Compute row-wise counts in subsets of columns in dplyr

Question

I want to count the number of instances of some text (or factor level) row wise, across a subset of columns using dplyr.

Here's the input:

> input_df
  num_col_1 num_col_2 text_col_1 text_col_2
1         1         4        yes        yes
2         2         5         no        yes
3         3         6         no       <NA>

And here's the desired output:

> output_df
  num_col_1 num_col_2 text_col_1 text_col_2 sum_yes
1         1         4        yes        yes       2
2         2         5         no        yes       1
3         3         6         no       <NA>       0

In sum_yes we have counted the number of "yes" in that row.

I have tried two methods:

Attempted solution 1:

text_cols = c("text_col_1","text_col_2")
df = input_df %>% mutate(sum_yes = rowSums( select(text_cols) == "yes" ), na.rm = TRUE)

Errors with:

Error in mutate_impl(.data, dots) : 
  Evaluation error: no applicable method for 'select_' applied to an object of class "character".

Attempted solution 2:

text_cols = c("text_col_1","text_col_2")
df = input_df %>% select(text_cols) %>% rowsum("yes", na.rm = TRUE)

Errors with:

Error in rowsum.data.frame(., "yes", na.rm = TRUE) : 
  incorrect length for 'group'

Answer 1

1. We can use mutate and take sum of number of "yes" for each row.

library(dplyr)    
df %>%  mutate(sum_yes = rowSums(.[text_cols] == "yes"))

#   num_col_1 num_col_2 text_col_1 text_col_2 sum_yes
#*     <int>     <int> <fct>      <fct>        <int>
#1         1         4 yes        yes              2
#2         2         5 no         yes              1
#3         3         6 no         <NA>             0

Inspired from this answer.

2. rowwise with c_across :

df %>%
  rowwise() %>%
  mutate(sum_yes = sum(c_across(all_of(text_cols)) == "yes"))

3. do with rowwise

df %>%
  rowwise() %>%
  do((.) %>% as.data.frame %>% 
  mutate(sum_yes = sum(.=="yes")))

4. without do and rowwise

df %>%
 select(text_cols) %>%
 mutate(sum_yes = rowSums(. == "yes")) 

5. In base R, it is actually more simple

df$sum_yes <- rowSums(df[text_cols] == "yes")

Answer 2

We can also use reduce with map

library(tidyverse)
df %>% 
  select(text_cols) %>% 
  map(~ .x == "yes" & !is.na(.x)) %>% 
              reduce(`+`) %>%
  bind_cols(df, sum_yes = .)
#   num_col_1 num_col_2 text_col_1 text_col_2 sum_yes
#1         1         4        yes        yes       2
#2         2         5         no        yes       1
#3         3         6         no       <NA>       0