Hands-On Exploratory Data Analysis with R电子书

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Preface

Who this book is for

What this book covers

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Code in Action

Conventions used

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Section 1: Setting Up Data Analysis Environment

Setting Up Our Data Analysis Environment

Technical requirements

The benefits of EDA across vertical markets

Manipulating data

Examining, cleaning, and filtering data

Visualizing data

Creating data reports

Installing the required R packages and tools

Installing R packages from the Terminal

Installing R packages from inside RStudio

Summary

Importing Diverse Datasets

Technical requirements

Converting rectangular data into R with the readr R package

readr read functions

read_tsv method

read_delim method

read_fwf method

read_table method

read_log method

Reading in Excel data with the readxl R package

Reading in JSON data with the jsonlite R package

Loading the jsonlite package

Getting data into R from web APIs using the httr R package

Getting data into R by scraping the web using the rvest package

Importing data into R from relational databases using the DBI R package

Summary

Examining, Cleaning, and Filtering

Technical requirements

About the dataset

Reshaping and tidying up erroneous data

The gather() function

The unite() function

The separate() function

The spread() function

Manipulating and mutating data

The mutate() function

The group_by() function

The summarize() function

The arrange() function

The glimpse() function

Selecting and filtering data

The select() function

The filter() function

Cleaning and manipulating time series data

Summary

Visualizing Data Graphically with ggplot2

Technical requirements

Advanced graphics grammar of ggplot2

Data

Layers

Scales

The coordinate system

Faceting

Theme

Installing ggplot2

Scatter plots

Histogram plots

Density plots

Probability plots

dnorm()

pnorm()

rnorm()

Box plots

Residual plots

Summary

Creating Aesthetically Pleasing Reports with knitr and R Markdown

Technical requirements

Installing R Markdown

Working with R Markdown

Reproducible data analysis reports with knitr

Exporting and customizing reports

Summary

Section 2: Univariate, Time Series, and Multivariate Data

Univariate and Control Datasets

Technical requirements

Reading the dataset

Cleaning and tidying up the data

Understanding the structure of the data

Hypothesis tests

Statistical hypothesis in R

The t-test in R

Directional hypothesis in R

Correlation in R

Tietjen-Moore test

Parsimonious models

Probability plots

The Shapiro-Wilk test

Summary

Time Series Datasets

Technical requirements

Introducing and reading the dataset

Cleaning the dataset

Mapping and understanding structure

Hypothesis test

t-test in R

Directional hypothesis in R

Grubbs' test and checking outliers

Parsimonious models

Bartlett's test

Data visualization

Autocorrelation plots

Spectrum plots

Phase plots

Summary

Multivariate Datasets

Technical requirements

Introducing and reading a dataset

Cleaning the data

Mapping and understanding the structure

Hypothesis test

t-test in R

Directional hypothesis in R

Parsimonious model

Levene's test

Data visualization

Principal Component Regression

Partial Least Squares Regression

Summary

Section 3: Multifactor, Optimization, and Regression Data Problems

Multi-Factor Datasets

Technical requirements

Introducing and reading the dataset

Cleaning the dataset

Mapping and understanding data structure

Hypothesis test

t-test in R

Directional hypothesis in R

Grubbs test and checking outliers

Parsimonious model

Multi-factor variance analysis

Exploring graphically the dataset

Summary

Handling Optimization and Regression Data Problems

Technical requirements

Introducing and reading a dataset

Cleaning the dataset

Mapping and understanding the data structure

Hypothesis test

t-test in R

Directional hypothesis in R

Grubbs' test and checking outliers

Parsimonious model

Exploration using graphics

Summary

Section 4: Conclusions

Next Steps

Technical requirements

What to learn next

Why R?

Environmental setup

R syntax

R packages

Understanding the help system

The data analysis workflow

Data import

Manipulating data

Visualizing data

Reporting results

Standout as R wizard

Building a data science portfolio

Datasets in R

Getting help with exploratory data analysis

Summary

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