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  1. 2
      .gitignore
  2. 3
      .gitmodules
  3. 3
      Jenkinsfile
  4. 29
      LICENSE
  5. 6
      Readme.md
  6. 4
      assets/fonts/font-awesome.css
  7. 13
      assets/fonts/material-icons.css
  8. BIN
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  9. BIN
      assets/fonts/specimen/FontAwesome.woff
  10. BIN
      assets/fonts/specimen/FontAwesome.woff2
  11. BIN
      assets/fonts/specimen/MaterialIcons-Regular.ttf
  12. BIN
      assets/fonts/specimen/MaterialIcons-Regular.woff
  13. BIN
      assets/fonts/specimen/MaterialIcons-Regular.woff2
  14. BIN
      assets/images/favicon.png
  15. 20
      assets/images/icons/bitbucket.1b09e088.svg
  16. 18
      assets/images/icons/github.f0b8504a.svg
  17. 38
      assets/images/icons/gitlab.6dd19c00.svg
  18. 1
      assets/javascripts/application.e72fd936.js
  19. 1
      assets/javascripts/lunr/lunr.da.js
  20. 1
      assets/javascripts/lunr/lunr.de.js
  21. 1
      assets/javascripts/lunr/lunr.du.js
  22. 1
      assets/javascripts/lunr/lunr.es.js
  23. 1
      assets/javascripts/lunr/lunr.fi.js
  24. 1
      assets/javascripts/lunr/lunr.fr.js
  25. 1
      assets/javascripts/lunr/lunr.hu.js
  26. 1
      assets/javascripts/lunr/lunr.it.js
  27. 1
      assets/javascripts/lunr/lunr.jp.js
  28. 1
      assets/javascripts/lunr/lunr.multi.js
  29. 1
      assets/javascripts/lunr/lunr.no.js
  30. 1
      assets/javascripts/lunr/lunr.pt.js
  31. 1
      assets/javascripts/lunr/lunr.ro.js
  32. 1
      assets/javascripts/lunr/lunr.ru.js
  33. 1
      assets/javascripts/lunr/lunr.stemmer.support.js
  34. 1
      assets/javascripts/lunr/lunr.sv.js
  35. 1
      assets/javascripts/lunr/lunr.tr.js
  36. 1
      assets/javascripts/lunr/tinyseg.js
  37. 1
      assets/javascripts/modernizr.1aa3b519.js
  38. 1176
      assets/stylesheets/application-palette.22915126.css
  39. 2552
      assets/stylesheets/application.451f80e5.css
  40. 1755
      awesome-big-data/index.html
  41. 2681
      awesome-public-datasets/index.html
  42. 2998
      awesome-python/index.html
  43. 1435
      awesome-r/index.html
  44. 1119
      awesome-rest/index.html
  45. 1075
      awesome-vim/index.html
  46. 729
      docs/awesome-big-data.md
  47. 1815
      docs/awesome-public-datasets.md
  48. 1339
      docs/awesome-python.md
  49. 615
      docs/awesome-r.md
  50. 252
      docs/awesome-rest.md
  51. 195
      docs/awesome-vim.md
  52. 23
      docs/index.md
  53. 422
      index.html
  54. 1
      mkdocs-material
  55. 33
      mkdocs.yml
  56. 2986
      search/lunr.js
  57. 94
      search/main.js
  58. 1
      search/search_index.json
  59. 127
      search/worker.js
  60. 38
      sitemap.xml
  61. BIN
      sitemap.xml.gz

2
.gitignore vendored

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# mkdocs site
site/

3
.gitmodules vendored

@ -1,3 +0,0 @@ @@ -1,3 +0,0 @@
[submodule "mkdocs-material"]
path = mkdocs-material
url = https://git.charlesreid1.com/charlesreid1/mkdocs-material

3
Jenkinsfile vendored

@ -1,3 +0,0 @@ @@ -1,3 +0,0 @@
node {
echo 'Hello from Pipeline'
}

29
LICENSE

@ -1,29 +0,0 @@ @@ -1,29 +0,0 @@
BSD 3-Clause License
Copyright (c) 2018, Charles Reid
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

6
Readme.md

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# search-demo-mkdocs-material
Using mkdocs-material to make a pile of markdown documents containing links
to useful resources into a searchable, beautiful HTML page.
See the demo in action here: [https://pages.charlesreid1.com/search-demo-mkdocs-material](https://pages.charlesreid1.com/search-demo-mkdocs-material)

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assets/fonts/material-icons.css

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/*!
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy
* of the License at:
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING, SOFTWARE
* DISTRIBUTED UNDER THE LICENSE IS DISTRIBUTED ON AN "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.
* SEE THE LICENSE FOR THE SPECIFIC LANGUAGE GOVERNING PERMISSIONS AND
* LIMITATIONS UNDER THE LICENSE.
*/@font-face{font-family:"Material Icons";font-style:normal;font-weight:400;src:local("Material Icons"),local("MaterialIcons-Regular"),url("specimen/MaterialIcons-Regular.woff2") format("woff2"),url("specimen/MaterialIcons-Regular.woff") format("woff"),url("specimen/MaterialIcons-Regular.ttf") format("truetype")}

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!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var i=Array.prototype.slice.call(arguments),t=i.join("-"),r="",n=[],s=[],p=0;p<i.length;++p)"en"==i[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[i[p]].wordCharacters,n.unshift(e[i[p]].stopWordFilter),n.push(e[i[p]].stemmer),s.push(e[i[p]].stemmer));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+t),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});

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!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,n,i;e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stA-Za-z",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=(r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){var e,i,t=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],o=[new r("dt",-1,-1),new r("vt",-1,-1)],s=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],a=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],m=[119,125,149,1],l=new n;this.setCurrent=function(e){l.setCurrent(e)},this.getCurrent=function(){return l.getCurrent()},this.stem=function(){var r,n,u,d,c=l.cursor;return function(){var r,n=l.cursor+3;if(i=l.limit,0<=n||n<=l.limit){for(e=n;;){if(r=l.cursor,l.in_grouping(a,97,248)){l.cursor=r;break}if(r>=l.limit)return;l.cursor=r+1}for(;!l.out_grouping(a,97,248);){if(l.cursor>=l.limit)return;l.cursor++}(i=l.cursor)<e&&(i=e)}}(),l.limit_backward=c,l.cursor=l.limit,function(){var e,r,n;if(l.cursor>=i&&(r=l.limit_backward,l.limit_backward=i,l.ket=l.cursor,e=l.find_among_b(t,29),l.limit_backward=r,e))switch(l.bra=l.cursor,e){case 1:l.slice_del();break;case 2:n=l.limit-l.cursor,l.in_grouping_b(m,98,122)?l.slice_del():(l.cursor=l.limit-n,l.eq_s_b(1,"k")&&l.out_grouping_b(a,97,248)&&l.slice_del());break;case 3:l.slice_from("er")}}(),l.cursor=l.limit,n=l.limit-l.cursor,l.cursor>=i&&(r=l.limit_backward,l.limit_backward=i,l.ket=l.cursor,l.find_among_b(o,2)?(l.bra=l.cursor,l.limit_backward=r,l.cursor=l.limit-n,l.cursor>l.limit_backward&&(l.cursor--,l.bra=l.cursor,l.slice_del())):l.limit_backward=r),l.cursor=l.limit,l.cursor>=i&&(d=l.limit_backward,l.limit_backward=i,l.ket=l.cursor,(u=l.find_among_b(s,11))?(l.bra=l.cursor,l.limit_backward=d,1==u&&l.slice_del()):l.limit_backward=d),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});

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assets/javascripts/lunr/lunr.stemmer.support.js

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!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,n,t;e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stA-Za-z",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=(r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){var e,t,i=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],s=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],o=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],u=[119,127,149],m=new n;this.setCurrent=function(e){m.setCurrent(e)},this.getCurrent=function(){return m.getCurrent()},this.stem=function(){var r,n=m.cursor;return function(){var r,n=m.cursor+3;if(t=m.limit,0<=n||n<=m.limit){for(e=n;;){if(r=m.cursor,m.in_grouping(o,97,246)){m.cursor=r;break}if(m.cursor=r,m.cursor>=m.limit)return;m.cursor++}for(;!m.out_grouping(o,97,246);){if(m.cursor>=m.limit)return;m.cursor++}(t=m.cursor)<e&&(t=e)}}(),m.limit_backward=n,m.cursor=m.limit,function(){var e,r=m.limit_backward;if(m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(i,37),m.limit_backward=r,e))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.in_grouping_b(u,98,121)&&m.slice_del()}}(),m.cursor=m.limit,r=m.limit_backward,m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.find_among_b(s,7)&&(m.cursor=m.limit,m.ket=m.cursor,m.cursor>m.limit_backward&&(m.bra=--m.cursor,m.slice_del())),m.limit_backward=r),m.cursor=m.limit,function(){var e,r;if(m.cursor>=t){if(r=m.limit_backward,m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(a,5))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.slice_from("lös");break;case 3:m.slice_from("full")}m.limit_backward=r}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});

