Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Joe-Rstats/Books

R & Stats Books and Websites that I think are good.
https://github.com/Joe-Rstats/Books

data-science econometrics education political-science r r-learning r-stats rstats statistics statistics-websites

Last synced: 3 months ago
JSON representation

R & Stats Books and Websites that I think are good.

Awesome Lists containing this project

README 

        

# 2022 VERSION FOR LEARNING R

# Note about Stars



- Three Stars = Excellent

- Two Stars = Great

- One Star = Good

- No Star = Still useful, but save for last



## Learning R

- [Hands On Programming in R](https://rstudio-education.github.io/hopr/) :star: :star:

- [Slow R](https://psu-psychology.github.io/r-bootcamp-2019/talks/slow-r.html) :star: :star: :star:



# R & Stats Books and Websites that I think are good (2019 Version)



# Note about Stars



- No Star = Good

- Star = Excellent



## Contents

- [R with Stats](#R-With-Stats)

- [R Without Stats](#R-Without-Stats)

  - [Books](#books)

    - [Data Visualization Books](#data-visualization-books) 

    - [Coding Books](#Coding-books)   

      - [R Markdown](#R-Markdown)

  - [Online Resources](#Online-Resources)

  - [List of R code](#List-of-R-code)

    - [Statistical Code](#Statistical-code)

    - [R Code](#R-code)

- [Staistics Without R](#statistics-without-r)

- [Research Design](#Research-design)

- [Dissertation Websites](#Dissertation-Websites)



## R With Stats

- [ Math camp] (http://www.csss.washington.edu/academics/math-camp/lectures)

- [nice courses](https://ditraglia.com)

- [useful](https://faculty.washington.edu/cadolph/)

- [Intro to linear regression in R](https://stats.idre.ucla.edu/r/seminars/introduction-to-regression-in-r/)

- [NYU carpentry - more](https://datacarpentry.org/r-socialsci/) - episodes

- [NYU carpentry - good] (https://swcarpentry.github.io/r-novice-inflammation/03-loops-R/index.html) - click on episodes.

- [Intro to R Workshop](https://github.com/UCIDataScienceInitiative/IntroR_Workshop) - :star: good stats stuff here too

- [R Bootcamp](https://www.jaredknowles.com/r-bootcamp/) - also some good stats tutorials here :star:

- [UCLA R & Stats Website](https://stats.idre.ucla.edu/) :star: :star: :star:

- [University of Cincinnati R programming guide](http://uc-r.github.io) - Click on top left to see all Info. :star:

- [Using R for introductory econometircs](http://www.urfie.net/read/mobile/index.html#p=1) - SHowing how to do everything in wooldridge in R. :star:

- [Swirl](https://swirlstats.com)

- https://iqss.github.io/prefresher/

- [R Course](https://github.com/jameslamb/teaching/tree/master/mu_rprog)

- [Advanced quant  methods](https://tvpollet.github.io/PY0794/#course-manual) - The courses (1-11) are very well done. Worth going through the slides and .rmd :star:

- [R workshops from harvard](https://dss.iq.harvard.edu/workshop-materials#widget-1) - Four short and concise courses. Regression, graphics, data wrangling. :star:

- [Categorical Regression Models](https://m-clark.github.io/docs/logregmodels.html) :star:

- [Biol 355/356: Intro to Data Science for Biology](https://biol355.github.io/schedule.html) :star:

- [GLM](https://rpubs.com/davoodastaraky/GLM) :star:

- [Learning Statistics With R](https://learningstatisticswithr.com/book/) :star:

- [Time Series R](https://rpubs.com/bensonsyd/389857) :star:

- [Simple linear regression](https://rpubs.com/bensonsyd/364877) :star:

- [more nyu r learning](https://nyu-cdsc.github.io/learningr/):star:

- [Math Prefresher for poli sci studnets](https://iqss.github.io/prefresher/) :star:

- [Advanced statistics course](http://statstools.com/learn/advanced-statistics/) :star:

  - [Notes to accompany the videos](https://osf.io/dnuyv/) :star:

- [Answering questions with Data](https://crumplab.github.io/statistics/) :star: - Doesn't even get to OLS, but what it does go over, it does well.

