Using map_df from purrr on a factor column - r

Question

I'm trying to count the number of responses in multiple columns for rows which all belong to one of four factors in the column Paper. I can sum the terms for each factor individually using map_df from purr as so
times <- in_all_waves %>%
filter(Paper =='Times') %>%
ungroup() %>% #function refuses to work without this
select(-Paper) %>%
map_df(table) %>% # use map_df from the purrr package to "table" each column
rownames_to_column("response") %>% #convert the rownames to a column named response
mutate(resp = case_when(response == 1 ~ "Remain", #change the resulting numbers to the correct responses
response == 2 ~ "Leave",
response ==3 ~ "Will Not Vote",
response == 4 ~ "Don't Know")) %>%
select(resp, everything(), -response) #reorder the columns with resp at the front, removing response
But when I try to do this without selecting just one column as so:
different_papers <- in_all_waves %>%
map_df(table) %>%
rownames_to_column("response") %>%
mutate(resp = case_when(response == 1 ~ "Remain", #change the resulting 1s to No in resp
response == 2 ~ "Leave",
response ==3 ~ "Will Not Vote",
response == 4 ~ "Don't Know")) %>%
select(resp, everything(), -response) #reorder the columns with resp at the front, removing response
I get the error Error: Argument 9 must be length 4, not 5 which is a reference to this last column of factors. Is there a way to keep all of the rows in the same tibble, or do they have to be in seperate ones for each factor?
No other suggested questions seem quite to match my query I'm afraid.
This is the dataframe I'm using in an rds format!
https://www.dropbox.com/s/nwq913lw13kxyw9/inallwaves.rds?dl=0

Answer 1

I found just adding the column back in worked best !
tally_reader_number <- function(input_dataframe,newspaper_name) {
#function takes the input of in_all_waves, tallies the number of different eu ref responses using map_df for a given newspaper factor (defined above)
# and returns a dataframe of responese for each wave with the newspaper factor as a column
returned_dataframe <- input_dataframe %>%
filter(Paper == newspaper_name) %>%
ungroup() %>% #function refuses to work without this
select(-Paper) %>%
map_df(table) %>% # use map_df from the purrr package to "table" each column
rownames_to_column("response") %>% #convert the rownames to a column named response
mutate(resp = case_when(response == 1 ~ "Remain", #change the resulting numbers to the correct responses
response == 2 ~ "Leave",
response ==3 ~ "Will Not Vote",
response == 4 ~ "Don't Know")) %>%
select(resp, everything(), -response) %>% #reorder the columns with resp at the front, removing response
mutate(Paper = newspaper_name)
returned_dataframe$Paper <- as.factor(returned_dataframe$Paper)
returned_dataframe$resp <- as.factor(returned_dataframe$resp)
returned_dataframe
}