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    R Notes for Professionals book

    Amazing collection of free programming books

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    Amazing collection of free programming books

    The Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. Text content is released under Creative Commons BY-SA. See credits at the end of this book whom contributed to the various chapters. Images may be copyright of their respective owners unless otherwise specified

    Book created for educational purposes and is not affiliated with group(s), company(s) nor Stack Overflow. All trademarks belong to their respective company owners

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    章节目录

    1-1Content list
    1-2About
    1-3Chapter 1: Getting started with R Language
    1-4Section 1.1: Installing R
    1-5Section 1.2: Hello World!
    1-6Section 1.3: Getting Help
    1-7Section 1.4: Interactive mode and R scripts
    1-8Chapter 2: Variables
    1-9Section 2.1: Variables, data structures and basic Operations
    1-10Chapter 3: Arithmetic Operators
    1-11Section 3.1: Range and addition
    1-12Section 3.2: Addition and subtraction
    1-13Chapter 4: Matrices
    1-14Section 4.1: Creating matrices
    1-15Chapter 5: Formula
    1-16Section 5.1: The basics of formula
    1-17Chapter 6: Reading and writing strings
    1-18Section 6.1: Printing and displaying strings
    1-19Section 6.2: Capture output of operating system command
    1-20Section 6.3: Reading from or writing to a file connection
    1-21Chapter 7: String manipulation with stringi package
    1-22Section 7.1: Count pattern inside string
    1-23Section 7.2: Duplicating strings
    1-24Section 7.3: Paste vectors
    1-25Section 7.4: Splitting text by some fixed pattern
    1-26Chapter 8: Classes
    1-27Section 8.1: Inspect classes
    1-28Section 8.2: Vectors and lists
    1-29Section 8.3: Vectors
    1-30Chapter 9: Lists
    1-31Section 9.1: Introduction to lists
    1-32Section 9.2: Quick Introduction to Lists
    1-33Section 9.3: Serialization: using lists to pass information
    1-34Chapter 10: Hashmaps
    1-35Section 10.1: Environments as hash maps
    1-36Section 10.2: package:hash
    1-37Section 10.3: package:listenv
    1-38Chapter 11: Creating vectors
    1-39Section 11.1: Vectors from build in constants: Sequences of letters & month names
    1-40Section 11.2: Creating named vectors
    1-41Section 11.3: Sequence of numbers
    1-42Section 11.4: seq()
    1-43Section 11.5: Vectors
    1-44Section 11.6: Expanding a vector with the rep() function
    1-45Chapter 12: Date and Time
    1-46Section 12.1: Current Date and Time
    1-47Section 12.2: Go to the End of the Month
    1-48Section 12.3: Go to First Day of the Month
    1-49Section 12.4: Move a date a number of months consistently by months
    1-50Chapter 13: The Date class
    1-51Section 13.1: Formatting Dates
    1-52Section 13.2: Parsing Strings into Date Objects
    1-53Section 13.3: Dates
    1-54Chapter 14: Date-time classes (POSIXct and POSIXlt)
    1-55Section 14.1: Formatting and printing date-time objects
    1-56Section 14.2: Date-time arithmetic
    1-57Section 14.3: Parsing strings into date-time objects
    1-58Chapter 15: The character class
    1-59Section 15.1: Coercion
    1-60Chapter 16: Numeric classes and storage modes
    1-61Section 16.1: Numeric
    1-62Chapter 17: The logical class
    1-63Section 17.1: Logical operators
    1-64Section 17.2: Coercion
    1-65Section 17.3: Interpretation of NAs
    1-66Chapter 18: Data frames
    1-67Section 18.1: Create an empty data.frame
    1-68Section 18.2: Subsetting rows and columns from a data frame
    1-69Section 18.3: Convenience functions to manipulate data.frames
    1-70Section 18.4: Introduction
    1-71Section 18.5: Convert all columns of a data.frame to character class
    1-72Chapter 19: Split function
    1-73Section 19.1: Using split in the split-apply-combine paradigm
    1-74Section 19.2: Basic usage of split
