R基础-向量，dataframe，dplyr操作
2022-12-17 本文已影响0人
余绕
1. 向量操作
A. creat a vector
v1<-c(1,2,3,4,5,6,7,8,9) # creat a vector
B. Get values
v1[c(2)] # obtain a value
[1] 2
v1[c(3:5)] #obtain values
[1] 3 4 5
C. assign names to the vector elements
names(v1)=c("one","two","three","four","five","six","seven","eight","night")
v1 # content of v1
one two three four five six seven eight night
1 2 3 4 5 6 7 8 9
D. get a value from its name
v1["two"]
two
2
v1[v1>3] #get list content basing on logic values
four five six seven eight night
4 5 6 7 8 9
2. Dataframe
A. 创建dataframe
df<-data.frame(row.names = c('张三','李四','王武','赵六','田七'),
性别= c('男','女','女','男','女'),
年龄= c( 39,65,34,28,76),
成绩=c(89,99,65,39,10))
df
性别 年龄 成绩
张三 男 39 89
李四 女 65 99
王武 女 34 65
赵六 男 28 39
田七 女 76 10
B. 1st column,all rows
df[1,]
性别 年龄 成绩
张三 男 39 89
C. all columns, first row
df[,1]
> df[,1]
[1] "男" "女" "女" "男" "女"
D. select data basing on the score
df[df$成绩>60,]
性别 年龄 成绩
张三 男 39 89
李四 女 65 99
王武 女 34 65
E. modify values
df
性别 年龄 成绩
张三 男 39 89
李四 女 65 99
王武 女 34 65
赵六 男 28 39
田七 女 76 10
df$成绩=df$成绩+100
df
性别 年龄 成绩
张三 男 39 189
李四 女 65 199
王武 女 34 165
赵六 男 28 139
田七 女 76 110
dplyr软件包学习
library(dplyr)
A. import data
de_test<-read.table("K:/生信学习/R语言基础绘图/R_basic/data/R_basic/de_result.txt",header=T)
de_test
gene_id logFC pvalue FDR
1 gene1 3.0 0.010 0.0300
2 gene2 1.0 0.300 0.3800
3 gene3 2.0 0.002 0.0025
4 gene4 -3.0 0.002 0.0030
5 gene5 -0.4 0.004 0.0048
B. select data basing on specific criterion---------Basic operation in R
de_test[de_test$logFC>=1 &de_test$pvalue<=0.05,]
gene_id logFC pvalue FDR
1 gene1 3 0.010 0.0300
3 gene3 2 0.002 0.0025
using dplyr
C. filter function #按行筛选
filter(de_test,logFC>= 1 & pvalue<=0.05)
gene_id logFC pvalue FDR
1 gene1 3 0.010 0.0300
2 gene3 2 0.002 0.0025
D. select #按列筛选
select(de_test,gene_id,logFC,pvalue)
gene_id logFC pvalue
1 gene1 3.0 0.010
2 gene2 1.0 0.300
3 gene3 2.0 0.002
4 gene4 -3.0 0.002
5 gene5 -0.4 0.004
select(de_test,-pvalue)
gene_id logFC pvalue
1 gene1 3.0 0.010
2 gene2 1.0 0.300
3 gene3 2.0 0.002
4 gene4 -3.0 0.002
5 gene5 -0.4 0.004
E. add a new column---mutate
mutate(de_test,FC=2**logFC)
gene_id logFC pvalue FDR FC
1 gene1 3.0 0.010 0.0300 8.0000000
2 gene2 1.0 0.300 0.3800 2.0000000
3 gene3 2.0 0.002 0.0025 4.0000000
4 gene4 -3.0 0.002 0.0030 0.1250000
5 gene5 -0.4 0.004 0.0048 0.7578583
F. sort the dataframe
arrange(de_test,logFC) #default is descending order
gene_id logFC pvalue FDR
1 gene4 -3.0 0.002 0.0030
2 gene5 -0.4 0.004 0.0048
3 gene2 1.0 0.300 0.3800
4 gene3 2.0 0.002 0.0025
5 gene1 3.0 0.010 0.0300
arrange(de_test,desc(logFC)) #ascending order
gene_id logFC pvalue FDR
1 gene1 3.0 0.010 0.0300
2 gene3 2.0 0.002 0.0025
3 gene2 1.0 0.300 0.3800
4 gene5 -0.4 0.004 0.0048
5 gene4 -3.0 0.002 0.0030
G. 利用管道（using the pipe %>%）
slected<-de_test %>% filter(logFC>= 1 & pvalue<=0.05) %>% select(-pvalue) %>% mutate(FC=2**logFC) %>% arrange(logFC)
slected
gene_id logFC FDR FC
1 gene3 2 0.0025 4
2 gene1 3 0.0300 8
Import data
gene_fuction<-read.table("K:/生信学习/R语言基础绘图/R_basic/data/R_basic/gene_function.txt",header=T,sep="\t")
gene_fuction
gene_name annotation
1 gene1 aaa
2 gene2 bbb
3 gene3 ccc
4 gene5 ddd
G. Merge dataframe basing on columns
left_join(slected,gene_fuction,by = c('gene_id'='gene_name')) #以左表为标准
gene_id logFC FDR FC annotation
1 gene3 2 0.0025 4 ccc
2 gene1 3 0.0300 8 aaa
right_join(slected,gene_fuction,by = c('gene_id'='gene_name'))#以右表为标准
1 gene3 2 0.0025 4 ccc
2 gene1 3 0.0300 8 aaa
3 gene2 NA NA NA bbb
4 gene5 NA NA NA dd
H. 存在重复的情况
gene_fuction<-edit(gene_fuction)
gene_fuction
gene_name annotation
1 gene1 aaa
2 gene2 bbb
3 gene3 ccc
4 gene5 ddd
5 gene1 xxx
6 gene3 new
left_join(slected,gene_fuction,by = c('gene_id'='gene_name')) #以左表为标准
gene_id logFC FDR FC annotation
1 gene3 2 0.0025 4 ccc
2 gene3 2 0.0025 4 new
3 gene1 3 0.0300 8 aaa
4 gene1 3 0.0300 8 xxx
