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RStudio for R Statistical Computing Cookbook
Andrea Cirillo 著
更新時間：2021-07-16 11:04:20
開會員，本書免費讀 >
最新章節(jié)：
Index
ThisbookistargetedatRstatisticians,datascientists,andRprogrammers.ReaderswithRexperiencewhoarelookingtotaketheplungeintostatisticalcomputingwillfindthisCookbookparticularlyindispensable.
最新章節(jié)
- Index
- Deploying your app on Amazon AWS with ramazon
- Sharing your work on RPubs
- Constructing RStudio add-ins
- Creating an interactive report with Shiny
- Changing a Shiny app UI based on user input
品牌：中圖公司
上架時間：2021-07-16 10:08:07
出版社：Packt Publishing
本書數(shù)字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發(fā)行
- Index 更新時間：2021-07-16 11:04:20
- Deploying your app on Amazon AWS with ramazon
- Sharing your work on RPubs
- Constructing RStudio add-ins
- Creating an interactive report with Shiny
- Changing a Shiny app UI based on user input
- Developing a single-file Shiny app
- Generating dynamic parametrized reports with R Markdown
- Introduction
- Chapter 8. Dynamic Reporting and Web Application Development
- Curating a blog through RStudio
- Sharing your code and plots with slides
- Writing wonderful tufte handouts with the tufte package and rmarkdown
- Writing and styling PDF documents with RStudio
- Using one markup language for all types of documents – rmarkdown
- Introduction
- Chapter 7. Developing Static Reports
- Forecasting the stock market
- Optimizing portfolio composition and maximising returns with the Portfolio Analytics package
- Tracking stock movements using the quantmod package
- Exploring time series forecasting with forecast()
- Performing time series decomposition using the stl() function
- Making a recommendation engine
- Measuring customer retention using cohort analysis in R
- Detecting fraud in e-commerce orders with Benford's law
- Performing a Twitter sentiment analysis
- Creating word clouds with the wordcloud package
- Analyzing PDF reports in a folder with the tm package
- Dealing with regular expressions
- Introduction
- Chapter 6. Domain-specific Applications
- Using GitHub with RStudio
- Comparing an alternative function's performance using the microbenchmarking package
- Evaluating your code performance using the profvis package
- Creating custom objects and methods in R using the S3 system
- Implementing parallel computation in R
- Writing modular code in RStudio
- Introduction
- Chapter 5. Power Programming with R
- Using the DiagrammeR package to produce a process flow diagram in RStudio
- Building a rotating 3D graph and exporting it as a GIF
- Creating a dynamic force network with the visNetwork package
- Producing a Sankey diagram with the networkD3 package
- Introduction
- Chapter 4. Advanced and Interactive Visualization
- Showing communities in a network with the linkcomm package
- Making use of the igraph package to draw a network
- Drawing a route on a map with ggmap
- Producing a matrix of graphs with ggplot2
- Changing axes appearance to ggplot2 plot (continous axes)
- Adding text to a ggplot2 plot at a custom location
- Using pairs.panel() to look at (visualize) correlations between variables
- Looking at your data using the plot() function
- Introduction
- Chapter 3. Basic Visualization Techniques
- Performing data filtering activities
- Detecting and removing outliers
- Substituting missing values using the mice package
- Detecting and removing missing values
- Preparing your data for analysis with the tidyr package
- Getting a sense of your data structure with R
- Introduction
- Chapter 2. Preparing for Analysis – Data Cleansing and Manipulation
- Converting file formats using the rio package
- Loading your data into R with rio packages
- Getting data from Google Analytics
- Getting data from Facebook with the Rfacebook package
- Getting data from Twitter with the twitteR package
- Accessing an API with R
- Acquiring data from the Web – web scraping tasks
- Introduction
- Chapter 1. Acquiring Data for Your Project
- Customer support
- Reader feedback
- Conventions
- Sections
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- eBooks discount offers and more
- www.PacktPub.com
- About the Reviewer
- About the Author
- Credits
- RStudio for R Statistical Computing Cookbook
- coverpage
- coverpage
- RStudio for R Statistical Computing Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- eBooks discount offers and more
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Sections
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Acquiring Data for Your Project
- Introduction
- Acquiring data from the Web – web scraping tasks
- Accessing an API with R
- Getting data from Twitter with the twitteR package
- Getting data from Facebook with the Rfacebook package
- Getting data from Google Analytics
- Loading your data into R with rio packages
- Converting file formats using the rio package
- Chapter 2. Preparing for Analysis – Data Cleansing and Manipulation
- Introduction
- Getting a sense of your data structure with R
- Preparing your data for analysis with the tidyr package
- Detecting and removing missing values
- Substituting missing values using the mice package
- Detecting and removing outliers
- Performing data filtering activities
- Chapter 3. Basic Visualization Techniques
- Introduction
- Looking at your data using the plot() function
- Using pairs.panel() to look at (visualize) correlations between variables
- Adding text to a ggplot2 plot at a custom location
- Changing axes appearance to ggplot2 plot (continous axes)
- Producing a matrix of graphs with ggplot2
- Drawing a route on a map with ggmap
- Making use of the igraph package to draw a network
- Showing communities in a network with the linkcomm package
- Chapter 4. Advanced and Interactive Visualization
- Introduction
- Producing a Sankey diagram with the networkD3 package
- Creating a dynamic force network with the visNetwork package
- Building a rotating 3D graph and exporting it as a GIF
- Using the DiagrammeR package to produce a process flow diagram in RStudio
- Chapter 5. Power Programming with R
- Introduction
- Writing modular code in RStudio
- Implementing parallel computation in R
- Creating custom objects and methods in R using the S3 system
- Evaluating your code performance using the profvis package
- Comparing an alternative function's performance using the microbenchmarking package
- Using GitHub with RStudio
- Chapter 6. Domain-specific Applications
- Introduction
- Dealing with regular expressions
- Analyzing PDF reports in a folder with the tm package
- Creating word clouds with the wordcloud package
- Performing a Twitter sentiment analysis
- Detecting fraud in e-commerce orders with Benford's law
- Measuring customer retention using cohort analysis in R
- Making a recommendation engine
- Performing time series decomposition using the stl() function
- Exploring time series forecasting with forecast()
- Tracking stock movements using the quantmod package
- Optimizing portfolio composition and maximising returns with the Portfolio Analytics package
- Forecasting the stock market
- Chapter 7. Developing Static Reports
- Introduction
- Using one markup language for all types of documents – rmarkdown
- Writing and styling PDF documents with RStudio
- Writing wonderful tufte handouts with the tufte package and rmarkdown
- Sharing your code and plots with slides
- Curating a blog through RStudio
- Chapter 8. Dynamic Reporting and Web Application Development
- Introduction
- Generating dynamic parametrized reports with R Markdown
- Developing a single-file Shiny app
- Changing a Shiny app UI based on user input
- Creating an interactive report with Shiny
- Constructing RStudio add-ins
- Sharing your work on RPubs
- Deploying your app on Amazon AWS with ramazon
- Index 更新時間：2021-07-16 11:04:20