    1-75Chapter 20: Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
    1-76Section 20.1: Importing .csv files
    1-77Section 20.2: Importing with data.table
    1-78Section 20.3: Exporting .csv files
    1-79Section 20.4: Import multiple csv files
    1-80Section 20.5: Importing fixed-width files
    1-81Chapter 21: Pipe operators (%>% and others)
    1-82Section 21.1: Basic use and chaining
    1-83Section 21.2: Functional sequences
    1-84Section 21.3: Assignment with %<>%
    1-85Section 21.4: Exposing contents with %$%
    1-86Section 21.5: Creating side eects with %T>%
    1-87Section 21.6: Using the pipe with dplyr and ggplot2
    1-88Chapter 22: Linear Models (Regression)
    1-89Section 22.1: Linear regression on the mtcars dataset
    1-90Section 22.2: Using the 'predict' function
    1-91Section 22.3: Weighting
    1-92Section 22.4: Checking for nonlinearity with polynomial regression
    1-93Section 22.5: Plotting The Regression (base)
    1-94Section 22.6: Quality assessment
    1-95Chapter 23: data.table
    1-96Section 23.1: Creating a data.table
    1-97Section 23.2: Special symbols in data.table
    1-98Section 23.3: Adding and modifying columns
    1-99Section 23.4: Writing code compatible with both data.frame and data.table
    1-100Section 23.5: Setting keys in data.table
    1-101Chapter 24: Pivot and unpivot with data.table
    1-102Section 24.1: Pivot and unpivot tabular data with data.table - I
    1-103Section 24.2: Pivot and unpivot tabular data with data.table - II
    1-104Chapter 25: Bar Chart
    1-105Section 25.1: barplot() function
    1-106Chapter 26: Base Plotting
    1-107Section 26.1: Density plot
    1-108Section 26.2: Combining Plots
    1-109Section 26.3: Getting Started with R_Plots
    1-110Section 26.4: Basic Plot
    1-111Section 26.5: Histograms
    1-112Section 26.6: Matplot
    1-113Section 26.7: Empirical Cumulative Distribution Function
    1-114Chapter 27: boxplot
    1-115Section 27.1: Create a box-and-whisker plot with boxplot() {graphics}
    1-116Section 27.2: Additional boxplot style parameters
    1-117Chapter 28: ggplot2
    1-118Section 28.1: Displaying multiple plots
    1-119Section 28.2: Prepare your data for plotting
    1-120Section 28.3: Add horizontal and vertical lines to plot
    1-121Section 28.4: Scatter Plots
    1-122Section 28.5: Produce basic plots with qplot
    1-123Section 28.6: Vertical and Horizontal Bar Chart
    1-124Section 28.7: Violin plot
    1-125Chapter 29: Factors
    1-126Section 29.1: Consolidating Factor Levels with a List
    1-127Section 29.2: Basic creation of factors
    1-128Section 29.3: Changing and reordering factors
    1-129Section 29.4: Rebuilding factors from zero
    1-130Chapter 30: Pattern Matching and Replacement
    1-131Section 30.1: Finding Matches
    1-132Section 30.2: Single and Global match
    1-133Section 30.3: Making substitutions
    1-134Section 30.4: Find matches in big data sets
    1-135Chapter 31: Run-length encoding
    1-136Section 31.1: Run-length Encoding with `rle`
    1-137Section 31.2: Identifying and grouping by runs in base R
    1-138Section 31.3: Run-length encoding to compress and decompress vectors
    1-139Section 31.4: Identifying and grouping by runs in data.table
    1-140Chapter 32: Speeding up tough-to-vectorize code
    1-141Section 32.1: Speeding tough-to-vectorize for loops with Rcpp
    1-142Section 32.2: Speeding tough-to-vectorize for loops by byte compiling
    1-143Chapter 33: Introduction to Geographical Maps
    1-144Section 33.1: Basic map-making with map() from the package maps
    1-145Section 33.2: 50 State Maps and Advanced Choropleths with Google Viz
    1-146Section 33.3: Interactive plotly maps
    1-147Section 33.4: Making Dynamic HTML Maps with Leaflet
    1-148Section 33.5: Dynamic Leaflet maps in Shiny applications
    1-149Chapter 34: Set operations
    1-150Section 34.1: Set operators for pairs of vectors
    1-151Section 34.2: Cartesian or "cross" products of vectors
    1-152Section 34.3: Set membership for vectors
