Count a list of specific strings in an R data frame

I have a data frame with 5 columns, but I am interested in one column "Conditions". Within conditions column, I need to find a way to count the number of specific entries in a cell. Each column cell can have one entry or more than one entry separated by ( , ) . So my data frame looks something like

S.NO                   Conditions
11            Eye Color 
12            Sound of your voice
13            Certain disease,Size of a palm,Eye Color
16            Eye Color,Hair color
17            Hair color,Height
18            Sound of your voice,Height

I want to count all the differnt entries/string at once. Total I have 35 list of different string in Conditions column and I want my Output something like this.

OUTPUT

Eye color   Sound of your voice   Certain disease    Size of a palm    Hair color   Height
    3           2                      1                   1              2          2

Solution

Since i do not know the exact structure of the data, i assumed the data to be as below

data

data <- tribble(
~Conditions, ~value,
'Eye color', '3',
'Sound of your voice', '2',
'Certain disease, Size of a palm, Eye color', '1,1,2',
'Eye color, Hair color', '2,2',
'Hair color, Height', '3,1',
'Sound of your voice, Height', '1,4'
)

for the above data we can write the below code to get the expected result

code

library(tidyverse)

Conditions <- unlist(strsplit(data$Conditions,','))
value <- unlist(strsplit(data$value,','))


df <- bind_cols(Conditions,value) %>% setNames(c('Conditions', 'value')) %>% 
mutate(across(everything(), ~trimws(.x)), value=as.numeric(value)) %>% 
  arrange(Conditions) %>% group_by(Conditions) %>% slice_head(n=1) %>% 
mutate(row=row_number()) %>% 
  pivot_wider(names_from = Conditions, values_from =value)

output

# A tibble: 1 × 7
    row `Certain disease` `Eye color` `Hair color` Height `Size of a palm` `Sound of your voice`
  <int>             <dbl>       <dbl>        <dbl>  <dbl>            <dbl>                 <dbl>
1     1                 1           3            2      1                1                     2