Skip to main content

Course

Exploratory Data Analysis in R

IntermediateSkill Level

4.7+

Updated 09/2024

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

Start Course for Free

RExploratory Data Analysis4 hr15 videos54 Exercises3,950 XP110K+Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

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.

Prerequisites

Introduction to Statistics in R Introduction to Data Visualization with ggplot2

1

Exploring Categorical Data

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

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

In this chapter, you will learn how to graphically summarize 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

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

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

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

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

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.7

from 1,134 reviews

81%

16%

2%

0%

0%

Sort by

Joshua

1 hour ago

Jerome

11 hours ago

Faruk

yesterday

Moira

2 days ago

‪IBRAHIM

2 days ago

Nestor

4 days ago

Joshua

Jerome

Faruk

FAQs

What R packages does this course use?

You will use dplyr for data manipulation and ggplot2 for data visualization, both part of the tidyverse ecosystem.

What will I learn to do by the end of this course?

You will be able to create graphical and numerical summaries, identify unusual observations, and uncover relationships between variables in your data.

What prior R experience do I need?

This is an intermediate course. You should have completed introductory R courses and be familiar with dplyr, ggplot2, and basic statistics in R.

Does this course include a case study?

Yes. The final chapter is a case study where you apply all the exploratory data analysis techniques you learned in the previous chapters.

How is the course organized?

It has 4 chapters covering categorical data, numerical data, numerical summaries, and a final case study, with 54 exercises total.

Join over 19 million learners and start Exploratory Data Analysis in R today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Grow your data skills with DataCamp for Mobile

Make progress on the go with our mobile courses and daily 5-minute coding challenges.