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
Credits
You may also like