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docs/awesome-big-data.md

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# Awesome Big Data
[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
A curated list of awesome big data frameworks, resources and other awesomeness. Inspired by [awesome-php](https://github.com/ziadoz/awesome-php), [awesome-python](https://github.com/vinta/awesome-python), [awesome-ruby](https://github.com/Sdogruyol/awesome-ruby), [hadoopecosystemtable](http://hadoopecosystemtable.github.io/) & [big-data](http://usefulstuff.io/big-data/).
Your contributions are always welcome!
- [Awesome Big Data](#awesome-bigdata)
- [RDBMS](#rdbms)
- [Frameworks](#frameworks)
- [Distributed Programming](#distributed-programming)
- [Distributed Filesystem](#distributed-filesystem)
- [Key-Map Data Model](#key-map-data-model)
- [Document Data Model](#document-data-model)
- [Key-value Data Model](#key-value-data-model)
- [Graph Data Model](#graph-data-model)
- [NewSQL Databases](#newsql-databases)
- [Columnar Databases](#columnar-databases)
- [Time-Series Databases](#time-series-databases)
- [SQL-like processing](#sql-like-processing)
- [Data Ingestion](#data-ingestion)
- [Service Programming](#service-programming)
- [Scheduling](#scheduling)
- [Machine Learning](#machine-learning)
- [Benchmarking](#benchmarking)
- [Security](#security)
- [System Deployment](#system-deployment)
- [Applications](#applications)
- [Search engine and framework](#search-engine-and-framework)
- [MySQL forks and evolutions](#mysql-forks-and-evolutions)
- [PostgreSQL forks and evolutions](#postgresql-forks-and-evolutions)
- [Memcached forks and evolutions](#memcached-forks-and-evolutions)
- [Embedded Databases](#embedded-databases)
- [Business Intelligence](#business-intelligence)
- [Data Visualization](#data-visualization)
- [Internet of things and sensor data](#internet-of-things-and-sensor-data)
- [Interesting Readings](#interesting-readings)
- [Interesting Papers](#interesting-papers)
- [Videos](#videos)
- [Books](#books)
- [Other Awesome Lists](#other-awesome-lists)
## RDBMS
* [MySQL](https://www.mysql.com/) The world's most popular open source database.
* [PostgreSQL](https://www.postgresql.org/) The world's most advanced open source database.
* [Oracle Database](http://www.oracle.com/us/corporate/features/database-12c/index.html) - object-relational database management system.
* [Teradata](http://www.teradata.com/products-and-services/teradata-database/) - high-performance MPP data warehouse platform.
## Frameworks
* [IBM Streams](https://www.ibm.com/analytics/us/en/technology/stream-computing/) - platform for distributed processing and real-time analytics. Integrates with many of the popular technologies in the Big Data ecosystem (Kafka, HDFS, Spark, etc.)
* [Apache Hadoop](http://hadoop.apache.org/) - framework for distributed processing. Integrates MapReduce (parallel processing), YARN (job scheduling) and HDFS (distributed file system).
* [Tigon](https://github.com/caskdata/tigon) - High Throughput Real-time Stream Processing Framework.
* [Pachyderm](http://pachyderm.io/) - Pachyderm is a data storage platform built on Docker and Kubernetes to provide reproducible data processing and analysis.
## Distributed Programming
* [AddThis Hydra](https://github.com/addthis/hydra) - distributed data processing and storage system originally developed at AddThis.
* [AMPLab SIMR](http://databricks.github.io/simr/) - run Spark on Hadoop MapReduce v1.
* [Apache APEX](https://apex.apache.org/) - a unified, enterprise platform for big data stream and batch processing.
* [Apache Beam](http://incubator.apache.org/projects/beam.html) - an unified model and set of language-specific SDKs for defining and executing data processing workflows.
* [Apache Crunch](http://crunch.apache.org/) - a simple Java API for tasks like joining and data aggregation that are tedious to implement on plain MapReduce.
* [Apache DataFu](http://incubator.apache.org/projects/datafu.html) - collection of user-defined functions for Hadoop and Pig developed by LinkedIn.
* [Apache Flink](http://flink.apache.org/) - high-performance runtime, and automatic program optimization.
* [Apache Gearpump](http://gearpump.apache.org/) - real-time big data streaming engine based on Akka.
* [Apache Gora](http://gora.apache.org/) - framework for in-memory data model and persistence.
* [Apache Hama](http://hama.apache.org/) - BSP (Bulk Synchronous Parallel) computing framework.
* [Apache MapReduce](https://wiki.apache.org/hadoop/MapReduce/) - programming model for processing large data sets with a parallel, distributed algorithm on a cluster.
* [Apache Pig](https://pig.apache.org/) - high level language to express data analysis programs for Hadoop.
* [Apache REEF](http://reef.apache.org/) - retainable evaluator execution framework to simplify and unify the lower layers of big data systems.
* [Apache S4](http://incubator.apache.org/projects/s4.html) - framework for stream processing, implementation of S4.
* [Apache Spark](http://spark.apache.org/) - framework for in-memory cluster computing.
* [Apache Spark Streaming](http://spark.apache.org/docs/0.7.3/streaming-programming-guide.html) - framework for stream processing, part of Spark.
* [Apache Storm](http://storm.apache.org) - framework for stream processing by Twitter also on YARN.
* [Apache Samza](http://samza.apache.org/) - stream processing framework, based on Kafka and YARN.
* [Apache Tez](http://tez.apache.org/) - application framework for executing a complex DAG (directed acyclic graph) of tasks, built on YARN.
* [Apache Twill](https://incubator.apache.org/projects/twill.html) - abstraction over YARN that reduces the complexity of developing distributed applications.
* [Baidu Bigflow](http://bigflow.cloud/en/index.html) - an interface that allows for writing distributed computing programs providing lots of simple, flexible, powerful APIs to easily handle data of any scale.
* [Cascalog](http://cascalog.org/) - data processing and querying library.
* [Cheetah](http://vldbarc.org/pvldb/vldb2010/pvldb_vol3/I08.pdf) - High Performance, Custom Data Warehouse on Top of MapReduce.
* [Concurrent Cascading](http://www.cascading.org/) - framework for data management/analytics on Hadoop.
* [Damballa Parkour](https://github.com/damballa/parkour) - MapReduce library for Clojure.
* [Datasalt Pangool](https://github.com/datasalt/pangool) - alternative MapReduce paradigm.
* [DataTorrent StrAM](https://www.datatorrent.com/) - real-time engine is designed to enable distributed, asynchronous, real time in-memory big-data computations in as unblocked a way as possible, with minimal overhead and impact on performance.
* [Facebook Corona](https://www.facebook.com/notes/facebook-engineering/under-the-hood-scheduling-mapreduce-jobs-more-efficiently-with-corona/10151142560538920) - Hadoop enhancement which removes single point of failure.
* [Facebook Peregrine](http://peregrine_mapreduce.bitbucket.org/) - Map Reduce framework.
* [Facebook Scuba](https://www.facebook.com/notes/facebook-engineering/under-the-hood-data-diving-with-scuba/10150599692628920) - distributed in-memory datastore.
* [Google Dataflow](https://googledevelopers.blogspot.it/2014/06/cloud-platform-at-google-io-new-big.html) - create data pipelines to help themæingest, transform and analyze data.
* [Google MapReduce](https://research.google.com/archive/mapreduce.html) - map reduce framework.
* [Google MillWheel](https://research.google.com/pubs/pub41378.html) - fault tolerant stream processing framework.
* [IBM Streams](https://www.ibm.com/analytics/us/en/technology/stream-computing/) - platform for distributed processing and real-time analytics. Provides toolkits for advanced analytics like geospatial, time series, etc. out of the box.
* [JAQL](https://code.google.com/p/jaql/) - declarative programming language for working with structured, semi-structured and unstructured data.
* [Kite](http://kitesdk.org/docs/current/) - is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
* [Metamarkets Druid](http://druid.io/) - framework for real-time analysis of large datasets.
* [Netflix PigPen](https://github.com/Netflix/PigPen) - map-reduce for Clojure which compiles to Apache Pig.
* [Nokia Disco](http://discoproject.org/) - MapReduce framework developed by Nokia.
* [Onyx](http://www.onyxplatform.org/) - Distributed computation for the cloud.
* [Pinterest Pinlater](https://medium.com/@Pinterest_Engineering/pinlater-an-asynchronous-job-execution-system-b8664cb8aa7d) - asynchronous job execution system.
* [Pydoop](http://crs4.github.io/pydoop/) - Python MapReduce and HDFS API for Hadoop.
* [Rackerlabs Blueflood](http://blueflood.io/) - multi-tenant distributed metric processing system
* [Skale](https://github.com/skale-me/skale-engine) - High performance distributed data processing in NodeJS.
* [Stratosphere](http://stratosphere.eu/) - general purpose cluster computing framework.
* [Streamdrill](https://streamdrill.com/) - useful for counting activities of event streams over different time windows and finding the most active one.
* [streamsx.topology](https://github.com/IBMStreams/streamsx.topology) - Libraries to enable building IBM Streams application in Java, Python or Scala.
* [Tuktu](https://github.com/UnderstandLingBV/Tuktu) - Easy-to-use platform for batch and streaming computation, built using Scala, Akka and Play!
* [Twitter Heron](https://github.com/twitter/heron) - Heron is a realtime, distributed, fault-tolerant stream processing engine from Twitter replacing Storm.
* [Twitter Scalding](https://github.com/twitter/scalding) - Scala library for Map Reduce jobs, built on Cascading.
* [Twitter Summingbird](https://github.com/twitter/summingbird) - Streaming MapReduce with Scalding and Storm, by Twitter.
* [Twitter TSAR](https://blog.twitter.com/engineering/en_us/a/2014/tsar-a-timeseries-aggregator.html) - TimeSeries AggregatoR by Twitter.
* [Wallaroo](http://www.wallaroolabs.com/community) - The ultrafast and elastic data processing engine. Big or fast data - no fuss, no Java needed.
## Distributed Filesystem
* [Ambry](https://github.com/linkedin/ambry) - a distributed object store that supports storage of trillion of small immutable objects as well as billions of large objects.
* [Apache HDFS](http://hadoop.apache.org/) - a way to store large files across multiple machines.
* [Apache Kudu](http://kudu.apache.org/) - Hadoop's storage layer to enable fast analytics on fast data.
* [BeeGFS](https://www.beegfs.io/content/) - formerly FhGFS, parallel distributed file system.
* [Ceph Filesystem](http://ceph.com/ceph-storage/file-system/) - software storage platform designed.
* [Disco DDFS](http://disco.readthedocs.org/en/latest/howto/ddfs.html) - distributed filesystem.
* [Facebook Haystack](https://www.facebook.com/note.php?note_id=76191543919) - object storage system.
* [Google Colossus](http://static.googleusercontent.com/media/research.google.reverse-proxy.org/en/us/university/relations/facultysummit2010/storage_architecture_and_challenges.pdf) - distributed filesystem (GFS2).
* [Google GFS](http://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf) - distributed filesystem.
* [Google Megastore](https://research.google.com/pubs/pub36971.html) - scalable, highly available storage.
* [GridGain](https://www.gridgain.com/) - GGFS, Hadoop compliant in-memory file system.
* [Lustre file system](http://wiki.lustre.org/) - high-performance distributed filesystem.
* [Microsoft Azure Data Lake Store](https://hadoop.apache.org/docs/current/hadoop-azure-datalake/index.html) - HDFS-compatible storage in Azure cloud
* [Quantcast File System QFS](https://www.quantcast.com/about-us/quantcast-file-system/) - open-source distributed file system.
* [Red Hat GlusterFS](http://gluster.org/) - scale-out network-attached storage file system.
* [Seaweed-FS](https://github.com/chrislusf/seaweedfs) - simple and highly scalable distributed file system.
* [Alluxio](http://www.alluxio.org/) - reliable file sharing at memory speed across cluster frameworks.
* [Tahoe-LAFS](https://www.tahoe-lafs.org/trac/tahoe-lafs) - decentralized cloud storage system.
* [Baidu File System](https://github.com/baidu/bfs) - distributed filesystem.
## Distributed Index
* [Pilosa](https://github.com/pilosa/pilosa) Open source distributed bitmap index that dramatically accelerates queries across multiple, massive data sets.
## Document Data Model
* [Actian Versant](https://www.actian.com/data-management/ingres-sql-rdbms/) - commercial object-oriented database management systems .
* [Crate Data](https://crate.io/) - is an open source massively scalable data store. It requires zero administration.
* [Facebook Apollo](http://www.infoq.com/news/2014/06/facebook-apollo) - Facebook’s Paxos-like NoSQL database.
* [jumboDB](http://comsysto.github.io/jumbodb/) - document oriented datastore over Hadoop.
* [LinkedIn Espresso](https://engineering.linkedin.com/data) - horizontally scalable document-oriented NoSQL data store.
* [MarkLogic](http://www.marklogic.com/) - Schema-agnostic Enterprise NoSQL database technology.
* [Microsoft Azure DocumentDB](https://azure.microsoft.com/en-us/services/cosmos-db/) - NoSQL cloud database service with protocol support for MongoDB
* [MongoDB](https://www.mongodb.com/) - Document-oriented database system.
* [RavenDB](https://ravendb.net/) - A transactional, open-source Document Database.
* [RethinkDB](https://rethinkdb.com/) - document database that supports queries like table joins and group by.
## Key Map Data Model
**Note**: There is some term confusion in the industry, and two different things are called "Columnar Databases". Some, listed here, are distributed, persistent databases built around the "key-map" data model: all data has a (possibly composite) key, with which a map of key-value pairs is associated. In some systems, multiple such value maps can be associated with a key, and these maps are referred to as "column families" (with value map keys being referred to as "columns").
Another group of technologies that can also be called "columnar databases" is distinguished by how it stores data, on disk or in memory -- rather than storing data the traditional way, where all column values for a given key are stored next to each other, "row by row", these systems store all *column* values next to each other. So more work is needed to get all columns for a given key, but less work is needed to get all values for a given column.
The former group is referred to as "key map data model" here. The line between these and the [Key-value Data Model](#key-value-data-model) stores is fairly blurry.
The latter, being more about the storage format than about the data model, is listed under [Columnar Databases](#columnar-databases).
You can read more about this distinction on Prof. Daniel Abadi's blog: [Distinguishing two major types of Column Stores](http://dbmsmusings.blogspot.com/2010/03/distinguishing-two-major-types-of_29.html).
* [Apache Accumulo](http://accumulo.apache.org/) - distributed key/value store, built on Hadoop.
* [Apache Cassandra](http://cassandra.apache.org/) - column-oriented distributed datastore, inspired by BigTable.
* [Apache HBase](http://hbase.apache.org/) - column-oriented distributed datastore, inspired by BigTable.
* [Baidu Tera](https://github.com/baidu/tera) - an Internet-scale database, inspired by BigTable.
* [Facebook HydraBase](https://code.facebook.com/posts/321111638043166/hydrabase-the-evolution-of-hbase-facebook/) - evolution of HBase made by Facebook.
* [Google BigTable](http://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf) - column-oriented distributed datastore.
* [Google Cloud Datastore](https://cloud.google.com/datastore/docs/concepts/overview) - is a fully managed, schemaless database for storing non-relational data over BigTable.
* [Hypertable](http://www.hypertable.org/) - column-oriented distributed datastore, inspired by BigTable.
* [InfiniDB](https://github.com/infinidb/infinidb/) - is accessed through a MySQL interface and use massive parallel processing to parallelize queries.
* [Tephra](https://github.com/caskdata/tephra) - Transactions for HBase.
* [Twitter Manhattan](https://blog.twitter.com/engineering/en_us/a/2014/manhattan-our-real-time-multi-tenant-distributed-database-for-twitter-scale.html) - real-time, multi-tenant distributed database for Twitter scale.
* [ScyllaDB](http://www.scylladb.com/) - column-oriented distributed datastore written in C++, totally compatible with Apache Cassandra.
## Key-value Data Model
* [Aerospike](http://www.aerospike.com/) - NoSQL flash-optimized, in-memory. Open source and "Server code in 'C' (not Java or Erlang) precisely tuned to avoid context switching and memory copies."
* [Amazon DynamoDB](https://aws.amazon.com/dynamodb/) - distributed key/value store, implementation of Dynamo paper.
* [Badger](https://open.dgraph.io/post/badger/) - a fast, simple, efficient, and persistent key-value store written natively in Go.
* [Bolt](https://github.com/boltdb/bolt) - an embedded key-value database for Go.
* [BTDB](https://github.com/Bobris/BTDB) - Key Value Database in .Net with Object DB Layer, RPC, dynamic IL and much more
* [BuntDB](https://github.com/tidwall/buntdb) - a fast, embeddable, in-memory key/value database for Go with custom indexing and geospatial support.
* [Edis](https://github.com/cbd/edis) - is a protocol-compatible Server replacement for Redis.
* [ElephantDB](https://github.com/nathanmarz/elephantdb) - Distributed database specialized in exporting data from Hadoop.
* [EventStore](https://geteventstore.com/) - distributed time series database.
* [GridDB](https://github.com/griddb/griddb_nosql) - suitable for sensor data stored in a timeseries.
* [HyperDex](https://github.com/rescrv/HyperDex) - a scalable, next generation key-value and document store with a wide array of features, including consistency, fault tolerance and high performance.
* [Ignite](https://ignite.apache.org/index.html) - is an in-memory key-value data store providing full SQL-compliant data access that can optionally be backed by disk storage.
* [LinkedIn Krati](https://github.com/linkedin-sna/sna-page/tree/master/krati) - is a simple persistent data store with very low latency and high throughput.
* [Linkedin Voldemort](http://www.project-voldemort.com/voldemort/) - distributed key/value storage system.
* [Oracle NoSQL Database](http://www.oracle.com/technetwork/database/database-technologies/nosqldb/overview/index.html) - distributed key-value database by Oracle Corporation.
* [Redis](https://redis.io/) - in memory key value datastore.
* [Riak](https://github.com/basho/riak) - a decentralized datastore.
* [Storehaus](https://github.com/twitter/storehaus) - library to work with asynchronous key value stores, by Twitter.
* [SummitDB](https://github.com/tidwall/summitdb) - an in-memory, NoSQL key/value database, with disk persistance and using the Raft consensus algorithm.
* [Tarantool](https://github.com/tarantool/tarantool) - an efficient NoSQL database and a Lua application server.
* [TiKV](https://github.com/pingcap/tikv) - a distributed key-value database powered by Rust and inspired by Google Spanner and HBase.
* [Tile38](https://github.com/tidwall/tile38) - a geolocation data store, spatial index, and realtime geofence, supporting a variety of object types including latitude/longitude points, bounding boxes, XYZ tiles, Geohashes, and GeoJSON
* [TreodeDB](https://github.com/Treode/store) - key-value store that's replicated and sharded and provides atomic multirow writes.
## Graph Data Model
* [AgensGraph](http://www.agensgraph.com/) - a new generation multi-model graph database for the modern complex data environment.
* [Apache Giraph](http://giraph.apache.org/) - implementation of Pregel, based on Hadoop.
* [Apache Spark Bagel](http://spark.apache.org/docs/0.7.3/bagel-programming-guide.html) - implementation of Pregel, part of Spark.
* [ArangoDB](https://www.arangodb.com/) - multi model distributed database.
* [DGraph](https://github.com/dgraph-io/dgraph) - A scalable, distributed, low latency, high throughput graph database aimed at providing Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data.
* [EliasDB](https://github.com/krotik/eliasdb) - a lightweight graph based database that does not require any third-party libraries.
* [Facebook TAO](https://www.facebook.com/notes/facebook-engineering/tao-the-power-of-the-graph/10151525983993920) - TAO is the distributed data store that is widely used at facebook to store and serve the social graph.
* [GCHQ Gaffer](https://github.com/gchq/Gaffer) - Gaffer by GCHQ is a framework that makes it easy to store large-scale graphs in which the nodes and edges have statistics.
* [Google Cayley](https://github.com/cayleygraph/cayley) - open-source graph database.
* [Google Pregel](http://kowshik.github.io/JPregel/pregel_paper.pdf) - graph processing framework.
* [GraphLab PowerGraph](https://turi.com/products/create/docs/) - a core C++ GraphLab API and a collection of high-performance machine learning and data mining toolkits built on top of the GraphLab API.
* [GraphX](https://amplab.cs.berkeley.edu/publication/graphx-grades/) - resilient Distributed Graph System on Spark.
* [Gremlin](https://github.com/tinkerpop/gremlin) - graph traversal Language.
* [Infovore](https://github.com/paulhoule/infovore) - RDF-centric Map/Reduce framework.
* [Intel GraphBuilder](https://01.org/graphbuilder/) - tools to construct large-scale graphs on top of Hadoop.
* [MapGraph](https://www.blazegraph.com/mapgraph-technology/) - Massively Parallel Graph processing on GPUs.
* [Neo4j](https://neo4j.com/) - graph database written entirely in Java.
* [OrientDB](http://orientdb.com/) - document and graph database.
* [Phoebus](https://github.com/xslogic/phoebus) - framework for large scale graph processing.
* [Titan](http://thinkaurelius.github.io/titan/) - distributed graph database, built over Cassandra.
* [Twitter FlockDB](https://github.com/twitter-archive/flockdb) - distributed graph database.
* [NodeXL](https://nodexl.codeplex.com/) - A free, open-source template for Microsoft® Excel® 2007, 2010, 2013 and 2016 that makes it easy to explore network graphs.
## Columnar Databases
**Note** please read the note on [Key-Map Data Model](#key-map-data-model) section.
* [Columnar Storage](http://the-paper-trail.org/blog/columnar-storage/) - an explanation of what columnar storage is and when you might want it.
* [Actian Vector](http://www.actian.com/) - column-oriented analytic database.
* [C-Store](http://db.lcs.mit.edu/projects/cstore/) - column oriented DBMS.
* [ClickHouse](https://clickhouse.yandex/) - an open-source column-oriented database management system that allows generating analytical data reports in real time.
* [EventQL](http://eventql.io/) - a distributed, column-oriented database built for large-scale event collection and analytics.
* [MonetDB](https://www.monetdb.org/) - column store database.
* [Parquet](http://parquet.apache.org/) - columnar storage format for Hadoop.
* [Pivotal Greenplum](https://pivotal.io/pivotal-greenplum) - purpose-built, dedicated analytic data warehouse that offers a columnar engine as well as a traditional row-based one.
* [Vertica](https://www.vertica.com/) - is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses.
* [SQream DB](http://sqream.com/) - A GPU powered big data database, designed for analytics and data warehousing, with ANSI-92 compliant SQL, suitable for data sets from 10TB to 1PB.
* [Google BigQuery](https://cloud.google.com/bigquery/what-is-bigquery) Google's cloud offering backed by their pioneering work on Dremel.
* [Amazon Redshift](https://aws.amazon.com/redshift/) Amazon's cloud offering, also based on a columnar datastore backend.
* [IndexR](https://github.com/shunfei/indexr) an open-source columnar storage format for fast & realtime analytic with big data.
## NewSQL Databases
* [Actian Ingres](http://www.actian.com/products/operational-databases/) - commercially supported, open-source SQL relational database management system.
* [Amazon RedShift](http://aws.amazon.com/redshift/) - data warehouse service, based on PostgreSQL.
* [BayesDB](http://probcomp.csail.mit.edu/bayesdb/index.html) - statistic oriented SQL database.
* [Bedrock](http://bedrockdb.com/) - a simple, modular, networked and distributed transaction layer built atop SQLite.
* [CitusDB](https://www.citusdata.com/) - scales out PostgreSQL through sharding and replication.
* [Cockroach](https://github.com/cockroachdb/cockroach) - Scalable, Geo-Replicated, Transactional Datastore.
* [Comdb2](https://github.com/bloomberg/comdb2) - a clustered RDBMS built on optimistic concurrency control techniques.
* [Datomic](http://www.datomic.com/) - distributed database designed to enable scalable, flexible and intelligent applications.
* [FoundationDB](https://foundationdb.com/) - distributed database, inspired by F1.
* [Google F1](https://research.google.com/pubs/pub41344.html) - distributed SQL database built on Spanner.
* [Google Spanner](https://research.google.com/archive/spanner.html) - globally distributed semi-relational database.
* [H-Store](http://hstore.cs.brown.edu/) - is an experimental main-memory, parallel database management system that is optimized for on-line transaction processing (OLTP) applications.
* [Haeinsa](https://github.com/VCNC/haeinsa) - linearly scalable multi-row, multi-table transaction library for HBase based on Percolator.
* [HandlerSocket](https://www.percona.com/doc/percona-server/5.5/performance/handlersocket.html) - NoSQL plugin for MySQL/MariaDB.
* [InfiniSQL](http://www.infinisql.org/) - infinity scalable RDBMS.
* [MemSQL](http://www.memsql.com/) - in memory SQL database witho optimized columnar storage on flash.
* [NuoDB](http://www.nuodb.com/) - SQL/ACID compliant distributed database.
* [Oracle TimesTen in-Memory Database](http://www.oracle.com/technetwork/database/database-technologies/timesten/overview/index.html) - in-memory, relational database management system with persistence and recoverability.
* [Pivotal GemFire XD](http://gemfirexd.docs.pivotal.io/latest/) - Low-latency, in-memory, distributed SQL data store. Provides SQL interface to in-memory table data, persistable in HDFS.
* [SAP HANA](https://hana.sap.com/abouthana.html) - is an in-memory, column-oriented, relational database management system.
* [SenseiDB](http://senseidb.github.io/sensei/) - distributed, realtime, semi-structured database.
* [Sky](http://skydb.io/) - database used for flexible, high performance analysis of behavioral data.
* [SymmetricDS](http://www.symmetricds.org/) - open source software for both file and database synchronization.
* [Map-D](https://www.mapd.com/) - GPU in-memory database, big data analysis and visualization platform
* [TiDB](https://github.com/pingcap/tidb) - TiDB is a distributed SQL database. Inspired by the design of Google F1.
* [VoltDB](https://www.voltdb.com/) - claims to be fastest in-memory database
## Time-Series Databases
* [Axibase Time Series Database](http://axibase.com/products/axibase-time-series-database/) - Integrated time series database on top of HBase with built-in visualization, rule-engine and SQL support.
* [Chronix](http://chronix.io/) - a time series storage built to store time series highly compressed and for fast access times.
* [Cube](http://square.github.io/cube/) - uses MongoDB to store time series data.
* [Heroic](https://spotify.github.io/heroic/#!/index) - is a scalable time series database based on Cassandra and Elasticsearch.
* [InfluxDB](https://www.influxdata.com/) - distributed time series database.
* [Kairosdb](https://github.com/kairosdb/kairosdb) - similar to OpenTSDB but allows for Cassandra.
* [Newts](https://opennms.github.io/newts/) - a time series database based on Apache Cassandra.
* [OpenTSDB](http://opentsdb.net) - distributed time series database on top of HBase.
* [Prometheus](https://prometheus.io/) - a time series database and service monitoring system.
* [Beringei](https://github.com/facebookincubator/beringei) - Facebook's in-memory time-series database.
* [TrailDB](http://traildb.io/) - an efficient tool for storing and querying series of events.
* [Druid](https://github.com/druid-io/druid/) Column oriented distributed data store ideal for powering interactive applications
* [Riak-TS](http://basho.com/products/riak-ts/) Riak TS is the only enterprise-grade NoSQL time series database optimized specifically for IoT and Time Series data.
* [Akumuli](https://github.com/akumuli/Akumuli) Akumuli is a numeric time-series database. It can be used to capture, store and process time-series data in real-time. The word "akumuli" can be translated from esperanto as "accumulate".
* [Rhombus](https://github.com/Pardot/Rhombus) A time-series object store for Cassandra that handles all the complexity of building wide row indexes.
* [Dalmatiner DB](https://github.com/dalmatinerdb/dalmatinerdb) Fast distributed metrics database
* [Blueflood](https://github.com/rackerlabs/blueflood) A distributed system designed to ingest and process time series data
* [Timely](https://github.com/NationalSecurityAgency/timely) Timely is a time series database application that provides secure access to time series data based on Accumulo and Grafana.
* [SiriDB](https://github.com/transceptor-technology/siridb-server) Highly-scalable, robust and fast, open source time series database with cluster functionality.
* [Thanos](https://github.com/improbable-eng/thanos) - Thanos is a set of components to create a highly available metric system with unlimited storage capacity using multiple (existing) Prometheus deployments.
## SQL-like processing
* [Actian SQL for Hadoop](http://www.actian.com/analytic-database/vectorh-sql-hadoop) - high performance interactive SQL access to all Hadoop data.
* [Apache Drill](http://drill.apache.org/) - framework for interactive analysis, inspired by Dremel.
* [Apache HCatalog](https://cwiki.apache.org/confluence/display/HCATALOG/Index) - table and storage management layer for Hadoop.
* [Apache Hive](http://hive.apache.org/) - SQL-like data warehouse system for Hadoop.
* [Apache Calcite](http://calcite.apache.org/) - framework that allows efficient translation of queries involving heterogeneous and federated data.
* [Apache Phoenix](http://phoenix.apache.org/index.html) - SQL skin over HBase.
* [Aster Database](http://www.teradata.com/products-and-services/Teradata-Aster/teradata-aster-database) - SQL-like analytic processing for MapReduce.
* [Cloudera Impala](https://www.cloudera.com/products/apache-hadoop/impala.html) - framework for interactive analysis, Inspired by Dremel.
* [Concurrent Lingual](http://www.cascading.org/projects/lingual/) - SQL-like query language for Cascading.
* [Datasalt Splout SQL](http://www.datasalt.com/products/splout-sql/) - full SQL query engine for big datasets.
* [Facebook PrestoDB](https://prestodb.io/) - distributed SQL query engine.
* [Google BigQuery](https://research.google.com/pubs/pub36632.html) - framework for interactive analysis, implementation of Dremel.
* [PipelineDB](https://www.pipelinedb.com/) - an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.
* [Pivotal HDB](https://pivotal.io/pivotal-hdb) - SQL-like data warehouse system for Hadoop.
* [RainstorDB](http://rainstor.com/products/rainstor-database/) - database for storing petabyte-scale volumes of structured and semi-structured data.
* [Spark Catalyst](https://github.com/apache/spark/tree/master/sql) - is a Query Optimization Framework for Spark and Shark.
* [SparkSQL](https://databricks.com/blog/2014/03/26/spark-sql-manipulating-structured-data-using-spark-2.html) - Manipulating Structured Data Using Spark.
* [Splice Machine](https://www.splicemachine.com/) - a full-featured SQL-on-Hadoop RDBMS with ACID transactions.
* [Stinger](https://hortonworks.com/innovation/stinger/) - interactive query for Hive.
* [Tajo](http://tajo.apache.org/) - distributed data warehouse system on Hadoop.
* [Trafodion](https://wiki.trafodion.org/wiki/index.php/Main_Page) - enterprise-class SQL-on-HBase solution targeting big data transactional or operational workloads.
## Data Ingestion
* [Amazon Kinesis](https://aws.amazon.com/kinesis/) - real-time processing of streaming data at massive scale.
* [Apache Chukwa](http://chukwa.apache.org/) - data collection system.
* [Apache Flume](http://flume.apache.org/) - service to manage large amount of log data.
* [Apache Kafka](http://kafka.apache.org/) - distributed publish-subscribe messaging system.
* [Apache NiFi](https://nifi.apache.org/) - Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems.
* [Apache Sqoop](http://sqoop.apache.org/) - tool to transfer data between Hadoop and a structured datastore.
* [Cloudera Morphlines](https://github.com/cloudera/cdk/tree/master/cdk-morphlines) - framework that help ETL to Solr, HBase and HDFS.
* [Embulk](http://www.embulk.org) - open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services.
* [Facebook Scribe](https://github.com/facebookarchive/scribe) - streamed log data aggregator.
* [Fluentd](http://www.fluentd.org) - tool to collect events and logs.
* [Google Photon](https://research.google.com/pubs/pub41318.html) - geographically distributed system for joining multiple continuously flowing streams of data in real-time with high scalability and low latency.
* [Heka](https://github.com/mozilla-services/heka) - open source stream processing software system.
* [HIHO](https://github.com/sonalgoyal/hiho) - framework for connecting disparate data sources with Hadoop.
* [Kestrel](https://github.com/papertrail/kestrel) - distributed message queue system.
* [LinkedIn Databus](https://engineering.linkedin.com/data) - stream of change capture events for a database.
* [LinkedIn Kamikaze](https://github.com/linkedin/kamikaze) - utility package for compressing sorted integer arrays.
* [LinkedIn White Elephant](https://github.com/linkedin/white-elephant) - log aggregator and dashboard.
* [Logstash](https://www.elastic.co/products/logstash) - a tool for managing events and logs.
* [Netflix Suro](https://github.com/Netflix/suro) - log agregattor like Storm and Samza based on Chukwa.
* [Pinterest Secor](https://github.com/pinterest/secor) - is a service implementing Kafka log persistance.
* [Linkedin Gobblin](https://github.com/linkedin/gobblin) - linkedin's universal data ingestion framework.
* [Skizze](https://github.com/skizzehq/skizze) - sketch data store to deal with all problems around counting and sketching using probabilistic data-structures.
* [StreamSets Data Collector](https://github.com/streamsets/datacollector) - continuous big data ingest infrastructure with a simple to use IDE.
* [Yahoo Pulsar](https://github.com/apache/incubator-pulsar) - a distributed pub-sub messaging platform with a very flexible messaging model and an intuitive client API.
* [Alooma](https://www.alooma.com/integrations/mysql) - data pipeline as a service enabling moving data sources such as MySQL into data warehouses.
## Service Programming
* [Akka Toolkit](http://akka.io/) - runtime for distributed, and fault tolerant event-driven applications on the JVM.
* [Apache Avro](http://avro.apache.org/) - data serialization system.
* [Apache Curator](http://curator.apache.org/) - Java libaries for Apache ZooKeeper.
* [Apache Karaf](http://karaf.apache.org/) - OSGi runtime that runs on top of any OSGi framework.
* [Apache Thrift](http://thrift.apache.org//) - framework to build binary protocols.
* [Apache Zookeeper](http://zookeeper.apache.org/) - centralized service for process management.
* [Google Chubby](https://research.google.com/archive/chubby.html) - a lock service for loosely-coupled distributed systems.
* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
* [Linkedin Norbert](https://engineering.linkedin.com/data) - cluster manager.
* [OpenMPI](https://www.open-mpi.org/) - message passing framework.
* [Serf](https://www.serf.io/) - decentralized solution for service discovery and orchestration.
* [Spotify Luigi](https://github.com/spotify/luigi) - a Python package for building complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more.
* [Spring XD](https://github.com/spring-projects/spring-xd) - distributed and extensible system for data ingestion, real time analytics, batch processing, and data export.
* [Twitter Elephant Bird](https://github.com/twitter/elephant-bird) - libraries for working with LZOP-compressed data.
* [Twitter Finagle](https://twitter.github.io/finagle/) - asynchronous network stack for the JVM.
## Scheduling
* [Apache Airflow](https://github.com/apache/incubator-airflow) - a platform to programmatically author, schedule and monitor workflows.
* [Apache Aurora](http://aurora.apache.org/) - is a service scheduler that runs on top of Apache Mesos.
* [Apache Falcon](http://falcon.apache.org/) - data management framework.
* [Apache Oozie](http://oozie.apache.org/) - workflow job scheduler.
* [Azure Data Factory](https://docs.microsoft.com/en-us/azure/data-factory/data-factory-introduction) - cloud-based pipeline orchestration for on-prem, cloud and HDInsight
* [Chronos](http://mesos.github.io/chronos/) - distributed and fault-tolerant scheduler.
* [Linkedin Azkaban](https://azkaban.github.io/) - batch workflow job scheduler.
* [Schedoscope](https://github.com/ottogroup/schedoscope) - Scala DSL for agile scheduling of Hadoop jobs.
* [Sparrow](https://github.com/radlab/sparrow) - scheduling platform.
## Machine Learning
* [Azure ML Studio](https://studio.azureml.net/) - Cloud-based AzureML, R, Python Machine Learning platform
* [brain](https://github.com/harthur/brain) - Neural networks in JavaScript.
* [Cloudera Oryx](https://github.com/cloudera/oryx) - real-time large-scale machine learning.
* [Concurrent Pattern](http://www.cascading.org/projects/pattern/) - machine learning library for Cascading.
* [convnetjs](https://github.com/karpathy/convnetjs) - Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
* [DataVec](https://github.com/deeplearning4j/DataVec) - A vectorization and data preprocessing library for deep learning in Java and Scala. Part of the Deeplearning4j ecosystem.
* [Deeplearning4j](https://github.com/deeplearning4j) - Fast, open deep learning for the JVM (Java, Scala, Clojure). A neural network configuration layer powered by a C++ library. Uses Spark and Hadoop to train nets on multiple GPUs and CPUs.
* [Decider](https://github.com/danielsdeleo/Decider) - Flexible and Extensible Machine Learning in Ruby.
* [ENCOG](http://www.heatonresearch.com/encog/) - machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data.
* [etcML](http://www.etcml.com/) - text classification with machine learning.
* [Etsy Conjecture](https://github.com/etsy/Conjecture) - scalable Machine Learning in Scalding.
* [GraphLab Create](https://dato.com/products/create/) - A machine learning platform in Python with a broad collection of ML toolkits, data engineering, and deployment tools.
* [H2O](https://github.com/h2oai/h2o-3/) - statistical, machine learning and math runtime with Hadoop. R and Python.
* [Keras](https://github.com/fchollet/keras) - An intuitive neural net API inspired by Torch that runs atop Theano and Tensorflow.
* [Mahout](http://mahout.apache.org/) - An Apache-backed machine learning library for Hadoop.
* [MLbase](http://www.mlbase.org/) - distributed machine learning libraries for the BDAS stack.
* [MLPNeuralNet](https://github.com/nikolaypavlov/MLPNeuralNet) - Fast multilayer perceptron neural network library for iOS and Mac OS X.
* [MOA](http://moa.cms.waikato.ac.nz) - MOA performs big data stream mining in real time, and large scale machine learning.
* [MonkeyLearn](https://monkeylearn.com/) - Text mining made easy. Extract and classify data from text.
* [ND4J](https://github.com/deeplearning4j/nd4j) - A matrix library for the JVM. Numpy for Java.
* [nupic](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.
* [PredictionIO](http://predictionio.incubator.apache.org/index.html) - machine learning server buit on Hadoop, Mahout and Cascading.
* [RL4J](https://github.com/deeplearning4j/rl4j) - Reinforcement learning for Java and Scala. Includes Deep-Q learning and A3C algorithms, and integrates with Open AI's Gym. Runs in the Deeplearning4j ecosystem.
* [SAMOA](http://samoa.incubator.apache.org/) - distributed streaming machine learning framework.
* [scikit-learn](https://github.com/scikit-learn/scikit-learn) - scikit-learn: machine learning in Python.
* [Spark MLlib](http://spark.apache.org/docs/0.9.0/mllib-guide.html) - a Spark implementation of some common machine learning (ML) functionality.
* [Sibyl](https://users.soe.ucsc.edu/~niejiazhong/slides/chandra.pdf) - System for Large Scale Machine Learning at Google.
* [TensorFlow](https://github.com/tensorflow/tensorflow) - Library from Google for machine learning using data flow graphs.
* [Theano](https://github.com/theano) - A Python-focused machine learning library supported by the University of Montreal.
* [Torch](https://github.com/torch) - A deep learning library with a Lua API, supported by NYU and Facebook.
* [Velox](https://github.com/amplab/velox-modelserver) - System for serving machine learning predictions.
* [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki) - learning system sponsored by Microsoft and Yahoo!.
* [WEKA](http://www.cs.waikato.ac.nz/ml/weka/) - suite of machine learning software.
* [BidMach](https://github.com/BIDData/BIDMach) - CPU and GPU-accelerated Machine Learning Library.
## Benchmarking
* [Apache Hadoop Benchmarking](https://issues.apache.org/jira/browse/MAPREDUCE-3561) - micro-benchmarks for testing Hadoop performances.
* [Berkeley SWIM Benchmark](https://github.com/SWIMProjectUCB/SWIM/wiki) - real-world big data workload benchmark.
* [Intel HiBench](https://github.com/intel-hadoop/HiBench) - a Hadoop benchmark suite.
* [PUMA Benchmarking](https://issues.apache.org/jira/browse/MAPREDUCE-5116) - benchmark suite for MapReduce applications.
* [Yahoo Gridmix3](http://yahoohadoop.tumblr.com/post/98294079296/gridmix3-emulating-production-workload-for) - Hadoop cluster benchmarking from Yahoo engineer team.
* [Deeplearning4j Benchmarks](https://github.com/deeplearning4j/dl4j-benchmark)
## Security
* [Apache Ranger](http://ranger.apache.org/) - Central security admin & fine-grained authorization for Hadoop
* [Apache Eagle](http://eagle.apache.org/) - real time monitoring solution
* [Apache Knox Gateway](http://knox.apache.org/) - single point of secure access for Hadoop clusters.
* [Apache Sentry](http://incubator.apache.org/projects/sentry.html) - security module for data stored in Hadoop.
* [BDA](https://github.com/kotobukki/BDA/) - The vulnerability detector for Hadoop and Spark
## System Deployment
* [Apache Ambari](http://ambari.apache.org/) - operational framework for Hadoop mangement.
* [Apache Bigtop](http://bigtop.apache.org//) - system deployment framework for the Hadoop ecosystem.
* [Apache Helix](http://helix.apache.org/) - cluster management framework.
* [Apache Mesos](http://mesos.apache.org/) - cluster manager.
* [Apache Slider](https://github.com/apache/incubator-slider) - is a YARN application to deploy existing distributed applications on YARN.
* [Apache Whirr](http://whirr.apache.org/) - set of libraries for running cloud services.
* [Apache YARN](https://hortonworks.com/hadoop/yarn/) - Cluster manager.
* [Brooklyn](http://brooklyncentral.github.io/) - library that simplifies application deployment and management.
* [Buildoop](http://buildoop.github.io/) - Similar to Apache BigTop based on Groovy language.
* [Cloudera HUE](http://gethue.com/) - web application for interacting with Hadoop.
* [Facebook Prism](http://www.wired.com/2012/08/facebook-prism/) - multi datacenters replication system.
* [Google Borg](https://www.wired.com/2013/03/google-borg-twitter-mesos/all/) - job scheduling and monitoring system.
* [Google Omega](https://www.youtube.com/watch?v=0ZFMlO98Jkc) - job scheduling and monitoring system.
* [Hortonworks HOYA](https://hortonworks.com/blog/introducing-hoya-hbase-on-yarn/) - application that can deploy HBase cluster on YARN.
* [Kubernetes](https://kubernetes.io/) - a system for automating deployment, scaling, and management of containerized applications.
* [Marathon](https://github.com/mesosphere/marathon) - Mesos framework for long-running services.
## Applications
* [411](https://github.com/etsy/411) - an web application for alert management resulting from scheduled searches into Elasticsearch.
* [Adobe spindle](https://github.com/adobe-research/spindle) - Next-generation web analytics processing with Scala, Spark, and Parquet.
* [Apache Kiji](http://www.kiji.org.s3-website-us-east-1.amazonaws.com) - framework to collect and analyze data in real-time, based on HBase.
* [Apache Metron](http://metron.apache.org/) - a platform that integrates a variety of open source big data technologies in order to offer a centralized tool for security monitoring and analysis.
* [Apache Nutch](http://nutch.apache.org/) - open source web crawler.
* [Apache OODT](http://oodt.apache.org/) - capturing, processing and sharing of data for NASA's scientific archives.
* [Apache Tika](https://tika.apache.org/) - content analysis toolkit.
* [Argus](https://github.com/salesforce/Argus) - Time series monitoring and alerting platform.
* [AthenaX](https://github.com/uber/AthenaX) - a streaming analytics platform that enables users to run production-quality, large scale streaming analytics using Structured Query Language (SQL).
* [Atlas](https://github.com/Netflix/atlas) - a backend for managing dimensional time series data.
* [Countly](https://count.ly/) - open source mobile and web analytics platform, based on Node.js & MongoDB.
* [Domino](https://www.dominodatalab.com/) - Run, scale, share, and deploy models — without any infrastructure.
* [Eclipse BIRT](http://www.eclipse.org/birt/) - Eclipse-based reporting system.
* [ElastAert](https://github.com/Yelp/elastalert) - ElastAlert is a simple framework for alerting on anomalies, spikes, or other patterns of interest from data in ElasticSearch.
* [Eventhub](https://github.com/Codecademy/EventHub) - open source event analytics platform.
* [Hermes](https://github.com/allegro/hermes) - asynchronous message broker built on top of Kafka.
* [HIPI Library](http://hipi.cs.virginia.edu/) - API for performing image processing tasks on Hadoop's MapReduce.
* [Hunk](https://www.splunk.com/en_us/download/hunk.html) - Splunk analytics for Hadoop.
* [Imhotep](http://opensource.indeedeng.io/imhotep/) - Large scale analytics platform by indeed.
* [MADlib](http://madlib.incubator.apache.org/community/) - data-processing library of an RDBMS to analyze data.
* [Kapacitor](https://github.com/influxdata/kapacitor) - an open source framework for processing, monitoring, and alerting on time series data.
* [Kylin](http://kylin.apache.org/) - open source Distributed Analytics Engine from eBay.
* [PivotalR](https://github.com/pivotalsoftware/PivotalR) - R on Pivotal HD / HAWQ and PostgreSQL.
* [Rakam](https://github.com/rakam-io/rakam) - open-source real-time custom analytics platform powered by Postgresql, Kinesis and PrestoDB.
* [Qubole](https://www.qubole.com/) - auto-scaling Hadoop cluster, built-in data connectors.
* [Sense](https://sense.io/) - Cloud Platform for Data Science and Big Data Analytics.
* [SnappyData](https://github.com/SnappyDataInc/snappydata) - a distributed in-memory data store for real-time operational analytics, delivering stream analytics, OLTP (online transaction processing) and OLAP (online analytical processing) built on Spark in a single integrated cluster.
* [Snowplow](https://github.com/snowplow/snowplow) - enterprise-strength web and event analytics, powered by Hadoop, Kinesis, Redshift and Postgres.
* [SparkR](http://amplab-extras.github.io/SparkR-pkg/) - R frontend for Spark.
* [Splunk](https://www.splunk.com/) - analyzer for machine-generated data.
* [Sumo Logic](https://www.sumologic.com/) - cloud based analyzer for machine-generated data.
* [Talend](http://www.talend.com/products/big-data/) - unified open source environment for YARN, Hadoop, HBASE, Hive, HCatalog & Pig.
* [Warp](https://warp.one//) - query by example tool for big data (OS X app)
## Search engine and framework
* [Apache Lucene](http://lucene.apache.org/) - Search engine library.
* [Apache Solr](http://lucene.apache.org/solr/) - Search platform for Apache Lucene.
* [Elassandra](https://github.com/strapdata/elassandra) - is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture.
* [ElasticSearch](https://www.elastic.co/) - Search and analytics engine based on Apache Lucene.
* [Enigma.io](https://www.enigma.com/) – Freemium robust web application for exploring, filtering, analyzing, searching and exporting massive datasets scraped from across the Web.
* [Facebook Unicorn](https://www.facebook.com/publications/219621248185635/) - social graph search platform.
* [Google Caffeine](https://googleblog.blogspot.it/2010/06/our-new-search-index-caffeine.html) - continuous indexing system.
* [Google Percolator](https://research.google.com/pubs/pub36726.html) - continuous indexing system.
* [TeraGoogle]() - large search index.
* [HBase Coprocessor](https://blogs.apache.org/hbase/entry/coprocessor_introduction) - implementation of Percolator, part of HBase.
* [Lily HBase Indexer](http://ngdata.github.io/hbase-indexer/) - quickly and easily search for any content stored in HBase.
* [LinkedIn Bobo](http://senseidb.github.io/bobo/) - is a Faceted Search implementation written purely in Java, an extension to Apache Lucene.
* [LinkedIn Cleo](https://github.com/linkedin/cleo) - is a flexible software library for enabling rapid development of partial, out-of-order and real-time typeahead search.
* [LinkedIn Galene](https://engineering.linkedin.com/search/did-you-mean-galene) - search architecture at LinkedIn.
* [LinkedIn Zoie](https://github.com/senseidb/zoie) - is a realtime search/indexing system written in Java.
* [MG4J](http://mg4j.di.unimi.it/) - MG4J (Managing Gigabytes for Java) is a full-text search engine for large document collections written in Java. It is highly customisable, high-performance and provides state-of-the-art features and new research algorithms.
* [Sphinx Search Server](http://sphinxsearch.com/) - fulltext search engine.
* [Vespa](http://vespa.ai/) - is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.
## MySQL forks and evolutions
* [Amazon RDS](https://aws.amazon.com/rds/) - MySQL databases in Amazon's cloud.
* [Drizzle](http://www.drizzle.org/) - evolution of MySQL 6.0.
* [Google Cloud SQL](https://cloud.google.com/sql/docs/) - MySQL databases in Google's cloud.
* [MariaDB](https://mariadb.org/) - enhanced, drop-in replacement for MySQL.
* [MySQL Cluster](https://www.mysql.com/products/cluster/) - MySQL implementation using NDB Cluster storage engine.
* [Percona Server](https://www.percona.com/software/mysql-database/percona-server) - enhanced, drop-in replacement for MySQL.
* [ProxySQL](https://github.com/renecannao/proxysql) - High Performance Proxy for MySQL.
* [TokuDB](https://www.percona.com/) - TokuDB is a storage engine for MySQL and MariaDB.
* [WebScaleSQL](http://webscalesql.org/) - is a collaboration among engineers from several companies that face similar challenges in running MySQL at scale.
## PostgreSQL forks and evolutions
* [HadoopDB](http://db.cs.yale.edu/hadoopdb/hadoopdb.html) - hybrid of MapReduce and DBMS.
* [IBM Netezza](http://www-01.ibm.com/software/data/netezza/) - high-performance data warehouse appliances.
* [Postgres-XL](http://www.postgres-xl.org/) - Scalable Open Source PostgreSQL-based Database Cluster.
* [RecDB](http://www-users.cs.umn.edu/~sarwat/RecDB/) - Open Source Recommendation Engine Built Entirely Inside PostgreSQL.
* [Stado](http://www.stormdb.com/community/stado) - open source MPP database system solely targeted at data warehousing and data mart applications.
* [Yahoo Everest](https://www.scribd.com/doc/3159239/70-Everest-PGCon-RT) - multi-peta-byte database / MPP derived by PostgreSQL.
* [TimescaleDB](http://www.timescale.com/) - An open-source time-series database optimized for fast ingest and complex queries
* [PipelineDB](https://www.pipelinedb.com/) - The Streaming SQL Database. An open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables
## Memcached forks and evolutions
* [Facebook McDipper](https://www.facebook.com/notes/facebook-engineering/mcdipper-a-key-value-cache-for-flash-storage/10151347090423920) - key/value cache for flash storage.
* [Facebook Memcached](https://www.facebook.com/notes/facebook-engineering/scaling-memcache-at-facebook/10151411410803920) - fork of Memcache.
* [Twemproxy](https://github.com/twitter/twemproxy) - A fast, light-weight proxy for memcached and redis.
* [Twitter Fatcache](https://github.com/twitter/fatcache) - key/value cache for flash storage.
* [Twitter Twemcache](https://github.com/twitter/twemcache) - fork of Memcache.
## Embedded Databases
* [Actian PSQL](http://www.actian.com/products/operational-databases/) - ACID-compliant DBMS developed by Pervasive Software, optimized for embedding in applications.
* [BerkeleyDB](https://www.oracle.com/database/berkeley-db/index.html) - a software library that provides a high-performance embedded database for key/value data.
* [HanoiDB](https://github.com/krestenkrab/hanoidb) - Erlang LSM BTree Storage.
* [LevelDB](https://github.com/google/leveldb) - a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
* [LMDB](https://symas.com/mdb/) - ultra-fast, ultra-compact key-value embedded data store developed by Symas.
* [RocksDB](http://rocksdb.org/) - embeddable persistent key-value store for fast storage based on LevelDB.
## Business Intelligence
* [BIME Analytics](https://www.bimeanalytics.com/?lang=en) - business intelligence platform in the cloud.
* [Chartio](https://chartio.com) - lean business intelligence platform to visualize and explore your data.
* [datapine](https://www.datapine.com/) - self-service business intelligence tool in the cloud.
* [GoodData](https://www.gooddata.com/) - platform for data products and embedded analytics.
* [Jaspersoft](https://www.jaspersoft.com/) - powerful business intelligence suite.
* [Jedox Palo](https://www.jedox.com/en/) - customisable Business Intelligence platform.
* [Jethrodata](https://jethro.io/) - Interactive Big Data Analytics.
* [Microsoft](http://www.microsoft.com/en-us/server-cloud/solutions/business-intelligence/default.aspx) - business intelligence software and platform.
* [Microstrategy](https://www.microstrategy.com/) - software platforms for business intelligence, mobile intelligence, and network applications.
* [Pentaho](http://www.pentaho.com/) - business intelligence platform.
* [Qlik](http://www.qlik.com/us/) - business intelligence and analytics platform.
* [Redash](https://redash.io/) - Open source business intelligence platform, supporting multiple data sources and planned queries.
* [Saiku](http://www.meteorite.bi/) - open source analytics platform.
* [SpagoBI](http://www.spagobi.org/) - open source business intelligence platform.
* [SparklineData SNAP](http://sparklinedata.com/) - modern B.I platform powered by Apache Spark.
* [Tableau](https://www.tableau.com/) - business intelligence platform.
* [Zoomdata](https://www.zoomdata.com/) - Big Data Analytics.
* [Metabase](https://github.com/metabase/metabase) - The simplest, fastest way to get business intelligence and analytics to everyone in your company
## Data Visualization
* [Airpal](https://github.com/airbnb/airpal) - Web UI for PrestoDB.
* [AnyChart](http://www.anychart.com) - fast, simple and flexible JavaScript (HTML5) charting library featuring pure JS API.
* [Arbor](https://github.com/samizdatco/arbor) - graph visualization library using web workers and jQuery.
* [Banana](https://github.com/LucidWorks/banana) - visualize logs and time-stamped data stored in Solr. Port of Kibana.
* [Bloomery](https://github.com/ufukomer/bloomery) - Web UI for Impala.
* [Bokeh](http://bokeh.pydata.org/en/latest/) - A powerful Python interactive visualization library that targets modern web browsers for presentation, with the goal of providing elegant, concise construction of novel graphics in the style of D3.js, but also delivering this capability with high-performance interactivity over very large or streaming datasets.
* [C3](http://c3js.org/) - D3-based reusable chart library
* [CartoDB](https://github.com/CartoDB/cartodb) - open-source or freemium hosting for geospatial databases with powerful front-end editing capabilities and a robust API.
* [chartd](http://chartd.co/) - responsive, retina-compatible charts with just an img tag.
* [Chart.js](http://www.chartjs.org/) - open source HTML5 Charts visualizations.
* [Chartist.js](https://github.com/gionkunz/chartist-js) - another open source HTML5 Charts visualization.
* [Crossfilter](http://square.github.io/crossfilter/) - JavaScript library for exploring large multivariate datasets in the browser. Works well with dc.js and d3.js.
* [Cubism](https://github.com/square/cubism) - JavaScript library for time series visualization.
* [Cytoscape](http://cytoscape.github.io/) - JavaScript library for visualizing complex networks.
* [DC.js](http://dc-js.github.io/dc.js/) - Dimensional charting built to work natively with crossfilter rendered using d3.js. Excellent for connecting charts/additional metadata to hover events in D3.
* [D3](https://d3js.org/) - javaScript library for manipulating documents.
* [D3.compose](https://github.com/CSNW/d3.compose) - Compose complex, data-driven visualizations from reusable charts and components.
* [D3Plus](http://d3plus.org) - A fairly robust set of reusable charts and styles for d3.js.
* [Echarts](https://github.com/ecomfe/echarts) - Baidus enterprise charts.
* [Envisionjs](https://github.com/HumbleSoftware/envisionjs) - dynamic HTML5 visualization.
* [FnordMetric](https://metrictools.org/) - write SQL queries that return SVG charts rather than tables
* [Freeboard](https://github.com/Freeboard/freeboard) - pen source real-time dashboard builder for IOT and other web mashups.
* [Gephi](https://github.com/gephi/gephi) - An award-winning open-source platform for visualizing and manipulating large graphs and network connections. It's like Photoshop, but for graphs. Available for Windows and Mac OS X.
* [Google Charts](https://developers.google.com/chart/) - simple charting API.
* [Grafana](https://grafana.com/) - graphite dashboard frontend, editor and graph composer.
* [Graphite](http://graphiteapp.org/) - scalable Realtime Graphing.
* [Highcharts](https://www.highcharts.com/) - simple and flexible charting API.
* [IPython](http://ipython.org/) - provides a rich architecture for interactive computing.
* [Kibana](https://www.elastic.co/products/kibana) - visualize logs and time-stamped data
* [Lumify](http://lumify.io/) - open source big data analysis and visualization platform
* [Matplotlib](https://github.com/matplotlib/matplotlib) - plotting with Python.
* [Metricsgraphic.js](https://metricsgraphicsjs.org/) - a library built on top of D3 that is optimized for time-series data
* [NVD3](http://nvd3.org/) - chart components for d3.js.
* [Peity](https://github.com/benpickles/peity) - Progressive SVG bar, line and pie charts.
* [Plot.ly](https://plot.ly/) - Easy-to-use web service that allows for rapid creation of complex charts, from heatmaps to histograms. Upload data to create and style charts with Plotly's online spreadsheet. Fork others' plots.
* [Plotly.js](https://github.com/plotly/plotly.js) The open source javascript graphing library that powers plotly.
* [Recline](https://github.com/okfn/recline) - simple but powerful library for building data applications in pure Javascript and HTML.
* [Redash](https://github.com/getredash/redash) - open-source platform to query and visualize data.
* [ReCharts](http://recharts.org/) - A composable charting library built on React components
* [Shiny](http://shiny.rstudio.com/) - a web application framework for R.
* [Sigma.js](https://github.com/jacomyal/sigma.js) - JavaScript library dedicated to graph drawing.
* [Superset](https://github.com/apache/incubator-superset) - a data exploration platform designed to be visual, intuitive and interactive, making it easy to slice, dice and visualize data and perform analytics at the speed of thought.
* [Vega](https://github.com/trifacta/vega) - a visualization grammar.
* [Zeppelin](https://github.com/ZEPL/zeppelin) - a notebook-style collaborative data analysis.
* [Zing Charts](https://www.zingchart.com/) - JavaScript charting library for big data.
## Internet of things and sensor data
* [Apache Edgent (Incubating)](http://edgent.apache.org/) - a programming model and micro-kernel style runtime that can be embedded in gateways and small footprint edge devices enabling local, real-time, analytics on the edge devices.
* [Azure IoT Hub](https://azure.microsoft.com/en-us/services/iot-hub/) - Cloud-based bi-directional monitoring and messaging hub
* [TempoIQ](https://www.tempoiq.com/) - Cloud-based sensor analytics.
* [2lemetry](http://2lemetry.com/) - Platform for Internet of things.
* [Pubnub](https://www.pubnub.com/) - Data stream network
* [ThingWorx](https://www.thingworx.com/) - Rapid development and connection of intelligent systems
* [IFTTT](https://ifttt.com/) - If this then that
* [Evrything](https://evrythng.com/)- Making products smart
* [NetLytics](https://github.com/marty90/netlytics/) - Analytics platform to process network data on Spark.
## Interesting Readings
* [Big Data Benchmark](https://amplab.cs.berkeley.edu/benchmark/) - Benchmark of Redshift, Hive, Shark, Impala and Stiger/Tez.
* [NoSQL Comparison](https://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis) - Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Neo4j vs Hypertable vs ElasticSearch vs Accumulo vs VoltDB vs Scalaris comparison.
* [Monitoring Kafka performance](https://www.datadoghq.com/blog/monitoring-kafka-performance-metrics?ref=awesome) - Guide to monitoring Apache Kafka, including native methods for metrics collection.
* [Monitoring Hadoop performance](https://www.datadoghq.com/blog/monitor-hadoop-metrics?ref=awesome) - Guide to monitoring Hadoop, with an overview of Hadoop architecture, and native methods for metrics collection.
## Interesting Papers
### 2015 - 2016
* [2015](http://www.vldb.org/pvldb/vol8/p1804-ching.pdf) - **Facebook** - One Trillion Edges: Graph Processing at Facebook-Scale.
### 2013 - 2014
* [2014](http://infolab.stanford.edu/~ullman/mmds/book.pdf) - **Stanford** - Mining of Massive Datasets.
* [2013](https://amplab.cs.berkeley.edu/wp-content/uploads/2013/03/eurosys13-paper83.pdf) - **AMPLab** - Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices.
* [2013](https://amplab.cs.berkeley.edu/wp-content/uploads/2013/01/dmx1.pdf) - **AMPLab** - MLbase: A Distributed Machine-learning System.
* [2013](https://amplab.cs.berkeley.edu/wp-content/uploads/2013/02/shark_sigmod2013.pdf) - **AMPLab** - Shark: SQL and Rich Analytics at Scale.
* [2013](https://amplab.cs.berkeley.edu/wp-content/uploads/2013/05/grades-graphx_with_fonts.pdf) - **AMPLab** - GraphX: A Resilient Distributed Graph System on Spark.
* [2013](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/40671.pdf) - **Google** - HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm.
* [2013](http://research.microsoft.com/pubs/200169/now-vldb.pdf) - **Microsoft** - Scalable Progressive Analytics on Big Data in the Cloud.
* [2013](http://static.druid.io/docs/druid.pdf) - **Metamarkets** - Druid: A Real-time Analytical Data Store.
* [2013](http://db.disi.unitn.eu/pages/VLDBProgram/pdf/industry/p764-rae.pdf) - **Google** - Online, Asynchronous Schema Change in F1.
* [2013](http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/41344.pdf) - **Google** - F1: A Distributed SQL Database That Scales.
* [2013](http://db.disi.unitn.eu/pages/VLDBProgram/pdf/industry/p734-akidau.pdf) - **Google** - MillWheel: Fault-Tolerant Stream Processing at Internet Scale.
* [2013](http://db.disi.unitn.eu/pages/VLDBProgram/pdf/industry/p767-wiener.pdf) - **Facebook** - Scuba: Diving into Data at Facebook.
* [2013](http://db.disi.unitn.eu/pages/VLDBProgram/pdf/industry/p871-curtiss.pdf) - **Facebook** - Unicorn: A System for Searching the Social Graph.
* [2013](https://www.usenix.org/system/files/conference/nsdi13/nsdi13-final170_update.pdf) - **Facebook** - Scaling Memcache at Facebook.
### 2011 - 2012
* [2012](http://vldb.org/pvldb/vol5/p1771_georgelee_vldb2012.pdf) - **Twitter** - The Unified Logging Infrastructure
for Data Analytics at Twitter.
* [2012](https://amplab.cs.berkeley.edu/wp-content/uploads/2013/04/blinkdb_vldb12_demo.pdf) - **AMPLab** - Blink and It’s Done: Interactive Queries on Very Large Data.
* [2012](https://www.usenix.org/system/files/login/articles/zaharia.pdf) - **AMPLab** - Fast and Interactive Analytics over Hadoop Data with Spark.
* [2012](https://amplab.cs.berkeley.edu/wp-content/uploads/2012/03/mod482-xin1.pdf) - **AMPLab** - Shark: Fast Data Analysis Using Coarse-grained Distributed Memory.
* [2012](https://www.usenix.org/legacy/event/nsdi11/tech/full_papers/Bolosky.pdf) - **Microsoft** - Paxos Replicated State Machines as the Basis of a High-Performance Data Store.
* [2012](http://research.microsoft.com/pubs/178045/ppaoxs-paper29.pdf) - **Microsoft** - Paxos Made Parallel.
* [2012](https://arxiv.org/pdf/1203.5485.pdf) - **AMPLab** - BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data.
* [2012](http://vldb.org/pvldb/vol5/p1436_alexanderhall_vldb2012.pdf) - **Google** - Processing a trillion cells per mouse click.
* [2012](http://static.googleusercontent.com/media/research.google.com/en//archive/spanner-osdi2012.pdf) - **Google** - Spanner: Google’s Globally-Distributed Database.
* [2011](https://amplab.cs.berkeley.edu/wp-content/uploads/2011/06/euro118-ananthanarayanan.pdf) - **AMPLab** - Scarlett: Coping with Skewed Popularity Content in MapReduce Clusters.
* [2011](https://amplab.cs.berkeley.edu/wp-content/uploads/2011/06/Mesos-A-Platform-for-Fine-Grained-Resource-Sharing-in-the-Data-Center.pdf) - **AMPLab** - Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center.
* [2011](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36971.pdf) - **Google** - Megastore: Providing Scalable, Highly Available Storage for Interactive Services.
### 2001 - 2010
* [2010](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf) - **Facebook** - Finding a needle in Haystack: Facebook’s photo storage.
* [2010](https://amplab.cs.berkeley.edu/wp-content/uploads/2011/06/Spark-Cluster-Computing-with-Working-Sets.pdf) - **AMPLab** - Spark: Cluster Computing with Working Sets.
* [2010](http://kowshik.github.io/JPregel/pregel_paper.pdf) - **Google** - Pregel: A System for Large-Scale Graph Processing.
* [2010](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36726.pdf) - **Google** - Large-scale Incremental Processing Using Distributed Transactions and Notifications base of Percolator and Caffeine.
* [2010](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36632.pdf) - **Google** - Dremel: Interactive Analysis of Web-Scale Datasets.
* [2010](http://leoneu.github.io/) - **Yahoo** - S4: Distributed Stream Computing Platform.
* [2009](http://www.vldb.org/pvldb/2/vldb09-861.pdf) - HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads.
* [2008](http://www.cca08.org/papers/Paper-13-Ariel-Rabkin.pdf) - **AMPLab** - Chukwa: A large-scale monitoring system.
* [2007](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) - **Amazon** - Dynamo: Amazon’s Highly Available Key-value Store.
* [2006](http://static.googleusercontent.com/media/research.google.com/en//archive/chubby-osdi06.pdf) - **Google** - The Chubby lock service for loosely-coupled distributed systems.
* [2006](http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//archive/bigtable-osdi06.pdf) - **Google** - Bigtable: A Distributed Storage System for Structured Data.
* [2004](http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf) - **Google** - MapReduce: Simplied Data Processing on Large Clusters.
* [2003](http://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf) - **Google** - The Google File System.
## Videos
* [Spark in Motion](https://www.manning.com/livevideo/spark-in-motion) - Spark in Motion teaches you how to use Spark for batch and streaming data analytics.
## Books
#### Streaming
* [Streaming Data](https://www.manning.com/books/streaming-data) - Streaming Data introduces the concepts and requirements of streaming and real-time data systems.
* [Storm Applied](https://www.manning.com/books/storm-applied) - Storm Applied is a practical guide to using Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams.
* [Fundamentals of Stream Processing: Application Design, Systems, and Analytics](http://www.cambridge.org/us/academic/subjects/engineering/communications-and-signal-processing/fundamentals-stream-processing-application-design-systems-and-analytics) - This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field.
* [Stream Data Processing: A Quality of Service Perspective](http://www.springer.com/us/book/9780387710020) - Presents a new paradigm suitable for stream and complex event processing.
* [Unified Log Processing](https://www.manning.com/books/event-streams-in-action) - Unified Log Processing is a practical guide to implementing a unified log of event streams (Kafka or Kinesis) in your business
* [Kafka Streams in Action](https://www.manning.com/books/kafka-streams-in-action) - Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort.
* [Big Data](https://www.manning.com/books/big-data) - Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data.
* [Spark in Action](https://www.manning.com/books/spark-in-action) - Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. Fully updated for Spark 2.0.
* [Kafka in Action](https://www.manning.com/books/kafka-in-action) - Kafka in Action is a fast-paced introduction to every aspect of working with Kafka you need to really reap its benefits.
* [Reactive Data Handling](https://www.manning.com/books/reactive-data-handling) - Reactive Data Handling is a collection of five hand-picked chapters, selected by Manuel Bernhardt, that introduce you to building reactive applications capable of handling real-time processing with large data loads--free eBook!
#### Distributed systems
* [Distributed Systems for fun and profit](http://book.mixu.net/distsys/) – Theory of distributed systems. Include parts about time and ordering, replication and impossibility results.
### Data Visualization
* [The beauty of data visualization](https://www.youtube.com/watch?v=5Zg-C8AAIGg)
* [Designing Data Visualizations with Noah Iliinsky](https://www.youtube.com/watch?v=R-oiKt7bUU8)
* [Hans Rosling's 200 Countries, 200 Years, 4 Minutes](https://www.youtube.com/watch?v=jbkSRLYSojo)
* [Ice Bucket Challenge Data Visualization](https://www.youtube.com/watch?v=qTEchen97rQ)
# Other Awesome Lists
- Other awesome lists [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness).
- Even more lists [awesome](https://github.com/sindresorhus/awesome).
- Another list? [list](https://github.com/jnv/lists).
- WTF! [awesome-awesome-awesome](https://github.com/t3chnoboy/awesome-awesome-awesome).
- Analytics [awesome-analytics](https://github.com/onurakpolat/awesome-analytics).
- Public Datasets [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets).

