Functional programming with dplyr

Question

Looking for a more efficient / elegant way to pass multiple arguments to a group-by using non-standard evaluation in a function using dplyr. I don\'t want to use the ... ope

Answer 1

The problem is that ggplot hasn't been updated to handle quosures yet, so you've got to pass it expressions, which you can create from quosures with rlang::quo_expr:

library(tidyverse)
set.seed(47)

my_df <- data_frame(month = sample(1:12, 1000, replace = TRUE),
                    category = sample(head(letters, 3), 1000, replace = TRUE),
                    approved = as.numeric(runif(1000) < 0.5),
                    converted = approved * as.numeric(runif(1000) < 0.5))

plot_lines <- function(df, x, y, group) {
    x <- enquo(x)
    y <- enquo(y)
    group <- enquo(group)

    df %>%
        group_by(!! x, !! group) %>%
        summarise(conversion_rate = sum(converted) / sum(approved)) %>%
        ggplot(aes_(x = rlang::quo_expr(x), 
                    y = rlang::quo_expr(y), 
                    color = rlang::quo_expr(group))) + 
        geom_line()
}

my_df %>% plot_lines(month, conversion_rate, category)

However, keep in mind that ggplot will almost inevitably be updated from lazyeval to rlang, so while this interface will probably keep working, a simpler, more consistent one will probably be possible shortly.