1. Exploring Data in R Introduction to R, Part II
Anna Blackstock
Statistician, Biostatistics and Information Management Office (BIMO)
NCEZID/DFWED

2. Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a term used to describe the process of exploring general dataset characteristics.
Three realms of EDA*:
Transformation
Visualization
Modelling
*From the book “R for Data Science” (

3. Exploratory Data Analysis
There is not a specific formula for doing EDA “the right way”
Goals of EDA:
Checking data quality
Any mistakes?
Any unusual or unexpected values?
Understanding distributions
Investigating relationships between variables

4. EDA Today
Will cover a few basic functions that you can use to get started with data exploration
Will NOT provide in-depth instruction on EDA tools (especially data transformation—stay tuned for other courses)

5. Exploring Data
When you first read your data into R, you will want to do a few data checks.
Functions you may consider:

6. EDA for Categorical Variables
Tables
Univariate and multi-way tables
Plots
Bar plots, stacked bar plots
Statistical tests
Chi-square tests

7. EDA for Continuous Variables
Summary statistics
Mean, variance, percentiles . . .
Plots
Scatterplots, Box plots
Statistical tests
T-tests

8. EDA for Categorical + Continuous Variables
Summary statistics
Mean, variance, etc. by category
Plots
Box plots, bubble plots
Statistical tests
T-tests

9. Where to next? Take an online course
Keep an eye out for future CDC courses
See the book “R for Data Science” by Garrett Grolemund and Hadley Wickham:
Find an online guide to EDA