Hi, I'm a media and communications student. My university has updated my bachelors curriculum and now uses R 4.6.1 to teach. If anyone has any tutorials or guides (does not have to be media related) it would be very helpful.

I have dyscalculia, so I have to practice twice as hard and adapt twice as fast for things to stick. So a slow or more rudimentary tutorial would work the best.

Thank you!

We're happy to welcome two new members on the R Consortium Board of Directors, and six that continue to serve!

Together, they are filling positions at the Premier and Core level.

• Francesca Lazzeri, Ph.D. of Microsoft joins as a Premier Member
• Mutaz M. Jaber of Gilead Sciences joins as a Core Member
• Mike K Smith of Pfizer continues as Board Chair, alongside our returning directors

We are honored to have their expertise at the R Consortium!

More here: https://r-consortium.org/posts/meet-our-2026-r-consortium-board-of-directors/

Hi! I'm working on an R package called ggalttext that takes a ggplot2 chart as input and returns alternative text (required for accessibility) for that chart.

The goal is to provide a very simple and lightweight way of adding meaningful alt texts to charts made with ggplot2.

It does not use AI or OCR technologies, but instead inspects the plot structure/content and uses some (more or less) naive heuristics to figure out what the chart looks like and how to describe it in a single sentence.

It's already available on CRAN, but I'm working on the next release, which fixes some edge cases.

Example usage:

library(ggplot2)
library(babynames)

plot_data <- babynames |>
    subset(name %in% c("Amanda", "Jessica", "Patricia", "Deborah", "Dorothy", "Helen"))

plot <- ggplot(plot_data, aes(x = year, y = n, group = name, fill = name)) +
    geom_area() +
    theme(legend.position = "none") +
    labs(title = "Popularity of American names in the previous 30 years") +
    theme(
        legend.position = "none",
        panel.spacing = unit(0.1, "lines"),
        strip.text.x = element_text(size = 8)
    ) +
    facet_wrap(~name, scale = "free_y")


ggalttext::generate_alt_text(plot)
# "Area chart split into 6 small charts arranged in a 2-row by 3-column grid,
# titled “Popularity of American names in the previous 30 years”."

I’m fitting a Beta GAM in mgcv to model the proportion of daytime outdoor-use time in laying hens. The response is the proportion of minutes spent outdoors during an 08:00–22:00 observation window, and the model contains repeated observations from individual hens.

My current model is approximately:

prop_outside ~
  coop_id +
  s(study_day, bs = "cr", k = 40) +
  s(max_temp, bs = "cr", k = 8) +
  s(mean_dew_point, bs = "cr", k = 7) +
  rain_any +
  s(log_total_rain, by = rain_status, bs = "cr", k = 7) +
  s(eid, bs = "re")

using:

family = betar(link = "logit")
method = "REML"

The dataset contains roughly 8,800 positive hen-day observations from 124 hens across about 137 study dates. The model explains about 60% of the deviance.

The issue is the study_day smooth. On a same-row model comparison, I get:

s(study_day)
k'       = 39
edf      = 34.58
k-index  = 0.949
p-value  < 0.001

I also tried an alternative weather specification using mean temperature, mean humidity and mean wind instead of maximum temperature and dew point:

prop_outside ~
  coop_id +
  s(study_day, bs = "cr", k = 40) +
  s(mean_temp, bs = "cr", k = 8) +
  s(mean_humidity, bs = "cr", k = 8) +
  s(mean_wind, bs = "cr", k = 8) +
  rain_any +
  s(log_total_rain, by = rain_status, bs = "cr", k = 7) +
  s(eid, bs = "re")

The same issue persists:

s(study_day)
k'       = 39
edf      = 34.84
k-index  = 0.966
p-value  = 0.0175

The study-day smooth is visually quite wiggly, particularly early in the study, and the EDF is already fairly close to the available basis dimension. Changing the weather specification does not materially improve overall fit: both models explain about 60% of deviance and have essentially identical AIC.

