Explain ungroup() in dplyr

Question

If I'm working with a dataset and I want to group the data (i.e. by country), compute a summary statistic (mean()) and then ungroup() the data.frame to have a dataset with the original dimensions (country-year) and a new column that lists the mean for each country (repeated over n years), how would I do that with dplyr? The ungroup() function doesn't return a data.frame with the original dimensions:

gapminder %>%
    group_by(country) %>%
    summarize(mn = mean(pop)) %>%
    ungroup() # returns data.frame with nrows == length(unique(gapminder$country))

Answer 1

ungroup() is useful if you want to do something like

gapminder %>%
group_by(country) %>%
mutate(mn = pop/mean(pop)) %>%
ungroup() 

where you want to do some sort of transformation that uses an entire group's statistics. In the above example, mn is the ratio of a population to the group's average population. When it is ungrouped, any further mutations called on it would not use the grouping for aggregate statistics.

summarize automatically reduces the dimensions, and there's no way to get that back. Perhaps you wanted to do

gapminder %>%
group_by(country) %>%
mutate(mn = mean(pop)) %>%
ungroup() 

Which creates mn as the mean for each group, replicated for each row within that group.

Answer 2

The summarize() reduced the number of rows. If you didn't want to change the number of rows, then use mutate() rather than summarize().