HomeRExploratory Data Analysis in R

# Exploratory Data Analysis in R

4.9+
20 reviews
Intermediate

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

4 hours15 videos54 exercises
101,654 learnersStatement of Accomplishment

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Training 2 or more people?Try DataCamp For Business

## 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.

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}Training 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.

### In the following Tracks

Certification Available

#### Data Analyst with R

Go To Track
Certification Available

Go To Track
1. 1

### Exploring Categorical Data

Free

In this chapter, you will learn how to create graphical and numerical summaries of two categorical variables.

Play Chapter Now
Exploring categorical data
50 xp
Bar chart expectations
50 xp
Contingency table review
100 xp
Dropping levels
100 xp
Side-by-side bar charts
100 xp
Bar chart interpretation
50 xp
Counts vs. proportions
50 xp
Conditional proportions
50 xp
Counts vs. proportions (2)
100 xp
Distribution of one variable
50 xp
Marginal bar chart
100 xp
Conditional bar chart
100 xp
Improve pie chart
100 xp
2. 2

### Exploring Numerical Data

In this chapter, you will learn how to graphically summarize numerical data.

3. 3

### Numerical Summaries

Now that we've looked at exploring categorical and numerical data, you'll learn some useful statistics for describing distributions of data.

4. 4

### Case Study

Apply what you've learned to explore and summarize a real world dataset in this case study of email spam.

### GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

### In the following Tracks

Certification Available

#### Data Analyst with R

Go To Track
Certification Available

#### Associate Data Scientist in R

Go To Track

datasets

Cars dataComics dataImmigration dataRaw life expectancy dataNames dataRaw U.S. income data

collaborators

Andrew Bray

Assistant Professor of Statistics at Reed College

Andrew Bray is an assistant professor of statistics at Reed College. His interests are in computing, differential privacy, environmental statistics, and statistics education. He is a co-author of the infer package for tidy statistical inference.
See More

## Don’t just take our word for it

*4.9
from 20 reviews
95%
5%
0%
0%
0%
Sort by
• Crystal E.
4 days

I loved this course. The instructor knew just how to present to an audience with varied experience - if I already knew something I was not bored, but if I didn't he would give just enough info so that I could keep up.

• Edmundo M.
8 months

This course in EDA with R gives you the fundamentals on statistics measures of center and variability, as well as to how discern the shape of a distribution and determine whether it is a skew distribution. The set of data provided to look and explore the effects of scale transformation on the shape of a distribution were very interesting. The use of boxplots, density distributions, histograms and bar charts, each one with their own properties, advantages and disadvantages prepare you with a good arsenal for discovering the behavior and relations of variables in your data.

• David C.
9 months

Got to learn a lot about what you can do with R

• Anna G.
9 months

very well explained

10 months

an interesting course well explained needs more practice

"I loved this course. The instructor knew just how to present to an audience with varied experience - if I already knew something I was not bored, but if I didn't he would give just enough info so that I could keep up."

Crystal E.

"Got to learn a lot about what you can do with R"

David C.

"very well explained"

Anna G.