R Notes for Professionals book

Content list
About
Chapter 1: Getting started with R Language
Section 1.1: Installing R
Section 1.2: Hello World!
Section 1.3: Getting Help
Section 1.4: Interactive mode and R scripts
Chapter 2: Variables
Section 2.1: Variables, data structures and basic Operations
Chapter 3: Arithmetic Operators
Section 3.1: Range and addition
Section 3.2: Addition and subtraction
Chapter 4: Matrices
Section 4.1: Creating matrices
Chapter 5: Formula
Section 5.1: The basics of formula
Chapter 6: Reading and writing strings
Section 6.1: Printing and displaying strings
Section 6.2: Capture output of operating system command
Section 6.3: Reading from or writing to a file connection
Chapter 7: String manipulation with stringi package
Section 7.1: Count pattern inside string
Section 7.2: Duplicating strings
Section 7.3: Paste vectors
Section 7.4: Splitting text by some fixed pattern
Chapter 8: Classes
Section 8.1: Inspect classes
Section 8.2: Vectors and lists
Section 8.3: Vectors
Chapter 9: Lists
Section 9.1: Introduction to lists
Section 9.2: Quick Introduction to Lists
Section 9.3: Serialization: using lists to pass information
Chapter 10: Hashmaps
Section 10.1: Environments as hash maps
Section 10.2: package:hash
Section 10.3: package:listenv
Chapter 11: Creating vectors
Section 11.1: Vectors from build in constants: Sequences of letters & month names
Section 11.2: Creating named vectors
Section 11.3: Sequence of numbers
Section 11.4: seq()
Section 11.5: Vectors
Section 11.6: Expanding a vector with the rep() function
Chapter 12: Date and Time
Section 12.1: Current Date and Time
Section 12.2: Go to the End of the Month
Section 12.3: Go to First Day of the Month
Section 12.4: Move a date a number of months consistently by months
Chapter 13: The Date class
Section 13.1: Formatting Dates
Section 13.2: Parsing Strings into Date Objects
Section 13.3: Dates
Chapter 14: Date-time classes (POSIXct and POSIXlt)
Section 14.1: Formatting and printing date-time objects
Section 14.2: Date-time arithmetic
Section 14.3: Parsing strings into date-time objects
Chapter 15: The character class
Section 15.1: Coercion
Chapter 16: Numeric classes and storage modes
Section 16.1: Numeric
Chapter 17: The logical class
Section 17.1: Logical operators
Section 17.2: Coercion
Section 17.3: Interpretation of NAs
Chapter 18: Data frames
Section 18.1: Create an empty data.frame
Section 18.2: Subsetting rows and columns from a data frame
Section 18.3: Convenience functions to manipulate data.frames
Section 18.4: Introduction
Section 18.5: Convert all columns of a data.frame to character class
Chapter 19: Split function
Section 19.1: Using split in the split-apply-combine paradigm
Section 19.2: Basic usage of split
Chapter 20: Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
Section 20.1: Importing .csv files
Section 20.2: Importing with data.table
Section 20.3: Exporting .csv files
Section 20.4: Import multiple csv files
Section 20.5: Importing fixed-width files
Chapter 21: Pipe operators (%>% and others)
Section 21.1: Basic use and chaining
Section 21.2: Functional sequences
Section 21.3: Assignment with %<>%
Section 21.4: Exposing contents with %$%
Section 21.5: Creating side eects with %T>%
Section 21.6: Using the pipe with dplyr and ggplot2
Chapter 22: Linear Models (Regression)
Section 22.1: Linear regression on the mtcars dataset
Section 22.2: Using the 'predict' function
Section 22.3: Weighting
Section 22.4: Checking for nonlinearity with polynomial regression
Section 22.5: Plotting The Regression (base)
Section 22.6: Quality assessment
Chapter 23: data.table
Section 23.1: Creating a data.table
Section 23.2: Special symbols in data.table
Section 23.3: Adding and modifying columns
Section 23.4: Writing code compatible with both data.frame and data.table
Section 23.5: Setting keys in data.table
Chapter 24: Pivot and unpivot with data.table
Section 24.1: Pivot and unpivot tabular data with data.table - I
Section 24.2: Pivot and unpivot tabular data with data.table - II
