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Exploratory Data Analysis in R

4.9+
19 reviews
Intermediate

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

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4 Hours15 Videos54 Exercises
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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.
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In the following Tracks

Certification Available

Data Analyst with R

Go To Track
Certification Available

Associate Data Scientist in R

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  1. 1

    Exploring Categorical Data

    Free

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

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    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
For Business

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

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Tom Jeon
Andrew Bray HeadshotAndrew 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.
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*4.9
from 19 reviews
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  • Edmundo M.
    5 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.
    6 months

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

  • Anna G.
    7 months

    very well explained

  • Mayada A.
    7 months

    an interesting course well explained needs more practice

  • Nils B.
    7 months

    Great teacher!

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

David C.

"very well explained"

Anna G.

"an interesting course well explained needs more practice"

Mayada A.

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