Buy me a coffee

Excerpt from site: Research Methods in R is a set of guides on how to use R as your central research methods tool. The target audience is psychology undergraduate students. Research Methods in R is Creative Commons, so you are free to reuse these materials and adapt them as you wish, as long as you attribute them to their authors, and as long as your modifications have a Creative Commons licence. They come with absolutely no warranty of any kind.

1. Link to site here: https://ajwills72.github.io/rminr/

Buy me a coffee

By Hasse Walum & Desirée De Leon

The site’s purpose is to introduce you to statistics with R. Very concise and clear 😄 !

1. Link to the “Introduction to the Normal Distribution” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/02_bellCurve.html
2. Link to the “Measures of centrality: Mean, Median, & Mode” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/03_mean.html
3. Link to the “The Spread of the Data: Variance * Standard Deviation” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/04_variance.html
4. Link to the “A tale of two variables: Covariance & Correlation” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/05_correlation.html
5. Link to the “Introduction to Inference: Standard Error” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/06_standardError.html

Buy me a coffee

Test if your model is a good model!

The primary goal of the performance package is to provide utilities for computing indices of model quality and goodness of fit. This includes measures like r-squared (R2), root mean squared error (RMSE) or intraclass correlation coefficient (ICC), but also functions to check (mixed) models for overdispersion, zero-inflation, convergence or singularity.

1. Link to information: https://easystats.github.io/performance/index.html

Buy me a coffee

By Dan Quintana

Dan Quintana made a screencast of how to visualise research trends for a paper introduction or grant application using the {europepmc}

1. Link to screencast: https://twitter.com/dsquintana/status/1166083585771802626?s=20
2. Link to code: https://gist.github.com/dsquintana/b512b715786088339b61a7fb79367d5e

Buy me a coffee

By Mattan S. Ben-Shachar

Excerpt from blog: Co-varying predictors can be a messy business. They make estimates unstable, reducing our statistical power and making interpretation more difficult. In this post I will demonstrate how ignoring the presence of co-variation between predictors when exploring our models can lead to odd results and how we might deal with this issue.

1. Link to blog here: https://shouldbewriting.netlify.app/posts/2020-08-11-when-predictors-covary/

Buy me a coffee

By Gina Reynolds

Excerpt from website: A data project often actually starts with a part than might present some frustration: data cleaning, manipulation, and management. Sometimes this is called “data wrangling” because of the possible struggles that arise in getting your data ready for data visualization and analysis. But wrangling can be satisfying. You might find yourself liking it!

1. Link to site here: https://evamaerey.github.io/data_manipulation/about

Buy me a coffee

By Desirée De Leon & Alison Hill

Now that many of us have to move to online platforms, this is a wonderful resource that walks you through on how you can use rMarkdown to upload your class materials.

1. Link to site here: https://rstudio-education.github.io/sharing-short-notice/#1

Buy me a coffee

Technically not a visualization resource but once you use it on your script, it is so pleasant to look at (at least for me).

1. Link to animations here: https://github.com/seasmith/AlignAssign

Buy me a coffee

Excerpt from site: The calendR package allows creating fully customizable ggplot2 calendar plots with a single function. In addition, the package provides arguments to create heatmap calendars. In this tutorial you will learn how to create ready to print yearly and monthly calendar plots in R.

1. Link to blog here: https://r-coder.com/calendar-plot-r/
2. Link to package here: https://cran.r-project.org/web/packages/calendR/

Buy me a coffee

By Jakub Nowosad

Deciding if a color palette is a colorblind-friendly is a hard task. This cannot be done in an entirely automatic fashion, as the decision needs to be confirmed by visual judgments. The goal of colorblindcheck is to provide tools to decide if the selected color palette is colorblind-friendly.

1. Link to repo here: https://github.com/Nowosad/colorblindcheck
2. Link to site here: https://jakubnowosad.com/colorblindcheck/

Buy me a coffee

Excerpt from site here: Use Large Language Models (LLM) to run Natural Language Processing (NLP) operations against your data. It takes advantage of the LLMs general language training in order to get the predictions, thus removing the need to train a new NLP model. mall is available for R and Python.

It works by running multiple LLM predictions against your data. The predictions are processed row-wise over a specified column. It relies on the “one-shot” prompt technique to instruct the LLM on a particular NLP operation to perform. The package includes prompts to perform the following specific NLP operations:

• Sentiment analysis

• Text summarizing

• Classify text

• Extract one, or several, specific pieces information from the text

• Translate text

• Verify that something is true about the text (binary)

1. Link to package here: https://mlverse.github.io/mall/
2. Link to repo here: https://github.com/mlverse/mall

Buy me a coffee

By David Gohel

Excerpt from book: This book deals with reporting from R with the packages ‘officer’, ‘officedown’, ‘flextable’, ‘rvg’ and ‘mschart’.