Chapter 25: Bar Chart
Section 25.1: barplot() function
Chapter 26: Base Plotting
Section 26.1: Density plot
Section 26.2: Combining Plots
Section 26.3: Getting Started with R_Plots
Section 26.4: Basic Plot
Section 26.5: Histograms
Section 26.6: Matplot
Section 26.7: Empirical Cumulative Distribution Function
Chapter 27: boxplot
Section 27.1: Create a box-and-whisker plot with boxplot() {graphics}
Section 27.2: Additional boxplot style parameters
Chapter 28: ggplot2
Section 28.1: Displaying multiple plots
Section 28.2: Prepare your data for plotting
Section 28.3: Add horizontal and vertical lines to plot
Section 28.4: Scatter Plots
Section 28.5: Produce basic plots with qplot
Section 28.6: Vertical and Horizontal Bar Chart
Section 28.7: Violin plot
Chapter 29: Factors
Section 29.1: Consolidating Factor Levels with a List
Section 29.2: Basic creation of factors
Section 29.3: Changing and reordering factors
Section 29.4: Rebuilding factors from zero
Chapter 30: Pattern Matching and Replacement
Section 30.1: Finding Matches
Section 30.2: Single and Global match
Section 30.3: Making substitutions
Section 30.4: Find matches in big data sets
Chapter 31: Run-length encoding
Section 31.1: Run-length Encoding with `rle`
Section 31.2: Identifying and grouping by runs in base R
Section 31.3: Run-length encoding to compress and decompress vectors
Section 31.4: Identifying and grouping by runs in data.table
Chapter 32: Speeding up tough-to-vectorize code
Section 32.1: Speeding tough-to-vectorize for loops with Rcpp
Section 32.2: Speeding tough-to-vectorize for loops by byte compiling
Chapter 33: Introduction to Geographical Maps
Section 33.1: Basic map-making with map() from the package maps
Section 33.2: 50 State Maps and Advanced Choropleths with Google Viz
Section 33.3: Interactive plotly maps
Section 33.4: Making Dynamic HTML Maps with Leaflet
Section 33.5: Dynamic Leaflet maps in Shiny applications
Chapter 34: Set operations
Section 34.1: Set operators for pairs of vectors
Section 34.2: Cartesian or "cross" products of vectors
Section 34.3: Set membership for vectors
Section 34.4: Make unique / drop duplicates / select distinct elements from a vector
Section 34.5: Measuring set overlaps / Venn diagrams for vectors
Chapter 35: tidyverse
Section 35.1: tidyverse: an overview
Section 35.2: Creating tbl_df’s
Chapter 36: Rcpp
Section 36.1: Extending Rcpp with Plugins
Section 36.2: Inline Code Compile
Section 36.3: Rcpp Attributes
Section 36.4: Specifying Additional Build Dependencies
Chapter 37: Random Numbers Generator
Section 37.1: Random permutations
Section 37.2: Generating random numbers using various density functions
Section 37.3: Random number generator's reproducibility
Chapter 38: Parallel processing
Section 38.1: Parallel processing with parallel package
Section 38.2: Parallel processing with foreach package
Section 38.3: Random Number Generation
Section 38.4: mcparallelDo
Chapter 39: Subsetting
Section 39.1: Data frames
Section 39.2: Atomic vectors
Section 39.3: Matrices
Section 39.4: Lists
Section 39.5: Vector indexing
Section 39.6: Other objects
Section 39.7: Elementwise Matrix Operations
Chapter 40: Debugging
Section 40.1: Using debug
Section 40.2: Using browser
Chapter 41: Installing packages
Section 41.1: Install packages from GitHub
Section 41.2: Download and install packages from repositories
Section 41.3: Install package from local source
Section 41.4: Install local development version of a package
Section 41.5: Using a CLI package manager -- basic pacman usage
Chapter 42: Inspecting packages
Section 42.1: View Package Version
Section 42.2: View Loaded packages in Current Session
Section 42.3: View package information
Section 42.4: View package's built-in data sets
Section 42.5: List a package's exported functions
Chapter 43: Creating packages with devtools
Section 43.1: Creating and distributing packages
Section 43.2: Creating vignettes
Chapter 44: Using pipe assignment in your own package %<>%: How to ?
