bigrquery - An interface to Google's bigquery from R.

  •        17

The low-level API provides thin wrappers over the underlying REST API. All the low-level functions start with bq_, and mostly have the form bq_noun_verb(). This level of abstraction is most appropriate if you’re familiar with the REST API and you want do something not supported in the higher-level APIs. The DBI interface wraps the low-level API and makes working with BigQuery like working with any other database system. This is most convenient layer if you want to execute SQL queries in BigQuery or upload smaller amounts (i.e. <100 MB) of data.



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sparklyr - R interface for Apache Spark

  •    R

If you use the RStudio IDE, you should also download the latest preview release of the IDE which includes several enhancements for interacting with Spark (see the RStudio IDE section below for more details). The returned Spark connection (sc) provides a remote dplyr data source to the Spark cluster.

SQL-Server-R-Services-Samples - Advanced analytics samples and templates using SQL Server R Services

  •    R

In these examples, we will demonstrate how to develop and deploy end-to-end advanced analytics solutions with SQL Server 2016 R Services.Develop models in R IDE. SQL Server 2016 R services allows Data Scientists to develop solutions in an R IDE (such as RStudio, Visual Studio R Tools) with Open Source R or Microsoft R Server, using data residing in SQL Server, and computing done in-database.

dplyr - dplyr: A grammar of data manipulation

  •    R

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").

nyc-taxi-data - Import public NYC taxi and Uber trip data into PostgreSQL / PostGIS database, analyze with R

  •    R

This repo provides scripts to download, process, and analyze data for over 1.8 billion taxi and for-hire vehicle (Uber, Lyft, etc.) trips originating in New York City since 2009. The data is stored in a PostgreSQL database, and uses PostGIS for spatial calculations, in particular mapping latitude/longitude coordinates to census tracts. Most of the raw data comes from the NYC Taxi & Limousine Commission. The 2014 Uber data comes via FiveThirtyEight, who obtained it via a FOIL request. In August 2016, the TLC began providing for-hire vehicle trip records in addition to taxi trips.

SQL Relay - Database Connection Pool library with API available in all programming languages

  •    C++

SQL Relay is a persistent database connection pooling, proxying and load balancing system for Unix and Linux supporting ODBC, and all major databases. It has APIs for C, C++, ODBC, Perl, Perl-DBI, Python, Python-DB, Zope, PHP, Ruby, Ruby-DBI, Java, TCL and Erlang, drop-in replacement libraries for MySQL and PostgreSQL clients.

swirl - :cyclone: Learn R, in R.

  •    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.

lubridate - Make working with dates in R just that little bit easier

  •    R

Date-time data can be frustrating to work with in R. R commands for date-times are generally unintuitive and change depending on the type of date-time object being used. Moreover, the methods we use with date-times must be robust to time zones, leap days, daylight savings times, and other time related quirks, and R lacks these capabilities in some situations. Lubridate makes it easier to do the things R does with date-times and possible to do the things R does not. If you are new to lubridate, the best place to start is the date and times chapter in R for data science.

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

  •    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).

linux-sgx - Intel SGX for Linux*

  •    C++

Intel(R) Software Guard Extensions (Intel(R) SGX) is an Intel technology for application developers seeking to protect select code and data from disclosure or modification. The Linux* Intel(R) SGX software stack is comprised of the Intel(R) SGX driver, the Intel(R) SGX SDK, and the Intel(R) SGX Platform Software (PSW). The Intel(R) SGX SDK and Intel(R) SGX PSW are hosted in the linux-sgx project.

rtweet - 🐦 R client for interacting with Twitter's [stream and REST] APIs

  •    R

R client for accessing Twitter’s REST and stream APIs. Check out the rtweet package documentation website. All users must be authorized to interact with Twitter’s APIs. To become authorized, follow the instructions below to (1) make a Twitter app and (2) create and save your access token (using one of the two authorization methods described below).

