1262 best open source r projects.

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Displaying 1 to 20 from 1262 results

plotly - An interactive graphing library for R

An R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.NOTE: The CRAN version of plotly is designed to work with the CRAN version of ggplot2, but at least for the time being, we recommend using the development versions of both plotly and ggplot2 (devtools::install_github("hadley/ggplot2")).

r ggplot2 data-visualization d3js shiny plotly webgl

adv-r - Advanced R programming: a book

This is code and text behind the Advanced R programming book. The site is built with bookdown.

book r programming bookdown

r4ds - R for data science

This is code and text behind the R for Data Science book.

book bookdown data-science r

knitr - A general-purpose tool for dynamic report generation in R

The R package knitr is a general-purpose literate programming engine, with lightweight API's designed to give users full control of the output without heavy coding work. It combines many features into one package with slight tweaks motivated from my everyday use of Sweave. See the package homepage for details and examples. See FAQ's for a list of frequently asked questions (including where to ask questions). Note that if you want to build the source package via R CMD INSTALL without a previously installed version of knitr, you must either pre-install knitr from CRAN, or run R CMD INSTALL on this source repo, otherwise R CMD build will fail (which is probably a bug of base R).

knitr r literate-programming sweave rmarkdown dynamic-documents

mal - mal - Make a Lisp

Assembly

Mal is a Clojure inspired Lisp interpreter.Mal is a learning tool. See the make-a-lisp process guide. Each implementation of mal is separated into 11 incremental, self-contained (and testable) steps that demonstrate core concepts of Lisp. The last step is capable of self-hosting (running the mal implementation of mal).

mal docker lisp lisp-interpreter c-plus-plus c-sharp r bash makefile learn-to-code

spack - A flexible package manager that supports multiple versions, configurations, platforms, and compilers

Python

Spack is a multi-platform package manager that builds and installs multiple versions and configurations of software. It works on Linux, macOS, and many supercomputers. Spack is non-destructive: installing a new version of a package does not break existing installations, so many configurations of the same package can coexist. Spack offers a simple "spec" syntax that allows users to specify versions and configuration options. Package files are written in pure Python, and specs allow package authors to write a single script for many different builds of the same package. With Spack, you can build your software all the ways you want to.

spack package-manager hpc scientific-computing cray supercomputer r gov

arl - lists of most popular repositories for most favoured programming languages (according to StackOverflow)

Python

Well, one of the best ways to learn something new is to watch how it is done by others. Feel free to create an issue.

awesome cpp nodejs typescript vbnet r matlab haskell

DataScienceR - a curated list of R tutorials for Data Science, NLP and Machine Learning

This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Curated list of Python tutorials for Data Science, NLP and Machine Learning.

datascience data-science r text-mining

h2o-tutorials - Tutorials and training material for the H2O Machine Learning Platform

Jupyter

This document contains tutorials and training materials for H2O-3. If you find any problems with the tutorial code, please open an issue in this repository. For general H2O questions, please post those to Stack Overflow using the "h2o" tag or join the H2O Stream Google Group for questions that don't fit into the Stack Overflow format.

h2o tutorial machine-learning data-science deep-learning r

devtools - Tools to make an R developer's life easier

The aim of devtools is to make package development easier by providing R functions that simplify and expedite common tasks. R Packages is a book based around this workflow. All devtools functions accept a path as an argument, e.g. load_all("path/to/mypkg"). If you don't specify a path, devtools will look in the current working directory - this is recommended practice.

r package-creation

httr - httr: a friendly http package for R

The aim of httr is to provide a wrapper for the curl package, customised to the demands of modern web APIs. Functions for the most important http verbs: GET(), HEAD(), PATCH(), PUT(), DELETE() and POST().

r curl http api

DiagrammeR - Graph and network visualization using tabular data in R

With the DiagrammeR package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into RMarkdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files. This package is made possible by the htmlwidgets R package, which provides an easy-to-use framework for bringing together R and JavaScript.

r network-graph graph visualization property-graph graph-functions

atom-beautify - :lipstick: Universal beautification package for Atom editor (:warning: Currently migrating to https://github

