最新章節(jié)

• Loading R packages
• Internal requirements – R packages
• macOS High Sierra
• Ubuntu 17.10
• Dependencies for the Rcpp package and the .Fortran() function
• macOS High Sierra

• Loading R packages 更新時(shí)間：2021-07-02 21:31:24
• Internal requirements – R packages
• macOS High Sierra
• Ubuntu 17.10
• Dependencies for the Rcpp package and the .Fortran() function
• macOS High Sierra
• Ubuntu 17.10
• Dependencies for the rgl and rgdal R packages
• Setting up user/password in both Linux and macOS
• macOS High Sierra
• Ubuntu 17.10
• Dependencies for the RMySQL R package
• External requirements – software outside of R
• Required Packages
• Summary
• Sharing your newly created application
• Adding custom CSS styling
• Adding HTML to your web application
• Adding static images
• Other topics of interest
• Styling our application with themes
• Adding interactivity with a secondary zoom-in graph
• Adding a simple moving average graph
• Adding a summary table with shared data
• Setting up dynamic input panels
• Setting up dynamic options in a drop-down
• Setting up static user inputs
• Introducing interactivity with user input
• Inserting a dynamic data table
• Introducing sections with panels
• Setting up a two-column distribution
• Designing our high-level application structure
• The input output and rendering functions
• The building blocks for reactivity in Shiny
• How is functional reactivity handled within Shiny?
• What is functional reactive programming and why is it useful?
• Introducing the Shiny application architecture and reactivity
• Required packages
• Adding Interactivity with Dashboards
• Summary
• Specialized R distributions
• Flexibility and power with cloud computing
• Using specialized packages for performance
• Improving our data and memory management
• Using memoization or cache layers
• Just-in-time (JIT) compilation of R code
• Making R code a bit faster with byte code compilation
• Preallocating memory to avoid duplication
• Other topics of interest to enhance performance
• Looking back at what we have achieved
• Using a modern approach with C++
• Using an old-school approach with Fortran
• Using C++ and Fortran to accelerate calculations
• Practical parallelization with R
• How deep does the parallelization rabbit hole go?
• Using parallelization to divide and conquer
• Avoiding sending data structures with overheads
• Using R's way of iterating efficiently
• If you can avoid iterating at all
• Moving checks out of iterative processes
• Removing unnecessary logic
• Vectorizing as much as possible
• Using the simple data structure for the job
• Easily achieving high benefit - cost improvements
• Benchmarking automatically with microbenchmark()
• Benchmarking manually with system.time()
• Profiling fundamentals with Rprof()
• Measuring by profiling and benchmarking
• Single-threaded processes
• Memory-bound processes
• Interpreted dynamic typings
• Object immutability
• Understanding why R can be slow
• Our first (very inefficient) attempt at an SMA
• Simulating the time-series
• Calculating simple moving averages inefficiently
• How fast is fast enough?
• Just how much impact can algorithm selection have?
• Starting by using good algorithms
• Required packages
• Implementing an Efficient Simple Moving Average
• Summary
• Some advice when working with object-oriented systems
• Activating our system with two simple functions
• Saving our initial user data into the system
• Helping ourselves with a centralized settings file
• Finally introducing users with S3 classes
• Implementing our wallet requesters
• Developing our wallets infrastructure
• Developing our exchanges infrastructure
• Creating a very simple requester to isolate API calls
• Retrieving live data for markets and wallets with R6 classes
• Encapsulating multiple databases with a storage layer
• Easily allowing new database integration with a factory
• Implementing a database-like storage system with CSV files
• Communicating available behavior with a database interface
• Implementing our storage layer with R6 classes
• Implementing cryptocurrency assets using S4 classes
• Starting simple with timestamps using S3 classes
• The architecture behind our cryptocurrencies system
• Finalizers
• Active bindings
• Inheritance
• Encapsulation and mutability
• Public methods and polymorphism
• Classes constructors and composition
• The R6 object model
• Inheritance
• Encapsulation and mutability
• Public methods and polymorphism
• Classes constructors and composition
• The S4 object model
• Inheritance
• Encapsulation and mutability
• Public methods and polymorphism
• Classes constructors and composition
• The S3 object model
• The second source of confusion – generic functions
• The first source of confusion – various object models
• Introducing three object models in R – S3 S4 and R6
• Interfaces factories and patterns in general
• Public and private methods
• Classes and constructors
• Hierarchies
• Polymorphism
• Encapsulation
• Important concepts behind object-oriented languages
• The purpose of object-oriented programming
• A brief introduction to object-oriented programming
• The cryptocurrencies example
• This chapter's required packages
• Object-Oriented System to Track Cryptocurrencies
• Summary
• Building our presentation with R Markdown
• Developing graphs and analysis as we normally would
• Producing the final output with knitr
• Caching
• Global chunk options
• Chunk options
• Graphs
• Tables
• Code chunks
• Extending Markdown with R Markdown
• Mathematics
• Code
• Quotes
• Images
• Links
• Tables
• Lists
• Header Level 4
• Header Level 3
• Header Level 2
• Header Level 1
• Headers
• Text
• A gentle introduction to Markdown
• The basic tools for an automation pipeline
• Reproducibility as a benefit of literate programming
• Literate programming as a content creation methodology
• Why invest in automation?