There is also substantial temporal/weather dependence. For example, in the alternative model, observed concurvity is about 0.90 for mean temperature and 0.48 for study day.

My questions are:

1. Is the low k-index plus EDF ≈ 35/39 sufficient reason to increase k for study_day, for example from 40 to 60?
2. If increasing k gives essentially the same fitted curve/predictions but the k-check remains significant, would you consider the current smooth adequate?
3. Could this be indicating residual temporal autocorrelation rather than simply an insufficient basis dimension?
4. Would you model study date differently in this setting—for example with an autocorrelation structure, a different smooth basis, or another temporal term?
5. Since weather variables themselves follow study date seasonally, how would you distinguish genuine temporal structure from weather-related temporal confounding?

My current plan is to compare k = 40 and k = 60 on the same observations and assess whether fitted values, the study-day effect, held-out-date prediction, and conclusions materially change, rather than selecting the model based only on the k-check p-value.

I’d appreciate advice on how to interpret this persistent study_day diagnostic and what additional diagnostic/model comparison would be most appropriate.

Just solved a problem that might be relevant for others and took me a while to pin down.

I have a project developed in Rstudio and using renv. At some point the .Rprofile was amended to place source("renv/activate.R") INSIDE a .First <- function(), with some other stuff that we wanted to run when starting R for this project.

One of our users transitioned to Positron, and we just couldn't get the .Rprofile to run properly and start renv.

The fix was to place source("renv/activate.R") at the top level of .Rprofile, outside of any .First function.

According to claude this is to do with the way that the Rstudio startup sequence differs from VS Code/Positron.

Hope this saves someone else a few hours...

Hey everyone,

I recently moved into data after studying law and communications, and I just finished my first complete R project using tidyverse and rmarkdown.

Spotify's API returns musical keys as pitch class integers (0–11).

I mapped them to their fundamental frequencies in Hz (f=440×2n/12) to see at what frequencies Coldplay actually vibrates across 20 years of albums.

Turns out their most-used fundamentals are E (330 Hz) and A (440 Hz), with very little pitch drift over time.

Repo is here: https://github.com/frequencymatch/coldplay-in-hz

As a beginner in R, I'd love any feedback on the Rmd structure or the code itself!

Hey everyone, math major here, looking to pursue a statistics minor. I've been working through Introduction to Statistical Learning with Python by Gareth James(ISLP), and others. I'm almost finished with it and have been looking at other books I might be interested in.

Just a quick summary for anyone that hasn't read it. ISLP covers topics including:, linear and non linear regression and classification, basic probability and statistics, cross validation, common shallow ML algorithms, deep learning, neural networks. From a very practical and non-techinal way. Basically you read a lot of "This works because reasons, moving on". Best way I would describe it is "intuition slop" .

I was looking for something more theoretical as ISLP is pretty light on the theory and focuses more on intuition and generalization. Although I want a more theoretical book, I still would prefer it to contain some applications and or assignments that I could do to practice my coding because as a math major I feel as though my programming is lagging behind. Are there any specific books you would recommend.

Also I'm taking machine learning this semester and our assigned book is "Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow" by Aurelien Geron. So I'll probably be reading it in bits and pieces, but I still want another book that I can focus on outside of classes. I appreciate any responses.

https://tidydesign.substack.com/p/y-code-when-ai?r=15f9ap&utm_campaign=post&utm_medium=web

I wanted to try the AI-in-the-IDE workflow Hadley Wickham has been writing about, so I used it on a project I already know well: survival analysis of Alone as each season unfolds.

After sitting with it for a week, going back to the old way sounds horrible. I’ll be using this again.

The biggest change was not that the models wrote cleverer models. It was that I ended up with something I was willing to show other people.

