Looping CI:s with infer and Purrr

Calculating CI:s

p_hat <- voter %>%
    mutate(party = fct_other(party, keep = "C")) %>%
    specify(response = party, success = "C") %>%
    calculate(stat = "prop") %>% 
    pull()

boot <- voter %>%
    mutate(party = fct_other(party, keep = "C")) %>%
    specify(response = party, success = "C") %>%
    generate(reps = 10000, type = "bootstrap") %>%
    calculate(stat = "prop") %>% 
    get_ci() %>% 
    mutate(estimate = p_hat,
           name = "C")
boot

## # A tibble: 1 x 4
##   lower_ci upper_ci estimate name 
##      <dbl>    <dbl>    <dbl> <chr>
## 1    0.064    0.097     0.08 C

and then repeat it all, with each single party. Tedious, and a lot of steps with repetition. A smarter idea, is to loop over each level in the party!

Reducing repetitiveness

First, let’s turn calculating CI:s into a function:

ci_calculation <- function(to_keep, voter){
  p_hat <- voter %>%
    mutate(party = fct_other(party, keep = to_keep)) %>%
    specify(response = party, success = to_keep) %>%
    calculate(stat = "prop") %>% 
    pull()
  
  voter%>% 
    mutate(party = fct_other(party, keep = to_keep)) %>%
    specify(response = party, success = to_keep) %>%
    generate(reps = 10000, type = "bootstrap") %>%
    calculate(stat = "prop") %>% 
    get_ci() %>% 
    mutate(estimate = p_hat,
           name = to_keep)
}

Let’s go through this piece by piece, just so we understand what has happened. First, we name the function ci_calculation, then give it two arguments: to_keep and voter, where to_keep is the party we want to get the CI:s for, and voter is the data we want to use.

The function then does everything we did before, just with variables that can change instead. Example:

ci_calculation("C", voter)

## # A tibble: 1 x 4
##   lower_ci upper_ci estimate name 
##      <dbl>    <dbl>    <dbl> <chr>
## 1    0.063    0.097     0.08 C

This did everything we did before, but with one row of code instead of 15, so instead of writing 15 lines of code for each of the 9 parties, we could write just 9 lines with only one difference.

But we can do even better! Using purrr we can reduce these 9 rows to just 1 row. What’s purrr, you may ask? It’s a way to apply a function in an iterative manner, over an index, like this:

complete_CI <- map_df(unique(voter$party), ci_calculation, voter)
complete_CI

## # A tibble: 9 x 4
##   lower_ci upper_ci estimate name          
##      <dbl>    <dbl>    <dbl> <chr>         
## 1    0.255    0.31     0.282 S             
## 2    0.085    0.123    0.104 V             
## 3    0.023    0.046    0.034 MP            
## 4    0.063    0.097    0.08  C             
## 5    0.026    0.049    0.037 L             
## 6    0.045    0.074    0.059 KD            
## 7    0.186    0.236    0.211 M             
## 8    0.156    0.202    0.179 SD            
## 9    0.007    0.022    0.014 Other/no reply

Here we use the function map_df which takes a vector, and uses that vector as the first argument in a function. We give it unique(voter$party), which find’s each unique value in the column party in the dataframe voter, like this:

unique(voter$party)

## [1] "S"              "V"              "MP"             "C"             
## [5] "L"              "KD"             "M"              "SD"            
## [9] "Other/no reply"

The second argument is the function we wrote, ci_calculation, and the third argument is the data we want to use, voter. Note that the function ci_calculation drops it’s parenthesis, and the third argument instead get’s fed into the function.

The resulting table still looks a bit hard to read, so let’s prettify it a bit using the package gt!

Much better!