Things that are easier in Julia than Python/R etc

[pdeffebach]

@DoktorMike

clean_data <- function(df, var1, var2) {
	df %>% mutate("{{var1}}_cleaned" := {{var1}} * 100,
		          "{{var2}}_cleaned" := {{var2}} * 100)
}
clean_data(df, x1, x2)

Sorry to keep harping on this, but I still don’t think this is 1:1.

To use variables representing column names, such as v1 = "x1", you have to use across, mutate_at, or some other construct which is not the nice $var = f($var) syntax which is the selling point of dplyr. This is my understanding from the vignette and from the snippet below.

@DoktorMike

clean_data <- function(df, var1, var2) {
	df %>% mutate(across(all_of(var1), function(x) x*100, .names = "{.col}_cleaned")) %>%
		  mutate(across({{var2}}, function(x) x*100, .names = "{.col}_cleaned"))
}
clean_data(df, "x1", x2)

The first expression is the one which you can use programmatically, i.e. inputting v1 = "x1", but you have to use all_of instead of a keyword argument.

All of the examples in the programming vignette work with symbol literals.

I really like dplyr and base R for data wrangling. But I really think easy prototype-to-function switches are a highlight of DataFramesMeta.jl, and I hope it can get people to good programming habits quickly since the change in code to support programmatic use of columns is so small, just add $.