CODEWITHSUNDEEP
rdata-manipulation
rg4s
rg4s

Reputation: 897

How to write a function that manipulates the data structure in R?

Some background information for better understanding

I have a very specific dataframe. It is basically tha sample of answers on my survey. Variables v1 represent a multiple choice question; the respondent were to choose variants from 1 to 4, he/she could choose the only or several options. Every chosen variant readressed the respondent to the block: if 1 was chosen, the respondent was readressed to o_v1_1, c_v1_1, and f_v1_1 and so on.

The problem

I want to write a function that will change my data structure from wide to long. But I am struggling with pivot_longer, because it does not produce a desirable output.

Here's the sample dataframe with some initial data processing:

structure(list(seance_id = c(1, 2, 3, 4), respondent = c("A", 
"B", "C", "D"), v1...3 = c(1, 1, NA, 1), v1...4 = c(2, NA, 2, 
NA), v1...5 = c(3, 4, 4, NA), v1...6 = c(4, NA, NA, NA), o_v1_1 = c(6, 
1, NA, 4), c_v1_1 = c(7, 1, NA, 1), f_v1_1 = c(8, 1, NA, 1), 
    o_v1_2 = c(10, NA, 4, NA), c_v1_2 = c(8, NA, 1, NA), f_v1_2 = c(3, 
    NA, 3, NA), o_v1_3 = c(4, NA, NA, NA), c_v1_3 = c(1, NA, 
    NA, NA), f_v1_3 = c(2, NA, NA, NA), o_v1_4 = c(10, 5, 4, 
    NA), c_v1_4 = c(9, 6, 5, NA), f_v1_4 = c(9, 6, 6, NA)), row.names = c(NA, 
-4L), class = c("tbl_df", "tbl", "data.frame")) 

data <- data %>%  mutate_if(is.numeric, as.character)

colnames(data) <- c("seance_id", "respondent", "v1", "v1", "v1", "v1", "o_v1_1",
                    "c_v1_1",   "f_v1_1",   "o_v1_2",   "c_v1_2",   "f_v1_2",   "o_v1_3",   "c_v1_3",
                    "f_v1_3",   "o_v1_4",   "c_v1_4",   "f_v1_4")

And here's how I tried to make my table long:

long <- data %>%
  pivot_longer(cols = -`seance_id`, names_to = "v1", values_to = "answer")

And this is what I want to get:

  `séance_id` respondent direction answer_dir criteria criteria_answer
        <dbl> <chr>      <chr>          <dbl> <chr>              <dbl>
1           1 A          v1                 1 o_v1_1                 6
2           1 A          v1                 1 c_v1_1                 7
3           1 A          v1                 1 f_v1_1                 8
4           1 A          v1                 2 o_v1_2                10
5           1 A          v1                 2 c_v1_2                 8
6           1 A          v1                 2 f_v1_2                 3

I have been researching SO for 2 days already and have not resolved my problem yet. How can I use pivot_longer effectively to get the desirable output? And is there any way to automate the process of longing my dfs? I have more than 30 of dfs, nested in different lists within one Excel file.

Upvotes: 0

Views: 37

Answers (1)

MrGumble
MrGumble

Reputation: 5766

You can drop you initial v1 columns. I instead propose the following:

 data %>% select(-starts_with('v1')) %>%
   pivot_longer(cols = contains('v1'), names_to = "v1", values_to = "criteria_answer") %>% 
   separate(v1, sep='_', into=c('w','direction','answer_dir'), remove=FALSE) %>%
   rename(creteria=v1) %>% select(-w)
# A tibble: 48 x 6
   seance_id respondent creteria direction answer_dir criteria_answer
   <chr>     <chr>      <chr>    <chr>     <chr>      <chr>          
 1 1         A          o_v1_1   v1        1          6              
 2 1         A          c_v1_1   v1        1          7              
 3 1         A          f_v1_1   v1        1          8              
 4 1         A          o_v1_2   v1        2          10             
 5 1         A          c_v1_2   v1        2          8              
 6 1         A          f_v1_2   v1        2          3              
 7 1         A          o_v1_3   v1        3          4              
 8 1         A          c_v1_3   v1        3          1              
 9 1         A          f_v1_3   v1        3          2              
10 1         A          o_v1_4   v1        4          10             
# ... with 38 more rows

The final select(-w) is to remove the w-column, an artefact from separate splitting o_v1_1,c_v1_1,etc. into 3 columns. Here w was for the first character.

Upvotes: 2

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