25  Home Exercises

Instructions

The at-home exercises should be completed using Posit cloud, Home Exercise 5. Create a new .Rmd file (use File -> New File -> R Markdown.

I advise saving the new file straight away. Because you’ll submit this file as an assignment, use a standardised name: lastname_firstname5. Regularly re-save the file.

When you have finished the exercise (or part of it), ‘knit’ the file. Export the .Rmd and the html as a .zip file, and upload this to the assignment area.

Task

Load the Bellevue Almshouse dataset we used a few weeks ago, which can be found in this project files tab. Load the Tidyverse library.

Create a series of charts using ggplot2, visualising the following aspects of the data. In most cases, you’ll need to prepare the data by filtering and/or summarising. Create each one in a separate code cell.

  • A bar chart of the number of male and female individuals. First filter out any missing or ambiguous data.

  • A bar chart of the ten most frequent diseases on the x axis, and the count of each on the y axis (look back to previous weeks for this one). Filter out NA values first.

Tip

To filter out NA values, the function is.na checks if a value is NA or not. So, filter(!is.na(disease))will keep any rows where the value is not NA.

  • A bar chart of the ten most frequent female first names on the y axis and the count of each on the x axis.

  • Using geom_histogram(), chart the distribution of the values for age, for male and female gender categories.

  • Create a line chart visualising the number of individuals admitted per week, with the week on the x axis and the count on the y axis.

Tip

The function floor_date(unit = 'week') will take a full date and round it down to the start of the week (starting on Sunday, because it’s American). So for example, running floor_date on today’s date and tomorrow’s date (2025-10-13 and 2025-10-14) would give 2025-10-12 for both.

Try creating a new column with the ‘floored’ dates, and then summarise using these as groups.

  • Do the same except using months instead of weeks, and visualise male and females separately.

Final challenge:

You’ve been asked to visualise the professions in the dataset, as a bar chart. Let’s take a look at a summary of this data first (this is not live code):

library(dplyr)
library(readr)

bellevue_dataset = read_csv('bellevue_almshouse_modified.csv')

bellevue_dataset |>
  group_by(profession) |>
  summarise(n = n()) |>
  arrange(desc(n))

As you can see, there are far too many professions listed to visualise as individual bars. How would you solve this? Try to think of a solution to this - you could use another visualisation method, or re-organise/rename the data to have a pragmatic solution.

If you can’t code the solution, you can simply write a description of what you would like to do, and we can try to solve it in the class!