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# Awesome R
[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
A curated list of awesome R packages and tools. Inspired by [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).
For better navigation, see https://awesome-r.com
<p><img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">
for <a target="_blank" href="https://github.com/rstudio/RStartHere/blob/master/top_downloads_2016/top_packages">Top 50</a> CRAN downloaded packages or repos with 400+
<img class="emoji" alt="star" src="https://awesome-r.com/star.png" height="20" align="absmiddle" width="20"></p>
- [Awesome R](#awesome-)
- [2017](#2017)
- [Integrated Development Environments](#integrated-development-environments)
- [Syntax](#syntax)
- [Data Manipulation](#data-manipulation)
- [Graphic Displays](#graphic-displays)
- [Html Widgets](#html-widgets)
- [Reproducible Research](#reproducible-research)
- [Web Technologies and Services](#web-technologies-and-services)
- [Parallel Computing](#parallel-computing)
- [High Performance](#high-performance)
- [Language API](#language-api)
- [Database Management](#database-management)
- [Machine Learning](#machine-learning)
- [Natural Language Processing](#natural-language-processing)
- [Bayesian](#bayesian)
- [Optimization](#optimization)
- [Finance](#finance)
- [Bioinformatics and Biostatistics](#bioinformatics-and-biostatistics)
- [Network Analysis](#network-analysis)
- [Spatial](#spatial)
- [R Development](#r-development)
- [Logging](#logging)
- [Data Packages](#data-packages)
- [Other Tools](#other-tools)
- [Other Interpreters](#other-interpreters)
- [Learning R](#learning-r)
- [Resources](#resources)
- [Websites](#websites)
- [Books](#books)
- [Podcasts](#podcasts)
- [Reference Cards](#reference-cards)
- [MOOCs](#moocs)
- [Lists](#lists)
- [Other Awesome Lists](#other-awesome-lists)
- [Contributing](#contributing)
## 2018
* [Readings in Applied Data Science](https://github.com/hadley/stats337) - These readings reflect Hadley's personal thoughts about applied data science.
* [promises](https://cran.r-project.org/web/packages/promises/index.html) - Abstractions for Promise-Based Asynchronous Programming
* [tinytex](https://yihui.name/tinytex/) - A lightweight and easy-to-maintain LaTeX distribution
## 2017
* [prophet](https://github.com/facebookincubator/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
* [tidyverse](https://github.com/tidyverse/tidyverse) - Easily install and load packages from the tidyverse
* [purrr](https://github.com/tidyverse/purrr) - A functional programming toolkit for R
* [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes) - 🔏 Opinionated, typographic-centric ggplot2 themes and theme components
* [xaringan](https://github.com/yihui/xaringan) - Create HTML5 slides with R Markdown and the JavaScript library
* [blogdown](https://github.com/rstudio/blogdown) - Create Blogs and Websites with R Markdown
* [glue](https://github.com/tidyverse/glue) - Glue strings to data in R. Small, fast, dependency free interpreted string literals.
* [covr](https://github.com/jimhester/covr) - Test coverage reports for R
* [lintr](https://github.com/jimhester/lintr) - Static Code Analysis for R
* [reprex](https://github.com/jennybc/reprex) - Render bits of R code for sharing, e.g., on GitHub or StackOverflow.
* [reticulate](https://github.com/rstudio/reticulate) - R Interface to Python
* [tensorflow](https://github.com/rstudio/tensorflow) - TensorFlow for R
* [utf8](https://github.com/patperry/r-utf8) - Manipulating and printing UTF-8 text that fixes multiple bugs in R's UTF-8 handling.
## Integrated Development Environments
*Integrated Development Environment*
* [RStudio <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://www.rstudio.org/) - A powerful and productive user interface for R. Works great on Windows, Mac, and Linux.
* [Emacs + ESS](http://ess.r-project.org/) - Emacs Speaks Statistics is an add-on package for emacs text editors.
* [Sublime Text + R-Box](http://github.com/randy3k/R-Box/) - Add-on package for Sublime Text 2/3.
* [TextMate + r.tmblundle](https://github.com/textmate/r.tmbundle) - Add-on package for TextMate 1/2.
* [StatET](http://www.walware.de/goto/statet) - An Eclipse based IDE for R.
* [Revolution R Enterprise](http://www.revolutionanalytics.com/get-revolution-r-enterprise) - Revolution R would be offered free to academic users and commercial software would focus on big data, large scale multiprocessor functionality.
* [R Commander](http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/) - A package that provides a basic graphical user interface.
* [IRkernel <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/IRkernel/IRkernel) - R kernel for Jupyter.
* [Deducer](http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual?from=Main.HomePage) - A Menu driven data analysis GUI with a spreadsheet like data editor.
* [Radiant](https://radiant-rstats.github.io/docs) - A platform-independent browser-based interface for business analytics in R, based on the Shiny.
* [Vim-R](https://github.com/vim-scripts/Vim-R-plugin) - Vim plugin for R.
* [Nvim-R](https://github.com/jalvesaq/Nvim-R) - Neovim plugin for R.
* [JASP](https://jasp-stats.org/) - A complete package for both Bayesian and Frequentist methods, that is familiar to users of SPSS.
* [Bio7](http://www.bio7.org/) - A IDE contains tools for model creation, scientific image analysis and statistical analysis for ecological modelling.
* [RTVS](http://microsoft.github.io/RTVS-docs/) - R Tools for Visual Studio.
* [rtichoke](https://github.com/randy3k/rtichoke) - A modern R console with syntax highlighting.
## Syntax
*Packages change the way you use R.*
* [magrittr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/smbache/magrittr) - Let's pipe it.
* [pipeR](https://github.com/renkun-ken/pipeR) - Multi-paradigm Pipeline Implementation.
* [lambda.r](https://github.com/zatonovo/lambda.r) - Functional programming and simple pattern matching in R.
* [purrr](https://github.com/hadley/purrr) - A FP package for R in the spirit of underscore.js.
## Data Manipulation
*Packages for cooking data.*
* [dplyr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/dplyr) - Fast data frames manipulation and database query.
* [data.table <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/Rdatatable/data.table) - Fast data manipulation in a short and flexible syntax.
* [reshape2 <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/reshape) - Flexible rearrange, reshape and aggregate data.
* [readr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/readr) - A fast and friendly way to read tabular data into R.
* [haven](https://github.com/hadley/haven) - Improved methods to import SPSS, Stata and SAS files in R.
* [tidyr](https://github.com/hadley/tidyr) - Easily tidy data with spread and gather functions.
* [broom <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/dgrtwo/broom) - Convert statistical analysis objects into tidy data frames.
* [rlist](https://github.com/renkun-ken/rlist) - A toolbox for non-tabular data manipulation with lists.
* [jsonlite](https://github.com/jeroenooms/jsonlite) - A robust and quick way to parse JSON files in R.
* [ff](http://ff.r-forge.r-project.org/) - Data structures designed to store large datasets.
* [lubridate](http://cran.r-project.org/web/packages/lubridate/index.html) - A set of functions to work with dates and times.
* [stringi <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://www.gagolewski.com/software/stringi/) - ICU based string processing package.
* [stringr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/stringr) - Consistent API for string processing, built on top of stringi.
* [bigmemory](http://cran.r-project.org/web/packages/bigmemory/index.html) - Shared memory and memory-mapped matrices. The big\* packages provide additional tools including linear models ([biglm](http://cran.r-project.org/web/packages/biglm/index.html)) and Random Forests ([bigrf](https://github.com/aloysius-lim/bigrf)).
* [fuzzyjoin](https://github.com/dgrtwo/fuzzyjoin) - Join tables together on inexact matching.
* [tidyverse](https://github.com/hadley/tidyverse) - Easily install and load packages from the tidyverse.
## Graphic Displays
*Packages for showing data.*
* [ggplot2 <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/ggplot2) - An implementation of the Grammar of Graphics.
* [ggfortify](https://github.com/sinhrks/ggfortify) - A unified interface to ggplot2 popular statistical packages using one line of code.
* [ggrepel](https://github.com/slowkow/ggrepel) - Repel overlapping text labels away from each other.
* [ggalt](https://github.com/hrbrmstr/ggalt) - Extra Coordinate Systems, Geoms and Statistical Transformations for ggplot2.
* [ggtree](https://github.com/GuangchuangYu/ggtree) - Visualization and annotation of phylogenetic tree.
* [ggtech](https://github.com/ricardo-bion/ggtech) - ggplot2 tech themes and scales
* [ggplot2 Extensions](https://ggplot2-exts.github.io/ggiraph.html) - Showcases of ggplot2 extensions.
* [lattice](http://lattice.r-forge.r-project.org/) - A powerful and elegant high-level data visualization system.
* [corrplot](https://github.com/taiyun/corrplot) - A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering.
* [rgl](http://cran.r-project.org/web/packages/rgl/index.html) - 3D visualization device system for R.
* [Cairo](http://cran.r-project.org/web/packages/Cairo/index.html) - R graphics device using cairo graphics library for creating high-quality display output.
* [extrafont](https://github.com/wch/extrafont) - Tools for using fonts in R graphics.
* [showtext](https://github.com/yixuan/showtext) - Enable R graphics device to show text using system fonts.
* [animation](http://yihui.name/animation/) - A simple way to produce animated graphics in R, using [ImageMagick](http://imagemagick.org/).
* [gganimate](https://github.com/dgrtwo/gganimate) - Create easy animations with ggplot2.
* [misc3d](https://cran.r-project.org/web/packages/misc3d/index.html) - Powerful functions to deal with 3d plots, isosurfaces, etc.
* [xkcd](https://cran.r-project.org/web/packages/xkcd/index.html) - Use xkcd style in graphs.
* [imager](http://dahtah.github.io/imager/) - An image processing package based on CImg library to work with images and display them.
* [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes) - 🔏 Opinionated, typographic-centric ggplot2 themes and theme components.
* [waffle](https://github.com/hrbrmstr/waffle) - 🍁 Make waffle (square pie) charts in R
## HTML Widgets
*Packages for interactive visualizations.*
* [d3heatmap](https://github.com/rstudio/d3heatmap) - Interactive heatmaps with D3.
* [DataTables](http://rstudio.github.io/DT/) - Displays R matrices or data frames as interactive HTML tables.
* [DiagrammeR <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/rich-iannone/DiagrammeR) - Create JS graph diagrams and flowcharts in R.
* [dygraphs](https://github.com/rstudio/dygraphs) - Charting time-series data in R.
* [formattable](http://renkun.me/formattable/) - Formattable Data Structures.
* [ggvis <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/rstudio/ggvis) - Interactive grammar of graphics for R.
* [Leaflet](http://rstudio.github.io/leaflet/) - One of the most popular JavaScript libraries interactive maps.
* [MetricsGraphics](http://hrbrmstr.github.io/metricsgraphics/) - Enables easy creation of D3 scatterplots, line charts, and histograms.
* [networkD3](http://christophergandrud.github.io/networkD3/) - D3 JavaScript Network Graphs from R.
* [scatterD3](https://github.com/juba/scatterD3) - Interactive scatterplots with D3.
* [plotly <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/ropensci/plotly) - Interactive ggplot2 and Shiny plotting with [plot.ly](https://plot.ly).
* [rCharts <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/ramnathv/rCharts) - Interactive JS Charts from R.
* [rbokeh](http://hafen.github.io/rbokeh/) - R Interface to [Bokeh](http://bokeh.pydata.org/en/latest/).
* [threejs](https://github.com/bwlewis/rthreejs) - Interactive 3D scatter plots and globes.
* [timevis](https://github.com/daattali/timevis) - Create fully interactive timeline visualizations.
* [visNetwork](https://github.com/datastorm-open/visNetwork) - Using vis.js library for network visualization.
* [wordcloud2](https://github.com/Lchiffon/wordcloud2) - R interface to wordcloud2.js.
* [highcharter](https://github.com/jbkunst/highcharter) - R wrapper for highcharts based on htmlwidgets
## Reproducible Research
*Packages for literate programming and reproducible workflows.*
* [knitr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://yihui.name/knitr/) - Easy dynamic report generation in R.
* [tinytex](https://yihui.name/tinytex/) - A lightweight and easy-to-maintain LaTeX distribution
* [xtable](http://cran.r-project.org/web/packages/xtable/index.html) - Export tables to LaTeX or HTML.
* [rapport](http://rapport-package.info/#intro) - An R templating system.
* [rmarkdown <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://rmarkdown.rstudio.com/) - Dynamic documents for R.
* [slidify <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/ramnathv/slidify) - Generate reproducible html5 slides from R markdown.
* [Sweave](https://www.statistik.lmu.de/~leisch/Sweave/) - A package designed to write LaTeX reports using R.
* [texreg](http://www.philipleifeld.de/software/texreg/texreg.html) - Formatting statistical models in LaTex and HTML.
* [checkpoint](https://github.com/RevolutionAnalytics/checkpoint) - Install packages from snapshots on the checkpoint server.
* [brew](https://cran.r-project.org/web/packages/brew/index.html) - Pre-compute data to enhance your report templates. Can be combined with knitr.
* [ReporteRs](http://davidgohel.github.io/ReporteRs/index.html) - An R package to generate Microsoft Word, Microsoft PowerPoint and HTML reports.
* [bookdown](https://bookdown.org/) - Authoring Books with R Markdown.
* [ezknitr](https://github.com/daattali/ezknitr) - Avoid the typical working directory pain when using 'knitr'
* [drake](https://github.com/ropensci/drake) - An [rOpenSci](https://ropensci.org/) package for reproducible data science workflows too big for [knitr](http://yihui.name/knitr/).
## Web Technologies and Services
*Packages to surf the web.*
* [Web Technologies List](https://github.com/ropensci/webservices) - Information about how to use R and the world wide web together.
* [shiny <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/rstudio/shiny) - Easy interactive web applications with R. See also [awesome-rshiny](https://github.com/grabear/awesome-rshiny)
* [shinyjs](https://github.com/daattali/shinyjs) - Easily improve the user interaction and user experience in your Shiny apps in seconds.
* [RCurl](http://cran.r-project.org/web/packages/RCurl/index.html) - General network (HTTP/FTP/...) client interface for R.
* [httr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/httr) - User-friendly RCurl wrapper.
* [httpuv](https://github.com/rstudio/httpuv) - HTTP and WebSocket server library.
* [XML <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://cran.r-project.org/web/packages/XML/index.html) - Tools for parsing and generating XML within R.
* [rvest <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/rvest) - Simple web scraping for R, using CSSSelect or XPath syntax.
* [OpenCPU <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://www.opencpu.org/) - HTTP API for R.
* [Rfacebook](https://github.com/pablobarbera/Rfacebook) - Access to Facebook API via R.
* [RSiteCatalyst](https://github.com/randyzwitch/RSiteCatalyst) - R client library for the Adobe Analytics.
* [plumber](https://github.com/trestletech/plumber) - A library to expose existing R code as web API.
## Parallel Computing
*Packages for parallel computing.*
* [parallel](http://cran.r-project.org/web/views/HighPerformanceComputing.html) - R started with release 2.14.0 which includes a new package parallel incorporating (slightly revised) copies of packages [multicore](http://cran.r-project.org/web/packages/multicore/index.html) and [snow](http://cran.r-project.org/web/packages/snow/index.html).
* [Rmpi](http://cran.r-project.org/web/packages/Rmpi/index.html) - Rmpi provides an interface (wrapper) to MPI APIs. It also provides interactive R slave environment.
* [foreach <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://cran.r-project.org/web/packages/foreach/index.html) - Executing the loop in parallel.
* [future](https://cran.r-project.org/package=future) - A minimal, efficient, cross-platform unified Future API for parallel and distributed processing in R; designed for beginners as well as advanced developers.
* [SparkR <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/amplab-extras/SparkR-pkg) - R frontend for Spark.
* [DistributedR](https://github.com/vertica/DistributedR) - A scalable high-performance platform from HP Vertica Analytics Team.
* [ddR](https://github.com/vertica/ddR) - Provides distributed data structures and simplifies distributed computing in R.
* [sparklyr](http://spark.rstudio.com/) - R interface for Apache Spark from RStudio.
* [batchtools](https://cran.r-project.org/package=batchtools) - High performance computing with LSF, TORQUE, Slurm, OpenLava, SGE and Docker Swarm.
## High Performance
*Packages for making R faster.*
* [Rcpp <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://rcpp.org/) - Rcpp provides a powerful API on top of R, make function in R extremely faster.
* [Rcpp11](https://github.com/Rcpp11/Rcpp11) - Rcpp11 is a complete redesign of Rcpp, targetting C++11.
* [compiler](http://stat.ethz.ch/R-manual/R-devel/library/compiler/html/compile.html) - speeding up your R code using the JIT
## Language API
*Packages for other languages.*
* [rJava](http://cran.r-project.org/web/packages/rJava/) - Low-level R to Java interface.
* [jvmr](https://github.com/cran/jvmr) - Integration of R, Java, and Scala.
* [rJython](http://cran.r-project.org/web/packages/rJython/index.html) - R interface to Python via Jython.
* [rPython](http://cran.r-project.org/web/packages/rPython/index.html) - Package allowing R to call Python.
* [runr](https://github.com/yihui/runr) - Run Julia and Bash from R.
* [RJulia](https://github.com/armgong/RJulia) - R package Call Julia.
* [JuliaCall](https://github.com/Non-Contradiction/JuliaCall) - Seamless Integration Between R and Julia.
* [RinRuby](https://sites.google.com/a/ddahl.org/rinruby-users/) - a Ruby library that integrates the R interpreter in Ruby.
* [R.matlab](http://cran.r-project.org/web/packages/R.matlab/index.html) - Read and write of MAT files together with R-to-MATLAB connectivity.
* [RcppOctave](https://github.com/renozao/RcppOctave) - Seamless Interface to Octave and Matlab.
* [RSPerl](http://www.omegahat.org/RSPerl/) - A bidirectional interface for calling R from Perl and Perl from R.
* [V8](https://github.com/jeroenooms/V8) - Embedded JavaScript Engine.
* [htmlwidgets](http://www.htmlwidgets.org/) - Bring the best of JavaScript data visualization to R.
* [rpy2](http://rpy.sourceforge.net/) - Python interface for R.
## Database Management
*Packages for managing data.*
* [RODBC](http://cran.r-project.org/web/packages/RODBC/) - ODBC database access for R.
* [DBI](https://github.com/rstats-db/DBI) - Defines a common interface between the R and database management systems.
* [elastic](https://github.com/ropensci/elastic) - Wrapper for the Elasticsearch HTTP API
* [mongolite](https://github.com/jeroenooms/mongolite) - Streaming Mongo Client for R
* [RMariaDB](https://github.com/rstats-db/RMariaDB) - An R interface to MariaDB (a replacement for the old RMySQL package)
* [RMySQL](http://cran.r-project.org/web/packages/RMySQL/) - R interface to the MySQL database.
* [ROracle](http://cran.r-project.org/web/packages/ROracle/index.html) - OCI based Oracle database interface for R.
* [RPostgreSQL](https://code.google.com/p/rpostgresql/) - R interface to the PostgreSQL database system.
* [RSQLite](http://cran.r-project.org/web/packages/RSQLite/) - SQLite interface for R
* [RJDBC](http://cran.r-project.org/web/packages/RJDBC/) - Provides access to databases through the JDBC interface.
* [rmongodb](https://github.com/mongosoup/rmongodb) - R driver for MongoDB.
* [rredis](http://cran.r-project.org/web/packages/rredis/) - Redis client for R.
* [RCassandra](http://cran.r-project.org/web/packages/RCassandra/index.html) - Direct interface (not Java) to the most basic functionality of Apache Cassanda.
* [RHive](https://github.com/nexr/RHive) - R extension facilitating distributed computing via Apache Hive.
* [RNeo4j](https://github.com/nicolewhite/Rneo4j) - Neo4j graph database driver.
* [rpostgis](https://github.com/mablab/rpostgis) - R interface to PostGIS database and get spatial objects in R.
## Machine Learning
*Packages for making R cleverer.*
* [AnomalyDetection <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/twitter/AnomalyDetection) - AnomalyDetection R package from Twitter.
* [ahaz](http://cran.r-project.org/web/packages/ahaz/index.html) - Regularization for semiparametric additive hazards regression.
* [arules](http://cran.r-project.org/web/packages/arules/index.html) - Mining Association Rules and Frequent Itemsets
* [bigrf](http://cran.r-project.org/web/packages/bigrf/index.html) - Big Random Forests: Classification and Regression Forests for
Large Data Sets
* [bigRR](http://cran.r-project.org/web/packages/bigRR/index.html) - Generalized Ridge Regression (with special advantage for p >> n
cases)
* [bmrm](http://cran.r-project.org/web/packages/bmrm/index.html) - Bundle Methods for Regularized Risk Minimization Package
* [Boruta](http://cran.r-project.org/web/packages/Boruta/index.html) - A wrapper algorithm for all-relevant feature selection
* [BreakoutDetection <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/twitter/BreakoutDetection) - Breakout Detection via Robust E-Statistics from Twitter.
* [bst](http://cran.r-project.org/web/packages/bst/index.html) - Gradient Boosting
* [CausalImpact <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/google/CausalImpact) - Causal inference using Bayesian structural time-series models.
* [C50](http://cran.r-project.org/web/packages/C50/index.html) - C5.0 Decision Trees and Rule-Based Models
* [caret <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://cran.r-project.org/web/packages/caret/index.html) - Classification and Regression Training
* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)
* [CORElearn](http://cran.r-project.org/web/packages/CORElearn/index.html) - Classification, regression, feature evaluation and ordinal
evaluation
* [CoxBoost](http://cran.r-project.org/web/packages/CoxBoost/index.html) - Cox models by likelihood based boosting for a single survival
endpoint or competing risks
* [Cubist](http://cran.r-project.org/web/packages/Cubist/index.html) - Rule- and Instance-Based Regression Modeling
* [e1071](http://cran.r-project.org/web/packages/e1071/index.html) - Misc Functions of the Department of Statistics (e1071), TU Wien
* [earth](http://cran.r-project.org/web/packages/earth/index.html) - Multivariate Adaptive Regression Spline Models
* [elasticnet](http://cran.r-project.org/web/packages/elasticnet/index.html) - Elastic-Net for Sparse Estimation and Sparse PCA
* [ElemStatLearn](http://cran.r-project.org/web/packages/ElemStatLearn/index.html) - Data sets, functions and examples from the book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman
* [evtree](http://cran.r-project.org/web/packages/evtree/index.html) - Evolutionary Learning of Globally Optimal Trees
* [forecast](http://cran.r-project.org/web/packages/forecast/index.html) - Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models
* [forecastHybrid](http://cran.r-project.org/web/packages/forecastHybrid/index.html) - Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package
* [prophet <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/facebookincubator/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
* [FSelector](https://cran.r-project.org/web/packages/FSelector/index.html) - A feature selection framework, based on subset-search or feature ranking approches.
* [frbs](http://cran.r-project.org/web/packages/frbs/index.html) - Fuzzy Rule-based Systems for Classification and Regression Tasks
* [GAMBoost](http://cran.r-project.org/web/packages/GAMBoost/index.html) - Generalized linear and additive models by likelihood based
boosting
* [gamboostLSS](http://cran.r-project.org/web/packages/gamboostLSS/index.html) - Boosting Methods for GAMLSS
* [gbm](http://cran.r-project.org/web/packages/gbm/index.html) - Generalized Boosted Regression Models
* [glmnet <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://cran.r-project.org/web/packages/glmnet/index.html) - Lasso and elastic-net regularized generalized linear models
* [glmpath](http://cran.r-project.org/web/packages/glmpath/index.html) - L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
* [GMMBoost](http://cran.r-project.org/web/packages/GMMBoost/index.html) - Likelihood-based Boosting for Generalized mixed models
* [grplasso](http://cran.r-project.org/web/packages/grplasso/index.html) - Fitting user specified models with Group Lasso penalty
* [grpreg](http://cran.r-project.org/web/packages/grpreg/index.html) - Regularization paths for regression models with grouped
covariates
* [h2o <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://cran.r-project.org/web/packages/h2o/index.html) - Deeplearning, Random forests, GBM, KMeans, PCA, GLM
* [hda](http://cran.r-project.org/web/packages/hda/index.html) - Heteroscedastic Discriminant Analysis
* [ipred](http://cran.r-project.org/web/packages/ipred/index.html) - Improved Predictors
* [kernlab](http://cran.r-project.org/web/packages/kernlab/index.html) - kernlab: Kernel-based Machine Learning Lab
* [klaR](http://cran.r-project.org/web/packages/klaR/index.html) - Classification and visualization
* [kohonen](http://cran.r-project.org/web/packages/kohonen/) - Supervised and Unsupervised Self-Organising Maps.
* [lars](http://cran.r-project.org/web/packages/lars/index.html) - Least Angle Regression, Lasso and Forward Stagewise
* [lasso2](http://cran.r-project.org/web/packages/lasso2/index.html) - L1 constrained estimation aka ‘lasso’
* [LiblineaR](http://cran.r-project.org/web/packages/LiblineaR/index.html) - Linear Predictive Models Based On The Liblinear C/C++ Library
* [lme4 <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/lme4/lme4) - Mixed-effects models
* [LogicReg](http://cran.r-project.org/web/packages/LogicReg/index.html) - Logic Regression
