學(xué)習(xí)R包
學(xué)習(xí)R包
生信必備Biocductor上生信分析R包
以dplyr為例
- 配置Rstudio鏡像
#代碼源生信星球
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")) #對(duì)應(yīng)清華源
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/") #對(duì)應(yīng)中科大源
- 安裝
install.packages(' ')
BiocManager::install(" ") - 加載
library()
require()
dplyr五個(gè)基礎(chǔ)函數(shù)
> test <- iris[c(1:2,51:52,101:102),]
> test
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
1 setosa
2 setosa
51 versicolor
52 versicolor
101 virginica
102 virginica
> library(dplyr)
-
mutate()新增列
> 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
-
select()按列篩選
按列號(hào)篩選
select(test,1)
select(test,c(1,5))
select(test,Sepal.Length)
按列名篩選
select(test, Petal.Length, Petal.Width)vars <- c("Petal.Length", "Petal.Width")
select(test, one_of(vars))
> select(test,Sepal.Length)
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8
-
filter()篩選行
filter(test, Species == "setosa")
#篩選Species == setosa的行
filter(test, Species == "setosa"&Sepal.Length > 5 )
#篩選Species == setosa并且Sepal.Length>5的行
filter(test, Species %in% c("setosa","versicolor"))
#篩選Species 屬于 setosa或versicolor的行
-
arrange(),按某1列或某幾列隊(duì)整個(gè)表格進(jìn)行排序
arrange(test, Sepal.Length)
#默認(rèn)從小到大排序
arrange(test, desc(Sepal.Length))
#用desc從大到小
5.summarise()匯總
summarise(test, mean(Sepal.Length), sd(Sepal.Length))# 計(jì)算Sepal.Length的平均值和標(biāo)準(zhǔn)差
# 先按照Species分組，計(jì)算每組Sepal.Length的平均值和標(biāo)準(zhǔn)差
group_by(test, Species)
summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
dplyr兩個(gè)實(shí)用技能
- 管道操作
%>%(cmd/ctr + shift + M)
test %>%
group_by(Species) %>%
summarise(mean(Sepal.Length), sd(Sepal.Length))
###等同于summarise(group_by(test, Species),mean(Sepal.Length), sd(Sepal.Length))
- count統(tǒng)計(jì)某列的unique值
count(test,Species)
dplyr 處理關(guān)系數(shù)據(jù)
> 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
The mutating joins add columns from y to x, matching rows based on the keys:
inner_join(): includes all rows in x and y.
left_join(): includes all rows in x.
right_join(): includes all rows in y.
full_join(): includes all rows in x or y.
If a row in x matches multiple rows in y, all the rows in y will be returned once for each matching row in x.
> inner_join(test1,test2,by='x')
x z y
1 b A 2
2 e B 5
3 f C 6
> 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
> 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
> semi_join(test1,test2,by='x')
x z
1 b A
2 e B
3 f C
> anti_join(test1,test2,by='x')
x z
1 x D
> anti_join(test2,test1,by='x')
x y
1 a 1
2 c 3
3 d 4
簡(jiǎn)單合并
cbind() rbind()
bind_rows()函數(shù)需要兩個(gè)表格列數(shù)相同，而bind_cols()函數(shù)則需要兩個(gè)數(shù)據(jù)框有相同的行數(shù)
