CODEWITHSUNDEEP
rdplyrpivot
wscampbell
wscampbell

Reputation: 379

pivot_longer to combine groups of columns: Advanced pivoting

Need: Pivot from wide to long, stacking groups of corresponding columns.
In essence, I have 3 sets of 5 columns with a need for each of the corresponding columns to be stacked into 1 (i.e., the first variable in each of the 3 sets becomes 1 column, the second variable in each each is the 2nd column, etc). For instance, I need: columns #2, 7, & 12 to all be in 1 column, 3, 8, & 13 in the next column, ... 6, 11, & 16 all in 1 column.

Data structure: I have a dataset that resembles this:

df <- tibble(
  pid = c(1, 2, 3, 4),
  
  v1_1 = c(19, NA, NA, NA),
  v1_2 = c(12, NA, NA, NA),
  v2_1 = c(15, NA, NA, NA),
  v2_2 = c(19, NA, NA, NA),
  v1_entry_3 = c(11, NA, NA, NA),
  
  v1_1_1 = c(NA, NA, 36, NA),
  v1_2_1 = c(NA, NA, 35, NA),
  v2_1_1 = c(NA, NA, 31, NA),
  v2_2_1 = c(NA, NA, 39, NA),
  v1_entry_3_1 = c(NA, NA, 33, NA),
  
  v1_1_2 = c(NA, 26, NA, 41),
  v1_2_2 = c(NA, 29, NA, 44),
  v2_1_2 = c(NA, 21, NA, 42),
  v2_2_2 = c(NA, 20, NA, 45),
  v1_entry_3_2 = c(NA, 22, NA, 44),
  
  age = c(19, 21, 33, 47)
)

In the end, I need data that look like this:

df_t <- tibble(
  pid = c(1, 2, 3, 4),
  
  v1_1 = c(19, 26, 36, 41),
  v1_2 = c(12, 29, 35, 44),
  v2_1 = c(15, 21, 31, 42),
  v2_2 = c(19, 20, 39, 45),
  v1_entry_3 = c(11, 22, 33, 44),
  
  age = c(19, 21, 33, 47)
)

Upvotes: 2

Views: 748

Answers (4)

Uwe
Uwe

Reputation: 42572

For the sake of completeness, here is an approach using the melt() function:

library(data.table)
cols <- names(df)[2:6]
melt(setDT(df), measure = patterns(cols), value.name = cols, na.rm = TRUE)[order(pid)]

   pid age variable v1_1 v1_2 v2_1 v2_2 v1_entry_3
1:   1  19        1   19   12   15   19         11
2:   2  21        3   26   29   21   20         22
3:   3  33        2   36   35   31   39         33
4:   4  47        3   41   44   42   45         44

Here, we benefit from the fact that the column names of the first set of columns to be reshaped can be reused as the column names of the reshaped output.

Upvotes: 2

Anoushiravan R
Anoushiravan R

Reputation: 21938

Here is a base R solution:

colnames <- startsWith(names(df), "v")

cbind(df[!colnames], 
      do.call(cbind, lapply(split.default(df[colnames], gsub("(v\\d_\\d|[[:alpha:]]+)_.*", "\\1", names(df)[colnames])), 
                            function(x) apply(x, 1, \(x) x[!is.na(x)]))))

  pid age v1_1 v1_2 v1_entry v2_1 v2_2
1   1  19   19   12       11   15   19
2   2  21   26   29       22   21   20
3   3  33   36   35       33   31   39
4   4  47   41   44       44   42   45

Upvotes: 4

lroha
lroha

Reputation: 34586

You need for column names to be matched on everything up to the second underscore:

library(tidyr)

df %>%
  pivot_longer(
    -c(pid, age),
    names_pattern =  "([^_]*_[^_]*)",
    names_to = ".value",
    values_drop_na = TRUE
  )

# A tibble: 4 x 7
    pid   age  v1_1  v1_2  v2_1  v2_2 v1_entry
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1    19    19    12    15    19       11
2     2    21    26    29    21    20       22
3     3    33    36    35    31    39       33
4     4    47    41    44    42    45       44

Upvotes: 6

akrun
akrun

Reputation: 887711

Consider renaming some of the columns before doing the pivot_longer

library(dplyr)
library(stringr)
library(tidyr)
df %>% 
  rename_with(~ str_c(., '_0'), matches("^v\\d+_\\d+$|^v\\d+_entry_\\d+$")) %>% 
    pivot_longer(cols = -c(pid, age), names_to = c(".value"), 
        names_pattern = "(.*)_\\d+$", values_drop_na = TRUE)
# A tibble: 4 x 7
    pid   age  v1_1  v1_2  v2_1  v2_2 v1_entry_3
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>      <dbl>
1     1    19    19    12    15    19         11
2     2    21    26    29    21    20         22
3     3    33    36    35    31    39         33
4     4    47    41    44    42    45         44

Upvotes: 4

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