• Required packages
• Developing Automatic Presentations
• Summary
• Retrieving text data from Twitter
• Testing our predictive model with unseen data
• Digging deeper with sentiment analysis
• Extending our analysis with cosine similarity
• Reducing dimensionality with SVD
• Adding flexibility with N-grams
• Improving our results with TF-IDF
• Computing predictive accuracy and confusion matrices
• Improving speed with parallelization
• Training our first predictive model
• Training models with cross validation
• Document feature matrices
• Building the corpus with tokenization and data cleaning
• Preparing training and testing data
• What is text analysis and how does it work?
• This chapter's required packages
• Understanding Reviews with Text Analysis
• Summary
• High-tech-looking interactive globe
• Maps you can navigate and zoom-in to
• Navigating geographical data with interactive maps
• Looking at geographical data with static maps
• Looking at dynamic data with time-series
• Exploring with interactive 3D scatter plots
• Developing our own graph type – radar graphs
• Client birth dates gender and ratings
• Pricing and profitability by protein source and continent
• Scatter plots with joint and marginal distributions
• Graphing disaggregated data with boxplots
• Graphing top performers with bar graphs
• Adding a third dimension with colors
• Starting with simple applications for bar graphs
• Building blocks for reusable high-quality graphs
• Extending our data with profit metrics
• Required packages
• Communicating Sales with Visualizations
• Summary
• Working with relational databases
• Simulating the client messages data
• Simulating the client data
• Putting everything together
• Simulating strings for complex identifiers
• Simulating numbers under shared restrictions
• Simulating dates within a range
• Simulating categorical values using factors
• Simulating numeric data according to distribution assumptions
• Simulating the sales data
• The too-much-repeated-data problem
• The too-much-empty-space problem
• Potential pitfalls
• Simplifying assumptions
• The basic variables
• Designing our data tables
• Required packages
• Simulating Sales Data and Working with Databases
• Summary
• Predicting votes from wards with unknown data
• Generating model combinations
• Programatically finding the best model
• Measuring accuracy with score functions
• Checking no collinearity with correlations
• Checking homoscedasticity with residual plots
• Checking normality with histograms and quantile-quantile plots
• Checking linearity with scatter plots
• Checking model assumptions
• Predicting votes with linear models
• Training and testing datasets
• Setting up the data
• Required packages
• Predicting Votes with Linear Models
• Summary
• Programming by visualizing the big picture
• Understanding the fundamentals of high-quality code
• Planning before programming
• Putting it all together into high-quality code
• Building new variables with principal components
• Creating a new dataset with what we've learned
• Understanding interactions with correlations
• Getting a better look with detailed scatter plots
• Using matrix scatter plots for a quick overview
• Visualizing variable distributions
• Getting intuition with graphs and correlations
• Summarizing the data into a data frame
• Cleaning and setting up the data
• The Brexit votes example
• This chapter's required packages
• Understanding Votes with Descriptive Statistics
• Summary
• The examples in this book
• While loops
• For loops
• If… else conditionals
• Complex logic with control structures
• Coercion
• Operators are functions
• Functions as arguments
• Optional arguments
• Divide and conquer with functions
• Data frames
• Lists
• Matrices
• Factors
• Vectors
• Logicals
• Characters
• Special values
• Numerics
• Working with data types and data structures
• Tracking state with symbols and variables
• How to use this book
• Executing R code in non-interactive sessions
• The efficient write-execute loop
• The send to console functionality
• Pick an IDE or a powerful editor
• Tools to work efficiently with R
• The interpreter and the console
• Comparing R with other software
• What R is not good for
• R is free as in freedom and as in free beer
• R is a flexible programming language
• R is a high quality statistical computing system
• The inspiration for R – the S language
• What R is and what it isn't
• Introduction to R
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• 版權(quán)信息
• 封面

• 封面
• 版權(quán)信息