    1-153Section 34.4: Make unique / drop duplicates / select distinct elements from a vector
    1-154Section 34.5: Measuring set overlaps / Venn diagrams for vectors
    1-155Chapter 35: tidyverse
    1-156Section 35.1: tidyverse: an overview
    1-157Section 35.2: Creating tbl_df’s
    1-158Chapter 36: Rcpp
    1-159Section 36.1: Extending Rcpp with Plugins
    1-160Section 36.2: Inline Code Compile
    1-161Section 36.3: Rcpp Attributes
    1-162Section 36.4: Specifying Additional Build Dependencies
    1-163Chapter 37: Random Numbers Generator
    1-164Section 37.1: Random permutations
    1-165Section 37.2: Generating random numbers using various density functions
    1-166Section 37.3: Random number generator's reproducibility
    1-167Chapter 38: Parallel processing
    1-168Section 38.1: Parallel processing with parallel package
    1-169Section 38.2: Parallel processing with foreach package
    1-170Section 38.3: Random Number Generation
    1-171Section 38.4: mcparallelDo
    1-172Chapter 39: Subsetting
    1-173Section 39.1: Data frames
    1-174Section 39.2: Atomic vectors
    1-175Section 39.3: Matrices
    1-176Section 39.4: Lists
    1-177Section 39.5: Vector indexing
    1-178Section 39.6: Other objects
    1-179Section 39.7: Elementwise Matrix Operations
    1-180Chapter 40: Debugging
    1-181Section 40.1: Using debug
    1-182Section 40.2: Using browser
    1-183Chapter 41: Installing packages
    1-184Section 41.1: Install packages from GitHub
    1-185Section 41.2: Download and install packages from repositories
    1-186Section 41.3: Install package from local source
    1-187Section 41.4: Install local development version of a package
    1-188Section 41.5: Using a CLI package manager -- basic pacman usage
    1-189Chapter 42: Inspecting packages
    1-190Section 42.1: View Package Version
    1-191Section 42.2: View Loaded packages in Current Session
    1-192Section 42.3: View package information
    1-193Section 42.4: View package's built-in data sets
    1-194Section 42.5: List a package's exported functions
    1-195Chapter 43: Creating packages with devtools
    1-196Section 43.1: Creating and distributing packages
    1-197Section 43.2: Creating vignettes
    1-198Chapter 44: Using pipe assignment in your own package %<>%: How to ?
    1-199Section 44.1: Putting the pipe in a utility-functions file
    1-200Chapter 45: Arima Models
    1-201Section 45.1: Modeling an AR1 Process with Arima
    1-202Chapter 46: Distribution Functions
    1-203Section 46.1: Normal distribution
    1-204Section 46.2: Binomial Distribution
    1-205Chapter 47: Shiny
    1-206Section 47.1: Create an app
    1-207Section 47.2: Checkbox Group
    1-208Section 47.3: Radio Button
    1-209Section 47.4: Debugging
    1-210Section 47.5: Select box
    1-211Section 47.6: Launch a Shiny app
    1-212Section 47.7: Control widgets
    1-213Chapter 48: spatial analysis
    1-214Section 48.1: Create spatial points from XY data set
    1-215Section 48.2: Importing a shape file (.shp)
    1-216Chapter 49: sqldf
    1-217Section 49.1: Basic Usage Examples
    1-218Chapter 50: Code profiling
    1-219Section 50.1: Benchmarking using microbenchmark
    1-220Section 50.2: proc.time()
    1-221Section 50.3: Microbenchmark
    1-222Section 50.4: System.time
    1-223Section 50.5: Line Profiling
    1-224Chapter 51: Control flow structures
    1-225Section 51.1: Optimal Construction of a For Loop
    1-226Section 51.2: Basic For Loop Construction
    1-227Section 51.3: The Other Looping Constructs: while and repeat
    1-228Chapter 52: Column wise operation
    1-229Section 52.1: sum of each column
    1-230Chapter 53: JSON
    1-231Section 53.1: JSON to / from R objects
    1-232Chapter 54: RODBC
    1-233Section 54.1: Connecting to Excel Files via RODBC
    1-234Section 54.2: SQL Server Management Database connection to get individual table
    1-235Section 54.3: Connecting to relational databases
    1-236Chapter 55: lubridate
    1-237Section 55.1: Parsing dates and datetimes from strings with lubridate
