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

Exploring Categorical 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

Marginal and conditional histograms

Marginal and conditional histograms interpretation

Three binwidths

Three binwidths interpretation

Box plots

Box plots for outliers

Plot selection

Visualization in higher dimensions

3 variable plot

Interpret 3 var plot

Exploring Numerical Data

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

Outliers

Identify outliers

Numerical Summaries

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

Check-in 1

Collapsing levels

Image and spam interpretation

Data Integrity

Answering questions with chains

Check-in 2

What's in a number?

What's in a number interpretation

Conclusion

Case Study

Cars data

Comics data

Immigration data

Raw life expectancy data

Names data

Raw U.S. income data

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.

Introduction to Statistics in R

Introduction to Data Visualization with ggplot2

Learn how to use graphical and numerical techniques for exploratory data analysis while generating insightful and beautiful graphics in R.

Exploratory Data Analysis in R

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

Associate Data Scientist in R

Datenanalyst  in R

Marginal and conditional histograms interpretation

Exploratory Data Analysis in R

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