rbtl - Data Visualisation

Lars Schöbitz

Global Health Engineering - ETH Zurich

2022-05-05

Today

  1. Research Project Report
  2. Solving coding problems
  3. Working collaboratively with git
    • Live Coding Exercise
  4. Exploratory Data Analysis with ggplot2
    • Live Coding Exercise
    • Programming Exercise
  5. Homework Assignment 11

Learning Objectives

  1. Learners can describe the four main aesthetic mappings that can be used to visualise data using the ggplot2 R Package
  2. Learners can control the colour scaling applied to a plot using colour as an aesthetic mapping
  3. Learners can compare three different geoms and their use case
  4. Learners can apply a theme to control font types and sizes within a plot

Research project report

GitHub issues / Slack

[TODO: List of aggregated questions and answers]

Solving coding problems

Tipps for search engines

  • Use actionable verbs that describe what you want to do
  • Be specific
  • Add R to the search query
  • Add the name of the R package name to the search query
  • Scroll through the top 5 results (don’t just pick the first)

Example: “How to remove a legend from a plot in R ggplot2”

Stack Overflow

What is it?

  • The biggest support network for (coding) problems
  • Can be initimidating at first
  • Upvote system

Workflow

  • First, briefly read the question that was posted
  • Then, read the answer marked as “correct”
  • Then, read one or two more answers with high votes
  • Then, check out the “Linked” posts
  • Always give credit for the solution

Give credit

Give credit

Give credit

ggplot(data = global_waste_data_kg_year,
       mapping = aes(x = income_id, 
                     y = capita_kg_year,
                     color = income_id)) +
  ## Remove legend ref: https://stackoverflow.com/a/35622358/6816220
  theme(legend.position = "none")

Other sources for help

  • RStudio Community Forum: https://community.rstudio.com/
  • Our rbtl Slack channel
  • Documentation websites: https://dplyr.tidyverse.org/
  • Twitter community: #rstats

Minimal reproducible example (reprex)

  • Needed when asking questions online
  • We will practice this in another class
  • Good support information: https://www.tidyverse.org/help/#reprex

.

Working collaboratively with git

pull first, and push often

Live Coding Exercise

  1. Open the repo for your team project report on RStudio Cloud
  2. Open the file: 01-introduction.qmd
  3. Use your Sticky Notes to let me know when you are ready.

Git help

You can find the merge conflict workflow documented in our git help document for the course:

rbtl-fs22/git-help

The best online resource for working with git is:

Happy Git and GitHub for the useR by Jenny Bryan

Exploratory Data Analysis with ggplot2

R Package ggplot2

  • ggplot2 is tidyverse’s data visualization package
  • gg in ggplot2 stands for Grammar of Graphics
  • Inspired by the book Grammar of Graphics by Leland Wilkinson
  • Documentation: https://ggplot2.tidyverse.org/
  • Book: https://ggplot2-book.org

Code structure

  • ggplot() is the main function in ggplot2
  • Plots are constructed in layers
  • Structure of the code for plots can be summarized as
ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], 
                     y = [y-variable])) +
   geom_xxx() +
   other options

Code structure

ggplot()

Code structure

ggplot(data = gapminder_yr_2007)

Code structure

ggplot(data = gapminder_yr_2007,
       mapping = aes())

Code structure

ggplot(data = gapminder_yr_2007,
       mapping = aes(x = continent,
                     y = lifeExp))

Code structure

ggplot(data = gapminder_yr_2007,
       mapping = aes(x = continent,
                     y = lifeExp)) +
  geom_boxplot()

Code structure

ggplot(data = gapminder_yr_2007,
       mapping = aes(x = continent,
                     y = lifeExp)) +
  geom_boxplot() +
  theme_minimal()

Code structure

ggplot(data = gapminder_yr_2007,
       mapping = aes(x = continent,
                     y = lifeExp)) +
  geom_boxplot() +
  theme_minimal(base_size = 14)

Break

15:00

Live Coding Exercise - Goal

Live Coding Exercise

ae-11-data-science-lifecycle

  1. Head over to the GitHub Organisation for the course.
  2. Find the repo for week 11 that has your GitHub username.
  3. Clone the repo with your username to the RStudio Cloud.
  4. Open the file: ae-11a-data-visualisation.qmd
  5. Use your Sticky Notes to let me know when you are ready.

Break

10:00

Live Coding Exercise - Result

Visualising numerical data

Types of variables

numerical

discrete variables

  • non-negative
  • whole numbers
  • e.g. number of students, roll of a dice

continuous variables

  • infinite number of values
  • also dates and times
  • e.g. length, weight, size

non-numerical

categorical variables

  • finite number of values
  • distinct groups (e.g. EU countries, continents)
  • ordinal if levels have natural ordering (e.g. week days, school grades)

data-to-viz.com

Homework Assignment

Submission

  • All details in assignment week 11
  • Due: Wednesday, 12th May at 23:59 (2 points)

Evaluation

  • 5 mins
  • anonymous
  • after each lecture

https://forms.gle/HbCPbG9Yb7iDJ2jW6

Programming

ae-11-data-visualisation

  1. Open the file: ae-11b-data-visualisation.qmd
  2. Work through the exercises
  3. Use your sticky notes to indicate if you need support
30:00

Thanks! 🌻

Slides created via revealjs and Quarto: https://quarto.org/docs/presentations/revealjs/ Access slides as PDF on GitHub

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