How do I specify pivot_wider for an entire dataframe? - r

Question

I am able to pivot_wider for a specific column using the following:
new_df <- pivot_wider(old_df, names_from = col10, values_from = value_col, values_fn = list)
I would like to pivot_wider with every column in a dataframe (minus an id column). What is the best way to do this? Should I use a loop or is there a way that this function takes the whole dataframe?
To clarify, using the below sample dataframes, I am able to go from old_df to new_df using the pivot_wider function I listed above. I would like to now go from old_df2 to new_df2.
old_df <- structure(list(id = c("1", "1", "2"), col10 = c("yellow",
"green", "green"), value_col = c("1", "1", "1")), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))
old_df2 <- structure(list(id = c("1", "1", "2"), col10 = c("yellow",
"green", "green"), col11 = c("dog",
"cat", "dog"), value_col = c("1", "1", "1")), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))
new_df <- pivot_wider(old_df, names_from = col10, values_from = value_col, values_fn = list)
new_df2 <- structure(list(id = c("1", "2"), yellow = c("1", "NULL"), green = c("1", "1"), dog = c("1", "1"), cat = c("1", "NULL")), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

If you would like to have separate column names for each value between these two columns (or any number of columns) you first need to use pivot_longer to put all the column names into a single column and then use pivot_wider to spread them:
library(tidyr)
old_df2 %>%
pivot_longer(!c(id, value_col), names_to = "Cols", values_to = "vals") %>%
pivot_wider(names_from = vals, values_from = value_col) %>%
select(-Cols) %>%
group_by(id) %>%
summarise(across(everything(), ~ sum(as.numeric(.x), na.rm = TRUE)))
# A tibble: 2 x 5
id yellow dog green cat
<chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 1 1
2 2 0 1 1 0

Answer 2

Update 1
As per your update, here comes with a data.table option
dcast(
melt(setDT(old_df),
id.var = "id",
measure.vars = patterns("^col\\d+")
),
id ~ value,
fun.aggregate = length,
fill = NA
)
which gives
id cat dog green yellow
1: 1 1 1 1 1
2: 2 NA 1 1 NA
Are you looking for something like below?
reshape(
transform(
old_df,
q = ave(id, id, FUN = seq_along)
),
direction = "wide",
idvar = "id",
timevar = "q"
)
The output is
id col10.1 col11.1 value_col.1 col10.2 col11.2 value_col.2
1 1 yellow dog 1 green cat 1
3 2 green dog 1 <NA> <NA> <NA>

Answer 3

You could combine those columns and unnest them followed by pivot_wider:
library(tidyr)
library(dplyr)
old_df2 <- structure(list(id = c("1", "1", "2"), col10 = c("yellow",
"green", "green"), col11 = c("dog",
"cat", "dog"), value_col = c("1", "1", "1")), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))
old_df2 %>%
mutate(new_col = strsplit(paste(col10, col11, sep = "_"), "_"), .keep = "unused") %>%
unnest(new_col) %>%
pivot_wider(names_from = new_col, values_from = value_col)
#> # A tibble: 2 x 5
#> id yellow dog green cat
#> <chr> <chr> <chr> <chr> <chr>
#> 1 1 1 1 1 1
#> 2 2 <NA> 1 1 <NA>
Created on 2021-08-25 by the reprex package (v2.0.1)