Understanding Datasets and R Fundamentals
- Reading data documentation/data dictionary
- Identifying a unit of observation
- Identifying types of variables in a dataset
- Characterizing different data objects in
R
- Interpreting functions and arguments in
R
- Identifying missing data in
R
Visualization Aesthetics
- Mapping appropriate variables onto plot aesthetics
- Adjusting plot attributes
- Recognizing and adjusting plot scales
- Contextualizing a plot with titles and labels
- Recognizing and dealing with overplotting
- Effectively selecting color palettes for plots
- Faceting plots into small multiples
- Defining and recognizing a plot’s graphical integrity (e.g. lie factor and data-to-ink ratio)
Plotting Freqencies
- Understanding how to visualize the frequency of both categorical and numeric values
- Recognizing how to visualize frequency for aggregate data
- Interpreting frequencies and distributions via a plot
- Interpreting a boxplot
GitHub
- Understanding
git vocabulary
- Identifying the steps of a collaborative GitHub workflow, including when to branch and merge changes
- Addressing common push/pull errors
- Resolving merge conflicts
Data Wrangling
- Understanding the 6 data wrangling verbs and when to appy them
- Understanding how a data structure transforms when wrangling data
- Writing pseduo-code to transform a dataset from one form to another