使用Purrr和Dplyr在多个数据帧中重新编码相似的因子水平-Java 学习之路

下面是两个简单的数据框架 . 我想重新编码（折叠） Sat1 和 Sat2 列，以便将所有满意度编码为 Satisfied ，并且所有不满意度都编码为 Dissatisfied . 中性仍为中性 . 因此，这些因素将有三个层次 - Satisfied, Dissatisfied, and Neutral .

我通常会通过绑定数据帧，使用 lapply 以及 car 包中的重新代码来完成此操作，例如：

DF1[2:3] <- lapply(DF1[2:3], recode, c('"Somewhat Satisfied"= "Satisfied","Satisfied"="Satisfied","Extremely Dissatisfied"="Dissatisfied"........etc, etc

我想用 Map 函数来完成这个，特别是 at_map （以保持数据框，但我是 purrr 的新手，所以随时可以建议其他版本的 Map ）来自 purrr ，以及 dplyr ，tidyr , stringr and ggplot2`所以一切都可以轻松流水线化 .

以下示例是我想要完成的，但是对于重新编码，但我无法使其工作 .

http://www.r-bloggers.com/using-purrr-with-dplyr/

我想使用at_map或类似的map函数，以便我可以保留 Sat1 和 Sat2 的原始列，因此重新编码的列将被添加到数据框并重命名 . 如果这个步骤也可以包含在一个函数中，那将会很棒 .

实际上，我将拥有许多数据帧，因此我只想重新编码因子级别一次，然后使用 purrr 中的函数使用最少量的代码在所有数据帧中进行更改 .

Names<-c("James","Chris","Jessica","Tomoki","Anna","Gerald")
Sat1<-c("Satisfied","Very Satisfied","Dissatisfied","Somewhat Satisfied","Dissatisfied","Neutral")
Sat2<-c("Very Dissatisfied","Somewhat Satisfied","Neutral","Neutral","Satisfied","Satisfied")
Program<-c("A","B","A","C","B","D")
Pets<-c("Snake","Dog","Dog","Dog","Cat","None")

DF1<-data.frame(Names,Sat1,Sat2,Program,Pets)

Names<-c("Tim","John","Amy","Alberto","Desrahi","Francesca")
Sat1<-c("Extremely Satisfied","Satisfied","Satisfed","Somewhat Dissatisfied","Dissatisfied","Satisfied")
Sat2<-c("Dissatisfied","Somewhat Dissatisfied","Neutral","Extremely Dissatisfied","Somewhat Satisfied","Somewhat Dissatisfied")
Program<-c("A","B","A","C","B","D")


DF2<-data.frame(Names,Sat1,Sat2,Program)

2 回答

一种方法是使用 mutate_each 与 map 函数之一结合工作来查看data.frames列表 . 使用dplyr_0.4.3.9001中的 mutate_each 或等效项可以重命名新列 .

在这种情况下，您可以使用字符串操作而不是重新编码 . 我相信你想从当前的字符串中拉出 Satisfied ， Dissatisfied 或 Neutral . 您可以使用正则表达式使用 sub 实现此目的 . 例如，

sub(".*(Satisfied|Dissatisfied|Neutral).*$", "\\1", DF2$Sat2)
"Dissatisfied" "Dissatisfied" "Neutral"      "Dissatisfied" "Satisfied"    "Dissatisfied"

包stringr有一个很好的函数来提取特定的字符串 str_extract .

library(stringr)
str_extract(DF2$Sat2, "Satisfied|Neutral|Dissatisfied")
 "Dissatisfied" "Dissatisfied" "Neutral"      "Dissatisfied" "Satisfied"    "Dissatisfied"

您可以在 mutate_each 中使用它在多列上使用这些函数之一 . 您在 funs 中为该函数指定的名称将添加到新列名称中 . 我用 recode . 对于您的一个数据集：

DF1 %>% 
    mutate_each( funs(recode = str_extract(., "Satisfied|Neutral|Dissatisfied") ), 
              starts_with("Sat") )

    Names               Sat1               Sat2 Program  Pets  Sat1_recode  Sat2_recode
1   James          Satisfied  Very Dissatisfied       A Snake    Satisfied Dissatisfied
2   Chris     Very Satisfied Somewhat Satisfied       B   Dog    Satisfied    Satisfied
3 Jessica       Dissatisfied            Neutral       A   Dog Dissatisfied      Neutral
4  Tomoki Somewhat Satisfied            Neutral       C   Dog    Satisfied      Neutral
5    Anna       Dissatisfied          Satisfied       B   Cat Dissatisfied    Satisfied
6  Gerald            Neutral          Satisfied       D  None      Neutral    Satisfied

要浏览存储在列表中的许多数据集，可以使用purrr中的 map 函数对列表中的每个元素执行一个函数 .

list(DF1, DF2) %>%
    map(~mutate_each(.x, 
                  funs(recode = str_extract(., "Satisfied|Neutral|Dissatisfied") ), 
                  starts_with("Sat")) )

[[1]]
    Names               Sat1               Sat2 Program  Pets  Sat1_recode  Sat2_recode
1   James          Satisfied  Very Dissatisfied       A Snake    Satisfied Dissatisfied
2   Chris     Very Satisfied Somewhat Satisfied       B   Dog    Satisfied    Satisfied
...
[[2]]
      Names                  Sat1                   Sat2 Program  Sat1_recode  Sat2_recode
1       Tim   Extremely Satisfied           Dissatisfied       A    Satisfied Dissatisfied
2      John             Satisfied  Somewhat Dissatisfied       B    Satisfied Dissatisfied
...

使用 map_df 会将列表中的所有元素绑定到data.frame中，这可能是您想要的，也可能不是 . 使用 .id 参数为每个原始数据集添加一个名称 .

list(DF1, DF2) %>%
    map_df(~mutate_each(.x, 
                  funs(recode = str_extract(., "Satisfied|Neutral|Dissatisfied")), 
                  starts_with("Sat")), .id = "Group")

   Group     Names                  Sat1                   Sat2 Program  Pets  Sat1_recode
1      1     James             Satisfied      Very Dissatisfied       A Snake    Satisfied
2      1     Chris        Very Satisfied     Somewhat Satisfied       B   Dog    Satisfied
3      1   Jessica          Dissatisfied                Neutral       A   Dog Dissatisfied
4      1    Tomoki    Somewhat Satisfied                Neutral       C   Dog    Satisfied
5      1      Anna          Dissatisfied              Satisfied       B   Cat Dissatisfied
6      1    Gerald               Neutral              Satisfied       D  None      Neutral
7      2       Tim   Extremely Satisfied           Dissatisfied       A  <NA>    Satisfied
8      2      John             Satisfied  Somewhat Dissatisfied       B  <NA>    Satisfied
...

回复于 2024-05-02T10:13:41+08:00

我通过连接进行这样大的重新编码，在这种情况下，我认为转换为长数据帧会使问题更容易思考 .

library(tidyr)
library(dplyr)

mdf <- DF1 %>% 
  gather(var, value, starts_with("Sat"))

recode_df <- data_frame( value = c("Extremely Satisfied","Satisfied","Somewhat Dissatisfied","Dissatisfied"),
                         recode = 1:4)
mdf <- left_join(mdf, recode_df)
mdf %>% spread(var, recode)

回复于 2024-05-02T10:13:41+08:00

使用Purrr和Dplyr在多个数据帧中重新编码相似的因子水平

2 回答

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