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Exercise

Biggest jumps in a name

Previously, you added a ratio column to describe the ratio of the frequency of a baby name between consecutive years to describe the changes in the popularity of a name. Now, you'll look at a subset of that data, called babynames_ratios_filtered, to look further into the names that experienced the biggest jumps in popularity in consecutive years. 

babynames_ratios_filtered <- babynames_fraction %>%
                     arrange(name, year) %>%
                     group_by(name) %>%
                     mutate(ratio = fraction / lag(fraction)) %>%
                     filter(fraction >= 0.00001)

Instructions

100 XP
  • From each name in the data, keep the observation (the year) with the largest ratio; note the data is already grouped by name.
  • Sort the ratio column in descending order.
  • Filter the babynames_ratios_filtered data further by filtering the fraction column to only display results greater than or equal to 0.001.