Learn R Programming

######## Learn R Programming ########

This app contains tutorials and reports for the all who want to learn R Programming.
They can learn easily from this application.

Following Chapters included in this Application:

#1 Getting started with R Language
#2 Variables
#3 Arithmetic Operators
#4 Matrices
#5 Formula
#6 Reading and writing strings
#7 String manipulation with stringi package
#8 Classes
#9 Lists
#10 Hashmaps
#11 Creating vectors
#12 Date and Time
#13 The Date class
#14 Date-time classes (POSIXct and POSIXlt)
#15 The character class
#16 Numeric classes and storage modes
#17 The logical class
#18 Data frames
#19 Split function
#20 Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
#21 Pipe operators (%>% and others)
#22 Linear Models (Regression)
#23 data.table
#24 Pivot and unpivot with data.table
#25 Bar Chart
#26 Base Plotting
#27 boxplot
#28 ggplot2
#29 Factors
#30 Pattern Matching and Replacement
#31 Run-length encoding
#32 Speeding up tough-to-vectorize code
#33 Introduction to Geographical Maps
#34 Set operations
#35 tidyverse
#36 Rcpp
#37 Random Numbers Generator
#38 Parallel processing
#39 Subsetting
#40 Debugging
#41 Installing packages
#42 Inspecting packages
#43 Creating packages with devtools
#44 Using pipe assignment in your own package %<>% How to ?
#45 Arima Models
#46 Distribution Functions
#47 Shiny
#48 spatial analysis
#49 sqldf
#50 Code profiling
#51 Control flow structures
#52 Column wise operation
#53 JSON
#54 RODBC
#55 lubridate
#56 Time Series and Forecasting
#57 strsplit function
#58 Web scraping and parsing
#59 Generalized linear models
#60 Reshaping data between long and wide forms
#61 RMarkdown and knitr presentation
#62 Scope of variables
#63 Performing a Permutation Test
#64 xgboost
#65 R code vectorization best practices
#66 Missing values
#67 Hierarchical Linear Modeling
#68 *apply family of functions (functionals)
#69 Text mining
#70 ANOVA
#71 Raster and Image Analysis
#72 Survival analysis
#73 Fault-tolerant/resilient code
#74 Reproducible R
#75 Fourier Series and Transformations
#76 .Rprofile
#77 dplyr
#78 caret
#79 Extracting and Listing Files in Compressed Archives
#80 Probability Distributions with R
#81 R in LaTeX with knitr
#82 Web Crawling in R
#83 Creating reports with RMarkdown
#84 GPU-accelerated computing
#85 heatmap and heatmap.2
#86 Network analysis with the igraph package
#87 Functional programming
#88 Get user input
#89 Spark API (SparkR)
#90 Meta Documentation Guidelines
#91 Input and output
#92 I/O for foreign tables (Excel, SAS, SPSS, Stata)
#93 I/O for database tables
#94 I/O for geographic data (shapefiles, etc.)
#95 I/O for raster images
#96 I/O for R’s binary format
#97 Recycling
#98 Expression parse + eval
#99 Regular Expression Syntax in R
#100 Regular Expressions (regex)
#101 Combinatorics
#102 Solving ODEs in R
#103 Feature Selection in R – Removing Extraneous Features
#104 Bibliography in RMD
#105 Writing functions in R
#106 Color schemes for graphics
#107 Hierarchical clustering with hclust
#108 Random Forest Algorithm
#109 RESTful R Services
#110 Machine learning
#111 Using texreg to export models in a paper-ready way
#112 Publishing
#113 Implement State Machine Pattern using S4 Class
#114 Reshape using tidyr
#115 Modifying strings by substitution
#116 Non-standard evaluation and standard evaluation
#117 Randomization
#118 Object-Oriented Programming in R
#119 Coercion
#120 Standardize analyses by writing standalone R scripts
#121 Analyze tweets with R
#122 Natural language processing
#123 R Markdown Notebooks (from RStudio)
#124 Aggregating data frames
#125 Data acquisition
#126 R memento by examples
#127 Updating R version