https://socviz.co/index.html#preface
https://serialmentor.com/dataviz/
Although not made in R is a good place to start before plotting your data, choosing the type of chart you need, just take a look:
https://gramener.github.io/visual-vocabulary-vega/
Make plots that are ready for publishing in scientific journals:
https://github.com/kassambara/ggpubr
Nice Kaplan-Meier plots (and analysis)
https://cran.r-project.org/web/packages/jskm/index.html
Make charts and graphics with D3 in R. Going beyond ggplot!
https://rstudio.github.io/r2d3/index.html
Plot world maps based on a butterfly projection:
https://github.com/fabianehmel/haacker & https://interface.fh-potsdam.de/butterfly/
Exploratory data analsysis (visually):
https://github.com/joachim-gassen/ExPanDaR
https://www.r-bloggers.com/accessing-openstreetmap-data-with-r/
https://www.sportscidata.com/2019/03/26/how-to-create-gps-heatmaps-in-ggplot/
Search OSM data by name and address and generate synthetic addresses of OpenStreetMaps points (reverse geocoding)
https://github.com/hrbrmstr/nominatim
Easily plot your data along with statistical information https://github.com/IndrajeetPatil/ggstatsplot
Not an R package, a program for checking if your visualization if colorblind-friendly:
https://colororacle.org/
Give an illusion of relief to your contours charts:
https://eliocamp.github.io/metR/reference/geom_contour_tanaka.html
A how-to guide:
https://datascienceplus.com/how-to-make-3d-scatter-plots-with-r-scatterplot3d-package/
Learn about R, the tidyverse and RStudio, interactively! https://rstudio.cloud/learn/primers
http://rpubs.com/acolumbus/ocrug-data-manipulation-with-tidy-tools
A free course from census.gov:
https://www.census.gov/data/academy/courses/choroplethr.html
Althought centered on journalism, it is a nice collection of video-tutorials covering almost all you need to work with R.
https://learn.r-journalism.com/en/introduction/
Copy and paste data to and from R without dying in the attempt. It is surely a must! https://github.com/MilesMcBain/datapasta
A great tool for work (and understand) regular expressions:
https://www.garrickadenbuie.com/project/regexplain/
A package for cleaning dataframes, the function tabyl() is very useful:
https://github.com/sfirke/janitor
A very nice tutorial:
https://datascienceplus.com/how-to-use-googlesheets-to-connect-r-to-google-sheets/
A nice guide about logistic regression with R:
https://uc-r.github.io/logistic_regression
How to perform a logistic regression:
https://www.r-bloggers.com/how-to-perform-ordinal-logistic-regression-in-r/
A tutorial about modeling data the tidyway:
https://rviews.rstudio.com/2019/06/19/a-gentle-intro-to-tidymodels/
Generalized additive models (GAMs):
https://noamross.github.io/gams-in-r-course/
Modeldown, a package for creating html reports with your modelling results, 5 stars!!!
https://github.com/MI2DataLab/modelDown
Multidomain data:
https://informationisbeautiful.net/data/
Global burden of disease:
http://ghdx.healthdata.org/gbd-results-tool
The Natural History Museum Specimen Collection:
https://data.nhm.ac.uk/
Data from elections in Spain:
https://github.com/hmeleiro/elecciones
Prepare your report with APA formatting:
https://github.com/crsh/papaja
Create documents following Tufte's style:
https://github.com/eddelbuettel/tint
The best tool for creating formatted tables in markdown, LaTex and html formats:
https://github.com/isubirana/compareGroups
https://jozef.io/r909-rmarkdown-tips/
An easy way to give formatting while you write rmarkdown reports:
https://thinkr-open.github.io/remedy/index.html
Join and split pdf files in R:
https://ropensci.org/technotes/2019/04/24/pdftools-22/
R package for functions on spanish data:
https://ropenspain.github.io/spanish/
A couple of things you should know when you are starting with R (from R-Bloggers):
https://www.r-bloggers.com/r-vocabulary-part-1/
https://www.r-bloggers.com/r-vocabulary-part-2/
https://resources.rstudio.com/webinars/programacio-n-con-r-edgar-ruiz
A step by step guide:
https://happygitwithr.com/index.html
Extract tables from pdf files:
https://github.com/ropensci/tabulizer
Add color to your RMarkdown output!
https://github.com/aedobbyn/multicolor
Probably the easiest time series analysis package:
https://www.tsbox.help/
Extract features from time series data https://cran.r-project.org/web/packages/tsfeatures/vignettes/tsfeatures.html
A set of tools for working with time series:
https://tidyverts.org/ (example datasets in: http://tsibbledata.tidyverts.org/)
https://statnmap.com/2018-07-14-introduction-to-mapping-with-sf-and-co/
Tools for analysis of interactions for regression models
https://github.com/jacob-long/interactions
The easiest way to create interactive dashboards or reports with R.
https://rmarkdown.rstudio.com/flexdashboard/
http://www.r-datacollection.com/blog/making-r-files-executable/
https://appsilon.com/r-studio-shortcuts-and-tips/
Create aRt, play with maths, images and R and have a lot of fun!:
https://github.com/cutterkom/generativeart
https://jef.works/R-style-guide/
https://style.tidyverse.org/
http://adv-r.had.co.nz/Style.html