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`

.