How to use the factor(f) syntax in dplyr/ forcats package in R?

Question

I am trying to do something very simple, which is use the forcats package in R to work with factors. I have a dataframe with some factor variables, one of which is gender, and I'm simply trying to count the occurrence of the variables using fct_count. The syntax is shown in the documentation as fct_count(f) (what could be easier.).

I'm trying to do this the dplyr way, using the pipe operator instead of the $ syntax to access the variables, but it doesn't seem to work. Am I just fundamentally misunderstanding the syntax?

pid <- c('id1','id2','id3','id4','id5','id6')
gender <- c('Male','Female','Other','Male','Female','Female')
df <- data.frame(pid, gender)
df <- as.tibble(df)
df

# A tibble: 6 x 2
  pid   gender
  <chr> <fct> 
1 id1   Male  
2 id2   Female
3 id3   Other 
4 id4   Male  
5 id5   Female
6 id6   Female

# This throws an error
df %>%
  mutate(gender = as.factor(gender)) %>%
  fct_count(gender) # Error: `f` must be a factor (or character vector).

# This works but doesn't use the nice dplyr select syntax
fct_count(df$gender)
# A tibble: 3 x 2
  f          n
  <fct>  <int>
1 Female     3
2 Male       2
3 Other      1

Where am I going wrong? New to dplyr and sorry for such a daft question but I can't seem to find a basic example anywhere!

Answer 1

fct_count takes a vector that is of type factor or char, it isn't especially aware of tibbles and dataframes. So the simplest pipe would be...

library(dplyr)
library(forcats)

df %>%
   pull(gender) %>%
   fct_count 
#> # A tibble: 3 x 2
#>   f          n
#>   <fct>  <int>
#> 1 Female     3
#> 2 Male       2
#> 3 Other      1

Your data

pid <- c('id1','id2','id3','id4','id5','id6')
gender <- c('Male','Female','Other','Male','Female','Female')
df <- data.frame(pid, gender)
df <- tibble::as_tibble(df)
df

Answer 2

you could just use group_by and n()

pid <- c('id1','id2','id3','id4','id5','id6')
gender <- c('Male','Female','Other','Male','Female','Female')
df <- data.frame(pid, gender)
df <- tibble::tibble(df)


df %>%
  dplyr::group_by(gender) %>%
  dplyr::summarise(cnt_gender = n()) %>% 
  dplyr::ungroup()