数据清洗神器之dplyr包（二）

上次跟大家一起学习了dplyr包的基本语法，大家已经对dplyr包有了初步的了解，接下来我们继续学习该包其他的函数。

安装并加载包

#install.packages("dplyr")
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

二.分组动作 group_by()

by_cyl=summarise(
  disp = mean(disp),
  hp = mean(hp),group_by(mtcars,cyl))#按照cyl分组计算变量disp和hp的平均值

三. 连接符 %>%

使用时把数据名作为开头, 然后依次对此数据进行多步操作.

iris %>% 
  mutate(Sepal.Length10 = Sepal.Length*10) %>%
  select(Sepal.Length10, Sepal.Length) %>% slice(1:15)#在iris中添加一个变量Sepal.Length10，选出变量名为Sepal.Length10和Sepal.Length的列，再选出这两列的前15行

##    Sepal.Length10 Sepal.Length
## 1              51          5.1
## 2              49          4.9
## 3              47          4.7
## 4              46          4.6
## 5              50          5.0
## 6              54          5.4
## 7              46          4.6
## 8              50          5.0
## 9              44          4.4
## 10             49          4.9
## 11             54          5.4
## 12             48          4.8
## 13             48          4.8
## 14             43          4.3## 15             58          5.8

四. 数据关联：join

注意：要连结的数据集中不要引入factor, 可令stringsAsFactors = F

以R自带的band_members和band_instruments为例，先查看数据

View(band_members)

View(band_instruments)

1.內连inner_join,取交集

band_members %>% inner_join(band_instruments, by = "name")

## # A tibble: 2 x 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar## 2 Paul  Beatles bass

上一行命令等同于下面这行命令

inner_join(band_members,band_instruments,by = "name")

## # A tibble: 2 x 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar## 2 Paul  Beatles bass

2.左连left_join

band_members %>% left_join(band_instruments,by = "name")

## # A tibble: 3 x 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 Mick  Stones  <NA>  
## 2 John  Beatles guitar## 3 Paul  Beatles bass

3.右连接

band_members %>% right_join(band_instruments,by = "name")

## # A tibble: 3 x 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar
## 2 Paul  Beatles bass  ## 3 Keith <NA>    guitar

4.全连full_join

band_members %>% full_join(band_instruments,by = "name")

## # A tibble: 4 x 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 Mick  Stones  <NA>  
## 2 John  Beatles guitar
## 3 Paul  Beatles bass  ## 4 Keith <NA>    guitar

五. 数据合并: bind

这里的bind函数相当于base包里的cbind()函数和rbind()函数

这里所使用的是R自带的数据集starwars

1.创建数据库

one <- starwars[1:4, ]
two <- starwars[9:12, ]

View(one)

View(two)

2.行合并

bind_rows(list(one, two))

## # A tibble: 8 x 14
##   name     height  mass hair_color  skin_color eye_color birth_year sex   gender
##   <chr>     <int> <dbl> <chr>       <chr>      <chr>          <dbl> <chr> <chr> 
## 1 Luke Sk~    172    77 blond       fair       blue            19   male  mascu~
## 2 C-3PO       167    75 <NA>        gold       yellow         112   none  mascu~
## 3 R2-D2        96    32 <NA>        white, bl~ red             33   none  mascu~
## 4 Darth V~    202   136 none        white      yellow          41.9 male  mascu~
## 5 Biggs D~    183    84 black       light      brown           24   male  mascu~
## 6 Obi-Wan~    182    77 auburn, wh~ fair       blue-gray       57   male  mascu~
## 7 Anakin ~    188    84 blond       fair       blue            41.9 male  mascu~
## 8 Wilhuff~    180    NA auburn, gr~ fair       blue            64   male  mascu~
## # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,## #   vehicles <list>, starships <list>

其他例子的行合并

bind_rows(
  c(a = 1, b = 2),
  tibble(a = 3:4, b = 5:6),
  c(a = 7, b = 8))

## # A tibble: 4 x 2
##       a     b
##   <dbl> <dbl>
## 1     1     2
## 2     3     5
## 3     4     6## 4     7     8

3.列合并

bind_cols(one, two)#等于：bind_cols(list(one, two))

## New names:
## * name -> name...1
## * height -> height...2
## * mass -> mass...3
## * hair_color -> hair_color...4
## * skin_color -> skin_color...5## * ...

## # A tibble: 4 x 28
##   name...1       height...2 mass...3 hair_color...4 skin_color...5 eye_color...6
##   <chr>               <int>    <dbl> <chr>          <chr>          <chr>        
## 1 Luke Skywalker        172       77 blond          fair           blue         
## 2 C-3PO                 167       75 <NA>           gold           yellow       
## 3 R2-D2                  96       32 <NA>           white, blue    red          
## 4 Darth Vader           202      136 none           white          yellow       
## # ... with 22 more variables: birth_year...7 <dbl>, sex...8 <chr>,
## #   gender...9 <chr>, homeworld...10 <chr>, species...11 <chr>,
## #   films...12 <list>, vehicles...13 <list>, starships...14 <list>,
## #   name...15 <chr>, height...16 <int>, mass...17 <dbl>, hair_color...18 <chr>,
## #   skin_color...19 <chr>, eye_color...20 <chr>, birth_year...21 <dbl>,
## #   sex...22 <chr>, gender...23 <chr>, homeworld...24 <chr>,
## #   species...25 <chr>, films...26 <list>, vehicles...27 <list>,## #   starships...28 <list>

六. 集合操作: set

intersect(band_members[,1],band_instruments[,1])#取两个集合的交集

## # A tibble: 2 x 1
##   name 
##   <chr>
## 1 John ## 2 Paul

union(band_members[,1],band_instruments[,1])#取两个集合的并集，并进行去重

## # A tibble: 4 x 1
##   name 
##   <chr>
## 1 Mick 
## 2 John 
## 3 Paul ## 4 Keith

union_all(band_members[,1],band_instruments[,1])#取两个集合的并集，不去重

## # A tibble: 6 x 1
##   name 
##   <chr>
## 1 Mick 
## 2 John 
## 3 Paul 
## 4 John 
## 5 Paul ## 6 Keith

setdiff(band_members[,1],band_instruments[,1])#取两个集合的差集

## # A tibble: 1 x 1
##   name 
##   <chr>## 1 Mick

setequal(band_members[,1],band_instruments[,1])#判断两个集合是否相等

## [1] FALSE

其他函数：count统计某列的unique值

count(iris,Species)

##      Species  n
## 1     setosa 50
## 2 versicolor 50## 3  virginica 50

欢迎大家关注我们的公众号：R语言与SPSS学习笔记

分享实用的SPSS及R处理数据、分析数据及做图的使用技巧