安裝和加載R包

1.鏡像設(shè)置

2.安裝

install.packages(“包”)
BiocManager::install(“包”)

取決于你要安裝的包存在于CRAN網(wǎng)站還是Biocductor

3.加載

library(包)
require(包)

dplyr五個(gè)基礎(chǔ)函數(shù)

iris
| 花萼長度 花萼寬度 花瓣長度 花瓣寬度
物種 |
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
test <- iris[c(1:2,51:52,101:102),]為例

1.新增列

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

2.按列篩選

select()

(1)按列號(hào)篩選

select(test,1)
select(test,c(1,5))
select(test,Sepal.Length)

(2)按列名篩選

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

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

3.篩選行

.filter()

`filter(test, Species == "setosa"&Sepal.Length > 5 )
filter(test, Species %in% c("setosa","versicolor"))

4.按某1列或某幾列對(duì)整個(gè)表格進(jìn)行排序

arrange()

arrange(test, Sepal.Length)#默認(rèn)從小到大排序
arrange(test, desc(Sepal.Length))#用desc從大到小
arrange(test, Sepal.Length, desc(Sepal.Width))

5.匯總

summarise()

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

1：管道操作

%>% (cmd/ctr + shift + M)

（加載任意一個(gè)tidyverse包即可用管道符號(hào)）

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

2：count統(tǒng)計(jì)某列的unique值

count(test,Species)

dplyr處理關(guān)系數(shù)據(jù)

• 準(zhǔn)備工作：即將2個(gè)表進(jìn)行連接

options(stringsAsFactors = F)

test1 <- data.frame(x = c('b','e','f','x'), 
                    z = c("A","B","C",'D'),
                    stringsAsFactors = F)
test1

test2 <- data.frame(x = c('a','b','c','d','e','f'), 
                    y = c(1,2,3,4,5,6),
                    stringsAsFactors = F)
test2 

1.內(nèi)連,取交集

inner_join

inner_join(test1, test2, by = "x")

2.左連

left_join

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

3.全連

full_join

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

4.半連接：返回能夠與y表匹配的x表所有記錄

semi_join

semi_join(x = test1, y = test2, by = 'x')

5.反連接：返回?zé)o法與y表匹配的x表的所記錄

anti_join

anti_join(x = test2, y = test1, by = 'x')

6.簡(jiǎn)單合并

test1 <- data.frame(x = c(1,2,3,4), y = c(10,20,30,40))
test1

test2 <- data.frame(x = c(5,6), y = c(50,60))
test2

test3 <- data.frame(z = c(100,200,300,400))
test3

bind_rows(test1, test2)

bind_cols(test1, test3)

18.png