The Median Data Scientist - curly operator

The “curly operator” was added in rlang a few years ago, and I have yet to really use it much. It came in handy during a data analyst consulting gig I had during the summer of 2021, and I should use it more.

library("palmerpenguins")
library("tidyverse")

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.1 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

For instance, can I make a helper function to reduce typing out the same few lines of code that I use often?

summary_stats <- function(data_frame, grouping_variable, numerical_variable){
  data_frame |>
    filter(!is.na({{grouping_variable}})) |>
    group_by({{grouping_variable}}) |>
    summarize(min = min({{numerical_variable}}, na.rm = TRUE),
            xbar = mean({{numerical_variable}}, na.rm = TRUE),
            med = median({{numerical_variable}}, na.rm = TRUE),
            s = sd({{numerical_variable}}, na.rm = TRUE),
            max = max({{numerical_variable}}, na.rm = TRUE))
}

summary_stats(penguins, species, bill_length_mm)

# A tibble: 3 × 6
  species     min  xbar   med     s   max
  <fct>     <dbl> <dbl> <dbl> <dbl> <dbl>
1 Adelie     32.1  38.8  38.8  2.66  46  
2 Chinstrap  40.9  48.8  49.6  3.34  58  
3 Gentoo     40.9  47.5  47.3  3.08  59.6

summary_stats(penguins, island, body_mass_g)

# A tibble: 3 × 6
  island      min  xbar   med     s   max
  <fct>     <int> <dbl> <dbl> <dbl> <int>
1 Biscoe     2850 4716. 4775   783.  6300
2 Dream      2700 3713. 3688.  417.  4800
3 Torgersen  2900 3706. 3700   445.  4700