R Notes for Professional
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List of Contents
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
|
Notes Details |
|
|
Notes
Name |
R
Notes for Professional |
|
Publisher |
GoalKicker |
|
Total
Chapter |
127 |
|
Total
Pages |
464 |
|
Category |
Free
Programming E-book |
[Preview ##eye##] [Download ##download##]
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