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137 items from iamericfletcher/awesome-r-learning-resources ★664

  1. TidyTuesday github.com

    TidyTuesday is a weekly data project aimed at the R ecosystem with an emphasis placed on understanding how to summarize and arrange data to make meaningful charts.

  2. Coolors coolors.co

    The super fast color schemes generator! Create the perfect palette or get inspired by thousands of beautiful color schemes. Features include color picker, pick palette from photo, create a collage, make your own gradient palette, create a gradient, contrast checker, etc.

  3. r-color-palettes github.com

    Comprehensive list of color palettes available in r. Author: Emil Hvitfeldt.

  4. Web Scraping Reference: Cheat Sheet for Web Scraping using R github.com

    Guide, reference and cheatsheet on web scraping using rvest, httr and Rselenium. Author: yifyan et al.

  5. R Bloggers r-bloggers.com

    R-Bloggers.com was created by Tal Galili and is a blog aggregator of content contributed by bloggers who write about R (in English). The site helps R bloggers and users to connect and follow the R blogosphere.

  6. R for Data Science by Garrett Grolemund & Hadley Wickham r4ds.had.co.nz

    This book will teach you how to do data science with R. You will learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it. Exercise Solutions Authors: Garrett Grolemund and Hadley Wickham.

  7. Text Mining with R tidytextmining.com

    This book serves as an introduction of text mining using the tidytext package and other tidy tools in R. Authors: Julia Silge and David Robinson.

  8. The R Graph Gallery r-graph-gallery.com

    A collection of charts made with the R programming language. Author: Yan Holtz.

  9. Not so Standard Deviations nssdeviations.com

    A data science podcast where Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.

  10. From Data to Viz data-to-viz.com

    From Data to Viz leads you to the most appropriate graph for your data. Author: Yan Holtz.

  11. The R-Podcast r-podcast.org

    Practical advice on how to take advantage of R to accomplish innovative and robust data analyses. Hosted by Eric Nantz.

  12. R Programming for Data Science by Roger D. Peng (2019) leanpub.com

    This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. Author: Roger Peng.

  13. htmlwidgets htmlwidgets.org

    Showcase and gallery of the various interactive web visualizations you can build using R.

  14. Efficient R Programming by Colin Gillespie & Robin Lovelace (2017) csgillespie.github.io

    Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. Authors: Colin Gillespie, Robin Lovelace.

  15. The R Inferno by Patrick Burns (2011) burns-stat.com

    A book about trouble spots, oddities, traps, and glitches in R. Author: Patrick Burns.

  16. Plot Spatial Data / Shapefiles in R youtube.com

    From the "math et al" YouTube channel.

  17. Help me help you: creating reproducible examples youtube.com

    Making a great reprex is both an art and a science and this webinar will cover both aspects. A reprex makes a conversation about code more efficient and pleasant for all. This comes up whenever you ask someone for help, report a bug in software, or propose a new feature. The…

  18. Ben Stenhaug youtube.com

    Topics include saving and reading data, map functions in purrr, t-tests, item response theory, and the basics of R and the tidyverse.

  19. Cédric Scherer youtube.com

    A collection of talks and seminars about R-related topics such as ggplot2 or Shiny, and data visualization in general.

  20. StatQuest with Josh Starmer youtube.com

    The Statistics and Machine Learning in R playlist deals with principal component analysis, random forest, regression, ROC and AUC, and ridge, lasso and elastic-net.

  21. RichardOnData youtube.com

    The R playlist includes videos on manipulating data with dplyr, visualizing data with ggplot2 and ggThemeAssist, data types and structures, important base r functions, handling datetimes with lubridate, conquering factors with forcats, manipulating text with stringr.

  22. Simplilearn youtube.com

    The R Programming for Beginners playlist includes videos on data science, charting, data visualization, algorithms, business analytics, regression, random forest, SVM, clustering, time series, modeling, and analytical techniques.

  23. IDG TECHtalk youtube.com

    Do More with R playlist includes tutorials on shiny, data.table, getting API data, using Git and Github with R, writing your own packages, run Python in R code, RStudio addins and keyboard shortcuts, dashboards and flexdashboards.

  24. David Jablonski youtube.com

    The UC Berkeley R Bootcamp playlists include videos on R basics, handling data, performing calculations, programming, graphics, workflows, and statistics.

  25. Statistics Globe youtube.com

    A collection of short but detailed tutorials on how to work through common problems you will face while using R. Topics include data formatting, reordering data, strings, and ggplot2.

  26. Andrew Couch youtube.com

    Topics include modeling, creating functions, dashboards, and forecasting.

  27. Data Analysis and Visualization Using R youtube.com

    Topics for the online course Data Analysis and Visualization Using R.

  28. David Robinson youtube.com

    Topics include graphing for EDA, data manipulation, animated mapping, visualization, text mining, time series, forecasting, regression, bootstrapping, package development, network graphs, ANOVA, JSON, simulation, survival analysis, and tidymetrics. Click here for detailed…

  29. Data Science with Tom youtube.com

    Topics include time series, analyzing word relationships with ggraph and tidytext, and tidymodels.

  30. Julia Silge youtube.com

    Topics include predictive text modeling, impute missing data, tidymodels, sentiment analysis, multinomial classification, principal component analysis, data preprocessing and resampling, and multinomial classification.

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