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• Introduction to R
• What R is and what it isn't
• The inspiration for R – the S language
• R is a high quality statistical computing system
• R is a flexible programming language
• R is free as in freedom and as in free beer
• What R is not good for
• Comparing R with other software
• The interpreter and the console
• Tools to work efficiently with R
• Pick an IDE or a powerful editor
• The send to console functionality
• The efficient write-execute loop
• Executing R code in non-interactive sessions
• How to use this book
• Tracking state with symbols and variables
• Working with data types and data structures
• Numerics
• Special values
• Characters
• Logicals
• Vectors
• Factors
• Matrices
• Lists
• Data frames
• Divide and conquer with functions
• Optional arguments
• Functions as arguments
• Operators are functions
• Coercion
• Complex logic with control structures
• If… else conditionals
• For loops
• While loops
• The examples in this book
• Summary
• Understanding Votes with Descriptive Statistics
• This chapter's required packages
• The Brexit votes example
• Cleaning and setting up the data
• Summarizing the data into a data frame
• Getting intuition with graphs and correlations
• Visualizing variable distributions
• Using matrix scatter plots for a quick overview
• Getting a better look with detailed scatter plots
• Understanding interactions with correlations
• Creating a new dataset with what we've learned
• Building new variables with principal components
• Putting it all together into high-quality code
• Planning before programming
• Understanding the fundamentals of high-quality code
• Programming by visualizing the big picture
• Summary
• Predicting Votes with Linear Models
• Required packages
• Setting up the data
• Training and testing datasets
• Predicting votes with linear models
• Checking model assumptions
• Checking linearity with scatter plots
• Checking normality with histograms and quantile-quantile plots
• Checking homoscedasticity with residual plots
• Checking no collinearity with correlations
• Measuring accuracy with score functions
• Programatically finding the best model
• Generating model combinations
• Predicting votes from wards with unknown data
• Summary
• Simulating Sales Data and Working with Databases
• Required packages
• Designing our data tables
• The basic variables
• Simplifying assumptions
• Potential pitfalls
• The too-much-empty-space problem
• The too-much-repeated-data problem
• Simulating the sales data
• Simulating numeric data according to distribution assumptions
• Simulating categorical values using factors
• Simulating dates within a range
• Simulating numbers under shared restrictions
• Simulating strings for complex identifiers
• Putting everything together
• Simulating the client data
• Simulating the client messages data
• Working with relational databases
• Summary
• Communicating Sales with Visualizations
• Required packages
• Extending our data with profit metrics
• Building blocks for reusable high-quality graphs
• Starting with simple applications for bar graphs
• Adding a third dimension with colors
• Graphing top performers with bar graphs
• Graphing disaggregated data with boxplots
• Scatter plots with joint and marginal distributions
• Pricing and profitability by protein source and continent
• Client birth dates gender and ratings
• Developing our own graph type – radar graphs
• Exploring with interactive 3D scatter plots
• Looking at dynamic data with time-series
• Looking at geographical data with static maps
• Navigating geographical data with interactive maps
• Maps you can navigate and zoom-in to
• High-tech-looking interactive globe
• Summary
• Understanding Reviews with Text Analysis
• This chapter's required packages
• What is text analysis and how does it work?
• Preparing training and testing data
• Building the corpus with tokenization and data cleaning
• Document feature matrices
• Training models with cross validation
• Training our first predictive model
• Improving speed with parallelization
• Computing predictive accuracy and confusion matrices
• Improving our results with TF-IDF
• Adding flexibility with N-grams
• Reducing dimensionality with SVD
• Extending our analysis with cosine similarity
• Digging deeper with sentiment analysis
• Testing our predictive model with unseen data
• Retrieving text data from Twitter
• Summary
• Developing Automatic Presentations
• Required packages
• Why invest in automation?