Section 44.1: Putting the pipe in a utility-functions file
Chapter 45: Arima Models
Section 45.1: Modeling an AR1 Process with Arima
Chapter 46: Distribution Functions
Section 46.1: Normal distribution
Section 46.2: Binomial Distribution
Chapter 47: Shiny
Section 47.1: Create an app
Section 47.2: Checkbox Group
Section 47.3: Radio Button
Section 47.4: Debugging
Section 47.5: Select box
Section 47.6: Launch a Shiny app
Section 47.7: Control widgets
Chapter 48: spatial analysis
Section 48.1: Create spatial points from XY data set
Section 48.2: Importing a shape file (.shp)
Chapter 49: sqldf
Section 49.1: Basic Usage Examples
Chapter 50: Code profiling
Section 50.1: Benchmarking using microbenchmark
Section 50.2: proc.time()
Section 50.3: Microbenchmark
Section 50.4: System.time
Section 50.5: Line Profiling
Chapter 51: Control flow structures
Section 51.1: Optimal Construction of a For Loop
Section 51.2: Basic For Loop Construction
Section 51.3: The Other Looping Constructs: while and repeat
Chapter 52: Column wise operation
Section 52.1: sum of each column
Chapter 53: JSON
Section 53.1: JSON to / from R objects
Chapter 54: RODBC
Section 54.1: Connecting to Excel Files via RODBC
Section 54.2: SQL Server Management Database connection to get individual table
Section 54.3: Connecting to relational databases
Chapter 55: lubridate
Section 55.1: Parsing dates and datetimes from strings with lubridate
Section 55.2: Dierence between period and duration
Section 55.3: Instants
Section 55.4: Intervals, Durations and Periods
Section 55.5: Manipulating date and time in lubridate
Section 55.6: Time Zones
Section 55.7: Parsing date and time in lubridate
Section 55.8: Rounding dates
Chapter 56: Time Series and Forecasting
Section 56.1: Creating a ts object
Section 56.2: Exploratory Data Analysis with time-series data
Chapter 57: strsplit function
Section 57.1: Introduction
Chapter 58: Web scraping and parsing
Section 58.1: Basic scraping with rvest
Section 58.2: Using rvest when login is required
Chapter 59: Generalized linear models
Section 59.1: Logistic regression on Titanic dataset
Chapter 60: Reshaping data between long and wide forms
Section 60.1: Reshaping data
Section 60.2: The reshape function
Chapter 61: RMarkdown and knitr presentation
Section 61.1: Adding a footer to an ioslides presentation
Section 61.2: Rstudio example
Chapter 62: Scope of variables
Section 62.1: Environments and Functions
Section 62.2: Function Exit
Section 62.3: Sub functions
Section 62.4: Global Assignment
Section 62.5: Explicit Assignment of Environments and Variables
Chapter 63: Performing a Permutation Test
Section 63.1: A fairly general function
Chapter 64: xgboost
Section 64.1: Cross Validation and Tuning with xgboost
Chapter 65: R code vectorization best practices
Section 65.1: By row operations
Chapter 66: Missing values
Section 66.1: Examining missing data
Section 66.2: Reading and writing data with NA values
Section 66.3: Using NAs of dierent classes
Section 66.4: TRUE/FALSE and/or NA
Chapter 67: Hierarchical Linear Modeling
Section 67.1: basic model fitting
Chapter 68: *apply family of functions (functionals)
Section 68.1: Using built-in functionals
Section 68.2: Combining multiple `data.frames` (`lapply`, `mapply`)