StatET - Eclipse based IDE and Tools for R

  •    Java

StatET is an Eclipse based IDE (integrated development environment) for R. It offers a set of mature tools for R coding and package building. This includes a fully integrated R Console, Object Browser and R Help System, whereas multiple local and remote installations of R are supported.

timevis - Create interactive timeline visualizations in R

  •    R

Copyright 2016 Dean Attali. Licensed under the MIT license. timevis lets you create rich and fully interactive timeline visualizations in R. Timelines can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. timevis includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. This package is based on the vis.js Timeline module and the htmlwidgets R package.

xaringan - Presentation Ninja 幻灯忍者 · 写轮眼

  •    R

An R package for creating slideshows with remark.js through R Markdown. The package name xaringan comes from Sharingan, a dōjutsu in Naruto with two abilities: the "Eye of Insight" and the "Eye of Hypnotism". A presentation ninja should have these basic abilities, and I think remark.js may help you acquire these abilities, even if you are not a member of the Uchiha clan. If you use RStudio, it is easy to get started from the menu File -> New File -> R Markdown -> From Template -> Ninja Presentation, and you will see an R Markdown example. Press the Knit button to compile it, or use the RStudio Addin Infinite Moon Reader to live preview the slides (every time you update and save the Rmd document, the slides will be automatically reloaded; make sure the Rmd document is on focus when you click the addin). Please see the issue #2 if you do not see the template or addin in RStudio.

DT - R Interface to the jQuery Plug-in DataTables

  •    R

This package provides a function datatable() to display R data via the DataTables library (N.B. not to be confused with the data.table package). See the full documentation at Please use Github issues only if you want to file bug reports or feature requests, and you are expected to ask questions on StackOverflow with at least the tags r and dt.

Creating-maps-in-R - Introductory tutorial on graphical display of geographical information in R.

  •    TeX

This tutorial is an introduction to visualising and analysing spatial data in R based on the sp class system. For a guide to the more recent sf package check out Chapter 2 of the in-development book Geocomputation with R, the source code of which can be found at Although sf supersedes sp in many ways, there is still merit in learning the content in this tutorial, which teaches principles that will be useful regardless of software. Specifically this tutorial focusses on map-making with R's 'base' graphics and various dedicated map-making packages for R including tmap and leaflet. It aims to teach the basics of using R as a fast, user-friendly and extremely powerful command-line Geographic Information System (GIS).

mkl-dnn - Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)

  •    C++

Intel MKL-DNN repository migrated to The old address will continue to be available and will redirect to the new repo. Please update your links. Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

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

  •    R

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

forcats - 🐈🐈🐈🐈: tools for working with categorical variables (factors)

  •    R

R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Historically, factors were much easier to work with than character vectors, so many base R functions automatically convert character vectors to factors. (For historical context, I recommend stringsAsFactors: An unauthorized biography by Roger Peng, and stringsAsFactors = <sigh> by Thomas Lumley. If you want to learn more about other approaches to working with factors and categorical data, I recommend Wrangling categorical data in R, by Amelia McNamara and Nicholas Horton.) These days, making factors automatically is no longer so helpful, so packages in the tidyverse never create them automatically. However, factors are still useful when you have true categorical data, and when you want to override the ordering of character vectors to improve display. The goal of the forcats package is to provide a suite of useful tools that solve common problems with factors. If you’re not familiar with strings, the best place to start is the chapter on factors in R for Data Science.

future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone

  •    R

The purpose of the future package is to provide a very simple and uniform way of evaluating R expressions asynchronously using various resources available to the user. In programming, a future is an abstraction for a value that may be available at some point in the future. The state of a future can either be unresolved or resolved. As soon as it is resolved, the value is available instantaneously. If the value is queried while the future is still unresolved, the current process is blocked until the future is resolved. It is possible to check whether a future is resolved or not without blocking. Exactly how and when futures are resolved depends on what strategy is used to evaluate them. For instance, a future can be resolved using a sequential strategy, which means it is resolved in the current R session. Other strategies may be to resolve futures asynchronously, for instance, by evaluating expressions in parallel on the current machine or concurrently on a compute cluster.