一、安装和加载
1、安装并加载tidyr和dplyr包
install.packages("tidyr")
library(tidyr)
install.packages("dplyr")
library(dplyr)
2、读取数据
expression <- read.delim("gene_expression.txt",sep="\t",header = T)
二、tidyr包操作
%>%
stocksm <- stocks %>% gather(stock, price, -time)
tidy_gather <- gather(data=expression,key=Samplename,value = Expression,-id)
data
key
value
...
tidy_spread <- spread(tidy_gather,key=Samplename,value = Expression)
separate(
data,
col,
into,
sep = "[^[:alnum:]]+",
remove = TRUE,
extra = "warn",
fill = "warn",
)
tidy_unite <-
unite(tidy_separate,col=Samplename,into=c("Source","Samplename"),sep="_")
三、dplyr包操作
dplyr_arrange <- arrange(tidy_gather , id )
dplyr_arrange1 <- arrange(tidy_gather,id,desc(Expression))
dplyr_arrange1 <- arrange(tidy_gather,id,-Expression)
mtcars %>% group_by(cyl)%>% arrange(desc(wt), .by_group = TRUE)
filter(starwars, hair_color == "none" & eye_color == "black")
filter(starwars, hair_color == "none", eye_color == "black")
filter(starwars, hair_color == "none" | eye_color == "black")
starwars %>% filter(mass > mean(mass, na.rm = TRUE))
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))
Result <- filter( tidy_gather , Expression>1 ) %>% arrange( Expression )
dplyr_select <- select( tidy_separate , id , Samplename , Expression )
dplyr_select <- select( tidy_separate , -Source )
dplyr_mutate <- mutate( tidy_gather , ID=sub( "gene", "Gene", id ) )
mtcars %>%
+ group_by(cyl) %>%
+ summarise(mean = mean(disp), n = n())
bind_rows(a , c)
bind_cols(a , c)
union(a , c)
setdiff(a , c)
inner_join(a,b,by=“x1”)
full_join(a,b,by=“x1”)
left_join(a,b,by=“x1”)
right_join(a,b,by=“x1”)
semi_join(a,b,by=“x1”)
anti_join(a,b,by="x1")
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