Bang-bang!!

Programming with dplyr

Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

Unpacking curly-curly

grouped_mean_gov_revenue <- function(group_col) {
  joined %>% 
    group_by({{ group_col }}) %>% 
    summarize(mean_gov_revenue = mean(gov_revenue_as_perc_gdp))
}
grouped_mean_gov_revenue(group_col = continent)

{{ }}

Forces function argument
Defuses function argument

Bang-bang-enquo forces and defuses

!!enquo() is the same as {{ }}

grouped_mean_gov_revenue <- function(group_col) {
  joined %>% 
    group_by(!!enquo(group_col)) %>% 
    summarize(
      mean_gov_revenue = mean(
        gov_revenue_as_perc_gdp
      )
   )
}
grouped_mean_gov_revenue(group_col = continent)

# A tibble: 17 × 2
    year mean_gov_revenue
   <dbl>            <dbl>
 1  2000             39.4
 2  2001             30.8
...
16  2015             34.7
17  2016             32.3

Multiple function arguments

grouped_mean_gov_revenue <- function(group_col) {
  joined %>% 
    group_by(!!enquo(group_col)) %>% 
    summarize(mean_gov_revenue = mean(gov_revenue_as_perc_gdp))
}

grouped_mean_for_column <- function(group_col, col_to_mean) {

  joined %>% 
    group_by(!!enquo(group_col)) %>%

    summarize(mean(!!enquo(col_to_mean)))
}

Calling grouped_mean_for_column()

grouped_mean_for_column(group_col = continent,
                        col_to_mean = perc_cvd_crd_70)

# A tibble: 5 × 2
  continent `mean(perc_cvd_crd_70)`
  <fct>                       <dbl>
1 Africa                      23.8 
2 Americas                    14.7 
3 Asia                        19.8 
4 Europe                      16.3 
5 Oceania                      9.91

Let's practice!

Programming with dplyr