CoffeeScript

Atom-Beautify respects the core.telemetryConsent configuration option from Atom editor. If you do not wish to have usage data sent to Google Analytics then please set core.telemetryConsent to no or undecided option before using Atom-Beautify. See Anonymous Analytics section of docs for details. Thank you. Atom-Beautify is going to be completely rewritten with Unibeautify at its core! See unibeautify branch for work in progress and Issue #1174.

atom beautifier formatter coffeescript beautify prettifier prettify js-beautify format pretty html handlebars mustache xml css json sass scss less sql markdown typescript cpp c-sharp coldfusion uncrustify apex pawn vala erb editorconfig yaml front-matter jekyll marko svg elm arduino crystal csv ejs gherkin haskell hindent brittany jade jsx latex ocaml puppet riot.js spacebars swig tss twig visualforce xtemplate autopep8 coffee-formatter coffee-fmt clang-format dfmt elm-format html-beautifier csscomb gherkin-formatter gofmt latex-beautify fortran-beautifier jscs-fixer eslint eslint-fixer lua-beautifier ocp-indent perltidy php-cs-fixer phpcbf pretty-diff pug-beautify puppet-lint r remark rubocop ruby-beautify rustfmt sqlformat stylish-haskell tidy-markdown typescript-formatter yapf erl_tidy marko-beautifier vue vue-beautifier sassconvert formatr clojure-beautifier nunjucks ux-markup pybeautifier cljfmt bash beautysh glsl hh_format nginx nginx-beautify golang-template align-yaml goimports terraform terraformfmt tsx prettier verilog emacs-verilog-mode vhdl vhdl-beautifier gn blade

swirl - :cyclone: Learn R, in R.

swirl is a platform for learning (and teaching) statistics and R simultaneously and interactively. It presents a choice of course lessons and interactively tutors a student through them. A student may be asked to watch a video, to answer a multiple-choice or fill-in-the-blanks question, or to enter a command in the R console precisely as if he or she were using R in practice. Emphasis is on the last, interacting with the R console. User responses are tested for correctness and hints are given if appropriate. Progress is automatically saved so that a user may quit at any time and later resume without losing work. swirl leans heavily on exercising a student's use of the R console. A callback mechanism, suggested and first demonstrated for the purpose by Hadley Wickham, is used to capture student input and to provide immediate feedback relevant to the course material at hand.

awesome-R - A curated list of awesome R packages, frameworks and software.

A curated list of awesome R packages and tools. Inspired by awesome-machine-learning. Packages change the way you use R.

r data-science data-analysis awesome list awesome-list

benchm-ml - A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc

This project aims at a minimal benchmark for scalability, speed and accuracy of commonly used implementations of a few machine learning algorithms. The target of this study is binary classification with numeric and categorical inputs (of limited cardinality i.e. not very sparse) and no missing data, perhaps the most common problem in business applications (e.g. credit scoring, fraud detection or churn prediction). If the input matrix is of n x p, n is varied as 10K, 100K, 1M, 10M, while p is ~1K (after expanding the categoricals into dummy variables/one-hot encoding). This particular type of data structure/size (the largest) stems from this author's interest in some particular business applications. Note: While a large part of this benchmark was done in Spring 2015 reflecting the state of ML implementations at that time, this repo is being updated if I see significant changes in implementations or new implementations have become widely available (e.g. lightgbm). Also, please find a summary of the progress and learnings from this benchmark at the end of this repo.

machine-learning data-science r gradient-boosting-machine random-forest deep-learning xgboost h2o spark

dplyr - dplyr: A grammar of data manipulation

These all combine naturally with group_by() which allows you to perform any operation “by group”. You can learn more about them in vignette("dplyr"). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette("two-table"). dplyr is designed to abstract over how the data is stored. That means as well as working with local data frames, you can also work with remote database tables, using exactly the same R code. Install the dbplyr package then read vignette("databases", package = "dbplyr").

r data-manipulation grammar

ggplot2 - An implementation of the Grammar of Graphics in R

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. It’s hard to succinctly describe how ggplot2 works because it embodies a deep philosophy of visualisation. However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).

r visualisation data-visualisation

tidyr - Easily tidy data with spread and gather functions.

Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis. gather() takes multiple columns, and gathers them into key-value pairs: it makes “wide” data longer.

r tidy-data

mlr - mlr: Machine Learning in R

Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.