Section 68.3: Bulk File Loading
Section 68.4: Using user-defined functionals
Chapter 69: Text mining
Section 69.1: Scraping Data to build N-gram Word Clouds
Chapter 70: ANOVA
Section 70.1: Basic usage of aov()
Section 70.2: Basic usage of Anova()
Chapter 71: Raster and Image Analysis
Section 71.1: Calculating GLCM Texture
Section 71.2: Mathematical Morphologies
Chapter 72: Survival analysis
Section 72.1: Random Forest Survival Analysis with randomForestSRC
Section 72.2: Introduction - basic fitting and plotting of parametric survival models with the survival package
Section 72.3: Kaplan Meier estimates of survival curves and risk set tables with survminer
Chapter 73: Fault-tolerant/resilient code
Section 73.1: Using tryCatch()
Chapter 74: Reproducible R
Section 74.1: Data reproducibility
Section 74.2: Package reproducibility
Chapter 75: Fourier Series and Transformations
Section 75.1: Fourier Series
Chapter 76: .Rprofile
Section 76.1: .Rprofile - the first chunk of code executed
Section 76.2: .Rprofile example
Chapter 77: dplyr
Section 77.1: dplyr's single table verbs
Section 77.2: Aggregating with %>% (pipe) operator
Section 77.3: Subset Observation (Rows)
Section 77.4: Examples of NSE and string variables in dpylr
Chapter 78: caret
Section 78.1: Preprocessing
Chapter 79: Extracting and Listing Files in Compressed Archives
Section 79.1: Extracting files from a .zip archive
Chapter 80: Probability Distributions with R
Section 80.1: PDF and PMF for dierent distributions in R
Chapter 81: R in LaTeX with knitr
Section 81.1: R in LaTeX with Knitr and Code Externalization
Section 81.2: R in LaTeX with Knitr and Inline Code Chunks
Section 81.3: R in LaTex with Knitr and Internal Code Chunks
Chapter 82: Web Crawling in R
Section 82.1: Standard scraping approach using the RCurl package
Chapter 83: Creating reports with RMarkdown
Section 83.1: Including bibliographies
Section 83.2: Including LaTeX Preample Commands
Section 83.3: Printing tables
Section 83.4: Basic R-markdown document structure
Chapter 84: GPU-accelerated computing
Section 84.1: gpuR gpuMatrix objects
Section 84.2: gpuR vclMatrix objects
Chapter 85: heatmap and heatmap.2
Section 85.1: Examples from the ocial documentation
Section 85.2: Tuning parameters in heatmap.2
Chapter 86: Network analysis with the igraph package
Section 86.1: Simple Directed and Non-directed Network Graphing
Chapter 87: Functional programming
Section 87.1: Built-in Higher Order Functions
Chapter 88: Get user input
Section 88.1: User input in R
Chapter 89: Spark API (SparkR)
Section 89.1: Setup Spark context
Section 89.2: Cache data
Section 89.3: Create RDDs (Resilient Distributed Datasets)
Chapter 90: Meta: Documentation Guidelines
Section 90.1: Style
Section 90.2: Making good examples
Chapter 91: Input and output
Section 91.1: Reading and writing data frames
Chapter 92: I/O for foreign tables (Excel, SAS, SPSS, Stata)
Section 92.1: Importing data with rio
Section 92.2: Read and write Stata, SPSS and SAS files
Section 92.3: Importing Excel files
Section 92.4: Import or Export of Feather file
Chapter 93: I/O for database tables
Section 93.1: Reading Data from MySQL Databases
Section 93.2: Reading Data from MongoDB Databases
Chapter 94: I/O for geographic data (shapefiles, etc.)
