Chapters
- 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
Credits
Thank you greatly to all the people from Stack Overflow Documentation who helped provide this content, more changes can be sent to web@petercv.com for new content to be published or updated. (See page 460 to watch all creedit over PDF file, please).
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