课程

Exploratory Data Analysis in R

中级技能水平

更新时间 2024年9月

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

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RExploratory Data Analysis4 小时15 视频54 练习3,950 经验值110K+成就声明

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课程描述

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.

先决条件

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

50 经验值

Bar chart expectations

50 经验值

Contingency table review

100 经验值

Dropping levels

100 经验值

Side-by-side bar charts

100 经验值

Bar chart interpretation

50 经验值

Counts vs. proportions

50 经验值

Conditional proportions

50 经验值

Counts vs. proportions (2)

100 经验值

Distribution of one variable

50 经验值

Marginal bar chart

100 经验值

Conditional bar chart

100 经验值

Improve pie chart

100 经验值

2

Exploring Numerical Data

In this chapter, you will learn how to graphically summarize numerical data.

Exploring numerical data

50 经验值

Faceted histogram

100 经验值

Boxplots and density plots

100 经验值

Compare distribution via plots

50 经验值

Distribution of one variable

50 经验值

Marginal and conditional histograms

100 经验值

Marginal and conditional histograms interpretation

50 经验值

Three binwidths

100 经验值

Three binwidths interpretation

50 经验值

50 经验值

Box plots for outliers

100 经验值

Plot selection

100 经验值

Visualization in higher dimensions

50 经验值

3 variable plot

100 经验值

Interpret 3 var plot

50 经验值

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

50 经验值

Choice of center measure

50 经验值

Calculate center measures

100 经验值

Measures of variability

50 经验值

Choice of spread measure

50 经验值

Calculate spread measures

100 经验值

Choose measures for center and spread

100 经验值

Shape and transformations

50 经验值

Describe the shape

50 经验值

Transformations

100 经验值

50 经验值

Identify outliers

100 经验值

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

50 经验值

Spam and num_char

100 经验值

Spam and num_char interpretation

50 经验值

Spam and !!!

100 经验值

Spam and !!! interpretation

50 经验值

50 经验值

Collapsing levels

100 经验值

Image and spam interpretation

50 经验值

Data Integrity

100 经验值

Answering questions with chains

100 经验值

50 经验值

What's in a number?

100 经验值

What's in a number interpretation

50 经验值

50 经验值

Exploratory Data Analysis in R

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