* [maptree](http://cran.r-project.org/web/packages/maptree/index.html) - Mapping, pruning, and graphing tree models
* [mboost](http://cran.r-project.org/web/packages/mboost/index.html) - Model-Based Boosting
* [Machine Learning For Hackers <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/johnmyleswhite/ML_for_Hackers)
* [mlr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/mlr-org/mlr) - Extensible framework for classification, regression, survival analysis and clustering
* [mvpart](http://cran.r-project.org/web/packages/mvpart/index.html) - Multivariate partitioning
* [MXNet <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/dmlc/mxnet/tree/master/R-package) - MXNet brings flexible and efficient GPU computing and state-of-art deep learning to R.
* [ncvreg](http://cran.r-project.org/web/packages/ncvreg/index.html) - Regularization paths for SCAD- and MCP-penalized regression
models
* [nnet](http://cran.r-project.org/web/packages/nnet/index.html) - eed-forward Neural Networks and Multinomial Log-Linear Models
* [oblique.tree](http://cran.r-project.org/web/packages/oblique.tree/index.html) - Oblique Trees for Classification Data
* [pamr](http://cran.r-project.org/web/packages/pamr/index.html) - Pam: prediction analysis for microarrays
* [party](http://cran.r-project.org/web/packages/party/index.html) - A Laboratory for Recursive Partytioning
* [partykit](http://cran.r-project.org/web/packages/partykit/index.html) - A Toolkit for Recursive Partytioning
* [penalized](http://cran.r-project.org/web/packages/penalized/index.html) - L1 (lasso and fused lasso) and L2 (ridge) penalized estimation
in GLMs and in the Cox model
* [penalizedLDA](http://cran.r-project.org/web/packages/penalizedLDA/index.html) - Penalized classification using Fisher's linear discriminant
* [penalizedSVM](http://cran.r-project.org/web/packages/penalizedSVM/index.html) - Feature Selection SVM using penalty functions
* [quantregForest](http://cran.r-project.org/web/packages/quantregForest/index.html) - quantregForest: Quantile Regression Forests
* [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) - randomForest: Breiman and Cutler's random forests for classification and regression.
* [randomForestSRC](http://cran.r-project.org/web/packages/randomForestSRC/index.html) - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
* [rattle](http://cran.r-project.org/web/packages/rattle/index.html) - Graphical user interface for data mining in R.
* [rda](http://cran.r-project.org/web/packages/rda/index.html) - Shrunken Centroids Regularized Discriminant Analysis
* [rdetools](http://cran.r-project.org/web/packages/rdetools/index.html) - Relevant Dimension Estimation (RDE) in Feature Spaces
* [REEMtree](http://cran.r-project.org/web/packages/REEMtree/index.html) - Regression Trees with Random Effects for Longitudinal (Panel)
Data
* [relaxo](http://cran.r-project.org/web/packages/relaxo/index.html) - Relaxed Lasso
* [rgenoud](http://cran.r-project.org/web/packages/rgenoud/index.html) - R version of GENetic Optimization Using Derivatives
* [rgp](http://cran.r-project.org/web/packages/rgp/index.html) - R genetic programming framework
* [Rmalschains](http://cran.r-project.org/web/packages/Rmalschains/index.html) - Continuous Optimization using Memetic Algorithms with Local
Search Chains (MA-LS-Chains) in R
* [rminer](http://cran.r-project.org/web/packages/rminer/index.html) - Simpler use of data mining methods (e.g. NN and SVM) in
classification and regression
* [ROCR](http://cran.r-project.org/web/packages/ROCR/index.html) - Visualizing the performance of scoring classifiers
* [RoughSets](http://cran.r-project.org/web/packages/RoughSets/index.html) - Data Analysis Using Rough Set and Fuzzy Rough Set Theories
* [rpart](http://cran.r-project.org/web/packages/rpart/index.html) - Recursive Partitioning and Regression Trees
* [RPMM](http://cran.r-project.org/web/packages/RPMM/index.html) - Recursively Partitioned Mixture Model
* [RSNNS](http://cran.r-project.org/web/packages/RSNNS/index.html) - Neural Networks in R using the Stuttgart Neural Network
Simulator (SNNS)
* [Rsomoclu](https://cran.r-project.org/web/packages/Rsomoclu/index.html) - Parallel implementation of self-organizing maps.
* [RWeka](http://cran.r-project.org/web/packages/RWeka/index.html) - R/Weka interface
* [RXshrink](http://cran.r-project.org/web/packages/RXshrink/index.html) - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least
Angle Regression
* [sda](http://cran.r-project.org/web/packages/sda/index.html) - Shrinkage Discriminant Analysis and CAT Score Variable Selection
* [SDDA](http://cran.r-project.org/web/packages/SDDA/index.html) - Stepwise Diagonal Discriminant Analysis
* [SuperLearner](https://github.com/ecpolley/SuperLearner) and [subsemble](http://cran.r-project.org/web/packages/subsemble/index.html) - Multi-algorithm ensemble learning packages.
* [svmpath](http://cran.r-project.org/web/packages/svmpath/index.html) - svmpath: the SVM Path algorithm
* [tgp](http://cran.r-project.org/web/packages/tgp/index.html) - Bayesian treed Gaussian process models
* [tree](http://cran.r-project.org/web/packages/tree/index.html) - Classification and regression trees
* [varSelRF](http://cran.r-project.org/web/packages/varSelRF/index.html) - Variable selection using random forests
* [xgboost <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/tqchen/xgboost/tree/master/R-package) - eXtreme Gradient Boosting Tree model, well known for its speed and performance.
## Natural Language Processing
*Packages for Natural Language Processing.*
* [text2vec](https://github.com/dselivanov/text2vec) - Fast Text Mining Framework for Vectorization and Word Embeddings.
* [tm](http://cran.r-project.org/web/packages/tm/index.html) - A comprehensive text mining framework for R.
* [openNLP](http://cran.r-project.org/web/packages/openNLP/index.html) - Apache OpenNLP Tools Interface.
* [koRpus](http://cran.r-project.org/web/packages/koRpus/index.html) - An R Package for Text Analysis.
* [zipfR](http://cran.r-project.org/web/packages/zipfR/index.html) - Statistical models for word frequency distributions.
* [NLP](http://cran.r-project.org/web/packages/NLP/index.html) - Basic functions for Natural Language Processing.
* [LDAvis](https://github.com/cpsievert/LDAvis) - Interactive visualization of topic models.
* [topicmodels](https://cran.r-project.org/web/packages/topicmodels/index.html) - Topic modeling interface to the C code developed by by David M. Blei for Topic Modeling (Latent Dirichlet Allocation (LDA), and Correlated Topics Models (CTM)).
* [syuzhet](https://cran.r-project.org/web/packages/syuzhet/index.html) - Extracts sentiment from text using three different sentiment dictionaries.
* [SnowballC](https://cran.rstudio.com/web/packages/SnowballC/index.html) - Snowball stemmers based on the C libstemmer UTF-8 library.
* [quanteda](https://github.com/kbenoit/quanteda) - R functions for Quantitative Analysis of Textual Data.
* [Topic Models Resources](https://github.com/trinker/topicmodels_learning) - Topic Models learning and R related resources.
* [NLP for <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f1e8-1f1f3.png" width="20" heigth="20" align="absmiddle" class="emoji" alt=":cn:">](https://github.com/BZRLC/R-notes/blob/master/NLP/readme.md) - NLP related resources in R. @Chinese
* [MonkeyLearn](https://github.com/masalmon/monkeylearn) - 🐒 R package for text analysis with Monkeylearn 🐒.
* [tidytext](http://tidytextmining.com/index.html) - Implementing tidy principles of Hadley Wickham to text mining.
* [utf8](https://github.com/patperry/r-utf8) - Manipulating and printing UTF-8 text that fixes multiple bugs in R's UTF-8 handling.
## Bayesian
*Packages for Bayesian Inference.*
* [coda](http://cran.r-project.org/web/packages/coda/index.html) - Output analysis and diagnostics for MCMC.
* [mcmc](http://cran.r-project.org/web/packages/mcmc/index.html) - Markov Chain Monte Carlo.
* [MCMCpack](http://mcmcpack.berkeley.edu/) - Markov chain Monte Carlo (MCMC) Package.
* [R2WinBUGS](http://cran.r-project.org/web/packages/R2WinBUGS/index.html) - Running WinBUGS and OpenBUGS from R / S-PLUS.
* [BRugs](http://cran.r-project.org/web/packages/BRugs/index.html) - R interface to the OpenBUGS MCMC software.
* [rjags](http://cran.r-project.org/web/packages/rjags/index.html) - R interface to the JAGS MCMC library.
* [rstan <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://mc-stan.org/interfaces/rstan.html) - R interface to the Stan MCMC software.
## Optimization
*Packages for Optimization.*
* [lpSolve](https://cran.rstudio.com/web/packages/lpSolve/index.html) - Interface to `Lp_solve` to Solve Linear/Integer Programs.
* [minqa](https://cran.rstudio.com/web/packages/minqa/index.html) - Derivative-free optimization algorithms by quadratic approximation.
* [nloptr](https://cran.rstudio.com/web/packages/nloptr/index.html) - NLopt is a free/open-source library for nonlinear optimization.
* [ompr](https://cran.rstudio.com/web/packages/ompr/index.html) - Model mixed integer linear programs in an algebraic way directly in R.
* [Rglpk](https://cran.rstudio.com/web/packages/Rglpk/index.html) - R/GNU Linear Programming Kit Interface
* [ROI](https://cran.rstudio.com/web/packages/ROI/index.html) - The R Optimization Infrastructure ('ROI') is a sophisticated framework for handling optimization problems in R.
## Finance
*Packages for dealing with money.*
* [quantmod <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://www.quantmod.com/) - Quantitative Financial Modelling & Trading Framework for R.
* [TTR](http://cran.r-project.org/web/packages/TTR/index.html) - Functions and data to construct technical trading rules with R.
* [PerformanceAnalytics](http://cran.r-project.org/web/packages/PerformanceAnalytics/index.html) - Econometric tools for performance and risk analysis.
* [zoo <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://cran.r-project.org/web/packages/zoo/index.html) - S3 Infrastructure for Regular and Irregular Time Series.
* [xts](http://cran.r-project.org/web/packages/xts/index.html) - eXtensible Time Series.
* [tseries](http://cran.r-project.org/web/packages/tseries/index.html) - Time series analysis and computational finance.
* [fAssets](http://cran.r-project.org/web/packages/fAssets/index.html) - Analysing and Modelling Financial Assets.
## Bioinformatics and Biostatistics
*Packages for processing biological datasets.*
* [Bioconductor <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://www.bioconductor.org/) - Tools for the analysis and comprehension of high-throughput genomic data.
* [genetics](http://cran.r-project.org/web/packages/genetics/index.html) - Classes and methods for handling genetic data.
* [gap](http://cran.r-project.org/web/packages/gap/index.html) - An integrated package for genetic data analysis of both population and family data.
* [ape](http://cran.r-project.org/web/packages/ape/index.html) - Analyses of Phylogenetics and Evolution.
* [pheatmap](http://cran.r-project.org/web/packages/pheatmap/index.html) - Pretty heatmaps made easy.
## Network Analysis
*Packages to construct, analyze and visualize network data.*
* [Network Analysis List](https://github.com/briatte/awesome-network-analysis) - Network Analysis related resources.
* [igraph <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://igraph.org/r/) - A collection of network analysis tools.
* [network](https://cran.r-project.org/web/packages/network/index.html) - Basic tools to manipulate relational data in R.
* [sna](https://cran.r-project.org/web/packages/sna/index.html) - Basic network measures and visualization tools.
* [netdiffuseR](https://github.com/USCCANA/netdiffuseR) - Tools for Analysis of Network Diffusion.
* [networkDynamic](https://cran.r-project.org/web/packages/networkDynamic/) - Support for dynamic, (inter)temporal networks.
* [ndtv](https://cran.r-project.org/web/packages/ndtv/) - Tools to construct animated visualizations of dynamic network data in various formats.
* [statnet](http://statnet.org/) - The project behind many R network analysis packages.
* [ergm](https://cran.r-project.org/web/packages/ergm/index.html) - Exponential random graph models in R.
* [latentnet](https://cran.r-project.org/web/packages/latentnet/index.html) - Latent position and cluster models for network objects.
* [tnet](https://cran.r-project.org/web/packages/tnet/index.html) - Network measures for weighted, two-mode and longitudinal networks.
* [rgexf](https://bitbucket.org/gvegayon/rgexf/wiki/Home) - Export network objects from R to [GEXF](http://gexf.net/format/), for manipulation with network software like [Gephi](https://gephi.org/) or [Sigma](http://sigmajs.org/).
* [visNetwork](https://github.com/datastorm-open/visNetwork) - Using vis.js library for network visualization.
## Spatial
*Packages to explore the earth.*
* [CRAN Task View: Analysis of Spatial Data](https://cran.r-project.org/web/views/Spatial.html)- Spatial Analysis related resources.
* [Leaflet](http://rstudio.github.io/leaflet/) - One of the most popular JavaScript libraries interactive maps.
* [ggmap](https://github.com/dkahle/ggmap) - Plotting maps in R with ggplot2.
* [REmap](https://github.com/Lchiffon/REmap) - R interface to the JavaScript library ECharts for interactive map data visualization.
* [sp](https://edzer.github.io/sp/) - Classes and Methods for Spatial Data.
* [rgeos](https://cran.r-project.org/web/packages/rgeos/index.html) - Interface to Geometry Engine - Open Source
* [rgdal](https://cran.r-project.org/web/packages/rgdal/index.html) - Bindings for the Geospatial Data Abstraction Library
* [maptools](https://cran.r-project.org/web/packages/maptools/index.html) - Tools for Reading and Handling Spatial Objects
* [gstat](https://github.com/edzer/gstat) - Spatial and spatio-temporal geostatistical modelling, prediction and simulation.
* [spacetime](https://github.com/edzer/spacetime) - R classes and methods for spatio-temporal data.
* [RColorBrewer](https://cran.r-project.org/web/packages/RColorBrewer/index.html) - Provides color schemes for maps
* [spatstat](https://github.com/spatstat/spatstat) - Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
* [spdep](https://cran.r-project.org/web/packages/spdep/index.html) - Spatial Dependence: Weighting Schemes, Statistics and Models
* [tigris](https://github.com/walkerke/tigris) - Download and use Census TIGER/Line shapefiles in R
## R Development
*Packages for packages.*
* [Package Development List](https://github.com/ropensci/PackageDevelopment) - R packages to improve package development.
* [promises](https://cran.r-project.org/web/packages/promises/index.html) - Abstractions for Promise-Based Asynchronous Programming
* [devtools <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/devtools) - Tools to make an R developer's life easier.
* [testthat <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/testthat) - An R package to make testing fun.
* [R6 <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/wch/R6) - simpler, faster, lighter-weight alternative to R's built-in classes.
* [pryr <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/hadley/pryr) - Make it easier to understand what's going on in R.
* [roxygen <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/klutometis/roxygen) - Describe your functions in comments next to their definitions.
* [lineprof](https://github.com/hadley/lineprof) - Visualise line profiling results in R.
* [packrat](https://github.com/rstudio/packrat) - Make your R projects more isolated, portable, and reproducible.
* [installr](https://github.com/talgalili/installr/) - Functions for installing softwares from within R (for Windows).
* [import](https://github.com/smbache/import/) - An import mechanism for R.
* [modules](https://github.com/klmr/modules) - An alternative (Python style) module system for R.
* [Rocker <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/rocker-org) - R configurations for [Docker](https://www.docker.com/).
* [RStudio Addins](https://github.com/daattali/rstudio-addins) - List of RStudio addins.
* [drat](https://github.com/eddelbuettel/drat) - Creation and use of R repositories on GitHub or other repos.
* [covr](https://github.com/jimhester/covr) - Test coverage for your R package and (optionally) upload the results to [coveralls](https://coveralls.io/) or [codecov](https://codecov.io/).
* [lintr](https://github.com/jimhester/lintr) - Static code analysis for R to enforce code style.
* [staticdocs](https://github.com/hadley/staticdocs) - Generate static html documentation for an R package.
## Logging
*Packages for Logging*
* [futile.logger](https://github.com/zatonovo/futile.logger) - A logging package in R similar to log4j
* [log4r](https://github.com/johnmyleswhite/log4r) - A log4j derivative for R
* [logging](https://cran.r-project.org/web/packages/logging/index.html) - A logging package emulating the python logging package.
## Data Packages
*Handy Data Packages*
* [engsoccerdata](https://github.com/jalapic/engsoccerdata) - English and European soccer results 1871-2016.
* [gapminder](http://github.com/jennybc/gapminder) - Excerpt from the Gapminder dataset (data about countries throught the past 50 years).
## Other Tools
*Handy Tools for R*
* [git2r](https://github.com/ropensci/git2r) - Gives you programmatic access to Git repositories from R.
## Other Interpreters
*Alternative R engines.*
* [CXXR](https://www.cs.kent.ac.uk/projects/cxxr/) - Refactorising R into C++.
* [fastR](https://bitbucket.org/allr/fastr/wiki/Home) - FastR is an implementation of the R Language in Java atop Truffle and Graal.
* [pqR](http://www.pqr-project.org/) - a "pretty quick" implementation of R
* [renjin](http://www.renjin.org/) - a JVM-based interpreter for R.
* [rho](https://github.com/rho-devel/rho) - Refactor the interpreter of the R language into a fully-compatible, efficient, VM for R.
* [riposte](https://github.com/jtalbot/riposte) - a fast interpreter and JIT for R.
* [TERR](http://spotfire.tibco.com/discover-spotfire/what-does-spotfire-do/predictive-analytics/tibco-enterprise-runtime-for-r-terr) - TIBCO Enterprise Runtime for R.
## Learning R
*Packages for Learning R.*
* [swirl <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://swirlstats.com/) - An interactive R tutorial directly in your R console.
* [DataScienceR <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](https://github.com/ujjwalkarn/DataScienceR) - a list of R tutorials for Data Science, NLP and Machine Learning.
# Resources
Where to discover new R-esources.
## Websites
* [R-project](http://www.r-project.org/) - The R Project for Statistical Computing.
* [R Weekly](https://rweekly.org) - Weekly updates about R and Data Science. R Weekly is openly developed on GitHub.
* [R Bloggers](http://www.r-bloggers.com/) - There are people scattered across the Web who blog about R. This is simply an aggregator of many of those feeds.
* [Quick-R](http://www.statmethods.net/) - An excellent quick reference.
* [Advanced R <img class="emoji" alt="heart" src="https://awesome-r.com/heart.png" height="20" align="absmiddle" width="20">](http://adv-r.had.co.nz/) - An online version of the Advanced R book.
* [Efficient R Programming](https://csgillespie.github.io/efficientR/) - An online home of the O’Reilly book: Efficient R Programming.
* [CRAN Task Views](http://cran.r-project.org/web/views/) - Task Views for CRAN packages.
* [The R Programming Wikibook](https://en.wikibooks.org/wiki/R_Programming) - A collaborative handbook for R.
* [R-users](https://www.r-users.com/) - A job board for R users (and the people who are looking to hire them)
* [R Cookbook](http://www.cookbook-r.com/) - A problem-oriented website that supports the [R Graphics Cookbook](http://shop.oreilly.com/product/0636920023135.do).
* [tryR](http://tryr.codeschool.com/) - A quick course for getting started with R.
* [RDocumentation](https://www.rdocumentation.org/) - Search through all CRAN, Bioconductor, Github packages and their archives with RDocumentation.
## Books
* [Readings in Applied Data Science](https://github.com/hadley/stats337) - These readings reflect Hadley's personal thoughts about applied data science.
* [R Books List](https://github.com/RomanTsegelskyi/rbooks) - List of R Books.
* [The Art of R Programming](http://shop.oreilly.com/product/9781593273842.do) - It's a good resource for systematically learning fundamentals such as types of objects, control statements, variable scope, classes and debugging in R.
* [Free Books](https://cran.r-project.org/other-docs.html) - CRAN Contributed Documentation in many languages.
* [R Cookbook](http://shop.oreilly.com/product/9780596809164.do) - A quick and simple introduction to conducting many common statistical tasks with R.
* Books written as part of the Johns Hopkins Data Science Specialization:
* [Exploratory Data Analysis with R](https://leanpub.com/exdata) - Basic analytical skills for all sorts of data in R.
* [R Programming for Data Science](https://leanpub.com/rprogramming) - More advanced data analysis that relies on R programming.
* [Report Writing for Data Science in R](https://leanpub.com/reportwriting) - R-based methods for reproducible research and report generation.
* [R Packages](http://r-pkgs.had.co.nz/) - A book (in paper and website formats) on writing R packages.
* [R in Action](http://www.manning.com/kabacoff2/) - This book aims at all levels of users, with sections for beginning, intermediate and advanced R ranging from "Exploring R data structures" to running regressions and conducting factor analyses.
* [Use R!](http://www.springer.com/series/6991?detailsPage=titles) - This series of inexpensive and focused books from Springer publish shorter books aimed at practitioners. Books can discuss the use of R in a particular subject area, such as Bayesian networks, ggplot2 and Rcpp.
* [R for SAS and SPSS users](http://r4stats.com/books/free-version/) - An excelllent resource for users already familiar with SAS or SPSS.
* [An Introduction to R](https://cran.r-project.org/doc/manuals/R-intro.pdf) - A very good introductory text on R, also covers some advanced topics.
* [Introduction to Statistical Learning with Application in R](http://www-bcf.usc.edu/~gareth/ISL/) - A simplified and "operational" version of *The Elements of Statistical Learning*. Free softcopy provided by its authors.
* [The R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf) - Patrick Burns gives insight into R's ins and outs along with its quirks!
* [R for Data Science](http://r4ds.had.co.nz/) - Free book from RStudio developers with emphasis on data science workflow.
* [Learning R Programming](https://www.packtpub.com/big-data-and-business-intelligence/learning-r-programming) - Learning R as a programming language from basics to advanced topics.
## Podcasts
* [Not So Standard Deviations](https://soundcloud.com/nssd-podcast) - The Data Science Podcast.
* [@Roger Peng](https://twitter.com/rdpeng) and [@Hilary Parker](https://twitter.com/hspter).
* [R World News](http://www.rworld.news/blog/) - R World News helps you keep up with happenings within the R community.
* [@Bob Rudis](https://twitter.com/hrbrmstr) and [@Jay Jacobs](https://twitter.com/jayjacobs).
* [The R-Podcast](https://r-podcast.org/) - Giving practical advice on how to use R.
* [@Eric Nantz](https://r-podcast.org/stories/contact.html).
* [R Talk](http://rtalk.org) - News and discussions of statistical software and language R.
* [@Oliver Keyes](https://twitter.com/quominus), [@Jasmine Dumas](https://twitter.com/jasdumas), [@Ted Hart](https://twitter.com/emhrt_) and [@Mikhail Popov](https://twitter.com/bearloga).
* [R Weekly](https://rweekly.org) - Weekly news updates about the R community.
## Reference Cards
* [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/)
* [R Reference Card 2.0](http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf) - Material from R for Beginners by permission of Emmanuel Paradis (Version 2 by Matt Baggott).
* [Regression Analysis Refcard](http://cran.r-project.org/doc/contrib/Ricci-refcard-regression.pdf) - R Reference Card for Regression Analysis.
* [Reference Card for ESS](http://ess.r-project.org/refcard.pdf) - Reference Card for ESS.
## MOOCs
*Massive open online courses.*
* [Johns Hopkins University Data Science Specialization](https://www.coursera.org/specialization/jhudatascience/1) - 9 courses including: Introduction to R, literate analysis tools, Shiny and some more.
* [HarvardX Biomedical Data Science](http://simplystatistics.org/2014/11/25/harvardx-biomedical-data-science-open-online-training-curriculum-launches-on-january-19/) - Introduction to R for the Life Sciences.
* [Explore Statistics with R](https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0) - Covers introduction, data handling and statistical analysis in R.
## Lists
*Great resources for learning domain knowledge.*
* [Books](https://github.com/RomanTsegelskyi/rbooks) - List of R Books.
* [ggplot2 Extensions](https://ggplot2-exts.github.io/ggiraph.html) - Showcases of ggplot2 extensions.
* [Natural Language Processing <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f1e8-1f1f3.png" width="20" heigth="20" align="absmiddle" class="emoji" alt=":cn:">](https://github.com/BZRLC/R-notes/blob/master/NLP/readme.md) - NLP related resources in R. @Chinese
* [Network Analysis](https://github.com/briatte/awesome-network-analysis) - Network Analysis related resources.
* [Open Data](https://github.com/ropensci/opendata) - Using R to obtain, parse, manipulate, create, and share open data.
* [Posts](https://github.com/qinwf/awesome-R/blob/master/posts.md) - Great R blog posts or Rticles.
* [Package Development](https://github.com/ropensci/PackageDevelopment) - R packages to improve package development.
* [R Project Conferences](https://www.r-project.org/conferences.html) - Information about useR! Conferences and DSC Conferences.
* [RStartHere](https://github.com/rstudio/RStartHere) - A guide to some of the most useful R packages, organized by workflow.
* [RStudio Addins](https://github.com/daattali/addinslist) - List of RStudio addins.
* [Topic Models](https://github.com/trinker/topicmodels_learning) - Topic Models learning and R related resources.
* [Web Technologies](https://github.com/ropensci/webservices) - Information about how to use R and the world wide web together.
# Other Awesome Lists
* [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness)
* [lists](https://github.com/jnv/lists)
* [awesome-rshiny](https://github.com/grabear/awesome-rshiny)
# Contributing
Your contributions are always welcome!
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License - [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)

