across() function appreciation

Wrangle and change multiple columns with the across() function from dplyr.


So quick! So simple!

Mutate all numeric variables and calculate the country mean across all years in the dataset.

Then use .names = argument to give a new column variable name!

  group_by(continent) %>% 
  mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%  
  mutate(across(where(is.numeric), mean, na.rm = TRUE,
                   .names = "avg_{col}")) %>% 
  mutate(across(where(is.numeric), log,
                   .names = "ln_{col}")) %>% 
  ggplot(aes(x = ln_avg_gdpPercap, 
             y = ln_avg_lifeExp, 
             group = continent)) + 
  geom_point() +  geom_label(aes(label = continent, 
                                 fill = continent), 
                             color = "#f0f0f0", 
                             size = 8) -> my_plot 

And optional code if you want to make the graph a bit prettier.

First dark hex colors:

my_palette <- c("570211","7e3110","004540","032c4d","360825")

add_hashtag <- function(my_vec){
  hash_vec <-  paste0('#', my_vec)

pal_hash <- add_hashtag(my_palette)

And some labelling and adjusting the look of the plot

my_plot + ggtitle("Scatterplot of average GDP and life expectancy, 1952-2007") +
  xlab("Average GDP per capita (logged)") +
  ylab("Average life expectancy (logged)") + 
  ggthemes::theme_fivethirtyeight() + xlim(7.5, 10.1) + 
    scale_fill_manual(values = pal_hash) +
  theme(legend.position = "none",
        plot.title = element_text(size = 25),
        text = element_text(family = "Arial")) 

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