舉報 
會員
Hands-On Exploratory Data Analysis with R
Hands-OnExploratoryDataAnalysiswithRwillhelpyoubuildnotjustafoundationbutalsoexpertiseintheelementarywaystoanalyzedata.Youwilllearnhowtounderstandyourdataandsummarizeitsmaincharacteristics.You'llalsouncoverthestructureofyourdata,andyou'lllearngraphicalandnumericaltechniquesusingtheRlanguage.Thisbookcoverstheentireexploratorydataanalysis(EDA)process—datacollection,generatingstatistics,distribution,andinvalidatingthehypothesis.Asyouprogressthroughthebook,youwilllearnhowtosetupadataanalysisenvironmentwithtoolssuchasggplot2,knitr,andRMarkdown,usingtoolssuchasDOEScatterPlotandSML2010formultifactor,optimization,andregressiondataproblems.Bytheendofthisbook,youwillbeabletosuccessfullycarryoutapreliminaryinvestigationonanydataset,identifyhiddeninsights,andpresentyourresultsinabusinesscontext.
目錄(189章)
倒序
- 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
- 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
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 14:11:08
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