问题
Below are two simple data frames. I would like to re-code (collapse) the Sat1 and Sat2 columns so that all degrees of satisfied are coded simply as Satisfied, and all degrees of Dissatisfied are coded as Dissatisfied. Neutral will remain as Neutral. These factors will therefore have three levels - Satisfied, Dissatisfied, and Neutral.
I would normally accomplish this by binding the data frames, and using lapply along with re-code from the car package, such as:
DF1[2:3] <- lapply(DF1[2:3], recode, c('"Somewhat Satisfied"= "Satisfied","Satisfied"="Satisfied","Extremely Dissatisfied"="Dissatisfied"........etc, etc
I would like to accomplish this using map functions, specifically at_map (to maintain the data frame, but I'm new to purrr so feel free to suggest other versions of map) from purrr, as well as dplyr, tidyr,stringrandggplot2` so everything can be easily pipelined.
The example below is what I would like to accomplish, but for re-coding, but I was unable to make it work.
http://www.r-bloggers.com/using-purrr-with-dplyr/
I would like to use at_map or a similar map function so that I can keep the original columns of Sat1 and Sat2, so the re-coded columns will be added to the data frame and renamed. It would be great if this step could also be included within a function.
In reality, I will have many data frames, so I only want to recode the factor levels once, and then use a function from purrr to make the changes across all the data frames using the least amount of code.
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)
回答1:
One way to do this is to use mutate_each to do the work combined with one of the map functions to go through a list of data.frames. Using mutate_each or equivalent from dplyr_0.4.3.9001 allows you to rename the new columns.
You could use string manipulation instead of recoding in this case. I believe you want to pull out Satisfied, Dissatisfied, or Neutral from the current strings that you have. You can achieve this with sub using regular expressions. For example,
sub(".*(Satisfied|Dissatisfied|Neutral).*$", "\\1", DF2$Sat2)
"Dissatisfied" "Dissatisfied" "Neutral" "Dissatisfied" "Satisfied" "Dissatisfied"
Package stringr has a nice function for extracting specific strings, str_extract.
library(stringr)
str_extract(DF2$Sat2, "Satisfied|Neutral|Dissatisfied")
"Dissatisfied" "Dissatisfied" "Neutral" "Dissatisfied" "Satisfied" "Dissatisfied"
You can use this within mutate_each to use one of these functions on multiple columns. The name you give for the function within funs is what will be added on to the new columns names. I used recode. For one of your datasets:
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
To go through many datasets stored in a list, you can use a map function from purrr to perform a function on every element in the list.
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
...
Using map_df instead will bind all of the elements in your list into a data.frame, which may or may not be what you want. Using the .id argument adds a name for each original dataset.
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
...
回答2:
I do big recodings like this with a join, in this case I think transforming to a long dataframe makes the problem easier to think about.
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)
来源：https://stackoverflow.com/questions/37951756/recoding-similar-factor-levels-across-multiple-data-frames-using-purrr-and-dplyr