- https://data.princeton.edu/wws509/R

- [Regression Models for Data science in R](https://leanpub.com/regmods/read#leanpub-auto-poisson-regression):star:

- [Data Science Specialization Course notes](http://sux13.github.io/DataScienceSpCourseNotes/) - :star:

- [Statistical Inference for data science](https://leanpub.com/LittleInferenceBook/read) - Very basic, but what it describes, it describes it well. Doesnt even do OLS. :star:

- [GLM](https://data.princeton.edu/wws509/R) - The R logs are quite good :star:

- [MSc Conversion in Psychological Studies/Science](https://psyteachr.github.io/msc-conv-f2f/) :star:

- [Quick-R](https://www.statmethods.net/) :star:

- [An Introduction to R](http://personality-project.org/r/short_courses/aps-short.pdf) :star:

- [Data Skills for Reproducible Scinece](https://psyteachr.github.io/msc-data-skills/index.html) - Goes over working with Data and Stats :star:

- [Interactive data viz](https://plotly-r.com)

- [Nice R Site](https://preludeinr.com)

- [Good stuff here](https://github.com/pmaji/data-science-toolkit)

- https://projects.iq.harvard.edu/gov2001

- [Organized list of good youtube videos](http://flavioazevedo.com/stats-and-r-blog/2016/9/13/learning-r-on-youtube) :star:

- [Good youtube series](https://www.youtube.com/channel/UCRJyvz_aLmJLFimTZ9kH1Lg) :star:

- [Econometrics in R](https://tyleransom.github.io/econometricslabs.html) :star:

- [Intro to Econometrics with R](https://www.econometrics-with-r.org/) :star:

- [Logit, probit and multinomial logit in R](https://dss.princeton.edu/training/LogitR101.pdf) :star:

- [some useful R tutorials here](http://www.econ.uiuc.edu/~econ508/e-ta.html) :star:

- [Linear Regression with R](https://dss.princeton.edu/training/Regression101R.pdf) :star:

- [Intro to data analysis](https://github.com/UCIDataScienceInitiative/IDA-with-R) :star:

- [R and Statistics](http://www.dartistics.com/index.html) :star:

- [Getting Started in R](https://rcatlord.github.io/GSinR/) :star:

- [YaRrr the pirates guide to R](https://bookdown.org/ndphillips/YaRrr/) :star:

- [Interpreting ols output in R](https://feliperego.github.io/blog/2015/10/23/Interpreting-Model-Output-In-R) :star:

- [Learning Statistics in R](https://ademos.people.uic.edu/index.html) :star:

- https://data.princeton.edu/R/GLMs

- [Practical Regression and Anova using R](https://people.bath.ac.uk/jjf23/book/pra.pdf) - Old but seems like it would still be useful.

- [Interesting book](https://rafalab.github.io/dsbook/random-variables.html)

- [R course] (https://github.com/uo-ec607/lectures)

- [Quantitative Research Methods for Political Science, Public Policy and Public Administration: 4th Edition With Applications in R](https://bookdown.org/ripberjt/qrmbook/)

- [Introduction to Quantitative Methods in R](https://bookdown.org/ejvanholm/Textbook/) - Most advanced it gets is OLS.