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# Awesome REST [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
A collaborative list of great resources about RESTful API architecture, development, test, and performance. Feel free to contribute to this on-going list.
* [Design](#design)
* [Standards](#standards)
* [Clients](#clients)
* [PHP](#php-clients)
* [Client-side JavaScript](#javascript-clients)
* [Node.js](#nodejs-clients)
* [Ruby](#ruby-clients)
* [Go](#go-clients)
* [Servers](#servers)
* [Directly On Top Of A RMDB](#directly-on-top-of-a-rmdb)
* [Node.js](#nodejs)
* [PHP](#php)
* [Symfony2](#symfony2)
* [Python](#python)
* [Ruby](#ruby)
* [Go](#go)
* [Java](#java)
* [Haskell](#haskell)
* [Testing](#testing)
* [Querying](#querying)
* [Mocking](#mocking)
* [Public REST APIs To Use In Tests](#public-rest-apis-to-use-in-tests)
* [Documentation](#documentation)
* [API Gateway](#api-gateway)
* [SaaS Tools](#saas-tools)
* [Miscellaneous](#miscellaneous)
## Design
* [Architectural Styles and
the Design of Network-based Software Architectures](https://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm) - Roy Fielding's dissertation defining REST
* [HTTP API design guide extracted from work on the Heroku Platform API](https://github.com/interagent/http-api-design)
* [Best Practices for Designing a Pragmatic RESTful API](http://www.vinaysahni.com/best-practices-for-a-pragmatic-restful-api)
* [How to design a REST API?](http://blog.octo.com/en/design-a-rest-api/) - Full guide tackling security, pagination, filtering, versioning, partial answers, CORS, etc.
* [Richardson Maturity Model](http://martinfowler.com/articles/richardsonMaturityModel.html) - Explained by Martin Fowler, originally presented by Leonard Richardson at the [QCon 2008](https://www.crummy.com/writing/speaking/2008-QCon/act3.html).
* [Enterprise Integration Using REST](http://martinfowler.com/articles/enterpriseREST.html) - Discusses the constraints and flexibility that you have with nonpublic APIs, and lessons learned from doing large scale RESTful integration across multiple teams.
* [HATEOAS](http://timelessrepo.com/haters-gonna-hateoas) - Clear explanation on what HATEOAS is, and why you should use it.
* [How to GET a cup of coffee](http://www.infoq.com/articles/webber-rest-workflow/)
* [REST API Tutorial](http://www.restapitutorial.com/) - RestApiTutorial.com is dedicated to tracking REST API best practices and making resources available to enable quick reference and self education for the development crafts-person.
* [Microsoft REST API Guidelines](https://github.com/Microsoft/api-guidelines/blob/vNext/Guidelines.md#readme) - The Microsoft REST API Guidelines, as a design principle, encourages application developers to have resources accessible to them via a RESTful HTTP interface.
* [API-Security-Checklist](https://github.com/shieldfy/API-Security-Checklist) - Best practices about REST API security
## Standards
* [JSON API](http://jsonapi.org/) - Standard for building APIs in JSON.
* [RAML](http://raml.org/) - Simple and succinct way to describe RESTful API.
* [JSend](http://labs.omniti.com/labs/jsend) - Simple specification that lays down some rules for how JSON responses from web servers should be formatted.
* [OData](http://www.odata.org/) - Open protocol to allow the creation and consumption of queryable and interoperable RESTful APIs. Quite complex.
* [HAL](http://stateless.co/hal_specification.html) - Simple format that gives a consistent and easy way to hyperlink between resources in your API (see: [HATEOAS](#hateoas)).
* [JSON-LD](http://json-ld.org/) - Standard for describing Linked Data and hypermedia relations in JSON (W3C).
* [Hydra](http://www.hydra-cg.com/) - Vocabulary for Hypermedia-Driven Web APIs (W3C).
* [Schema.org](http://schema.org) - Collection of schemas describing common data models.
* [OpenAPI](https://openapis.org/) - Formerly known as the Swagger Specification, OpenAPI specifcation is the world’s most popular description format for defining Restful APIs.
## Clients
### PHP Clients
* [Guzzle](http://guzzle.readthedocs.org/en/latest/) - HTTP client and framework for consuming RESTful web services.
* [Buzz](https://github.com/kriswallsmith/buzz) - Another lightweight HTTP client.
* [unirest for PHP](https://github.com/Mashape/unirest-php) - Simplified, lightweight HTTP client library.
### JavaScript Clients
* [restangular](https://github.com/mgonto/restangular) - AngularJS service to handle REST API properly and easily.
* [restful.js](https://github.com/marmelab/restful.js) - JS client for interacting with server-side RESTful resources.
* [traverson](https://github.com/basti1302/traverson) - A Hypermedia API/HATEOAS Client for Node.js and the Browser
* [raml-client-generator](https://github.com/mulesoft/raml-client-generator) - Generates static client libs for js.
### Node.js Clients
* [restler](https://github.com/danwrong/restler) - REST client library for node.js.
* [unirest for Node.js](https://github.com/Mashape/unirest-nodejs) - Simplified, lightweight HTTP client library.
### Ruby Clients
* [RESTClient](https://github.com/rest-client/rest-client) - Simple HTTP and REST client for Ruby, inspired by microframework syntax for specifying actions.
* [Spyke](https://github.com/balvig/spyke) - Interact with REST services in an ActiveRecord-like manner.
* [excon](https://github.com/excon/excon) - Usable, fast, simple Ruby HTTP 1.1. It works great as a general HTTP(s) client and is particularly well suited to usage in API clients.
* [httparty](https://github.com/jnunemaker/httparty) - Makes HTTP fun again!
* [Net::HTTP](http://ruby-doc.org/stdlib/libdoc/net/http/rdoc/Net/HTTP.html) - Net::HTTP provides a rich library which can be used to build HTTP user-agents.
* [raml-ruby-client-generator](https://github.com/zlx/raml-ruby-client-generator) - Auto generate API client from a RAML file.
### Go Clients
* [gopencils](https://github.com/bndr/gopencils) - Small and simple package to easily consume REST APIs.
* [resty](https://github.com/go-resty/resty) - Simple HTTP and REST client for Go inspired by Ruby rest-client.
## Servers
### Directly On Top Of A RMDB
* [postgrest](https://github.com/begriffs/postgrest) - Serve a fully RESTful API directly from an existing PostgreSQL database.
* [MySQL HTTP plugin](http://blog.ulf-wendel.de/2014/mysql-5-7-http-plugin-mysql/) - Simple REST-like / CRUD server for any MySQL database.
* [pREST](https://github.com/prest/prest) - A fully RESTful API from any existing PostgreSQL database written in Go.
### Node.js
* [node-restify](https://github.com/restify/node-restify) - Framework specifically meant for REST API.
* [Sails.js](http://sailsjs.org/) - Node.js Web framework embedding a command to generate automatically a REST API.
* [mers](https://github.com/jspears/mers) - Express service exposing Mongoose finders as RESTful API.
* [Baucis](https://github.com/wprl/baucis) - Build scalable REST API based on your Mongoose entities.
* [flatiron/resourceful](https://github.com/flatiron/resourceful) - Isomorphic Resource engine for JavaScript.
* [loopback](http://loopback.io/) - Powerful Node.js framework for creating APIs and easily connecting to backend data sources.
* [Feathers](http://feathersjs.com/) - is a real-time, micro-service web framework that gives you control over your data via RESTful resources, sockets and flexible plug-ins.
* [Expressa](https://github.com/thomas4019/expressa) - Express middleware for creating APIs from JSON schemas with a simple admin editor and permissions model.
### PHP
* [Microrest](https://github.com/marmelab/microrest.php) - Micro-web application providing a REST API on top of any relational database.
* [Negotiation](https://github.com/willdurand/Negotiation) - Content negotiation library.
* [Drest](https://github.com/leedavis81/drest) - Library for exposing Doctrine entities as REST resource endpoints.
* [Restler](https://github.com/Luracast/Restler) - Lightweight framework to expose PHP methods as RESTful web API.
* [HAL](https://github.com/blongden/hal) - Hypertext Application Language (HAL) builder library.
* [Apigility](https://github.com/zfcampus/zf-apigility-skeleton) - API builder built with Zend Framework 2.
* [phprest](https://github.com/phprest/phprest) - Specialized REST microframework for PHP.
* [Hateoas](https://github.com/willdurand/Hateoas) - PHP library to support implementing representations for HATEOAS REST web services.
* [Fusio](https://github.com/apioo/fusio) - Open source API management platform.
#### Symfony2
* [REST APIs with Symfony2: the Right Way](http://williamdurand.fr/2012/08/02/rest-apis-with-symfony2-the-right-way/) - Complete guide to build a state-of-the-art REST API with Symfony2 framework.
* [FOSRestBundle](https://github.com/FriendsOfSymfony/FOSRestBundle) - Bundle handling view, routing, error handling, etc. for your REST API.
* [stanlemon/rest-bundle](https://github.com/stanlemon/rest-bundle) - Build a REST API based on Doctrine entities using conventions over configuration.
* [lakion/Lionframe](http://lakion.com/lionframe) - Glu between several community libraries to ease API development.
* [BazingaHateoasBundle](https://github.com/willdurand/BazingaHateoasBundle) - Integrate the [Hateoas](https://github.com/willdurand/Hateoas) library into a Symfony2 application.
* [Symfony REST Edition](https://github.com/gimler/symfony-rest-edition) - Start with a Symfony2 application with all REST-friendly bundles pre-configured.
* [NgAdminGeneratorBundle](https://github.com/marmelab/NgAdminGeneratorBundle) - Boostrap ng-admin configuration based on `stanlemon/rest-bundle`.
* [DunglasApiBundle](https://github.com/dunglas/DunglasApiBundle) - Build a REST API which follow Hydra/JSON-LD specification.
* [API Platform](https://github.com/api-platform/api-platform) - Specialize Symfony edition for the creation of hypermedia REST APIs.
* [NelmioApiDocBundle](https://github.com/nelmio/NelmioApiDocBundle) - Generate documentation for your REST API from annotations.
### Python
* [Django REST framework](http://www.django-rest-framework.org/) - Powerful and flexible toolkit that makes it easy to build Web APIs.
* [django-tastypie](http://tastypieapi.org/) - Creating delicious APIs for Django apps.
* [flask-restful](http://flask-restful.readthedocs.org/) - Extension for Flask that adds support for quickly building REST APIs.
* [flask-restless](https://flask-restless.readthedocs.org/en/latest/) - Flask extension for generating ReSTful APIs for database models defined with SQLAlchemy (or Flask-SQLAlchemy).
* [hug](http://www.hug.rest/) - Lightweight and fast API Framework.
* [sandman](https://github.com/jeffknupp/sandman) - Automated REST APIs for existing database-driven systems.
* [restless](http://restless.readthedocs.org/en/latest/) - Framework agnostic REST framework based on lessons learned from TastyPie.
* [savory-pie](https://github.com/RueLaLa/savory-pie/) - REST API building library (django, and others).
* [Python Eve](http://python-eve.org/) - Eve is an open source Python REST API framework designed for human beings. It allows to effortlessly build and deploy highly customizable, fully featured RESTful Web Services.
* [Ramses](https://ramses.readthedocs.org/en/stable/) - Makes RAML files executable by generating production-ready APIs from them at runtime.
* [Flask-Potion](https://github.com/biosustain/potion) - Flask-Potion is a powerful Flask extension for building RESTful JSON APIs. It also provides several Clients for easier access to the API.
* [apistar](https://github.com/encode/apistar) - A smart Web API framework, designed for Python 3.
### Ruby
* [Grape](http://www.ruby-grape.org) - Opinionated micro-framework for creating REST-like APIs in Ruby.
* [Rails](http://guides.rubyonrails.org/api_app.html) - RailsGuides: Using Rails for API-only applications.
### Go
* [gocrud](https://github.com/manishrjain/gocrud): Go library to simplify creating, updating and deleting arbitrary depth structured data — to make building REST services fast and easy.
* [go-json-rest](https://github.com/ant0ine/go-json-rest) - Thin layer on top of `net/http` that helps building RESTful APIs easily.
* [sleepy](https://github.com/dougblack/sleepy) - RESTful micro-framework written in Go.
* [restit](https://github.com/yookoala/restit) - Go micro framework to help writing RESTful API integration test.
* [go-relax](https://github.com/codehack/go-relax) - Framework of pluggable components to build RESTful API's.
* [go-rest](https://github.com/ungerik/go-rest) - Small and evil REST framework for Go.
* [go-restful](https://github.com/emicklei/go-restful) - A declarative highly readable framework for building restful API's.
* [Goat](https://github.com/bahlo/goat) - Minimalistic REST API server in Go.
* [Resoursea](https://github.com/resoursea/api) - REST framework for quickly writing resource based services.
* [Zerver](https://github.com/cosiner/zerver) - Zerver is a expressive, modular, feature completed RESTful framework.
### Java
* [RestExpress](https://github.com/RestExpress/RestExpress) - Netty-based, highly performant, lightweight, container-less, plugin-extensible, framework that is ideal for microservice architectures.
* [Vertx-Web](https://github.com/vert-x3/vertx-web) - Vert.x-Web is a set of building blocks for building web applications with Vert.x, a toolkit for building reactive applications on the JVM.
* [Dropwizard](https://github.com/dropwizard/dropwizard) - A framework for developing ops-friendly, high-performance, RESTful web services.
### Haskell
* [Rest for Haskell](https://github.com/silkapp/rest) - This package allows you to create REST APIs in Haskell. These APIs can be run in different web frameworks. They can also be used to automatically generate documentation as well as client libraries.
## Testing
### Querying
* [Hurl.it](https://www.hurl.it/) - Make HTTP requests with a simple web-based HTTP client -- like `curl` in the cloud.
* [httpie](https://github.com/jkbrzt/httpie) - Command line HTTP client, far more dev-friendly than `curl`.
* [Postman REST Client](https://chrome.google.com/webstore/detail/postman-rest-client/fdmmgilgnpjigdojojpjoooidkmcomcm) - Chrome extension essential to test manually REST API.
* [resty](https://github.com/micha/resty) - Little command line REST client that you can use in pipelines (bash or zsh).
* [jq](https://github.com/stedolan/jq) - Command line JSON processor, to use in combination with a command-line HTTP client like cURL.
* [HttpMaster](http://www.httpmaster.net) - GUI tool for testing REST APIs and services. Windows OS only.
* [Http-console](https://github.com/cloudhead/http-console) - Command line interface for HTTP that let you *speak HTTP like a local*
* [rest-assured](https://github.com/rest-assured/rest-assured) - Java DSL for easy testing of REST services.
### Mocking
* [RequestBin](http://requestb.in/) - Inspect and debug webhook requests sent by your clients or third-party APIs.
* [httpbin](http://httpbin.org) - HTTP request and response service - a/k/a Swiss Army Knife for HTTP.
* [FakeRest](https://github.com/marmelab/FakeRest) - Patch XMLHttpRequest to fake a REST API client-side.
* [json-server](https://github.com/typicode/json-server) - Serve a REST API from fixture files using quick prototyping.
* [Mocky.io](http://www.mocky.io/) - Free online service to create fake HTTP responses.
* [Swagger API Mock](https://github.com/bulkismaslom/swagger-api-mock) - Mock RESTful API based on swagger schema
* [Request Baskets](https://github.com/darklynx/request-baskets) - Service to collect HTTP requests and inspect them via RESTful API or web UI.
* [DuckRails](https://github.com/iridakos/duckrails) - Mock quickly & dynamically API endpoints.
### Public REST APIs To Use In Tests
* [Deck of Cards API](http://deckofcardsapi.com) - Open API for simulating a deck of cards.
* [ProgrammableWeb](http://www.programmableweb.com/apis/directory) - The world's largest API repository.
* [Public APIS](https://www.publicapis.com/) - Explore The Largest API Directory In The Galaxy.
* [Marvel Comics API](http://developer.marvel.com/) - Query characters, stories, events about Marvel superheroes.
* [JSON Placeholder](http://jsonplaceholder.typicode.com/) - Free online REST service that you can use whenever you need some fake data.
* [APIs.guru](http://APIs.guru) - Wikipedia for Web APIs, each API has OpenAPI/Swagger description.
## Documentation
* [Swagger](http://swagger.io/) - Documentation/querying web interface for REST APIs.
* [API doc](http://apidocjs.com/) - Inline Documentation for RESTful web APIs.
* [raml2html](https://github.com/raml2html/raml2html) - Generates HTML documentation from a RAML file.
* [ReDoc](https://github.com/Rebilly/ReDoc/) - OpenAPI/Swagger-powered three-panel documentation.
* [Slate](https://github.com/lord/slate) - Beautiful and responsive three-panel API documentation using Middleman.
## API Gateway
* [Kong](https://github.com/Kong/kong) - Scalable, distributed, and plugin oriented API gateway backed by Nginx.
* [Tyk API Gateway](https://github.com/TykTechnologies/tyk) - Lightweight API gateway with analytics logging, written in Go.
* [API Umbrella](https://github.com/NREL/api-umbrella) - API management platform for exposing web services, with web interface and analytics, written in Lua.
* [WSO2 API Management](https://github.com/wso2/product-apim) - API management tool with lightweight gateway and API lifecycle manangement, written in Java.
## SaaS Tools
* [Runscope](https://www.runscope.com/) - Automated API Monitoring & Testing.
* [Ping-API](https://ping-api.com/) - Automated API Monitoring & Testing.
* [import.io Magic](https://magic.import.io/) - Create a REST API from any website in one click.
* [Apiary](https://apiary.io/) - Collaborative design, instant API mock, generated documentation, integrated code samples, debugging and automated testing.
* [Amazon API Gateway](https://aws.amazon.com/api-gateway/) - Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale.
* [Apigee](https://apigee.com) - Apigee is the leading provider of API technology and services for enterprises and developers.
* [3scale](https://www.3scale.net/) - Nginx based API gateway to integrate internal and external API services with 3scale's API Management Platform.
* [Assertible](https://assertible.com) - Continuously test and monitor your APIs after deployments and across environments.
* [Moesif](https://www.moesif.com) - API Analytics for Debugging, Monitoring, and Usage Tracking for RESTful and GraphQL.
## Miscellaneous
* [ng-admin](https://github.com/marmelab/ng-admin) - Add an AngularJS admin GUI to any RESTful API.
* [admin-on-rest](https://github.com/marmelab/admin-on-rest) - Add a ReactJS admin GUI to any RESTful API.
* [swagger-codegen](https://github.com/swagger-api/swagger-codegen) - Auto generation of client libraries or server stubs given an OpenAPI specification (formerly known as the Swagger Specification).
* [Lumber](https://github.com/ForestAdmin/lumber) - Generate the admin interface of your application.
## License
[![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)](http://creativecommons.org/licenses/by/4.0/)
This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).

