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Paul Love has completed

Exploratory Data Analysis in R

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4 hours
3,950 XP
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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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  1. 1

    Exploring Categorical Data


    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

In the following tracks

Associate Data ScientistAssociate Data Scientist Data Analyst Machine Learning ScientistSupervised Machine Learning


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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