R Notes for Professionals

• 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

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