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# Awesome Vim
Plugins are organized by section and ordered alphabetically.
## Table of Contents
1. [Learning Vim](#learning-vim)
2. [Plugin Management](#plugin-management)
3. [Colors](#colors)
4. [Tools](#tools)
5. [Language Specific](#language-specific)
6. [Framework Specific](#framework-specific)
7. [Distributions](#distributions)
8. [Websites](#websites)
9. [Contributing](#contributing)
10. [License](#license)
## Learning Vim
* [Seven habits of effective text editing](http://www.moolenaar.net/habits.html)
* [The Way of the Vim Warrior](https://github.com/dahu/LearnVim)
* [Learn Vimscript the Hard Way](http://learnvimscriptthehardway.stevelosh.com/)
* [Vim Adventures](http://vim-adventures.com/)
* [Vim Genius](http://www.vimgenius.com/)
* [Vim Tips](http://zzapper.co.uk/vimtips.html)
* [Fortune vimtips](https://github.com/hobbestigrou/vimtips-fortune)
* [Vim Galore](https://github.com/mhinz/vim-galore)
## Plugin Management
* [Neobundle](https://github.com/Shougo/neobundle.vim)
* [Pathogen](https://github.com/tpope/vim-pathogen)
* [VAM](https://github.com/MarcWeber/vim-addon-manager)
* [Vim-plug](https://github.com/junegunn/vim-plug)
* [Vundle](https://github.com/gmarik/Vundle.vim)
## Colors
* [Apprentice](https://github.com/romainl/Apprentice)
* [Base16](https://github.com/chriskempson/base16-vim/)
* [Jellybeans](https://github.com/nanotech/jellybeans.vim)
* [Molokai](https://github.com/tomasr/molokai)
* [Solarized](https://github.com/altercation/vim-colors-solarized)
## Tools
### Editing
* [Abolish](https://github.com/tpope/vim-abolish)
* [Align](https://github.com/vim-scripts/Align)
* [DelimitMate](https://github.com/Raimondi/delimitMate)
* [EditorConfig](https://github.com/editorconfig/editorconfig-vim)
* [ExpandRegion](https://github.com/terryma/vim-expand-region)
* [Gundo](https://github.com/sjl/gundo.vim)
* [IndentGuides](https://github.com/nathanaelkane/vim-indent-guides)
* [NerdCommenter](https://github.com/scrooloose/nerdcommenter)
* [Repeat](https://github.com/tpope/vim-repeat)
* [Surround](https://github.com/tpope/vim-surround)
* [Tabular](https://github.com/godlygeek/tabular)
* [Targets](https://github.com/wellle/targets.vim)
* [TComment](https://github.com/tomtom/tcomment_vim)
* [TextobjIndent](https://github.com/kana/vim-textobj-indent)
* [TextobjUser](https://github.com/kana/vim-textobj-user)
* [TextobjWordColumn](https://github.com/coderifous/textobj-word-column.vim)
* [Unimpaired](https://github.com/tpope/vim-unimpaired)
* [VisualSplit](https://github.com/wellle/visual-split.vim)
* [YankStack](https://github.com/maxbrunsfeld/vim-yankstack)
### File Management
* [Dirvish](https://github.com/justinmk/vim-dirvish)
* [NERDTree](https://github.com/scrooloose/nerdtree)
* [Vinegar](https://github.com/tpope/vim-vinegar)
### Git
* [Fugitive](https://github.com/tpope/vim-fugitive)
* [vim-gitgutter](https://github.com/airblade/vim-gitgutter)
### Interface
* [Airline](https://github.com/bling/vim-airline) + [Airline Themes](https://github.com/vim-airline/vim-airline-themes)
* [vim-lastplace](https://github.com/farmergreg/vim-lastplace)
* [Signify](https://github.com/mhinz/vim-signify)
* [Startify](https://github.com/mhinz/vim-startify)
### Searching
* [Ack](https://github.com/mileszs/ack.vim)
* [CtrlP](https://github.com/ctrlpvim/ctrlp.vim)
* [vim-codequery](https://github.com/devjoe/vim-codequery)
### Task Running
* [Dispatch](https://github.com/tpope/vim-dispatch)
### Text Navigation
* [EasyMotion](https://github.com/easymotion/vim-easymotion)
* [Sneak](https://github.com/justinmk/vim-sneak)
### Snippets
* [Snippets](https://github.com/honza/vim-snippets)
* [UltiSnips](https://github.com/sirver/UltiSnips)
### Syntax/Completion
* [Asynchronous Lint Engine](https://github.com/w0rp/ale)
* [Neocomplete](https://github.com/Shougo/neocomplete.vim)
* [Syntastic](https://github.com/scrooloose/syntastic)
* [TmuxComplete](https://github.com/wellle/tmux-complete.vim)
* [YouCompleteMe](https://github.com/Valloric/YouCompleteMe)
## Language Specific
* [Codi](https://github.com/metakirby5/codi.vim)
* [Polyglot](https://github.com/sheerun/vim-polyglot)
### HTML
* [MatchTagAlways](https://github.com/valloric/MatchTagAlways)
* [vim-ionic2](https://github.com/akz92/vim-ionic2)
### Java
* [Eclim](http://eclim.org/)
* [Ensime](http://ensime.org/editors/vim/)
### Javascript
* [JavascriptLibrariesSyntax](https://github.com/othree/javascript-libraries-syntax.vim)
* [TernForVim](https://github.com/marijnh/tern_for_vim)
### Markdown
* [vim-instant-markdown](https://github.com/suan/vim-instant-markdown)
* [vim-markdown-toc](https://github.com/mzlogin/vim-markdown-toc)
### PHP
* [PDV](https://github.com/tobyS/pdv)
### Python
* [PythonMode](https://github.com/klen/python-mode)
### Ruby
* [Endwise](https://github.com/tpope/vim-endwise)
### XML
* [xmledit](https://github.com/sukima/xmledit/)
## Framework Specific
### Django
* [htmldjango autocomplete](https://github.com/mjbrownie/vim-htmldjango_omnicomplete)
* [Pony](https://github.com/jmcomets/vim-pony/)
### Rails
* [Rails](https://github.com/tpope/vim-rails)
## Distributions
* [Janus](https://github.com/carlhuda/janus)
* [spf13](https://github.com/spf13/spf13-vim)
## Websites
* [Vim Bootstrap](http://vim-bootstrap.com/)
* [usevim](http://usevim.com/)
* [Vim Awesome](http://vimawesome.com/)
* [Vivify](http://bytefluent.com/vivify/)
* [/r/vim](http://www.reddit.com/r/vim)
* [Vimcasts](http://vimcasts.org/)
## Contributing
* Please read the [contribution guidelines](https://github.com/akrawchyk/awesome-vim/blob/master/contributing.md)
## License
* [MIT License](https://github.com/akrawchyk/awesome-vim/blob/master/LICENSE)
Shameless plug for my vimrc [akrawchyk/dotfiles](https://github.com/akrawchyk/dotfiles/tree/master/vim).

