更新时间：2021-06-24 14:11:08
coverpage
Title Page
Copyright and Credits
Hands-On Exploratory Data Analysis with R
Dedication
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Code in Action
Conventions used
Get in touch
Reviews
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
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
Examining Cleaning and Filtering
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
Visualizing Data Graphically with ggplot2
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
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Installing R Markdown
Working with R Markdown
Reproducible data analysis reports with knitr
Exporting and customizing reports
Section 2: Univariate Time Series and Multivariate Data
Univariate and Control Datasets
Reading the dataset