Previously I would not have spent the effort polishing comments, a data dictionary, and structure so someone else could follow it — or had much confidence I wasn’t doing something quietly dumb. The useful part was the admin and the second opinion: documentation, comments, and a sense-check of the methods.Repo: https://gitlab.com/weekly_analysis/alone-hw-method

Setup: LibreChat with Anthropic as the “strategic thinker,” and the Continue extension with three Gemini models and two DeepSeek models as the “hands.”Weekend cost was about $3. Some of that was me burning tokens while I learned how to talk to the add-on; next time should be cheaper. Hadley flags Gemini as the cost-effective option. In my case DeepSeek was cheaper, and Gemini kept returning “model unavailable” for the Flash version I wanted, which meant waiting or switching to something worse or more expensive. DeepSeek never did that. When I needed it 3.1 pro was excellent and never stalled me out, but I was missing the most useful option, a reliable affordable 'daily driver' without going outside Gemini which is a shame, I tried again this weekend and found the problem worse, but maybe I could get what I need without waiting by using a SLIGHTLY different model than 3.7 Flash.

If you already use AI to help write R and want the next step beyond paste-into-a-chatbot, this setup is worth a weekend. Happy to share the Continue/LibreChat bits if useful;

I'm working on a Linux-based workstation setup aimed at economists and other quantitative researchers. R support is one of the last major pieces I need to settle.

I mainly use Python and VS Code, so I don't want to assume that my preferred setup makes sense for regular R users. I'm interested in what people actually use:

1. What operating system do you use for R?
2. What is your main editor or IDE? RStudio Desktop, RStudio Server, Positron, VS Code, or something else?
3. Do you use one shared R package library, project-specific environments with renv, or a mixture?
4. Do you install R or R packages through Conda/Mamba? Is that mainly for projects that mix R and Python?
5. Do you work locally, on remote servers or HPC systems, or in containers?
6. What supporting tools do you regularly need, such as Quarto, Jupyter, TinyTeX, compilers, or external libraries?
7. Do you need multiple R versions?

I'm also interested in package developers. Does your development setup differ from your normal analysis setup? What would you expect to find on a fresh workstation before you considered it ready for R work?

You don't need to answer everything. Short descriptions of your setup, unusual requirements, and recurring setup problems would all help.

If useful, here is a template:

OS:
Main editor/IDE:
Local or remote:
Package-library strategy:
How R is installed:
Conda/Mamba:
Mix R and Python:
Develop R packages:
Biggest setup annoyance:

Edit: Thank you all for taking the time to respond. I wasn’t expecting such a wide variety of responses.

hi everyone,

does anyone have the dataset used by Mike Marin in this tutorial video series?

https://www.youtube.com/watch?v=riONFzJdXcs&list=PLqzoL9-eJTNBDdKgJgJzaQcY6OXmsXAHU&index=2

I'm new to R and I wanted to get to know it a little bit and try it out to see if I'll be able to use it or not. I've already search this sub for recommended free resources for beginners to learn the basics. However, I found this nice playlist that I'd like to follow along just to get an overview, and I'd like to imitate and reproduce what Mike does, that's why I'm looking for the dataset he uses.

Thanks for the help. if you have any other suggestion, it's more than welcomed!

My blog post covering two small packages for styling altdoc sites:

• altdown: Style altdoc site like a pkgdown (Bootstrap 5) site (reason why explained in the blog post).
• reftip: Adds preview on function signature on hover, and more robust handling of hyperlinks.

They can be used alone or together, as shown in my toy package alttip.

Please let me know what you think!

Hi everyone!

I’m an early-career marine biologist currently completing my BSc in Marine Biology, and I’m looking to connect with researchers, students, conservationists, and others working with biological or environmental data.

I’ve been developing my skills in R, particularly for data cleaning, statistical analysis, ecological modelling, and data visualization. I genuinely really enjoy working in R, and at this stage I want to get involved with as many different projects and datasets as I reasonably can.

