R - How to create multiple datasets based on levels of factor in multiple columns?

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I'm kinda new to R and still looking for ways to make my code more elegant. I want to create multiple datasets in a more efficient way, each based on a particular value over different columns.

This is my dataset:

df<-data.frame(A=c(1,2,2,3,4,5,1,1,2,3),
               B=c(4,4,2,3,4,2,1,5,2,2),
               C=c(3,3,3,3,4,2,5,1,2,3),
               D=c(1,2,5,5,5,4,5,5,2,3),
               E=c(1,4,2,3,4,2,5,1,2,3),
               dummy1=c("yes","yes","no","no","no","no","yes","no","yes","yes"),
               dummy2=c("high","low","low","low","high","high","high","low","low","high"))

And I need each column to be a factor:

df[colnames(df)] <- lapply(df[colnames(df)], factor)

Now, what I want to obtain is one dataframe called "Likert_rank_yes" that contains all the observations that in the column "dummy1" have "yes", one dataframe called "Likert_rank_no" that contains all the observations that in the column "dummy1" have "no", one dataframe called "Likert_rank_high" that contains all the observations that in the column "dummy2" have "high" and so on for all my other dummies. I want to loop or streamline the process in some way, so that there are few commands to run to get all the datasets I need.

The first two dataframes should look something like this:

Dataframe called "Likert_rank_yes" that contains all the observations that in the column "dummy1" have "yes"

Dataframe called "Likert_rank_no" that contains all the observations that in the column "dummy1" have "no"

I have to do this with several dummies with multiple levels and would like to automate/loop the process or make it more efficient, so that I don't have to subset and rename every dataframe for each dummy level. Ideally I would also need to drop the last column in each df created (the one containing the dummy considered).

I tried splitting like below but it seems it is not possible using multiple values, I just get 4 dfs (yes AND high observations, yes AND low obs, no AND high obs etc.) like so:

Splitting with a list of columns doesn't work

list_df <-   split(df[c(1:5)], list(df$dummy1,df$dummy2), sep=".")

Can you help? Thanks in advance!

dataframe r for-loop loops split
1 Answers

You need two lapplys:

vals <- colnames(df)[1:5]
dummies <- colnames(df)[-(1:5)]
step1 <- lapply(dummies, function(x) df[, c(vals, x)])
step2 <- lapply(step1, function(x) split(x, x[, 6]))
names(step2) <- dummies
step2
# $dummy1
# $dummy1$no
#   A B C D E dummy1
# 3 2 2 3 5 2     no
# 4 3 3 3 5 3     no
# 5 4 4 4 5 4     no
# 6 5 2 2 4 2     no
# 8 1 5 1 5 1     no
# 
# $dummy1$yes
#    A B C D E dummy1
# 1  1 4 3 1 1    yes
# 2  2 4 3 2 4    yes
# 7  1 1 5 5 5    yes
# 9  2 2 2 2 2    yes
# 10 3 2 3 3 3    yes
# 
# 
# $dummy2
# $dummy2$high
#    A B C D E dummy2
# 1  1 4 3 1 1   high
# 5  4 4 4 5 4   high
# 6  5 2 2 4 2   high
# 7  1 1 5 5 5   high
# 10 3 2 3 3 3   high
# 
# $dummy2$low
#   A B C D E dummy2
# 2 2 4 3 2 4    low
# 3 2 2 3 5 2    low
# 4 3 3 3 5 3    low
# 8 1 5 1 5 1    low
# 9 2 2 2 2 2    low

For the first data set ("dummy1" and "no") use step2$dummy1$no or step2[[1]][[1]] or step2[["dummy1"]][["no"]].

For programming purposes it is usually better to keep the list intact since it makes it simple to write code that processes all of the data frames in the list without having to specify them individually.

You are very close:

tbls <- unlist(step2, recursive=FALSE)
list2env(tbls, envir=.GlobalEnv)
ls()
#  [1] "df"          "dummies"     "dummy1.no"   "dummy1.yes"  "dummy2.high" "dummy2.low"  "step1"       "step2"       "tbls"        "vals"

This will create the same set of tables.

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