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Course

Exploratory Data Analysis in R

Intermediate

4.7+

Updated 04/2025

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

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RExploratory Data Analysis4 hours15 videos54 Exercises3,950 XP108,703Statement of Accomplishment

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Course Description

When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

Prerequisites

Introduction to Statistics in R Introduction to Data Visualization with ggplot2

1

Exploring Categorical Data

Exploring categorical data

Bar chart expectations

Contingency table review

Dropping levels

Side-by-side bar charts

Bar chart interpretation

Counts vs. proportions

Conditional proportions

Counts vs. proportions (2)

Distribution of one variable

Marginal bar chart

Conditional bar chart

Improve pie chart

2

Exploring Numerical Data

Exploring numerical data

Faceted histogram

Boxplots and density plots

Compare distribution via plots

Distribution of one variable

Marginal and conditional histograms

Marginal and conditional histograms interpretation

Three binwidths

Three binwidths interpretation

Box plots for outliers

Plot selection

Visualization in higher dimensions

3 variable plot

Interpret 3 var plot

3

Numerical Summaries

Measures of center

Choice of center measure

Calculate center measures

Measures of variability

Choice of spread measure

Calculate spread measures

Choose measures for center and spread

Shape and transformations

Describe the shape

Transformations

Identify outliers

4

Case Study

Introducing the data

Spam and num_char

Spam and num_char interpretation

Spam and !!!

Spam and !!! interpretation

Collapsing levels

Image and spam interpretation

Data Integrity

Answering questions with chains

What's in a number?

What's in a number interpretation

Exploratory Data Analysis in R

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