update lists

terminal/R

@@ -1,4 +1,4 @@
-                                                                                                                 Awesome R
+                                                                                                                      Awesome R
 
 !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
 
@@ -106,8 +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.
@@ -203,8 +203,8 @@
 ⟡ 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/).
@@ -422,8 +422,7 @@
 ⟡ 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.
@@ -437,6 +436,7 @@
 Bayesian
 Packages for Bayesian Inference.
 
+⟡ brms (https://cran.r-project.org/web/packages/brms/index.html) - High-level interface for Bayesian regression models using Stan.
 ⟡ 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.
@@ -584,7 +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.
 
@@ -624,8 +624,8 @@
   ⟡ _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.
@@ -634,10 +634,9 @@
 
 ⟡ 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
@@ -722,13 +721,15 @@
 ⟡ 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)
+
+R Github: https://github.com/qinwf/awesome-R