create a list of dataframes using purrr to select columns

Question

These are my data:

set.seed(1234321)

# Original dataframe (i.e. a questionnaire survey data)
answer <- c("Yes", "No")
likert_scale <- c("strongly disagree", "disagree", "undecided", "agree", "strongly agree")
d1 <- c(rnorm(10)*10)
d2 <- sample(x = c(letters), size = 10, replace = TRUE)
d3 <- sample(x = likert_scale, size = 10, replace = TRUE)
d4 <- sample(x = likert_scale, size = 10, replace = TRUE)
d5 <- sample(x = likert_scale, size = 10, replace = TRUE)
d6 <- sample(x = answer, size = 10, replace = TRUE)
d7 <- sample(x = answer, size = 10, replace = TRUE)
original_df <- data.frame(d1, d2, d3, d4, d5, d6, d7)

# Questionnaire codebook 
quest_section <- c("generic", "likert scale", "specific approval")
starting_column <- c(1, 3, 6)
ending_column <- c(2, 5, 7)
df_codebook <- data.frame(quest_section, starting_column, ending_column)

I would like to obtain the following list of dataframes:

> my_df_list
$generic
           d1 d2
1   12.369081  z
2   15.616230  x
3   18.396185  f
4    3.173245  q
5   10.715115  j
6  -11.459955  p
7    2.488894  j
8    1.158625  n
9   26.200816  a
10  12.624048  b

$`likert scale`
                  d3                d4                d5
1           disagree    strongly agree    strongly agree
2          undecided         undecided strongly disagree
3     strongly agree         undecided strongly disagree
4              agree         undecided         undecided
5  strongly disagree             agree         undecided
6           disagree strongly disagree         undecided
7           disagree             agree          disagree
8           disagree strongly disagree         undecided
9          undecided strongly disagree          disagree
10 strongly disagree          disagree    strongly agree

$`specific approval`
    d6  d7
1   No  No
2   No  No
3  Yes  No
4  Yes Yes
5  Yes Yes
6  Yes Yes
7  Yes  No
8   No Yes
9   No  No
10  No Yes

To obtain the previous result using purrr approach, I performed the following code:

my_list_2 <- pmap(list(c(1:3)), ~dplyr::select(original_df,df_codebook[,2]:df_codebook[,3])) %>% 
        set_names(df_codebook[,1])

But the result was this one:

> my_list_2
$generic
           d1 d2
1   12.369081  z
2   15.616230  x
3   18.396185  f
4    3.173245  q
5   10.715115  j
6  -11.459955  p
7    2.488894  j
8    1.158625  n
9   26.200816  a
10  12.624048  b

$`likert scale`
           d1 d2
1   12.369081  z
2   15.616230  x
3   18.396185  f
4    3.173245  q
5   10.715115  j
6  -11.459955  p
7    2.488894  j
8    1.158625  n
9   26.200816  a
10  12.624048  b

$`specific approval`
           d1 d2
1   12.369081  z
2   15.616230  x
3   18.396185  f
4    3.173245  q
5   10.715115  j
6  -11.459955  p
7    2.488894  j
8    1.158625  n
9   26.200816  a
10  12.624048  b

I have just obtained a solution for a similar question, but now I am interested in perform it using and understanding purrr approach.

Answer 1

If you would like to use purrr you could try this:

library(purrr)

my_list <- map2(.x = df_codebook$starting_column, 
                .y = df_codebook$ending_column, 
                ~ original_df[, .x:.y]) %>%
  set_names(df_codebook$quest_section)

map2 allows you to iterate over two inputs - in this case, the start and end columns.

> my_list
$generic
           d1 d2
1   12.369081  z
2   15.616230  x
3   18.396185  f
4    3.173245  q
5   10.715115  j
6  -11.459955  p
7    2.488894  j
8    1.158625  n
9   26.200816  a
10  12.624048  b

$`likert scale`
                  d3                d4                d5
1           disagree    strongly agree    strongly agree
2          undecided         undecided strongly disagree
3     strongly agree         undecided strongly disagree
4              agree         undecided         undecided
5  strongly disagree             agree         undecided
6           disagree strongly disagree         undecided
7           disagree             agree          disagree
8           disagree strongly disagree         undecided
9          undecided strongly disagree          disagree
10 strongly disagree          disagree    strongly agree

$`specific approval`
    d6  d7
1   No  No
2   No  No
3  Yes  No
4  Yes Yes
5  Yes Yes
6  Yes Yes
7  Yes  No
8   No Yes
9   No  No
10  No Yes