Section 94.1: Import and Export Shapefiles
Chapter 95: I/O for raster images
Section 95.1: Load a multilayer raster
Chapter 96: I/O for R's binary format
Section 96.1: Rds and RData (Rda) files
Section 96.2: Enviromments
Chapter 97: Recycling
Section 97.1: Recycling use in subsetting
Chapter 98: Expression: parse + eval
Section 98.1: Execute code in string format
Chapter 99: Regular Expression Syntax in R
Section 99.1: Use `grep` to find a string in a character vector
Chapter 100: Regular Expressions (regex)
Section 100.1: Dierences between Perl and POSIX regex
Section 100.2: Validate a date in a "YYYYMMDD" format
Section 100.3: Escaping characters in R regex patterns
Section 100.4: Validate US States postal abbreviations
Section 100.5: Validate US phone numbers
Chapter 101: Combinatorics
Section 101.1: Enumerating combinations of a specified length
Section 101.2: Counting combinations of a specified length
Chapter 102: Solving ODEs in R
Section 102.1: The Lorenz model
Section 102.2: Lotka-Volterra or: Prey vs. predator
Section 102.3: ODEs in compiled languages - definition in R
Section 102.4: ODEs in compiled languages - definition in C
Section 102.5: ODEs in compiled languages - definition in fortran
Section 102.6: ODEs in compiled languages - a benchmark test
Chapter 103: Feature Selection in R -- Removing Extraneous Features
Section 103.1: Removing features with zero or near-zero variance
Section 103.2: Removing features with high numbers of NA
Section 103.3: Removing closely correlated features
Chapter 104: Bibliography in RMD
Section 104.1: Specifying a bibliography and cite authors
Section 104.2: Inline references
Section 104.3: Citation styles
Chapter 105: Writing functions in R
Section 105.1: Anonymous functions
Section 105.2: RStudio code snippets
Section 105.3: Named functions
Chapter 106: Color schemes for graphics
Section 106.1: viridis - print and colorblind friendly palettes
Section 106.2: A handy function to glimse a vector of colors
Section 106.3: colorspace - click&drag interface for colors
Section 106.4: Colorblind-friendly palettes
Section 106.5: RColorBrewer
Section 106.6: basic R color functions
Chapter 107: Hierarchical clustering with hclust
Section 107.1: Example 1 - Basic use of hclust, display of dendrogram, plot clusters
Section 107.2: Example 2 - hclust and outliers
Chapter 108: Random Forest Algorithm
Section 108.1: Basic examples - Classification and Regression
Chapter 109: RESTful R Services
Section 109.1: opencpu Apps
Chapter 110: Machine learning
Section 110.1: Creating a Random Forest model
Chapter 111: Using texreg to export models in a paper-ready way
Section 111.1: Printing linear regression results
Chapter 112: Publishing
Section 112.1: Formatting tables
Section 112.2: Formatting entire documents
Chapter 113: Implement State Machine Pattern using S4 Class
Section 113.1: Parsing Lines using State Machine
Chapter 114: Reshape using tidyr
Section 114.1: Reshape from long to wide format with spread()
Section 114.2: Reshape from wide to long format with gather()
Chapter 115: Modifying strings by substitution
Section 115.1: Rearrange character strings using capture groups
Section 115.2: Eliminate duplicated consecutive elements
Chapter 116: Non-standard evaluation and standard evaluation
Section 116.1: Examples with standard dplyr verbs
Chapter 117: Randomization
Section 117.1: Random draws and permutations
Section 117.2: Setting the seed
Chapter 118: Object-Oriented Programming in R
Section 118.1: S3
Chapter 119: Coercion
Section 119.1: Implicit Coercion
Chapter 120: Standardize analyses by writing standalone R scripts
Section 120.1: The basic structure of standalone R program and how to call it
Section 120.2: Using littler to execute R scripts
Chapter 121: Analyze tweets with R
Section 121.1: Download Tweets
Section 121.2: Get text of tweets
Chapter 122: Natural language processing
Section 122.1: Create a term frequency matrix
Chapter 123: R Markdown Notebooks (from RStudio)
Section 123.1: Creating a Notebook
Section 123.2: Inserting Chunks
Section 123.3: Executing Chunk Code
Section 123.4: Execution Progress
Section 123.5: Preview Output
Section 123.6: Saving and Sharing
Chapter 124: Aggregating data frames
Section 124.1: Aggregating with data.table
Section 124.2: Aggregating with base R
Section 124.3: Aggregating with dplyr
Chapter 125: Data acquisition
Section 125.1: Built-in datasets
Section 125.2: Packages to access open databases
Section 125.3: Packages to access restricted data
Section 125.4: Datasets within packages
Chapter 126: R memento by examples
Section 126.1: Plotting (using plot)
Section 126.2: Commonly used functions
Section 126.3: Data types
Chapter 127: Updating R version
Section 127.1: Installing from R Website
Section 127.2: Updating from within R using installr Package
Section 127.3: Deciding on the old packages
Section 127.4: Updating Packages
Section 127.5: Check R Version
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