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Exploratory Data Analysis in R

4.9+
12 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.
  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.

    Play Chapter Now
    Exploring numerical data
    50 xp
    Faceted histogram
    100 xp
    Boxplots and density plots
    100 xp
    Compare distribution via plots
    50 xp
    Distribution of one variable
    50 xp
    Marginal and conditional histograms
    100 xp
    Marginal and conditional histograms interpretation
    50 xp
    Three binwidths
    100 xp
    Three binwidths interpretation
    50 xp
    Box plots
    50 xp
    Box plots for outliers
    100 xp
    Plot selection
    100 xp
    Visualization in higher dimensions
    50 xp
    3 variable plot
    100 xp
    Interpret 3 var plot
    50 xp
  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.

    Play Chapter Now
    Measures of center
    50 xp
    Choice of center measure
    50 xp
    Calculate center measures
    100 xp
    Measures of variability
    50 xp
    Choice of spread measure
    50 xp
    Calculate spread measures
    100 xp
    Choose measures for center and spread
    100 xp
    Shape and transformations
    50 xp
    Describe the shape
    50 xp
    Transformations
    100 xp
    Outliers
    50 xp
    Identify outliers
    100 xp
  4. 4

    Case Study

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

    Play Chapter Now
    Introducing the data
    50 xp
    Spam and num_char
    100 xp
    Spam and num_char interpretation
    50 xp
    Spam and !!!
    100 xp
    Spam and !!! interpretation
    50 xp
    Check-in 1
    50 xp
    Collapsing levels
    100 xp
    Image and spam interpretation
    50 xp
    Data Integrity
    100 xp
    Answering questions with chains
    100 xp
    Check-in 2
    50 xp
    What's in a number?
    100 xp
    What's in a number interpretation
    50 xp
    Conclusion
    50 xp

In the following tracks

Data Analyst with RData Scientist with RData Scientist Professional with R

Datasets

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

Collaborators

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Tom Jeon

Prerequisites

Introduction to Statistics in RIntroduction to Data Visualization with ggplot2
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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  • Jorge E.
    3 months

    10/10

  • Youngkee J.
    5 months

    Thank you!

  • Martina B.
    7 months

    I really like the way the instructor structures the knowledge and skill, he wanted us to acquire and also the fact, that we actually had to think for ourselves.

  • Nicolas F.
    7 months

    This course was so practical and helpful - it gave excellent strategies on how to explore your data and understand its structure! Well done, all.

  • Oladapo A.
    8 months

    Am rating it five, because it cost me nothing, but, in my fair review, the course wasn't as simplified as her sisters courses, proved quite challenging to understand its concept especially its application.

"10/10"

Jorge E.

"Thank you!"

Youngkee J.

"I really like the way the instructor structures the knowledge and skill, he wanted us to acquire and also the fact, that we actually had to think for ourselves."

Martina B.

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