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# Searching with Mkdocs-Material
This is a quick demonstration of how to index a large body of links
and make it searchable using the search functionality built into the
mkdocs-material theme for mkdocs.
This is as simple as it gets: create a directory, put a pile of markdown
files into the directory, and run `mkdocs build` to create a site *and*
a search index.
We document a couple of awesome lists, which are large curated lists of links:
* [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets/blob/master/README.rst)
* [awesome-big-data](https://github.com/onurakpolat/awesome-bigdata)
* [awesome-vim](https://github.com/akrawchyk/awesome-vim)
* [awesome-rest](https://github.com/marmelab/awesome-rest)
* [awesome-python](https://github.com/vinta/awesome-python)
* [awesome-r](https://github.com/qinwf/awesome-R)
To take this thing for a test drive, just start typing into the search box
at the top of the page.
Try searching for the term **healthcare**.

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<!DOCTYPE html>
<html lang="en" class="no-js">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<link rel="canonical" href="https://pages.charlesreid1.com/search-demo-mkdocs-material/">
<meta name="lang:clipboard.copy" content="Copy to clipboard">
<meta name="lang:clipboard.copied" content="Copied to clipboard">
<meta name="lang:search.language" content="en">
<meta name="lang:search.pipeline.stopwords" content="True">
<meta name="lang:search.pipeline.trimmer" content="True">
<meta name="lang:search.result.none" content="No matching documents">
<meta name="lang:search.result.one" content="1 matching document">
<meta name="lang:search.result.other" content="# matching documents">
<meta name="lang:search.tokenizer" content="[\s\-]+">
<link rel="shortcut icon" href=".">
<meta name="generator" content="mkdocs-1.0, mkdocs-material-3.0.3">
<title>search-demo-mkdocs-material</title>
<link rel="stylesheet" href="assets/stylesheets/application.451f80e5.css">
<link rel="stylesheet" href="assets/stylesheets/application-palette.22915126.css">
<meta name="theme-color" content="">
<script src="assets/javascripts/modernizr.1aa3b519.js"></script>
<link href="https://fonts.gstatic.com" rel="preconnect" crossorigin>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,400i,700|Roboto+Mono">
<style>body,input{font-family:"Roboto","Helvetica Neue",Helvetica,Arial,sans-serif}code,kbd,pre{font-family:"Roboto Mono","Courier New",Courier,monospace}</style>
<link rel="stylesheet" href="assets/fonts/material-icons.css">
<link rel="stylesheet" href="css/custom.css">
</head>
<body dir="ltr" data-md-color-primary="dark-blue" data-md-color-accent="dark-blue">
<svg class="md-svg">
<defs>
</defs>
</svg>
<input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
<input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
<label class="md-overlay" data-md-component="overlay" for="__drawer"></label>
<a href="#searching-with-mkdocs-material" tabindex="1" class="md-skip">
Skip to content
</a>
<header class="md-header" data-md-component="header">
<nav class="md-header-nav md-grid">
<div class="md-flex">
<div class="md-flex__cell md-flex__cell--shrink">
<a href="https://pages.charlesreid1.com/search-demo-mkdocs-material" title="search-demo-mkdocs-material" class="md-header-nav__button md-logo">
<i class="md-icon">search</i>
</a>
</div>
<div class="md-flex__cell md-flex__cell--shrink">
<label class="md-icon md-icon--menu md-header-nav__button" for="__drawer"></label>
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<div class="md-flex__cell md-flex__cell--stretch">
<div class="md-flex__ellipsis md-header-nav__title" data-md-component="title">
<span class="md-header-nav__topic">
search-demo-mkdocs-material
</span>
<span class="md-header-nav__topic">
Searching with Mkdocs-Material
</span>
</div>
</div>
<div class="md-flex__cell md-flex__cell--shrink">
<label class="md-icon md-icon--search md-header-nav__button" for="__search"></label>
<div class="md-search" data-md-component="search" role="dialog">
<label class="md-search__overlay" for="__search"></label>
<div class="md-search__inner" role="search">
<form class="md-search__form" name="search">
<input type="text" class="md-search__input" name="query" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="query" data-md-state="active">
<label class="md-icon md-search__icon" for="__search"></label>
<button type="reset" class="md-icon md-search__icon" data-md-component="reset" tabindex="-1">
&#xE5CD;
</button>
</form>
<div class="md-search__output">
<div class="md-search__scrollwrap" data-md-scrollfix>
<div class="md-search-result" data-md-component="result">
<div class="md-search-result__meta">
Type to start searching
</div>
<ol class="md-search-result__list"></ol>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="md-flex__cell md-flex__cell--shrink">
<div class="md-header-nav__source">
<a href="https://git.charlesreid1.com/charlesreid1/search-demo-mkdocs-material" title="Go to repository" class="md-source" data-md-source="">
<div class="md-source__repository">
charlesreid1/search-demo-mkdocs-material
</div>
</a>
</div>
</div>
</div>
</nav>
</header>
<div class="md-container">
<main class="md-main">
<div class="md-main__inner md-grid" data-md-component="container">
<div class="md-sidebar md-sidebar--primary" data-md-component="navigation">
<div class="md-sidebar__scrollwrap">
<div class="md-sidebar__inner">
<nav class="md-nav md-nav--primary" data-md-level="0">
<label class="md-nav__title md-nav__title--site" for="__drawer">
<a href="https://pages.charlesreid1.com/search-demo-mkdocs-material" title="search-demo-mkdocs-material" class="md-nav__button md-logo">
<i class="md-icon">search</i>
</a>
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</label>
<div class="md-nav__source">
<a href="https://git.charlesreid1.com/charlesreid1/search-demo-mkdocs-material" title="Go to repository" class="md-source" data-md-source="">
<div class="md-source__repository">
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</a>
</div>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item md-nav__item--active">
<input class="md-toggle md-nav__toggle" data-md-toggle="toc" type="checkbox" id="__toc">
<a href="." title="Searching with Mkdocs-Material" class="md-nav__link md-nav__link--active">
Searching with Mkdocs-Material
</a>
</li>
<li class="md-nav__item">
<a href="awesome-big-data/" title="Awesome Big Data" class="md-nav__link">
Awesome Big Data
</a>
</li>
<li class="md-nav__item">
<a href="awesome-public-datasets/" title="Awesome Public Datasets" class="md-nav__link">
Awesome Public Datasets
</a>
</li>
<li class="md-nav__item">
<a href="awesome-python/" title="Awesome Python [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)" class="md-nav__link">
Awesome Python [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
</a>
</li>
<li class="md-nav__item">
<a href="awesome-r/" title="Awesome R" class="md-nav__link">
Awesome R
</a>
</li>
<li class="md-nav__item">
<a href="awesome-rest/" title="Awesome REST [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)" class="md-nav__link">
Awesome REST [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
</a>
</li>
<li class="md-nav__item">
<a href="awesome-vim/" title="Awesome Vim" class="md-nav__link">
Awesome Vim
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-sidebar md-sidebar--secondary" data-md-component="toc">
<div class="md-sidebar__scrollwrap">
<div class="md-sidebar__inner">
<nav class="md-nav md-nav--secondary">
</nav>
</div>
</div>
</div>
<div class="md-content">
<article class="md-content__inner md-typeset">
<h1 id="searching-with-mkdocs-material">Searching with Mkdocs-Material</h1>
<p>This is a quick demonstration of how to index a large body of links
and make it searchable using the search functionality built into the
mkdocs-material theme for mkdocs.</p>
<p>This is as simple as it gets: create a directory, put a pile of markdown
files into the directory, and run <code>mkdocs build</code> to create a site <em>and</em>
a search index.</p>
<p>We document a couple of awesome lists, which are large curated lists of links:</p>
<ul>
<li><a href="https://github.com/awesomedata/awesome-public-datasets/blob/master/README.rst">awesome-public-datasets</a></li>
<li><a href="https://github.com/onurakpolat/awesome-bigdata">awesome-big-data</a></li>
<li><a href="https://github.com/akrawchyk/awesome-vim">awesome-vim</a></li>
<li><a href="https://github.com/marmelab/awesome-rest">awesome-rest</a></li>
<li><a href="https://github.com/vinta/awesome-python">awesome-python</a></li>
<li><a href="https://github.com/qinwf/awesome-R">awesome-r</a></li>
</ul>
<p>To take this thing for a test drive, just start typing into the search box
at the top of the page.</p>
<p>Try searching for the term <strong>healthcare</strong>.</p>
</article>
</div>
</div>
</main>
<footer class="md-footer">
<div class="md-footer-nav">
<nav class="md-footer-nav__inner md-grid">
<a href="awesome-big-data/" title="Awesome Big Data" class="md-flex md-footer-nav__link md-footer-nav__link--next" rel="next">
<div class="md-flex__cell md-flex__cell--stretch md-footer-nav__title">
<span class="md-flex__ellipsis">
<span class="md-footer-nav__direction">
Next
</span>
Awesome Big Data
</span>
</div>
<div class="md-flex__cell md-flex__cell--shrink">
<i class="md-icon md-icon--arrow-forward md-footer-nav__button"></i>
</div>
</a>
</nav>
</div>
<div class="md-footer-meta md-typeset">
<div class="md-footer-meta__inner md-grid">
<div class="md-footer-copyright">
<div class="md-footer-copyright__highlight">
Copyright &copy; 2018 Charles Reid, released under the <a href="https://opensource.org/licenses/BSD-3-Clause">BSD 3-Clause License</a>
</div>
powered by
<a href="https://www.mkdocs.org">MkDocs</a>
and
<a href="https://squidfunk.github.io/mkdocs-material/">
Material for MkDocs</a>
</div>
</div>
</div>
</footer>
</div>
<script src="assets/javascripts/application.e72fd936.js"></script>
<script>app.initialize({version:"1.0",url:{base:"."}})</script>
<script src="search/main.js"></script>
</body>
</html>

1
mkdocs-material

@ -1 +0,0 @@ @@ -1 +0,0 @@
Subproject commit b0c6890853aa9138baf5f9749862b927518ab656

33
mkdocs.yml

@ -1,33 +0,0 @@ @@ -1,33 +0,0 @@
site_name: search-demo-mkdocs-material
site_url: https://pages.charlesreid1.com/search-demo-mkdocs-material
repo_name: charlesreid1/search-demo-mkdocs-material
repo_url: https://git.charlesreid1.com/charlesreid1/search-demo-mkdocs-material
edit_uri: ""
copyright: 'Copyright &copy; 2018 Charles Reid, released under the <a href="https://opensource.org/licenses/BSD-3-Clause">BSD 3-Clause License</a>'
docs_dir: docs
site_dir: site
theme:
name: null
custom_dir: 'mkdocs-material/material'
# pretty colors! see https://squidfunk.github.io/mkdocs-material/getting-started/#primary-colors
palette:
primary: 'dark blue'
accent: 'dark blue'
# fun logos! see https://material.io/icons/
logo:
icon: 'search'
font:
text: 'Roboto'
code: 'Roboto Mono'
# this will add docs/css/custom.css to all your docs
extra_css:
- css/custom.css

2986
search/lunr.js

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94
search/main.js

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function getSearchTermFromLocation() {
var sPageURL = window.location.search.substring(1);
var sURLVariables = sPageURL.split('&');
for (var i = 0; i < sURLVariables.length; i++) {
var sParameterName = sURLVariables[i].split('=');
if (sParameterName[0] == 'q') {
return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20'));
}
}
}
function formatResult (location, title, summary) {
return '<article><h3><a href="' + base_url + '/' + location + '">'+ title + '</a></h3><p>' + summary +'</p></article>';
}
function displayResults (results) {
var search_results = document.getElementById("mkdocs-search-results");
while (search_results.firstChild) {
search_results.removeChild(search_results.firstChild);
}
if (results.length > 0){
for (var i=0; i < results.length; i++){
var result = results[i];
var html = formatResult(result.location, result.title, result.summary);
search_results.insertAdjacentHTML('beforeend', html);
}
} else {
search_results.insertAdjacentHTML('beforeend', "<p>No results found</p>");
}
}
function doSearch () {
var query = document.getElementById('mkdocs-search-query').value;
if (query.length > 2) {
console.log('Searching with query: ' + query);
if (!window.Worker) {
displayResults(search(query));
} else {
searchWorker.postMessage({query: query});
}
} else {
// Clear results for short queries
displayResults([]);
}
}
function initSearch () {
var search_input = document.getElementById('mkdocs-search-query');
if (search_input) {
search_input.addEventListener("keyup", doSearch);
}
var term = getSearchTermFromLocation();
if (term) {
search_input.value = term;
doSearch();
}
}
function onWorkerMessage (e) {
if (e.data.results) {
var results = e.data.results;
displayResults(results);
}
}
if (!window.Worker) {
console.log('Web Worker API not supported');
// load index in main thread
$.getScript(base_url + "/search/worker.js").done(function () {
console.log('Loaded worker');
init();
}).fail(function (jqxhr, settings, exception) {
console.error('Could not load worker.js');
});
} else {
// Wrap search in a web worker
var searchWorker = new Worker(base_url + "/search/worker.js");
searchWorker.postMessage({init: true});
searchWorker.onmessage = onWorkerMessage;
}
$(function() {
var search_input = document.getElementById('mkdocs-search-query');
if (search_input) {
search_input.addEventListener("keyup", doSearch);
}
var term = getSearchTermFromLocation();
if (term) {
search_input.value = term;
doSearch();
}
});

1
search/search_index.json

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127
search/worker.js

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var base_path = 'function' === typeof importScripts ? '.' : '/search/';
var allowSearch = false;
var index;
var documents = {};
var lang = ['en'];
var data;
function getScript(script, callback) {
console.log('Loading script: ' + script);
$.getScript(base_path + script).done(function () {
callback();
}).fail(function (jqxhr, settings, exception) {
console.log('Error: ' + exception);
});
}
function getScriptsInOrder(scripts, callback) {
if (scripts.length === 0) {
callback();
return;
}
getScript(scripts[0], function() {
getScriptsInOrder(scripts.slice(1), callback);
});
}
function loadScripts(urls, callback) {
if( 'function' === typeof importScripts ) {
importScripts.apply(null, urls);
callback();
} else {
getScriptsInOrder(urls, callback);
}
}
function onJSONLoaded () {
data = JSON.parse(this.responseText);
var scriptsToLoad = ['lunr.js'];
if (data.config && data.config.lang && data.config.lang.length) {
lang = data.config.lang;
}
if (lang.length > 1 || lang[0] !== "en") {
scriptsToLoad.push('lunr.stemmer.support.js');
if (lang.length > 1) {
scriptsToLoad.push('lunr.multi.js');
}
for (var i=0; i < lang.length; i++) {
if (lang[i] != 'en') {
scriptsToLoad.push(['lunr', lang[i], 'js'].join('.'));
}
}
}
loadScripts(scriptsToLoad, onScriptsLoaded);
}
function onScriptsLoaded () {
console.log('All search scripts loaded, building Lunr index...');
if (data.config && data.config.separator && data.config.separator.length) {
lunr.tokenizer.separator = new RegExp(data.config.separator);
}
if (data.index) {
index = lunr.Index.load(data.index);
data.docs.forEach(function (doc) {
documents[doc.location] = doc;
});
console.log('Lunr pre-built index loaded, search ready');
} else {
index = lunr(function () {
if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) {
this.use(lunr[lang[0]]);
} else if (lang.length > 1) {
this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility
}
this.field('title');
this.field('text');
this.ref('location');
for (var i=0; i < data.docs.length; i++) {
var doc = data.docs[i];
this.add(doc);
documents[doc.location] = doc;
}
});
console.log('Lunr index built, search ready');
}
allowSearch = true;
}
function init () {
var oReq = new XMLHttpRequest();
oReq.addEventListener("load", onJSONLoaded);
var index_path = base_path + '/search_index.json';
if( 'function' === typeof importScripts ){
index_path = 'search_index.json';
}
oReq.open("GET", index_path);
oReq.send();
}
function search (query) {
if (!allowSearch) {
console.error('Assets for search still loading');
return;
}
var resultDocuments = [];
var results = index.search(query);
for (var i=0; i < results.length; i++){
var result = results[i];
doc = documents[result.ref];
doc.summary = doc.text.substring(0, 200);
resultDocuments.push(doc);
}
return resultDocuments;
}
if( 'function' === typeof importScripts ) {
onmessage = function (e) {
if (e.data.init) {
init();
} else if (e.data.query) {
postMessage({ results: search(e.data.query) });
} else {
console.error("Worker - Unrecognized message: " + e);
}
};
}

38
sitemap.xml

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<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/awesome-big-data/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/awesome-public-datasets/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/awesome-python/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/awesome-r/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/awesome-rest/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>https://pages.charlesreid1.com/search-demo-mkdocs-material/awesome-vim/</loc>
<lastmod>2018-08-11</lastmod>
<changefreq>daily</changefreq>
</url>
</urlset>

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