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Updating conversion, creating readmes

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Jonas Zeunert
2024-04-19 23:37:46 +02:00
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 Awesome R
 Awesome R
!Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
@@ -54,8 +54,7 @@
2020
⟡ VSCode (https://code.visualstudio.com/) - vscode-R (https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + vscode-r-lsp (https://marketplace.visualstudio.com/items?itemName=REditorSupport.r-lsp) 
VSCode R Langauage Support
⟡ VSCode (https://code.visualstudio.com/) - vscode-R (https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + vscode-r-lsp (https://marketplace.visualstudio.com/items?itemName=REditorSupport.r-lsp) VSCode R Langauage Support
⟡ gt (https://github.com/rstudio/gt) - Easily generate information-rich, publication-quality tables from R
⟡ lightgbm  (https://cran.r-project.org/web/packages/lightgbm/index.html) - Light Gradient Boosting Machine.
⟡ torch (https://cran.r-project.org/web/packages/torch/index.html) - Tensors and Neural Networks with 'GPU' Acceleration.
@@ -69,8 +68,7 @@
Integrated Development Environments
Integrated Development Environment
⟡ VSCode  (https://code.visualstudio.com/) - vscode-R (https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + vscode-r-lsp (https://marketplace.visualstudio.com/items?itemName=REditorSupport.r-lsp) 
VSCode R Langauage Support
⟡ VSCode  (https://code.visualstudio.com/) - vscode-R (https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r) + vscode-r-lsp (https://marketplace.visualstudio.com/items?itemName=REditorSupport.r-lsp) VSCode R Langauage Support
⟡ RStudio  (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-IDE (https://github.com/REditorSupport/sublime-ide-r) - Add-on package for Sublime Text 2/3.
@@ -108,8 +106,8 @@
⟡ lubridate (https://github.com/tidyverse/lubridate) - A set of functions to work with dates and times.
⟡ stringi  (https://github.com/gagolews/stringi) - ICU based string processing package.
⟡ stringr  (https://github.com/hadley/stringr) - Consistent API for string processing, built on top of stringi.
⟡ bigmemory (https://github.com/kaneplusplus/bigmemory) - 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)).
⟡ bigmemory (https://github.com/kaneplusplus/bigmemory) - 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.
⟡ snakecase (https://github.com/Tazinho/snakecase) - Automatically parse and convert strings into cases like snake or camel among others.
@@ -205,12 +203,11 @@
⟡ 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.
⟡ officer (https://davidgohel.github.io/officer/index.html) - An R package to generate Microsoft Word, Microsoft PowerPoint and HTML reports.
⟡ flextable (https://davidgohel.github.io/flextable/index.html) - An R package to embed complex tables (merged cells, multi-level headers and footers, conditional formatting) in Microsoft Word, Microsoft 
PowerPoint and HTML reports. It cooperates with the officer package and integrates with rmarkdown reports.
⟡ flextable (https://davidgohel.github.io/flextable/index.html) - An R package to embed complex tables (merged cells, multi-level headers and footers, conditional formatting) in Microsoft Word, Microsoft PowerPoint and HTML reports. It 
cooperates with the officer package and integrates with rmarkdown 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'
⟡ targets (https://docs.ropensci.org/targets/) - Make-like pipeline tool for organizing and running data science workflows, automatically skipping steps that have already been done. Supported by rOpenSci 
(https://ropensci.org/).
⟡ targets (https://docs.ropensci.org/targets/) - Make-like pipeline tool for organizing and running data science workflows, automatically skipping steps that have already been done. Supported by rOpenSci (https://ropensci.org/).
⟡ R Suite (http://rsuite.io) - A package to design flexible and reproducible deployment workflows for R.
⟡ kable (https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html) - Build fancy HTML or 'LaTeX' tables using 'kable()' from 'knitr'.
@@ -240,8 +237,7 @@
(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  (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.
⟡ 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  (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.
@@ -356,10 +352,8 @@
⟡ LiblineaR (http://cran.r-project.org/web/packages/LiblineaR/index.html) - Linear Predictive Models Based On The Liblinear C/C++ Library
⟡ lightgbm  (https://cran.r-project.org/web/packages/lightgbm/index.html) - Light Gradient Boosting Machine.
⟡ lme4  (https://github.com/lme4/lme4) - Mixed-effects models
⟡ nlme 
 (https://cran.r-project.org/web/packages/nlme/index.html) - Mixed-effects models, handling user-specified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials
⟡ glmmTMB (https://cran.r-project.org/web/packages/glmmTMB/index.html) - Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the analysis of repeated 
observations in longitudinal trials
⟡ nlme  (https://cran.r-project.org/web/packages/nlme/index.html) - Mixed-effects models, handling user-specified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials
⟡ glmmTMB (https://cran.r-project.org/web/packages/glmmTMB/index.html) - Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the analysis of repeated observations in longitudinal trials
⟡ 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
@@ -412,8 +406,7 @@
⟡ survival (https://cran.r-project.org/web/packages/survival/index.html) - Survival Analysis
⟡ 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
⟡ tidymodels (https://cran.r-project.org/web/packages/tidymodels/index.html) - A collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data 
structures of the tidyverse.
⟡ tidymodels (https://cran.r-project.org/web/packages/tidymodels/index.html) - A collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.
⟡ torch (https://cran.r-project.org/web/packages/torch/index.html) - Tensors and Neural Networks with 'GPU' Acceleration.
⟡ 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
@@ -429,8 +422,8 @@
⟡ 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)).
⟡ 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.