- [Regression analysis in R](https://raw.githack.com/uo-ec607/lectures/master/08-regression/08-regression.html)

- [Good Econometric Course](https://github.com/edrubin/EC525S19)

    - Notes (http://edrub.in/ARE212/index.html)

- [Multiple linear regression R](https://rpubs.com/bensonsyd/385183)

- [Data analysis and predictin algorithms with r](https://rafalab.github.io/dsbook/)

- [Using R for Data Analysis and Graphics](https://cran.r-project.org/doc/contrib/usingR.pdf)

- [Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/)

- [Applied Econometrics with R](https://eeecon.uibk.ac.at/~zeileis/teaching/AER/)-

- [statstools](http://statstools.com/learn/)

- [Good Youtube two minute tutorials](https://www.youtube.com/playlist?list=PLcgz5kNZFCkzSyBG3H-rUaPHoBXgijHfC)

- [Purdue R tutorial](https://www.stat.purdue.edu/scs/docs/R_Introduction.pdf)

- [Quantitative Analysis - Applied Inferential Statistics](https://slu-soc5050.github.io)

- http://www.stephenpettigrew.com/r/

- [Open data science masters](http://datasciencemasters.org)

- [Modeling Social Data](http://modelingsocialdata.org/syllabus/)

- [(Very) basic steps to weight a survey sample](https://bookdown.org/jespasareig/Book_How_to_weight_a_survey/)

- [Step by Step how to analyze public datasets](http://asdfree.com) - includes ANES

- [Intro to data science](https://rafalab.github.io/dsbook/importing-data.html)

- [CUrated listed of how to learn R](https://osf.io/be7yt/wiki/Learning%20R/)

## R Without Stats

### Books

#### Coding Books

- [R for the rest of us](https://rfortherestofus.com/courses/getting-started/)

 -[shiny](https://laderast.github.io/gradual_shiny/)

- [What they forgot to teach you about R](https://rstats.wtf)

- [Nice R and data viz courses](https://www.yan-holtz.com/teaching)

 -[ An introduction to data cleaning with R](https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf) :star:

- [Advanced R](https://adv-r.hadley.nz) :star:

- [ R Programming for Data Science](https://bookdown.org/rdpeng/rprogdatascience/) :star:

- [Introduction to Data Science](https://beanumber.github.io/sds192/index.html) :star:

- [Efficient R programming](https://csgillespie.github.io/efficientR/) :star:

- [R for data science](https://r4ds.had.co.nz) :star:

- [Exploratory data analysis with R](https://bookdown.org/rdpeng/exdata/) :star:

- [R-Data Analysis & Visualization In Science](https://gge-ucd.github.io/R-DAVIS/index.html) :star:

- [Intro to R Workshop](https://github.com/UCIDataScienceInitiative/IntroR_Workshop) - :star: good stats stuff here too

- [R Bootcamp](https://www.jaredknowles.com/r-bootcamp/) - also some good stats tutorials here :star:

- [some nyu courses](https://guides.nyu.edu/r) -the class materials are good (intro, wrangling, data viz). download the rmarkdown and your good. :star:

- [An introduction to Data Cleaning with R](https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf) :star:

- [RStudio Primers](https://rstudio.cloud/learn/primers) :star:

- [R For Psychological Science](https://psyr.org/) - Goes over basic R stuff, like loops, really well. :star:

- [an introduction to R for non-programmers](http://swcarpentry.github.io/r-novice-gapminder/) :star:

- [Programming with R](http://swcarpentry.github.io/r-novice-inflammation/) :star:

- [Data wrangling, exploration, and analysis with R](https://stat545.com) :star:

- [Data Cleaning](https://www.kaggle.com/rtatman/data-cleaning-challenge-json-txt-and-xls/) :star:

- [Data cleaning tricks](https://github.com/underthecurve/r-data-cleaning-tricks) :star:

- [Hands-On Programming with R](https://rstudio-education.github.io/hopr/) :star:

- [Github for the R user](https://happygitwithr.com) :star:

- [Slow Intro to R](https://psu-psychology.github.io/r-bootcamp-2019/talks/slow-r.html) :star:

- [Intro to R](https://billpetti.github.io/Crash_course_in_R/)

- [Ton of R courses](https://robchavez.github.io/datascience_gallery/)

- [An Introduction to R](https://cran.r-project.org/doc/manuals/R-intro.pdf)