• Literate programming as a content creation methodology
• Reproducibility as a benefit of literate programming
• The basic tools for an automation pipeline
• A gentle introduction to Markdown
• Text
• Headers
• Header Level 1
• Header Level 2
• Header Level 3
• Header Level 4
• Lists
• Tables
• Links
• Images
• Quotes
• Code
• Mathematics
• Extending Markdown with R Markdown
• Code chunks
• Tables
• Graphs
• Chunk options
• Global chunk options
• Caching
• Producing the final output with knitr
• Developing graphs and analysis as we normally would
• Building our presentation with R Markdown
• Summary
• Object-Oriented System to Track Cryptocurrencies
• This chapter's required packages
• The cryptocurrencies example
• A brief introduction to object-oriented programming
• The purpose of object-oriented programming
• Important concepts behind object-oriented languages
• Encapsulation
• Polymorphism
• Hierarchies
• Classes and constructors
• Public and private methods
• Interfaces factories and patterns in general
• Introducing three object models in R – S3 S4 and R6
• The first source of confusion – various object models
• The second source of confusion – generic functions
• The S3 object model
• Classes constructors and composition
• Public methods and polymorphism
• Encapsulation and mutability
• Inheritance
• The S4 object model
• Classes constructors and composition
• Public methods and polymorphism
• Encapsulation and mutability
• Inheritance
• The R6 object model
• Classes constructors and composition
• Public methods and polymorphism
• Encapsulation and mutability
• Inheritance
• Active bindings
• Finalizers
• The architecture behind our cryptocurrencies system
• Starting simple with timestamps using S3 classes
• Implementing cryptocurrency assets using S4 classes
• Implementing our storage layer with R6 classes
• Communicating available behavior with a database interface
• Implementing a database-like storage system with CSV files
• Easily allowing new database integration with a factory
• Encapsulating multiple databases with a storage layer
• Retrieving live data for markets and wallets with R6 classes
• Creating a very simple requester to isolate API calls
• Developing our exchanges infrastructure
• Developing our wallets infrastructure
• Implementing our wallet requesters
• Finally introducing users with S3 classes
• Helping ourselves with a centralized settings file
• Saving our initial user data into the system
• Activating our system with two simple functions
• Some advice when working with object-oriented systems
• Summary
• Implementing an Efficient Simple Moving Average
• Required packages
• Starting by using good algorithms
• Just how much impact can algorithm selection have?
• How fast is fast enough?
• Calculating simple moving averages inefficiently
• Simulating the time-series
• Our first (very inefficient) attempt at an SMA
• Understanding why R can be slow
• Object immutability
• Interpreted dynamic typings
• Memory-bound processes
• Single-threaded processes
• Measuring by profiling and benchmarking
• Profiling fundamentals with Rprof()
• Benchmarking manually with system.time()
• Benchmarking automatically with microbenchmark()
• Easily achieving high benefit - cost improvements
• Using the simple data structure for the job
• Vectorizing as much as possible
• Removing unnecessary logic
• Moving checks out of iterative processes
• If you can avoid iterating at all
• Using R's way of iterating efficiently
• Avoiding sending data structures with overheads
• Using parallelization to divide and conquer
• How deep does the parallelization rabbit hole go?
• Practical parallelization with R
• Using C++ and Fortran to accelerate calculations
• Using an old-school approach with Fortran
• Using a modern approach with C++
• Looking back at what we have achieved
• Other topics of interest to enhance performance
• Preallocating memory to avoid duplication
• Making R code a bit faster with byte code compilation
• Just-in-time (JIT) compilation of R code
• Using memoization or cache layers
• Improving our data and memory management
• Using specialized packages for performance
• Flexibility and power with cloud computing
• Specialized R distributions
• Summary
• Adding Interactivity with Dashboards
• Required packages
• Introducing the Shiny application architecture and reactivity
• What is functional reactive programming and why is it useful?
• How is functional reactivity handled within Shiny?
• The building blocks for reactivity in Shiny
• The input output and rendering functions
• Designing our high-level application structure
• Setting up a two-column distribution
• Introducing sections with panels
• Inserting a dynamic data table
• Introducing interactivity with user input
• Setting up static user inputs
• Setting up dynamic options in a drop-down
• Setting up dynamic input panels
• Adding a summary table with shared data
• Adding a simple moving average graph
• Adding interactivity with a secondary zoom-in graph
• Styling our application with themes
• Other topics of interest
• Adding static images
• Adding HTML to your web application
• Adding custom CSS styling
• Sharing your newly created application
• Summary
• Required Packages
• External requirements – software outside of R
• Dependencies for the RMySQL R package
• Ubuntu 17.10
• macOS High Sierra
• Setting up user/password in both Linux and macOS
• Dependencies for the rgl and rgdal R packages
• Ubuntu 17.10
• macOS High Sierra
• Dependencies for the Rcpp package and the .Fortran() function
• Ubuntu 17.10
• macOS High Sierra
• Internal requirements – R packages
• Loading R packages 更新時(shí)間：2021-07-02 21:31:24