@@ -484,10 +477,8 @@
⟡ 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.
⟡ lme4 (https://github.com/lme4/lme4) - Generalized mixed-effects models.
⟡ nlme
 (https://cran.r-project.org/web/packages/nlme/index.html) - Mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.
⟡ glmmTMB (https://cran.r-project.org/web/packages/glmmTMB/index.html) - Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations
in longitudinal trials.
⟡ nlme (https://cran.r-project.org/web/packages/nlme/index.html) - Mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.
⟡ glmmTMB (https://cran.r-project.org/web/packages/glmmTMB/index.html) - Generalized mixed-effects models, handling user-specified matrix of residual covariance, relevant for the anaysis of repeated observations in longitudinal trials.
Network Analysis
Packages to construct, analyze and visualize network data.
@@ -503,8 +494,7 @@
⟡ 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/).
⟡ 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.
⟡ tidygraph (https://github.com/thomasp85/tidygraph) - A tidy API for graph manipulation
@@ -567,8 +557,8 @@
⟡ gapminder (http://github.com/jennybc/gapminder) - Excerpt from the Gapminder dataset (data about countries through the past 50 years).
⟡ wbstats (https://cran.r-project.org/web/packages/wbstats/index.html) - Tools for searching and downloading data and statistics from the World Bank Data API and the World Bank Data Catalog API.
⟡ ICON (https://github.com/rrrlw/ICON) - complex systems & networks datasets from the Index of COmplex Networks (ICON) database webpage (http://icon.colorado.edu).
⟡ RCOBOLDI (https://github.com/thospfuller/rcoboldi) - Import COBOL CopyBook data files directly into R as properly structured data frames. Package builds are available via Drat 
(https://github.com/thospfuller/drat) and DockerHub (https://hub.docker.com/r/thospfuller/rcoboldi-rocker-rstudio).
⟡ RCOBOLDI (https://github.com/thospfuller/rcoboldi) - Import COBOL CopyBook data files directly into R as properly structured data frames. Package builds are available via Drat (https://github.com/thospfuller/drat) and DockerHub 
(https://hub.docker.com/r/thospfuller/rcoboldi-rocker-rstudio).
Other Tools
Handy Tools for R
@@ -594,7 +584,7 @@
⟡ swirl  (http://swirlstats.com/) - An interactive R tutorial directly in your R console.
⟡ DataScienceR  (https://github.com/ujjwalkarn/DataScienceR) - a list of R tutorials for Data Science, NLP and Machine Learning.
 Resources
 Resources
Where to discover new R-esources.
@@ -603,8 +593,7 @@
Manuals
⟡ R-project (http://www.r-project.org/) - The R Project for Statistical Computing.
⟡ 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 topic. See also the Manuals section on CRAN 
(https://cran.r-project.org/manuals.html)
⟡ 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 topic. See also the Manuals section on CRAN (https://cran.r-project.org/manuals.html)
⟡ CRAN Contributed Docs (https://cran.r-project.org/other-docs.html) - CRAN Contributed Documentation in many languages.
⟡ Quick-R (http://www.statmethods.net/) - An excellent quick reference
⟡ tryR (http://tryr.codeschool.com/) - A quick course for getting started with R.
@@ -635,21 +624,20 @@
  ⟡ _R Programming for Data Science_ by Roger D. Peng (2019) (https://leanpub.com/rprogramming) - More advanced data analysis that relies on R programming.
  ⟡ _Report Writing for Data Science in R_ by Roger D. Peng (2019) (https://leanpub.com/reportwriting) - R-based methods for reproducible research and report generation.
⟡ _R for SAS and SPSS users_ by Bob Muenchen (2012) (http://r4stats.com/books/free-version/) - An excellent resource for users already familiar with SAS or SPSS.
⟡ _Introduction to Statistical Learning with Application in R_ by Gareth James et al. (2017) (http://faculty.marshall.usc.edu/gareth-james/ISL/) - A simplified and "operational" version of The Elements of 
Statistical Learning. Free softcopy provided by its authors.
⟡ _Introduction to Statistical Learning with Application in R_ by Gareth James et al. (2017) (http://faculty.marshall.usc.edu/gareth-james/ISL/) - A simplified and "operational" version of The Elements of Statistical Learning. Free 
softcopy provided by its authors.
⟡ _The R Inferno_ by Patrick Burns (2011) (http://www.burns-stat.com/pages/Tutor/R_inferno.pdf) - Patrick Burns gives insight into R's ins and outs along with its quirks!
⟡ _Efficient R Programming_ by Colin Gillespie & Robin Lovelace (2017) (https://csgillespie.github.io/efficientR/) - An online version of the O’Reilly book: Efficient R Programming.
⟡ The R Programming Wikibook (https://en.wikibooks.org/wiki/R_Programming) - A collaborative handbook for R.
Paid
⟡ 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.
⟡ 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.
⟡ _R Cookbook_, 2nd ed. by JD Long & Paul Teetor (2019) (http://shop.oreilly.com/product/0636920174851.do) - A quick and simple introduction to conducting many common statistical tasks with R.
⟡ 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!_ Series by Springer (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 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!_ Series by Springer (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.
⟡ 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.
Book/monograph Lists and Reviews
@@ -734,13 +722,13 @@
⟡ utf8 (https://github.com/patperry/r-utf8) - Manipulating and printing UTF-8 text that fixes multiple bugs in R's UTF-8 handling.
⟡ Patchwork (https://github.com/thomasp85/patchwork) - Combine separate ggplots into the same graphic.
 Other Awesome Lists
 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
 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)