Learning R

Download free course Learning R, pdf file on 619 pages by Stack Overflow Community.
R is a programming language and free software environment for statistical computing and graphics. It is an unofficial and free R ebook created for educational purposes. All the content is extracted from Stack Overflow Documentation, which is written by many hardworking individuals at Stack Overflow.

Table of contents

  • Getting started with R Language
  • *apply family of functions (functionals)
  • .Rprofile
  • Aggregating data frames
  • Analyze tweets with R
  • Arima Models
  • Arithmetic Operators
  • Bar Chart
  • Base Plotting
  • Bibliography in RMD
  • boxplot
  • caret
  • Classes
  • Cleaning data
  • Code profiling
  • Coercion
  • Color schemes for graphics
  • Column wise operation
  • Combinatorics
  • Control flow structures
  • Creating packages with devtools
  • Creating reports with RMarkdown
  • Creating vectors
  • Data acquisition
  • Data frames
  • data.table
  • Date and Time
  • Date-time classes (POSIXct and POSIXlt)
  • Debugging
  • Distribution Functions
  • dplyr
  • Expression: parse + eval
  • Extracting and Listing Files in Compressed Archives
  • Factors
  • Fault-tolerant/resilient code
  • Feature Selection in R - Removing Extraneous Features
  • Formula
  • Fourier Series and Transformations
  • Functional programming
  • Generalized linear models
  • Get user input
  • ggplot2
  • GPU-accelerated computing
  • Hashmaps
  • heatmap and heatmap.2
  • Hierarchical clustering with hclust
  • Hierarchical Linear Modeling
  • I/O for database tables
  • I/O for foreign tables (Excel, SAS, SPSS, Stata)
  • I/O for geographic data (shapefiles, etc.)
  • I/O for raster images
  • I/O for R's binary format
  • Implement State Machine Pattern using S4 Class
  • Input and output
  • Inspecting packages
  • Installing packages
  • Introduction to Geographical Maps
  • Introspection
  • JSON
  • Linear Models (Regression)
  • Lists
  • lubridate
  • Machine learning
  • Matrices
  • Meta: Documentation Guidelines
  • Missing values
  • Modifying strings by substitution
  • Natural language processing
  • Network analysis with the igraph package
  • Non-standard evaluation and standard evaluation
  • Numeric classes and storage modes
  • Object-Oriented Programming in R
  • Parallel processing
  • Pattern Matching and Replacement
  • Performing a Permutation Test
  • Pipe operators (%>% and others)
  • Pivot and unpivot with data.table
  • Probability Distributions with R
  • Publishing
  • R code vectorization best practices
  • R in LaTeX with knitr
  • R Markdown Notebooks (from RStudio)
  • R memento by examples
  • Random Forest Algorithm
  • Random Numbers Generator
  • Randomization
  • Raster and Image Analysis
  • Rcpp
  • Reading and writing strings
  • Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
  • Recycling
  • Regular Expression Syntax in R
  • Regular Expressions (regex)
  • Reproducible R
  • Reshape using tidyr
  • Reshaping data between long and wide forms
  • RESTful R Services
  • RMarkdown and knitr presentation
  • roxygen2
  • Run-length encoding
  • Scope of variables
  • Set operations
  • Shiny
  • Solving ODEs in R
  • Spark API (SparkR)
  • spatial analysis
  • Speeding up tough-to-vectorize code
  • Split function
  • sqldf
  • Standardize analyses by writing standalone R scripts
  • String manipulation with stringi package
  • strsplit function
  • Subsetting
  • Survival analysis
  • Text mining
  • The character class
  • The Date class
  • The logical class
  • tidyverse
  • Time Series and Forecasting
  • Updating R and the package library
  • Updating R version
  • Using pipe assignment in your own package %<>%: How to ?
  • Using texreg to export models in a paper-ready way
  • Variables
  • Web Crawling in R
  • Web scraping and parsing
  • Writing functions in R
  • xgboost
Pages : 619
Size : 10.2 MB
File type : PDF
Downloads: 39
Created: 2022-02-03
License: CC BY-SA
Author(s): Stack Overflow Community
Learning R

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