宏宏的学习笔记Day6 学习R包

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咕咚咕叽

发布于 2024-04-19 07:04:46

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发布于 2024-04-19 07:04:46

文章被收录于专栏：生信新手保护小组学习

安装和加载R包

设置镜像、安装、加载（以dplyr包为例）

options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")) 

install.packages("dplyr")
also installing the dependencies ‘cli’, ‘lifecycle’, ‘pillar’, ‘rlang’, ‘tibble’, ‘tidyselect’, ‘vctrs’


  There are binary versions available but the source
  versions are later:
           binary source needs_compilation
rlang       1.1.2  1.1.3              TRUE
tidyselect  1.2.0  1.2.1              TRUE

Do you want to install from sources the packages which need compilation? (Yes/no/cancel) Yes
trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/macosx/contrib/4.2/cli_3.6.2.tgz'
Content type 'application/octet-stream' length 1369741 bytes (1.3 MB)

downloaded 1.3 MB

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/macosx/contrib/4.2/lifecycle_1.0.4.tgz'
Content type 'application/octet-stream' length 121623 bytes (118 KB)

downloaded 118 KB

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/macosx/contrib/4.2/pillar_1.9.0.tgz'
Content type 'application/octet-stream' length 643056 bytes (627 KB)
downloaded 627 KB

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/macosx/contrib/4.2/tibble_3.2.1.tgz'
Content type 'application/octet-stream' length 676165 bytes (660 KB)
downloaded 660 KB

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/macosx/contrib/4.2/vctrs_0.6.5.tgz'
Content type 'application/octet-stream' length 1852246 bytes (1.8 MB)
downloaded 1.8 MB

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/macosx/contrib/4.2/dplyr_1.1.4.tgz'
Content type 'application/octet-stream' length 1570597 bytes (1.5 MB)
downloaded 1.5 MB


The downloaded binary packages are in
	/var/folders/v_/r2n80_ls6yx_37pjtzps5yqh0000gn/T//RtmpsObKqI/downloaded_packages
installing the source packages ‘rlang’, ‘tidyselect’

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/src/contrib/rlang_1.1.3.tar.gz'
Content type 'application/octet-stream' length 763765 bytes (745 KB)
downloaded 745 KB

trying URL 'https://mirrors.tuna.tsinghua.edu.cn/CRAN/src/contrib/tidyselect_1.2.1.tar.gz'
Content type 'application/octet-stream' length 103591 bytes (101 KB)

downloaded 101 KB

* installing *source* package ‘rlang’ ...
** package ‘rlang’ successfully unpacked and MD5 sums checked
** using staged installation
** libs
clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I./rlang/  -I/usr/local/include   -fPIC  -Wall -g -O2  -c capture.c -o capture.o
clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I./rlang/  -I/usr/local/include   -fPIC  -Wall -g -O2  -c internal.c -o internal.o
clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I./rlang/  -I/usr/local/include   -fPIC  -Wall -g -O2  -c rlang.c -o rlang.o
clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I./rlang/  -I/usr/local/include   -fPIC  -Wall -g -O2  -c version.c -o version.o
clang -mmacosx-version-min=10.13 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o rlang.so capture.o internal.o rlang.o version.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
installing to /Library/Frameworks/R.framework/Versions/4.2/Resources/library/00LOCK-rlang/00new/rlang/libs
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
*** copying figures
** building package indices
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (rlang)
* installing *source* package ‘tidyselect’ ...
** package ‘tidyselect’ successfully unpacked and MD5 sums checked
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (tidyselect)

The downloaded source packages are in
	‘/private/var/folders/v_/r2n80_ls6yx_37pjtzps5yqh0000gn/T/RtmpsObKqI/downloaded_packages’
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

dplyr五个基础函数

1.mutate()新增列

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

意为新增一列，test数据框中 Sepal.Length与Sepal.Width相乘的结果

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

按列号筛选

select(test,1)

意为筛选出，test数据框中的第一列

> select(test,1)
    Sepal.Length
1            5.1
2            4.9
51           7.0
52           6.4
101          6.3

select(test,c(1,5))

意思为筛选出，test数据框中的第一和第五列

> 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

select(test,Sepal.Length)

意为筛选出，test数据框中列名为Sepal.Length的列

> select(test,Sepal.Length)
    Sepal.Length
1            5.1
2            4.9
51           7.0
52           6.4
101          6.3

按列名筛选

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

意为筛选出，test数据框中列名为Sepal.Length和Petal.Width的列

> 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

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

select(test, one_of(vars))

将"Petal.Length", "Petal.Width"赋值给vas

意为筛选出test数据框，vas向量中包含名字的列

> 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

3.filter()按列筛选

filter(test, Species == "setosa")

意为筛选出test数据框中，Species列中setosa所在的行

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 )

意为筛选出test数据框中，Species列中setosa且Sepal.Length列值大于5的行

> 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"))

意为筛选出test数据框中，Species列中setosa和versicolor的行

> 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(),按某1列或某几列对整个表格进行排序

arrange()#默认从小到大排序

所以arrange(test, Sepal.Length)，意为在test数据框中，将Sepal.Length一列从小到大排序

> 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

#用desc从大到小

所以arrange(test, desc(Sepal.Length))，意为在test数据框中，将Sepal.Length一列从大到小排序

> 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))

#计算Sepal.Length的平均值和标准差

> summarise(test, mean(Sepal.Length), sd(Sepal.Length))
  mean(Sepal.Length) sd(Sepal.Length)
1           5.916667        0.8084965

summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))

意为先按照Species分组，计算每组Sepal.Length的平均值和标准差

> summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
# A tibble: 3 × 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两个使用技能

1.管道（cmd+shift+M）

%>%

上述summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))可使用%>% 拆解为

group_by(test,Species) %>%

summarise(mean(Sepal.Length), sd(Sepal.Length))

 group_by(test,Species) %>% 
+     summarise(mean(Sepal.Length), sd(Sepal.Length))
# A tibble: 3 × 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

将两个表进行连接

> 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))
> test1
  x z
1 b A
2 e B
3 f C
4 x D
> 
> test2
  x y
1 a 1
2 b 2
3 c 3
4 d 4
5 e 5
6 f 6

1.内连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

左连left_join

left_join(test1, test2, by = 'x')

根据test1数据框中的x列，取test2中test1x列对应的y值

> 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')

根据test2数据框中的x列，取test1中test2x列对应z值

> 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

full_join( test1, test2, by = 'x')

将test1级test2数据框按x列取并集，并补齐相应的y列z列

> 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：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')
  x z
1 b A
2 e B
3 f C

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')
  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))
> 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()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

> 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

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。

学习笔记

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。

学习笔记

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