    1-238Section 55.2: Dierence between period and duration
    1-239Section 55.3: Instants
    1-240Section 55.4: Intervals, Durations and Periods
    1-241Section 55.5: Manipulating date and time in lubridate
    1-242Section 55.6: Time Zones
    1-243Section 55.7: Parsing date and time in lubridate
    1-244Section 55.8: Rounding dates
    1-245Chapter 56: Time Series and Forecasting
    1-246Section 56.1: Creating a ts object
    1-247Section 56.2: Exploratory Data Analysis with time-series data
    1-248Chapter 57: strsplit function
    1-249Section 57.1: Introduction
    1-250Chapter 58: Web scraping and parsing
    1-251Section 58.1: Basic scraping with rvest
    1-252Section 58.2: Using rvest when login is required
    1-253Chapter 59: Generalized linear models
    1-254Section 59.1: Logistic regression on Titanic dataset
    1-255Chapter 60: Reshaping data between long and wide forms
    1-256Section 60.1: Reshaping data
    1-257Section 60.2: The reshape function
    1-258Chapter 61: RMarkdown and knitr presentation
    1-259Section 61.1: Adding a footer to an ioslides presentation
    1-260Section 61.2: Rstudio example
    1-261Chapter 62: Scope of variables
    1-262Section 62.1: Environments and Functions
    1-263Section 62.2: Function Exit
    1-264Section 62.3: Sub functions
    1-265Section 62.4: Global Assignment
    1-266Section 62.5: Explicit Assignment of Environments and Variables
    1-267Chapter 63: Performing a Permutation Test
    1-268Section 63.1: A fairly general function
    1-269Chapter 64: xgboost
    1-270Section 64.1: Cross Validation and Tuning with xgboost
    1-271Chapter 65: R code vectorization best practices
    1-272Section 65.1: By row operations
    1-273Chapter 66: Missing values
    1-274Section 66.1: Examining missing data
    1-275Section 66.2: Reading and writing data with NA values
    1-276Section 66.3: Using NAs of dierent classes
    1-277Section 66.4: TRUE/FALSE and/or NA
    1-278Chapter 67: Hierarchical Linear Modeling
    1-279Section 67.1: basic model fitting
    1-280Chapter 68: *apply family of functions (functionals)
    1-281Section 68.1: Using built-in functionals
    1-282Section 68.2: Combining multiple `data.frames` (`lapply`, `mapply`)
    1-283Section 68.3: Bulk File Loading
    1-284Section 68.4: Using user-defined functionals
    1-285Chapter 69: Text mining
    1-286Section 69.1: Scraping Data to build N-gram Word Clouds
    1-287Chapter 70: ANOVA
    1-288Section 70.1: Basic usage of aov()
    1-289Section 70.2: Basic usage of Anova()
    1-290Chapter 71: Raster and Image Analysis
    1-291Section 71.1: Calculating GLCM Texture
    1-292Section 71.2: Mathematical Morphologies
    1-293Chapter 72: Survival analysis
    1-294Section 72.1: Random Forest Survival Analysis with randomForestSRC
    1-295Section 72.2: Introduction - basic fitting and plotting of parametric survival models with the survival package
    1-296Section 72.3: Kaplan Meier estimates of survival curves and risk set tables with survminer
    1-297Chapter 73: Fault-tolerant/resilient code
    1-298Section 73.1: Using tryCatch()
    1-299Chapter 74: Reproducible R
    1-300Section 74.1: Data reproducibility
    1-301Section 74.2: Package reproducibility
    1-302Chapter 75: Fourier Series and Transformations
    1-303Section 75.1: Fourier Series
    1-304Chapter 76: .Rprofile
    1-305Section 76.1: .Rprofile - the first chunk of code executed
    1-306Section 76.2: .Rprofile example
    1-307Chapter 77: dplyr
    1-308Section 77.1: dplyr's single table verbs
    1-309Section 77.2: Aggregating with %>% (pipe) operator
    1-310Section 77.3: Subset Observation (Rows)
    1-311Section 77.4: Examples of NSE and string variables in dpylr
    1-312Chapter 78: caret
    1-313Section 78.1: Preprocessing
    1-314Chapter 79: Extracting and Listing Files in Compressed Archives
    1-315Section 79.1: Extracting files from a .zip archive
    1-316Chapter 80: Probability Distributions with R
    1-317Section 80.1: PDF and PMF for dierent distributions in R
    1-318Chapter 81: R in LaTeX with knitr
