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()
andhead()
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()
, andmutate()
for data manipulation - Understanding logical operators for data filtering
Module 3.2–Data Grouping, Summarization, and Sorting
- Techniques for using
group_by()
andsummarize()
functions to aggregate data - Applying
arrange()
for sorting data frames and usingdesc()
for descending order - Introduction to common functions for summarizing data like
mean()
,median()
, andsd()
- Strategies for dealing with common errors and warnings in R