ETC5513: Collaborative and Reproducible Practices

Tutorial 2

Author

Michael Lydeamore

Published

20 May 2025

🎯 Objectives

  • Working on a reproducible RStudio Project
  • Working on a HTML report and example different YAML themes
  • Practice Markdown syntax
  • Practice R
  • Practice R chunk options
  • Gain experience on data wrangling using the tidyverse suite of packages
  • Producing exploratory data analysis figures using the package ggplot2
  • Learn how to add figure captions
  • Create HTML tables and learn how to add captions

Exercise 1: Hands on practice with COVID-19 Data

  1. The data for the tutorial is inside a folder called data, which is bundled with the RStudio Project you made in this week’s workshop. Find that file in the lower right pane where all your files are listed.
  2. Create a new section heading in your qmd document to read the data with the title “Reading Avian Influenza Data”
    Hint: Use #
  3. Inside this new section, create an R Code Chunk with options echo: true, warning: false, message: false called “Reading data” and insert the following code:
dat <- read_csv("data/avian_influenza_numbers.csv")
  1. Insert a new R Chunk and find out what information you can get from the command head(dat)
  2. Modify the head command to display 10 rows.
  3. Create another two R chunks and use in each of them the R functions glimpse() and str(). What information can you get from those commands? Hint: For more information on R functions, type in the R console ?glimpse().
  4. Using an R inline command, write the dimension of the dataset in a sentence.
    Hint: Have a look at ncol and nrow.
  5. Add a new subsection heading (###) with “Why is it important to know the dimension of your dataset?” and write a brief sentence with the explanation
  6. Add a new subsection heading (###) with “What are the variable names in the dataset?” and display the names of the dataset variables using R.
    Hint: ?names() in the R consolee
  7. Select two variables and use a markdown list to briefly explain what each of the variables are measuring.

Exercise 4: Data Wrangling

  1. Using the R package dplyr (which is loaded with tidyverse), and using the pipe (|>), create a new dataset called data_cleaned that only contains the following variables:
    • Month
    • Australia
    • Egypt
    • United States
  1. Inspect data_cleaned and describe using a markdown list the type of variables in this new dataset. Write the names of the variables in bold. Do you think the variable attributes are correct?
  1. Convert the variable date into a date vector using lubridate::mdy. What do you notice?
  1. Remove cases for which the data is aggregated or doesn’t have a valid month.
  1. What is the dimension of this new data set? Compare it with the dimension of cleaned_data. How many cases have we lost?
  1. Provide a table summary of the three countries using the kable() function from the kableExtra package. Give it the caption “Summary of number of cases of Avian Influenza”.
  1. Visualize the case counts using a histogram and give an explanation about the information that a histogram convey. In addition change the x label in the plot to Age and remove the y axis label.
    Hint: As a first step, do this for just one country. To do multiple countries at once, you will need to pivot_longer your dataset.
  1. Extension: Change this plot to a time series plot, with one bar per month. As an extra challenge, split this out into three separate plots - one per country.
Important

At the end of this tutorial, you should have a full QMD file that renders, including your code and the outputs from it. This means you can read it from top to bottom and remember what you did.