Aggregations with rowwise()
rowwise() can be a handy tool in your dplyr programming toolbox when combined with c_across(). Together, they allow you to perform calculations across different variables on each row. For example, this can be useful for counting missing values across each row for chosen variables.
This exercise is part of the course
Programming with dplyr
Exercise instructions
- Set the pipeline up for calculations across each row.
- Create a column
num_missingthat contains each row's number of missing values in the columnsgdp_in_billions_of_usdthrough to the last column inimf_data. - Sort the results by number of missing entries in decreasing order.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
imf_data %>%
# Specify that calculations are done across the row
___() %>%
# Count missings in gdp_in_billions_of_usd to last column
mutate(num_missing = sum(is.na(
___(___:___))
)) %>%
select(country:year, num_missing) %>%
# Arrange by descending number of missing entries
___