- Getting started with R Language
- Variables
- Arithmetic Operators
- Matrices
- Formula
- Reading and writing strings
- String manipulation with stringi package
- Classes
- Lists
- Hashmaps
- Creating vectors
- Date and Time
- The Date class
- Date-time classes (POSIXct and POSIXlt)
- The character class
- Numeric classes and storage modes
- The logical class
- Data frames
- Split function
- Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
- Pipe operators (%>% and others)
- Linear Models (Regression)
- data.table
- Pivot and unpivot with data.table
- Bar Chart
- Base Plotting
- boxplot
- ggplot2
- Factors
- Pattern Matching and Replacement
- Run-length encoding
- Speeding up tough-to-vectorize code
- Introduction to Geographical Maps
- Set operations
- tidyverse
- Rcpp
- Random Numbers Generator
- Parallel processing
- Subsetting
- Debugging
- Installing packages
- Inspecting packages
- Creating packages with devtools
- Using pipe assignment in your own package %<>%: How to ?
- Arima Models
- Distribution Functions
- Shiny
- spatial analysis
- sqldf
- Code profiling
- Control flow structures
- Column wise operation
- JSON
- RODBC
- lubridate
- Time Series and Forecasting
- strsplit function
- Web scraping and parsing
- Generalized linear models
- Reshaping data between long and wide forms
- RMarkdown and knitr presentation
- Scope of variables
- Performing a Permutation Test
- xgboost
- R code vectorization best practices
- Missing values
- Hierarchical Linear Modeling
- *apply family of functions (functionals)
- Text mining
- ANOVA
- Raster and Image Analysis
- Survival analysis
- Fault-tolerant/resilient code
- Reproducible R
- Fourier Series and Transformations
- .Rprofile
- dplyr
- caret
- Extracting and Listing Files in Compressed Archives
- Probability Distributions with R
- R in LaTeX with knitr
- Web Crawling in R
- Creating reports with RMarkdown
- GPU-accelerated computing
- heatmap and heatmap.2
- Network analysis with the igraph package
- Functional programming
- Get user input
- Spark API (SparkR)
- Meta: Documentation Guidelines
- Input and output
- I/O for foreign tables (Excel, SAS, SPSS, Stata)
- I/O for database tables
- I/O for geographic data (shapefiles, etc.)
- I/O for raster images
- I/O for R's binary format
- Recycling
- Expression: parse + eval
- Regular Expression Syntax in R
- Regular Expressions (regex)
- Combinatorics
- Solving ODEs in R
- Feature Selection in R -- Removing Extraneous Features
- Bibliography in RMD
- Writing functions in R
- Color schemes for graphics
- Hierarchical clustering with hclust
- Random Forest Algorithm
- RESTful R Services
- Machine learning
- Using texreg to export models in a paper-ready way
- Publishing
- Implement State Machine Pattern using S4 Class
- Reshape using tidyr
- Modifying strings by substitution
- Non-standard evaluation and standard evaluation
- Randomization
- Object-Oriented Programming in R
- Coercion
- Standardize analyses by writing standalone R scripts
- Analyze tweets with R
- Natural language processing
- R Markdown Notebooks (from RStudio)
- Aggregating data frames
- Data acquisition
- R memento by examples
- Updating R version

The *R Notes for Professionals* book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. Text content is released under Creative Commons BY-SA. See credits at the end of this book whom contributed to the various chapters. Images may be copyright of their respective owners unless otherwise specified

Book created for educational purposes and is not affiliated with R group(s), company(s) nor Stack Overflow. All trademarks belong to their respective company owners

472 pages, published on January 2018

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