Group by, summarize, spread in R not working - r

Question

I have a data frame that looks like the following:
ID Code Desc
1 0A Red
1 NA Red
2 1A Blue
3 2B Green
I want to first create a new column where I concatenate the values in the Code column where the IDs are the same. So:
ID Combined_Code Desc
1 0A | NA Red
2 1A Blue
3 2B Green
Then I want to take the original Code column and spread it. The values in this case would be a count of how many times each Code shows up for a given ID. So:
ID Combined_Code 0A NA 1A 2B Desc
1 0A | NA 1 1 0 0 Red
2 1A 0 0 1 0 Blue
3 2B 0 0 0 1 Green
I've tried:
sample_data %>%
group_by(ID) %>%
summarise(Combined_Code = paste(unique(Combined_Code), collapse ='|'))
This works for creating the concatenation. However, I can't get this to work in tandem with spread:
sample_data %>%
group_by(ID) %>%
summarise(Combined_Code = paste(unique(Combined_Code), collapse ='|'))
sample_data <- spread(count(sample_data, ID, Combined_Code, Desc., Code), Code, n, fill = 0)
Doing this spreads, but drops the concatenation. I've also tried this with filter instead of summarise, which gives the same result. This results in:
ID Combined_Code 0A NA 1A 2B Desc
1 0A 1 0 0 0 Red
1 NA 0 1 0 0 Red
2 1A 0 0 1 0 Blue
3 2B 0 0 0 1 Green
Finally, I've tried piping spread through the summarise function:
sample_data %>%
group_by(ID) %>%
summarise(Combined_Code = paste(unique(Combined_Code), collapse ='|')) %>%
spread(count(sample_data, ID, Combined_Code, Desc., Code), Code, n, fill = 0)
This results in the error:
Error: `var` must evaluate to a single number or a column name, not a list
Run `rlang::last_error()` to see where the error occurred.
What can I do to solve these problems?

Answer 1

We can do a group by paste
library(dplyr)
library(stringr)
df1 %>%
group_by(ID, Desc) %>%
summarise(Combined_Code = str_c(Code, collapse="|"))
# A tibble: 3 x 3
# Groups: ID [3]
# ID Desc Combined_Code
# <int> <chr> <chr>
#1 1 Red 0A|0B
#2 2 Blue 1A
#3 3 Green 2B
For the second case, after creating a 'val' column of 1s, paste the 'Code' elements afte grouping by 'ID', 'Desc', then use pivot_wider from tidyr to reshape from 'long' to 'wide format.
library(tidyr)
df1 %>%
mutate(val = 1) %>%
group_by(ID, Desc) %>%
mutate(Combined_Code = str_c(Code, collapse="|")) %>%
pivot_wider(names_from = Code, values_from = val, values_fill = list(val = 0))
# A tibble: 3 x 7
# Groups: ID, Desc [3]
# ID Desc Combined_Code `0A` `0B` `1A` `2B`
# <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#1 1 Red 0A|0B 1 1 0 0
#2 2 Blue 1A 0 0 1 0
#3 3 Green 2B 0 0 0 1
The OP's expected output is
ID Combined_Code 0A 0B 1A 2B Desc
1 0A | 0B 1 1 0 0 Red
2 1A 0 0 1 0 Blue
3 2B 0 0 0 1 Green
Update
For the updated dataset, there are NA elements in the 'Code', and by default str_c returns NA if there any NA as one of the elements, while paste still returns the NA along with the other elements. Here, we replace the str_c with paste
df2 %>%
mutate(val = 1) %>%
group_by(ID, Desc) %>%
mutate(Combined_Code = paste(Code, collapse="|")) %>%
pivot_wider(names_from = Code, values_from = val, values_fill = list(val = 0))
# A tibble: 3 x 7
# Groups: ID, Desc [3]
# ID Desc Combined_Code `0A` `NA` `1A` `2B`
# <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#1 1 Red 0A|NA 1 1 0 0
#2 2 Blue 1A 0 0 1 0
#3 3 Green 2B 0 0 0 1
data
df1 <- structure(list(ID = c(1L, 1L, 2L, 3L), Code = c("0A", "0B", "1A",
"2B"), Desc = c("Red", "Red", "Blue", "Green")),
class = "data.frame", row.names = c(NA,
-4L))
df2 <- structure(list(ID = c(1L, 1L, 2L, 3L), Code = c("0A", NA, "1A",
"2B"), Desc = c("Red", "Red", "Blue", "Green")), class = "data.frame",
row.names = c(NA,
-4L))