    1-319Section 81.1: R in LaTeX with Knitr and Code Externalization
    1-320Section 81.2: R in LaTeX with Knitr and Inline Code Chunks
    1-321Section 81.3: R in LaTex with Knitr and Internal Code Chunks
    1-322Chapter 82: Web Crawling in R
    1-323Section 82.1: Standard scraping approach using the RCurl package
    1-324Chapter 83: Creating reports with RMarkdown
    1-325Section 83.1: Including bibliographies
    1-326Section 83.2: Including LaTeX Preample Commands
    1-327Section 83.3: Printing tables
    1-328Section 83.4: Basic R-markdown document structure
    1-329Chapter 84: GPU-accelerated computing
    1-330Section 84.1: gpuR gpuMatrix objects
    1-331Section 84.2: gpuR vclMatrix objects
    1-332Chapter 85: heatmap and heatmap.2
    1-333Section 85.1: Examples from the ocial documentation
    1-334Section 85.2: Tuning parameters in heatmap.2
    1-335Chapter 86: Network analysis with the igraph package
    1-336Section 86.1: Simple Directed and Non-directed Network Graphing
    1-337Chapter 87: Functional programming
    1-338Section 87.1: Built-in Higher Order Functions
    1-339Chapter 88: Get user input
    1-340Section 88.1: User input in R
    1-341Chapter 89: Spark API (SparkR)
    1-342Section 89.1: Setup Spark context
    1-343Section 89.2: Cache data
    1-344Section 89.3: Create RDDs (Resilient Distributed Datasets)
    1-345Chapter 90: Meta: Documentation Guidelines
    1-346Section 90.1: Style
    1-347Section 90.2: Making good examples
    1-348Chapter 91: Input and output
    1-349Section 91.1: Reading and writing data frames
    1-350Chapter 92: I/O for foreign tables (Excel, SAS, SPSS, Stata)
    1-351Section 92.1: Importing data with rio
    1-352Section 92.2: Read and write Stata, SPSS and SAS files
    1-353Section 92.3: Importing Excel files
    1-354Section 92.4: Import or Export of Feather file
    1-355Chapter 93: I/O for database tables
    1-356Section 93.1: Reading Data from MySQL Databases
    1-357Section 93.2: Reading Data from MongoDB Databases
    1-358Chapter 94: I/O for geographic data (shapefiles, etc.)
    1-359Section 94.1: Import and Export Shapefiles
    1-360Chapter 95: I/O for raster images
    1-361Section 95.1: Load a multilayer raster
    1-362Chapter 96: I/O for R's binary format
    1-363Section 96.1: Rds and RData (Rda) files
    1-364Section 96.2: Enviromments
    1-365Chapter 97: Recycling
    1-366Section 97.1: Recycling use in subsetting
    1-367Chapter 98: Expression: parse + eval
    1-368Section 98.1: Execute code in string format
    1-369Chapter 99: Regular Expression Syntax in R
    1-370Section 99.1: Use `grep` to find a string in a character vector
    1-371Chapter 100: Regular Expressions (regex)
    1-372Section 100.1: Dierences between Perl and POSIX regex
    1-373Section 100.2: Validate a date in a "YYYYMMDD" format
    1-374Section 100.3: Escaping characters in R regex patterns
    1-375Section 100.4: Validate US States postal abbreviations
    1-376Section 100.5: Validate US phone numbers
    1-377Chapter 101: Combinatorics
    1-378Section 101.1: Enumerating combinations of a specified length
    1-379Section 101.2: Counting combinations of a specified length
    1-380Chapter 102: Solving ODEs in R
    1-381Section 102.1: The Lorenz model
    1-382Section 102.2: Lotka-Volterra or: Prey vs. predator
    1-383Section 102.3: ODEs in compiled languages - definition in R
    1-384Section 102.4: ODEs in compiled languages - definition in C
    1-385Section 102.5: ODEs in compiled languages - definition in fortran
    1-386Section 102.6: ODEs in compiled languages - a benchmark test
    1-387Chapter 103: Feature Selection in R -- Removing Extraneous Features
    1-388Section 103.1: Removing features with zero or near-zero variance
    1-389Section 103.2: Removing features with high numbers of NA
    1-390Section 103.3: Removing closely correlated features
    1-391Chapter 104: Bibliography in RMD
    1-392Section 104.1: Specifying a bibliography and cite authors
    1-393Section 104.2: Inline references
