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