- [Introduction to Data Exploration and Analysis with R](https://bookdown.org/mikemahoney218/IDEAR/)

- [R for academics](https://bookdown.org/marius_mather/Rad/)

- [Data science for psychologists](https://bookdown.org/hneth/ds4psy/)

- [Importing and Cleaning Data in R: Case Studies](https://rpubs.com/williamsurles/291422)

##### R Markdown

- [How to start a bookdown book](http://seankross.com/2016/11/17/How-to-Start-a-Bookdown-Book.html)

- [R for reproducible scientific analysis](http://swcarpentry.github.io/r-novice-gapminder/)

- [Has some useful R markdown tips](https://cran.r-project.org/web/views/ReproducibleResearch.html)

- [Manuscripts in Rmarkdown](https://stirlingcodingclub.github.io/Manuscripts_in_Rmarkdown/Rmarkdown_notes.html)- 

- [Youtube Video about R markdown](https://www.youtube.com/watch?v=Nj9J5iCSMB0)

- [Official R markdown site with tutorial](https://rmarkdown.io.com/index.html)

- [How to R markdown](http://rpubs.com/collnell/howto_rmd)

- [topics in R](https://github.com/UCIDataScienceInitiative/Topics_In_R) -some good markdown stuff

- [R Markdown Basics](https://stats.idre.ucla.edu/r/seminars/r-markdown-basics/)

- [Slides](https://geocompr.github.io/user_19/presentation/#12)

- [Markdown tutorial](https://github.com/rstudio-education/communicate-rmd-workshop)

- [R Markdown video](https://www.youtube.com/watch?v=CHBOVuo6RCo&feature=youtu.be)

- [bookdown: Authoring Books and Technical Documents with R Markdown](https://bookdown.org/yihui/bookdown/)

- [Intro to R Markdown](https://m-clark.github.io/Introduction-to-Rmarkdown/)

- [Pimp my Rmarkdown](https://holtzy.github.io/Pimp-my-rmd/#text_formating)

- [R Markdown: The definitive guide](https://bookdown.org/yihui/rmarkdown/)

- [R markdwon for scientists](https://rmd4sci.njtierney.com)

- [R markdown cookbook](https://bookdown.org/yihui/rmarkdown-cookbook/)

#### Data Visualization Books

- [some nyu courses](https://guides.nyu.edu/r) -the class materials are good (intro, wrangling, data viz). download the rmarkdown and your good. :star:

 - [R graphics - an idiots guide](http://rpubs.com/SusanEJohnston/7953):star:

 - [BBC Visual and Data Journalism cookbook for R graphics](https://bbc.github.io/rcookbook/):star:

- [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/):star:

- [Data viz book] (http://socviz.co/workgeoms.html#label-outliers)

### Online Resources

- [R Tips](http://pj.freefaculty.org/R/Rtips.html) - Basically R FAQ.

- [Various R tutorials](https://ourcodingclub.github.io/tutorials/)

- [Package to Teach you R](https://swirlstats.com/)

- [Rtips](https://twitter.com/rlangtip)

- [Cool R shiny apps](http://wiki.mgto.org/doku.php/r_shiny_apps)

- [RWeekly](https://rweekly.org)

- [CRANberries](http://dirk.eddelbuettel.com/cranberries/about/) - info about new packages

- [R FAQ](https://stackoverflow.com/questions/tagged/r-faq)

- [How to get help in R](https://stackoverflow.com/questions/15289995/how-to-get-help-in-r)

- [Most useful R tricks](https://stackoverflow.com/questions/1295955/what-is-the-most-useful-r-trick)

- [Useful slack group for questions](https://www.rfordatasci.com/about/)

- [Stack-Overflow R Resourcees](https://stackoverflow.com/tags/r/info)



### List of R Code

#### Statistical Code

- [R Codebook](http://www.cookbook-r.com) :star:

- [R code examples for a number of common data analysis tasks](http://dwoll.de/rexrepos/) - This is very good. Basically shows how to do code for common things we do.:star:

- [Learn R](https://learnxinyminutes.com/docs/r/):star:

- [a compendium of r commands to teach statistics](http://mosaic-web.org/go/Master-Core.pdf) :star:

- [R Functions for Reegression Analysis] (https://cran.r-project.org/doc/contrib/Ricci-refcard-regression.pdf) :star:

- [Common R commands used in Data Analysis and Statistical Inference](http://www2.stat.duke.edu/~mc301/R/Rcommands.pdf)

#### R Code

- [Good R reference card](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)- :star:

- [Psychometric Models and Methods](https://cran.r-project.org/web/views/Psychometrics.html)- :star:

- [How to do Econometric Stats in R](https://cran.r-project.org/web/views/Econometrics.html)- :star:

- [How to do 604 Stats in R](https://cran.r-project.org/web/views/SocialSciences.html)- :star:

- [How to deal with missing data in R](https://cran.r-project.org/web/views/MissingData.html)- :star:

- [How Do I?... In R](https://smach.github.io/R4JournalismBook/HowDoI.html) - :star:

- [R Cheat Sheet](https://www.sas.upenn.edu/~baron/from_cattell/refcard.pdf)- :star:

- [R Cheat Sheet](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)- :star:

- [R <-> Stata](https://www.princeton.edu/~otorres/RStata.pdf)

- [Modern R with tidyverse](https://b-rodrigues.github.io/modern_R/graphs.html#resources)

### Statistics Without R

- [good stuff in courses](https://www3.nd.edu/~rwilliam/)

- [Math Prefresher for political science students](https://bookdown.org/kuriwaki/prefresher/prefresher.pdf)- :star:

- [Basic Statistics](https://crumplab.github.io/statistics/foundations-for-inference.html#the-crump-test)- :star:

- [Advanced Data Analysisfrom an Elementary Point of View](http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf)- :star:

- [StatTrek](https://stattrek.com)- :star:

- [Introduction to mathematics for political scientists](http://brendancooley.com/imps2019/)- :star:

- [Good List of workshops from Cornell](http://www.cscu.cornell.edu/workshops/catalog.php)- :star:

- [Econometric Academy](https://sites.google.com/site/econometricsacademy/)- :star:

- [JB statistics](https://www.jbstatistics.com)- :star:

- [statquest](https://statquest.org/video-index/)- :star:

- [GLM and multilevel models](https://bookdown.org/roback/bookdown-bysh/)- :star:

- [Duke Math Camp](http://people.duke.edu/~das76/Mathematics%20for%20Political%20and%20Social%20Research%20Syllabus_Siegel.pdf)- :star:



## other

- [Machine learning course](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)

- [Version control w git hub, learn it ehre](https://swcarpentry.github.io/git-novice/)

- [Links to r resources](https://awesomeopensource.com/projects/r?categoryPage=2)

 - [Courses](https://docs.google.com/spreadsheets/d/1LtaeWPzEhRiy-kdNZBn0gPwc6aTYkWtt6Cau6PzcXuo/edit#gid=0)

 - [600 R websites](https://www.datasciencecentral.com/profiles/blogs/600-websites-about-r)

 - [ML tutorioal](https://koalaverse.github.io/machine-learning-in-R/)

## Research Design

- [Research design course from LSE](https://thomasleeper.com/designcourse/)



## Dissertation Websites

- [Advice](https://github.com/edrubin/Advice)

- [Writing your thesis with r markdown](https://paulvanderlaken.com/2017/09/01/writing-your-thesis-with-r-markdown-1-getting-started/)

- [Dissertating with Bookdown](https://bookdown.org/thea_knowles/dissertating_rmd_presentation/nitty-gritty-stuff.html#predefined-functions)

- [One year to dissertate](https://livefreeordichotomize.com/2018/09/14/one-year-to-dissertate/)