Counting string mentions on reddit by period (dplyr)

I have reddit data and I am trying to count the mentions of different private firms before and after the introduction of a minimum wage policy in Oregon. All companies mentioned by sub-reddit users were coded under the "directed_to_whom" variable, which refers to the name of the private firm that mentioned in a given reddit post.

I would like to then create a bar graph that shows the share of mentions per company relative to the minimum wage policy using a string variable coded "pre" if the company mention is before the policy and "post" otherwise.

My data are structured as follows:

dput(df[1:6,c(2, 3)]) # Print data example with specific columns

data example:

structure(list(directed_to_whom = c("nike", "nike", 
"amazon", "walmart", "walmart", "walmart"), treatment_announcement = c("pre", 
"pre", "pre", "pre", "post", "post")), class = c("tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -6L)) -> df

I then calculated the share of company mentions as follows:

df2  <- df %>%
  select(directed_to_whom, treatment_announcement) %>%
  group_by(treatment_announcement) %>%
  summarise(total_posts = n(),
            entity_count = sum(directed_to_whom == "nike"),
            entity_count = sum(directed_to_whom == "amazon"),
            entity_count = sum(directed_to_whom == "walmart"),
            entity_share = entity_count/total_posts * 100) 

The code works without errors but it isn't capturing the indicator I am interested in, which is the share of each company mentions pre & post the minimum wage policy, rather than the average share of All companies pre and post policy.

Here is the data example of what this produces:

dput(df2[1:2,c(1,2, 3,4)])

output:

structure(list(treatment_announcement = c("post", "pre"), total_posts = c(1013L, 
179L), entity_count = c(152L, 26L), entity_share = c(15.004935834156, 
14.5251396648045)), row.names = c(NA, -2L), class = c("tbl_df", 
"tbl", "data.frame"))

Is it possible to instead compute the share of say "walmart" mentions before the policy and be able to compare it to "amazon" mentions also pre-policy.

Here is the desired df output:

"directed_to_whom"      "treatment_announcement" "entity_share"
walmart                  pre                          45%
amazon                    pre                          10%
nike                      pre                           45%
walmart                  post                          60%
amazon                    post                          15%
nike                      post                           25%

I created a dataframe with more rows to showcase the solution; see below.

library(dplyr)

df %>% 
 group_by(directed_to_whom, treatment_announcement) %>% 
 summarise(val_count = n()) %>% 
 group_by(treatment_announcement) %>% 
 summarise(directed_to_whom = directed_to_whom,
                              entity_share = prop.table(val_count),
                              .groups = "drop")

#> # A tibble: 6 x 3
#>   treatment_announcement directed_to_whom entity_share
#>   <chr>                  <chr>                   <dbl>
#> 1 post                   amazon                   0.25
#> 2 post                   nike                     0.25
#> 3 post                   walmart                  0.5 
#> 4 pre                    amazon                   0.25
#> 5 pre                    nike                     0.5 
#> 6 pre                    walmart                  0.25

You can also plot it like this:

ggplot(df_out) +
  geom_bar(aes(x = directed_to_whom, y = entity_share, fill = treatment_announcement), 
           stat = "identity", show.legend = FALSE) + 
  facet_wrap(~treatment_announcement, ncol = 1) +
  theme_bw()

Data:

df <- structure(list(directed_to_whom = c("nike", "nike", "amazon", "walmart", 
                                          "walmart", "walmart","nike", "amazon"), 
                     treatment_announcement = c("pre", "pre", "pre", "pre", 
                                                "post", "post", "post", "post")), 
                class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -8L))

#> 1 nike             pre                   
#> 2 nike             pre                   
#> 3 amazon           pre                   
#> 4 walmart          pre                   
#> 5 walmart          post                  
#> 6 walmart          post                  
#> 7 nike             post                  
#> 8 amazon           post