creating summary of subset filtered dataframe - r

Question

I am trying filtered data with value having 1 but the dataframe is already labelled . so the objective is to create a summary of filtered dataset
df <- data.frame(NY = c(1,2,1,1,2,1,1,1,2,1,1,1,2,1),
DE = c(2,1,1,1,1,2,2,1,1,1,2,2,2,1) )
df$NY<- factor(df$NY, levels =c(1,2), labels = c("unavailable","available"))
df$DE<- factor(df$DE, levels =c(1,2), labels = c("rejected","recieved"))
output is the frequency of "available" in both column
available/ total frequency in NY and DE for "recieved"
the output should be look like

Answer 1

If output in this format is useful?
library(janitor)
library(tidyverse)
df %>% pivot_longer(everything()) %>%
tabyl(name, value) %>%
adorn_percentages() %>%
adorn_pct_formatting(digits = 2)
#> name available unavailable
#> DE 50.00% 50.00%
#> NY 71.43% 28.57%
In case of revised scenario
df %>% pivot_longer(everything()) %>%
tabyl(value, name) %>%
adorn_percentages('col') %>%
filter(value %in% c('available', 'recieved')) %>%
adorn_totals('row') %>%
adorn_pct_formatting(digits = 2) %>%
tail(1)
value DE NY
Total 50.00% 71.43%

Answer 2

Here's a tidyverse approach to your problem which outputs the percentages as decimal:
library(tidyverse)
df %>% summarise(across(everything(), ~ sum(. == "available")/n()))
Output:
NY DE
1 0.7142857 0.5

Answer 3

You can try map_df() through each column.
df %>%
map_df(
~ (mean(. == "available") * 100) %>%
round() %>%
paste("%")
)
# # A tibble: 1 x 2
# NY DE
# <chr> <chr>
# 1 71 % 50 %
For different desired values, one approach is to create a named vector as below and pass it into a customised function. Note that the output is a character vector but you can change it as necessary.
values <- c(NY = "available",
DE = "received")
get_percent <- function(.data, .values) {
vars <- names(.values)
pct <- sapply(
seq_along(.values),
function(.) round(mean(.data[[ vars[.] ]] == .values[vars[.]]) * 100)
)
pct <- paste0(pct, "%")
names(pct) <- vars
pct
}
res <- get_percent(df, values)
res
# NY DE
# "29%" "43%"