If anyone has a marine science, ecology, conservation, fisheries, wildlife, or other biological dataset that could use some additional help with analysis in R, I’d be happy to contribute. I’m also open to projects outside of marine science if there is an opportunity to work with interesting data and learn something new.

I’m not looking to charge anyone. My main goal is to gain experience working with different types of real-world data, improve my R skills, contribute where I can, and hopefully meet and build connections with people working in science and research.

I also recently created a GitHub where I’ll be archiving my projects as I continue learning. I uploaded a project I finished about a week ago involving the analysis of BRUV data and whitetip reef shark habitat associations.

GitHub: https://github.com/kylealibz

If you have a project where another person helping with the r/data side would be useful, feel free to message me. I’d also be happy just to connect with other early-career researchers and R users.

Thanks!

nlmixr2 is an R Consortium Working Group building open-source nonlinear mixed-effects modeling in R suitable for regulatory submissions.

In a follow-up to the 7.0 release, Matthew Fidler covers a frequently requested feature - a fuller, more flexible covariance step after model fitting.

What's new:

• Nearly any covariance method can be requested from nearly any estimation method

• Switch a finished fit to a different covariance method without refitting

• The default covariance step now covers every estimated parameter, not just structural ones - so residual-error terms can return SEs, %RSE, and confidence intervals in $parFixed

Read the cross-post on the R Consortium blog: https://r-consortium.org/posts/nlmixr2-7-0-covariance-step/

Hey, just wanted to share ggvariant: A simple, opinionated toolkit for visualizing genomic variant data using a 'ggplot2'-native grammar. Accepts VCF files or plain data frames and produces lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons as standard 'ggplot2' objects.  It's on CRAN

Put out a new release of c5tree, my C5.0 decision tree implementation for Python. Main change is a rewritten C++ core replacing the old pure-Python fit routine.

Ran benchmarks against the previous PyPI release and against sklearn's CART, across breast_cancer, iris, and wine.

Fit time: new version is somewhere between 4x and 50x faster than the old pure-Python one depending on dataset size. Still slower than sklearn CART, which isn't surprising given how optimized that codebase is.

Predict time: roughly the same as before, and both versions are a bit slower than sklearn here — this is probably where I'll focus next.

Accuracy: identical between old and new versions on every dataset, both holdout and 5-fold CV. So no regressions, just faster training. Comparable to CART overall, slightly ahead on the wine dataset.

Chart with all four comparisons is attached. Happy to answer questions about the implementation or take suggestions for other datasets to test against.

Repo/install: https://pypi.org/project/c5tree/#description

https://github.com/vinaykumarkv/c5tree

I’m excited to announce that evoFE 1.0.0 is now on CRAN (a major leap forward from the initial 0.1 release).

What is evoFE?

evoFE is an R package for automated feature engineering using genetic programming. Instead of manually brainstorming interaction terms, nonlinear scalings, or encodings, evoFE evolves candidate transformation recipes and evaluates them directly against gradient boosted trees or linear models.

What's New in 1.0.0?

1. Zero-Boilerplate "One-Liner" Bayesian Tuners: Pass evaluator = "lightgbm_mbo" to automatically tune tree depth, learning rate, and subsampling via mlr3mbo during evolution. Or wrap any custom model (like XGBoost) with make_tunable().
2. 42+ Built-in Transformers: Arithmetic, group-by aggregations (mean, median, SD, quantiles), target encodings, WoE, UMAP embeddings, Genie & Lumbermark MST graph clustering, date differencing (date_diff), and custom transformer registration.
3. Hybrid Active Feature Masking: Mutates and selects raw input features simultaneously with derived features, guided by baseline feature importance.
4. Hierarchical Gene Chaining: High-performing features from earlier generations serve as inputs for subsequent compound transformations.
5. Island Models & Topologies: Runs independent sub-populations across Ring, Torus, Grid, or Hypercube topologies with demand-driven Gibbs pull migration.
6. Caruana Island Ensembling (ensemble_islands()): Combines diverse island champion recipes using Caruana post-hoc forward selection with replacement.
7. Leakage-Safe Validation: Native support for time-series (cv_strategy = "time"), grouped entity validation (cv_strategy = "group"), and untouched confirmation holdouts (holdout_frac) with search-gap diagnostics.
8. Dynamic BIC Regularization: Asymptotic BIC / PAC-Bayes penalties scaling with sample size NN to prevent feature bloat.

