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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.

Este exercício faz parte do curso

Programming with dplyr

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Instruções do exercício

  • Set the pipeline up for calculations across each row.
  • Create a column num_missing that contains each row's number of missing values in the columns gdp_in_billions_of_usd through to the last column in imf_data.
  • Sort the results by number of missing entries in decreasing order.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

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
  ___
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