Making a list in r-CodePudding

I am trying to split the data frame into list.

My data frame is as follows:

enter image description here

I want to split this dataframe into lists as follows list

[1] Country Iso_code Year cow Supdem (the forth column in the picture)

[[2]] Country Iso_code Year cow Supdem (the fifth colum in the picture)

[[3]] Country Iso_code Year cow Supdem (the sixth colum in the picture)

....

Could you help me with this?

CodePudding user response：

load package

require(data.table)

since we had a jpeg as sample dataframe (df), we shall create one as our example

df <- data.table(country = letters[1:3]
                 , iso_code = 1:3
                 , year = c(2000, 2001, 2002)
                 , cow = 1:3
                 , supdem = 1:3
                 , supdem = 4:6
                 ); df

   country iso_code year cow supdem supdem
1:       a        1 2000   1      1      4
2:       b        2 2001   2      2      5
3:       c        3 2002   3      3      6

run this if your dataframe is not a data table already

setDT(df)

Not the best practice to give our columns same names i.e. supdem. As this entails identifying columns using their index (which is also bad practice as future column location may change). Anyway, we first identify column location of columns: supdem

x <- 5:ncol(df)

Then iteratively put the first 4 columns then 1 variable (changing) column into lists

y <- lapply(x, function(i) df[, .SD, .SDcols=c(1:4, i)]); y

[[1]]
   country iso_code year cow supdem
1:       a        1 2000   1      1
2:       b        2 2001   2      2
3:       c        3 2002   3      3

[[2]]
   country iso_code year cow supdem
1:       a        1 2000   1      4
2:       b        2 2001   2      5
3:       c        3 2002   3      6

CodePudding user response：

One way could be to use Map to bind the columns between your 4 key columns to each other column using split.default, which will create individual dataframes.

Map(cbind, list(mtcars[1:4]), split.default(mtcars[-(1:4)], 1:7))

Another way could be:

library(tidyverse)

purrr::map(names(mtcars[-c(1:4)]), function(x) bind_cols(mtcars[c(1:4)], mtcars[x]))

This one could also be done with the apply family too.

lapply(names(mtcars[-c(1:4)]), function(x) cbind(mtcars[c(1:4)], mtcars[x]))

Another way would be to create two lists of dataframes, one with the main information replicated n times (depending on number of columns not repeated) and another with the individual columns (excluding your key columns). First, I determined the number of columns that you have minus the number of columns that would be repeated in each dataframe (i.e., 4). Then, I replicate the dataframe with the key columns (i.e., mtcars[, 1:4]). Then, I use split.default to put each remaining column in its own dataframe in a list. Finally, I use purrr::map2 to bind the columns from the two dataframes for each index position.

library(purrr)

nocols <- ncol(mtcars) - 4
lst1 <- replicate(nocols, mtcars[, 1:4], simplify = FALSE)
lst2 <-
  split.default(mtcars[, 5:ncol(mtcars)], seq_along(mtcars[, 5:ncol(mtcars)]))

results <- map2(lst1, lst2, bind_cols)

Output

str(results)

List of 7
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ wt  : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ vs  : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
 $ :'data.frame':   32 obs. of  5 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...