    1-394Section 104.3: Citation styles
    1-395Chapter 105: Writing functions in R
    1-396Section 105.1: Anonymous functions
    1-397Section 105.2: RStudio code snippets
    1-398Section 105.3: Named functions
    1-399Chapter 106: Color schemes for graphics
    1-400Section 106.1: viridis - print and colorblind friendly palettes
    1-401Section 106.2: A handy function to glimse a vector of colors
    1-402Section 106.3: colorspace - click&drag interface for colors
    1-403Section 106.4: Colorblind-friendly palettes
    1-404Section 106.5: RColorBrewer
    1-405Section 106.6: basic R color functions
    1-406Chapter 107: Hierarchical clustering with hclust
    1-407Section 107.1: Example 1 - Basic use of hclust, display of dendrogram, plot clusters
    1-408Section 107.2: Example 2 - hclust and outliers
    1-409Chapter 108: Random Forest Algorithm
    1-410Section 108.1: Basic examples - Classification and Regression
    1-411Chapter 109: RESTful R Services
    1-412Section 109.1: opencpu Apps
    1-413Chapter 110: Machine learning
    1-414Section 110.1: Creating a Random Forest model
    1-415Chapter 111: Using texreg to export models in a paper-ready way
    1-416Section 111.1: Printing linear regression results
    1-417Chapter 112: Publishing
    1-418Section 112.1: Formatting tables
    1-419Section 112.2: Formatting entire documents
    1-420Chapter 113: Implement State Machine Pattern using S4 Class
    1-421Section 113.1: Parsing Lines using State Machine
    1-422Chapter 114: Reshape using tidyr
    1-423Section 114.1: Reshape from long to wide format with spread()
    1-424Section 114.2: Reshape from wide to long format with gather()
    1-425Chapter 115: Modifying strings by substitution
    1-426Section 115.1: Rearrange character strings using capture groups
    1-427Section 115.2: Eliminate duplicated consecutive elements
    1-428Chapter 116: Non-standard evaluation and standard evaluation
    1-429Section 116.1: Examples with standard dplyr verbs
    1-430Chapter 117: Randomization
    1-431Section 117.1: Random draws and permutations
    1-432Section 117.2: Setting the seed
    1-433Chapter 118: Object-Oriented Programming in R
    1-434Section 118.1: S3
    1-435Chapter 119: Coercion
    1-436Section 119.1: Implicit Coercion
    1-437Chapter 120: Standardize analyses by writing standalone R scripts
    1-438Section 120.1: The basic structure of standalone R program and how to call it
    1-439Section 120.2: Using littler to execute R scripts
    1-440Chapter 121: Analyze tweets with R
    1-441Section 121.1: Download Tweets
    1-442Section 121.2: Get text of tweets
    1-443Chapter 122: Natural language processing
    1-444Section 122.1: Create a term frequency matrix
    1-445Chapter 123: R Markdown Notebooks (from RStudio)
    1-446Section 123.1: Creating a Notebook
    1-447Section 123.2: Inserting Chunks
    1-448Section 123.3: Executing Chunk Code
    1-449Section 123.4: Execution Progress
    1-450Section 123.5: Preview Output
    1-451Section 123.6: Saving and Sharing
    1-452Chapter 124: Aggregating data frames
    1-453Section 124.1: Aggregating with data.table
    1-454Section 124.2: Aggregating with base R
    1-455Section 124.3: Aggregating with dplyr
    1-456Chapter 125: Data acquisition
    1-457Section 125.1: Built-in datasets
    1-458Section 125.2: Packages to access open databases
    1-459Section 125.3: Packages to access restricted data
    1-460Section 125.4: Datasets within packages
    1-461Chapter 126: R memento by examples
    1-462Section 126.1: Plotting (using plot)
    1-463Section 126.2: Commonly used functions
    1-464Section 126.3: Data types
    1-465Chapter 127: Updating R version
    1-466Section 127.1: Installing from R Website
    1-467Section 127.2: Updating from within R using installr Package
    1-468Section 127.3: Deciding on the old packages
    1-469Section 127.4: Updating Packages
    1-470Section 127.5: Check R Version
    1-471Credits
    1-472You may also like

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