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