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Download R Tools

Download r tools

Download R Tools

Install R package. From Alteryx Designer version 8.5 or greater, go to Help- Install Predictive Tools. This will install the R program and the Predictive tools that use R. If you do not see this option and are running version 8.5 or greater, you may use the link below to download the R installer. Find R-tools Technology software downloads at CNET Download.com, the most comprehensive source for safe, trusted, and spyware-free downloads on the Web. The R tool is a code editor for users of R, an open-source code base used for statistical and predictive analysis. Proficiency in R scripting is recommended before using this tool.

The aim of devtools is to make your life as a package developer easier by providing R functions that simplify many common tasks. R packages are actually really simple, and with the right tools it should be easier to use the package structure than not. Package development in R can feel intimidating, but devtools does every thing it can to make it as welcoming as possible. devtools comes with a small guarantee: if because of a bug in devtools a member of R-core gets angry with you, I will send you a handwritten apology note. Just forward me the email and your address, and I'll get a card in the mail.

devtools is opinionated about how to do package development, and requires that you use roxygen2 for documentation and testthat for testing. Not everyone agrees with these opinions, and they are by no means perfect, but they have evolved during the process of writing over 30 R packages. I'm always happy to hear about what doesn't work for you, and any places where devtools gets in your way. Either send an email to the rdevtools mailing list or file an issue.

Updating to the latest version of devtools

You can track (and contribute to) development of devtools at https://github.com/hadley/devtools. To install it:

  1. Install the release version of devtools from CRAN with install.packages('devtools').

  2. Make sure you have a working development environment.
    • Windows: Install Rtools.
    • Mac: Install Xcode from the Mac App Store.
    • Linux: Install a compiler and various development libraries (details vary across differnet flavors of Linux).

  3. Follow the instructions below depending on platform.

    • Mac and Linux:

    • Windows:

Package development tools

All devtools functions accept a path as an argument, e.g. load_all('path/to/path/mypkg'). If you don't specify a path, devtools will look in the current working directory - this is recommend practice.

Frequent development tasks:

  • load_all() simulates installing and reloading your package, loading R code in R/, compiled shared objects in src/ and data files in data/. During development you usually want to access all functions so load_all() ignores the package NAMESPACE. load_all() will automatically create a DESCRIPTION if needed.

  • document() updates documentation, file collation and NAMESPACE.

  • test() reloads your code, then runs all testthat tests.

Building and installing:

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  • install() reinstalls the package, detaches the currently loaded version then reloads the new version with library(). Reloading a package is not guaranteed to work: see the documentation to unload() for caveats.

  • build() builds a package file from package sources. You can can use it to build a binary version of your package.

  • install_github() installs an R package from github, install_gitorious() from gitorious, install_bitbucket() from bitbucket, install_url() from an arbitrary url and install_file() from a local file on disk. install_version() installs a specified version from cran.

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Check and release:

  • check() updates the documentation, then builds and checks the package. build_win() builds a package using win-builder, allowing you to easily check your package on windows.

  • run_examples() will run all examples to make sure they work. This is useful because example checking is the last step of R CMD check.

  • check_doc() runs most of the documentation checking components of R CMD check

  • release() makes sure everything is ok with your package (including asking you a number of questions), then builds and uploads to CRAN. It also drafts an email to let the CRAN maintainers know that you've uploaded a new package.

Other commands:

  • bash() opens a bash shell in your package directory so you can use git or other command line tools.

  • wd() changes the working directory to a path relative to the package root.

Development mode

Calling dev_mode() will switch your version of R into 'development mode'. In this mode, R will install packages to ~/R-dev. This is useful to avoid clobbering the existing versions of CRAN packages that you need for other tasks. Calling dev_mode() again will turn development mode off, and return you to your default library setup.

Alteryx

Download R Tools For R Studio

Other tips

I recommend adding the following code to your .Rprofile:

See the complete list in ?devtools

This will set up R to:

  • always install packages from the RStudio CRAN mirror
  • ignore newlines when browse()ing
  • give minimal output from traceback()
  • automatically load devtools in interactive sessions

There are also a number of options you might want to set (in .Rprofile) to customise the default behaviour when creating packages and drafting emails:

  • devtools.name: your name, used to sign emails
  • devtools.desc.author: your R author string, in the form of 'Hadley Wickham <h.wickham@gmail.com> [aut, cre]'. Used when creating default DESCRIPTION files.
  • devtools.desc.license: a default license used when creating new packages

Alteryx Designer Desktop includes a suite of Predictive Analytics tools that use R, an open-source code base used for statistical and predictive analysis. In order to use the Predictive Macros in Alteryx, users must install R and the packages used by the R tool.

Alteryx versions 8.5 and greater have an integrated R install that includes: R version 3.0.1, Alteryx Macros, Alteryx sample modules, and the R Packages listed below.

Download R Tools
  • R for Windows
  • AlteryxRDataX: This package provides connectivity between Alteryx and R as well as a number of functions to facilitate the interaction between Alteryx and R.

CRAN Packages:

  • arules: Mining Association Rules and Frequent Itemsets
  • arulesNBMiner: Mining NB-Frequent Itemsets and NB-Precise Rules
  • arulesSequences: Mining frequent sequences
  • arulesViz: arulesViz - Visualizing Association Rules and Frequent Itemsets
  • car: Companion to Applied Regression
  • colorspace: Color Space Manipulation
  • dichromat: Color Schemes for Dichromats
  • flexclust: Flexible Cluster Algorithms
  • FNN: Fast Nearest Neighbor Search Algorithms and Applications
  • forecast: Forecasting functions for time series and linear models
  • fracdiff: Fractionally differenced ARIMA aka ARFIMA(p,d,q) models
  • gclus: Clustering Graphics
  • GPArotation: GPA Factor Rotation
  • Hmisc: Harrell Miscellaneous
  • igraph0: Network analysis and visualization
  • limSolve: Solving Linear Inverse Models
  • lpSolve: Interface to Lp_solve v. 5.5 to solve linear/integer programs
  • modeltools: Tools and Classes for Statistical Models
  • mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation
  • munsell: Munsell colour system
  • plyr: Tools for splitting, applying and combining data
  • quadprog: Functions to solve Quadratic Programming Problems
  • randomForest: Breiman and Cutler's random forests for classification and regression
  • Rcpp: Seamless R and C++ Integration
  • RcppArmadillo: Rcpp integration for Armadillo templated linear algebra library
  • rgeos: Interface to Geometry Engine - Open Source (GEOS)
  • rjava: Low-level R to Java interface
  • rpart.plot: Plot rpart models. An enhanced version of plot.rpart
  • scales: Scale functions for graphics
  • scatterplot3d: 3D Scatter Plot
  • seriation: Infrastructure for seriation
  • sm: Smoothing methods for nonparametric regression and density estimation
  • sp: Classes and methods for spatial data
  • stringr: Make it easier to work with strings
  • tseries: Time series analysis and computational finance
  • TSP: Traveling Salesperson Problem (TSP)
  • vcd: Visualizing Categorical Data
  • vioplot: Violin plot
  • zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's ordered observations)

For more information on R, please visit their website: http://www.r-project.org/