作

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)
# A tibble: 3 x 2
  Species        n
  <fct>      <int>
1 setosa         2
2 versicolor     2
3 virginica      2

Manage Relational data

处理表连接时，不要引入 factor

1. 内连接

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
inner_join(test1,test2,by = 'x')
  x z y
1 b A 2
2 e B 5
3 f C 6

2. 左连接

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

3. 全连接

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

4. 半连接

semi_join(x= test1,y= test2,by = 'x')
  x z
1 b A
2 e B
3 f C

5. 反连接

anti_join(x=test2,y=test1,by='x')
  x y
1 a 1
2 c 3
3 d 4

6. 简单合并

test1 <- data.frame(x= c(1,2,3,4),y=c(10,20,30,40))
test1
  x  y
1 1 10
2 2 20
3 3 30
4 4 40
test2 <- data.frame(x=c(5,6),y=c(50,60))
test2
  x  y
1 5 50
2 6 60
test3 <- data.frame(z=c(100,200,300,400))
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_cols(test1,test3)
  x  y   z
1 1 10 100
2 2 20 200
3 3 30 300
4 4 40 400