Module 4.1

Data Viz Review

Data Viz Review

Module 1.1–Introduction to R and RStudio

  • Introduction to the RStudio interface and script execution
  • Practices for running and modifying R code
  • Use of the pipe operator (|>) for chaining operations
  • Installation and loading of essential packages like tidyverse
  • Basic data manipulation functions like read.csv() and head()

Module 1.2–Introduction to Quarto

  • Understanding Quarto’s integration with R and its benefits for reproducibility
  • Detailed instruction on Markdown for text formatting, including headers, lists, and links
  • Embedding R code within Quarto using code chunks
  • Setting chunk options for better control over code execution and output

Module 2.1–Data Visualization with ggplot2 (Bar Charts and Histograms)

  • Overview of Leland Wilkinson’s grammar of graphics as implemented in ggplot2
  • Introduction to ggplot() function and its parameters
  • Understanding aesthetic mappings using aes() function
  • Utilization of geom_bar() for bar chart creation
  • Constructing histograms using geom_histogram()
  • Customizing charts with labels, colors, and themes

Module 2.2–Advanced Data Visualization (Line Charts and Scatter Plots)

  • Techniques for creating line charts using geom_line()
  • Building scatter plots with geom_point() and applying color scales and themes
  • Adding layers and annotations
  • Introduction to interactive graphs with plotly
  • Considerations for color blindness and visual accessibility in data visualization

Module 3.1–Data Handling and API Integration

  • Techniques for importing and cleaning data in R
  • Discussion on principles of tidy data and its importance
  • Methods for retrieving data from APIs
  • Utilizing filter(), select(), and mutate() for data manipulation
  • Understanding logical operators for data filtering

Module 3.2–Data Grouping, Summarization, and Sorting

  • Techniques for using group_by() and summarize() functions to aggregate data
  • Applying arrange() for sorting data frames and using desc() for descending order
  • Introduction to common functions for summarizing data like mean(), median(), and sd()
  • Strategies for dealing with common errors and warnings in R