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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.

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Programming with dplyr

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Instrucciones del ejercicio

  • 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.

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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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