Exercise

Radar chart

Remember Plutchik's wheel of emotion? The NRC lexicon has the 8 emotions corresponding to the first ring of the wheel. Previously you created a comparison.cloud() according to the 8 primary emotions. Now you will create a radar chart similar to the wheel in this exercise.

A radarchart is a two-dimensional representation of multidimensional data (at least 3). In this case the tally of the different emotions for a book are represented in the chart. Using a radar chart, you can review all 8 emotions simultaneously.

As before we've loaded the "nrc" lexicon as nrc and moby_huck which is a combined tidy version of both Moby Dick and Huck Finn.

In this exercise you once again use a negated grepl() to remove "positive|negative" emotional classes from the chart. As a refresher here is an example:

object <- tibble %>%
  filter(!grepl("positive|negative", column_name))

This exercise reintroduces pivot_wider() which rearranges the tallied emotional words. As a refresher consider this raw data called datacamp.

people food like
Nicole bread 78
Nicole salad 66
Ted bread 99
Ted salad 21

If you applied pivot_wider() as in datacamp %>% pivot_wider(names_from = people, values_from = like) the data looks like this.

food Nicole Ted
bread 78 99
salad 66 21

Instructions 1/2

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  • Review rows 945 to 950 of moby_huck using square brackets.
  • Create scores by piping moby_huck to [inner_join()].(https://www.rdocumentation.org/packages/dplyr/topics/join) with nrc. Specify the by parameter to be "term = "word".
  • Overwrite scores by piping it to filter(). Within filter() negate grepl() using pattern "positive|negative" applied to the sentiment column.
  • Apply another%>% using pivot_wider() along with names_from = book, values_from = n, values_fill = 0.