In jr-packages/jrTidyverse: Jumping Rivers: Getting to Grips with the Tidyverse

Some of the questions in this practical might not exactly be things you would do in the real world, they are just intended to get you comfortable using some of the stringr functions you have seen so far.

Question 1

We'll start by loading the necessary packages and data sets

library("tidyverse")
data(names, package = "jrTidyverse")

Here we have a data set containing 800 people with the names: "Abigail", "Alexander", "Ava", "Benjamin", "Charlotte", "Emily", "Emma", "Ethan", "Harper", "Isabella", "Jacob", "James", "Liam", "Mason", "Mia", "Michael", "Noah", "Olivia", "Sophia" and "William".

Using various functions from stringr and count() from dplyr, work out the frequency of each name. Which name occcurs the most?

names %>%
  mutate(name = str_trim(name)) %>%
  mutate(name = str_to_title(name)) %>%
  count(name) %>%
  arrange(n)

Question 2: Movies

We'll start off by loading the data

data(movies, package = "jrTidyverse")

1. How many movie titles contain the word "The" ?

length(str_subset(movies$title, pattern = "The"))

1. Do any titles contain your name?

# not for me!
str_subset("movies$title", pattern = "Theo")

1. Mutate a new column that is the count of how many characters each movie title contains, call this new column title_length

movies = movies %>%
  mutate(title_length = str_count(title))

1. How many characters does the longest title contain? Hint: Use summarise() and max()

movies %>%
  summarise(max(title_length))

1. Which film has the longest title?

movies %>%
  filter(title_length == max(title_length)) %>%
  select(title)

1. Use ggplot2 to produce a histogram of the title lengths

movies %>%
  ggplot(aes(x = title_length)) +
  geom_histogram()

1. Produce a scatter plot of title length against rating, do you think longer title effect the rating?

movies %>%
  ggplot(aes(x = title_length, y = rating)) +
  geom_point()

jr-packages/jrTidyverse documentation built on Oct. 11, 2020, 9:03 p.m.