These packages are available from CRAN or from github in their development version.

These packages have been developed to facilitate the production of Word documents and PowerPoint presentations from and with R. It was written specifically to offer a competitive solution to SAS ODS for tabular and graphical reporting.

1. Link to book: https://ardata-fr.github.io/officeverse/index.html

Buy me a coffee

By Maureen Linke, The Wall Street Journal @maureenlinke

Excerpt from site: How to create publishable graphics from R and using the tidyverse for data processing and analysis. We’ll look at examples of published work from the Wall Street Journal and dive into the code it took to produce them.

1. Link to slides here: https://docs.google.com/presentation/d/1-2toLjn_6dK2VC__UmKp-AowD8rknZ4NQbn3P5IKLQ0/edit?slide=id.p#slide=id.p
2. Link to repo here: https://bit.ly/3aHmOgX

Buy me a coffee

Excerpt from site: These are the files supporting the walkthroughs in the NICAR 2024 session Tidycensus will convince you to learn R

1. Link to repo here: https://github.com/r-journalism/nicar-2024-tidycensus?tab=readme-ov-file
2. Link to site here: https://nicar.r-journalism.com/2024/

Buy me a coffee

by Peter Aldhous

Excerpt from site: The sf (Simple Features) package in R (https://r-spatial.github.io/sf/) represents vector geodata as data frames with the geometry held in a single list-column. As such, it allows you to process geodata in pipelines that consistent with the syntax of dplyr and other tidyverse packages. This session will provide an introduction to the Simple Features in R package, show how to draw maps from sf objects with ggplot2, and how to run spatial queries on geodata much as you would in PostGIS.

1. Link to site here: https://paldhous.github.io/NICAR/2022/r-sf-mapping-geo-analysis.html
2. Link to materials here: https://paldhous.github.io/NICAR/2022/r-sf-mapping-geo-analysis.zip

Buy me a coffee

By Shreya Vuttaluru

Excerpt from site: A class on geospatial analysis in R for NICAR25 in Minneapolis

1. Link to repo here: https://github.com/shreyavut/spatial-analysis-in-r?tab=readme-ov-file

Buy me a coffee

Excerpt from site: For this class, we’ll be learning the basics of the R programming language, and interacting with R through RStudio which is an efficient and user-friendly tool.

A bit of background: R is a language that was created for statistical analysis and graphics; it does these two things very well. Over time, additional functionality has been added to R (because it is an open standard language, so anyone can contribute packages or libraries) so that it now works very well for data analysis, web scraping, app building, and even natural language processing.

Learning a programming language requires some investment of time. We’re going to cover the basics of what you need to know to get started, but I can’t teach you everything in 3 hours (and frankly I don’t know everything anyway). You will be able to do data analysis in R after this class, but you will also need to keep exploring, learning, and troubleshooting. I will provide resources to help with all that.

1. Link to the repo here: https://github.com/ireapps/nicar24-intro-to-R

Buy me a coffee

By Alistair Bailey

Excerpt from site: Coding togetheR is a series of collaborative workshops to teach foundational R coding and data science skills at the University of Southampton in 2019. This book contains the materials covered over eight, two-hour sessions.

1. Link to web-book here: https://ab604.github.io/docs/coding-together-2019/

Buy me a coffee

By Lisa Lendway

Excerpt from the site: This website serves the students who take COMP/STAT 112 at Macalester College taught by Lisa Lendway. Other people interested in learning Data Science skills using R may also be interested in this material!

1. Link to tutorials: https://ds112-lendway.netlify.app/

List of tutorials

1. R basics:https://r-basics.netlify.app/
2. ggplot dplyr: https://ggplot-dplyr-intro.netlify.app/
3. Expanding data wrangling: https://03-wrangling-tutorial.netlify.app/
4. Mapping: https://mapping-in-r.netlify.app/
5. Animation and Interactivity in R: https://animation-and-interactivity-in-r.netlify.app/
6. Importing, scraping, and more!: https://importing-and-more-in-r.netlify.app/

Buy me a coffee

By Meghan Hall

Excerpt from the site: The goal of this talk is to hopefully convince you that you can make your life a little bit easier by creating an internal package! An internal package is an R package, created by you, that’s just for you, to serve your work. And depending on your specific working scenario, that “you” can be substituted for you and your direct report, you and a handful of the colleagues that you closely collaborate with, or even you and your whole team.

1. Link to slides here: https://meghan.rbind.io/slides/boston_user/hall_boston_user_2022#1