Minimal Example:

install.packages("evoFE")
library(evoFE)
# Evolve features + Bayesian tuned model
recipe <- evolve_features(
  data         = mtcars,
  target_col   = "am",
  task         = "classification",
  evaluator    = "lightgbm_mbo",  # Built-in one-liner Bayesian Optimization tuner
  generations  = 5,
  pop_size     = 8,
  holdout_frac = 0.20
)
# Inspect evolved recipe & search gap
summary(recipe)
# Predict on new data
test_features <- predict(recipe, newdata)
predictions   <- predict_model(recipe, newdata)

• CRAN: https://cran.r-project.org/package=evoFE
• GitHub: https://github.com/tanopereira/evoFE
• Vignette: vignette("evoFE")

Feedback, suggestions, and bug reports are very welcome!

I wanted to share a new method called the Entropic Scree. If you need to estimate the dimensionality of a dataset but standard tools are struggling with noise or giving crazy estimates, it might be worth your time to take a look.

Instead of evaluating linear variance, rank, or Euclidean distance like established PCA methods, this new approach evaluates a transformed mutual information metric. It’s meant to be more effective for dealing with non-linearities, mixed data types, or environments where your variables exceed your samples.

The original function and the simulations are available in R (see GitHub repo). There is also a preprint that covers the methods in detail.

Would love to hear your thoughts/experiences if you give it a try.

GitHub Repo: https://github.com/tjleestjohn/Entropic-Scree

Zenodo Preprint: https://doi.org/10.5281/zenodo.22028087

I have a database of patients who have received a cancer diagnosis, some are metastatic, some are non metastatic. This includes the date of each diagnosis.

I have a different document with the recorded PSA values and the date it was recorded on. Each patient has more than one PSA value recorded.

The two data frames share a UserID that I can use to find out which PSA values belong to which patient. I am trying to determine which PSA values pertain to metastatic patients and which pertain to non metastatic patients.

What I think I should do is for each patient determine the turning point where they moved from the non metastatic group to the metastatic group.

Then merge the two documents so that PSA values get associated with cancer stage according to whether the PSA test was done before or after the turning point.

First data frame looks like:

UserID CancerStage DateofDx

5131 non-metastatic 12/05/2023

5131 non-metastatic 16/07/2024

5131 metastatic 21/02/2026

3285 metastatic 08/07/2025

2387 non-metastatic 01/03/2025

Second data frame looks like:

UserID PSA_value DateofTest

5131 2 13/07/2023

5131 3 18/09/2024

5131 18 22/03/2026

3285 23 06/08/2025

3285 12 13/04/2026

2387 1 05/06/2025

2387 0.5 06/03/2026

Hey all, honours student here — deadline in ~2 months, hoping for some guidance on the stats approach for my project.

I have three separate experiments, each with a factorial design, testing an outcome measure over time under different treatment combinations. Design structure:

• Each experiment used 3 replicates (biological replicate units)
• Each replicate was split into treatment groups (e.g. 4 groups in one experiment, a 2×2 or 2×4 crossed design in others)
• Each treatment group within a replicate was then measured at multiple time points (e.g. 20/30/60/120 min)

Experiments as below:

• Experiment 1: 2×2 — Factor A present/absent × Condition 1/Condition 2 (e.g. two temperatures)
• Experiment 2: 2×4 — Factor A present/absent × Treatment (none / B / C / B+C)
• Experiment 3: 2×2 — control / +D / +E / +D+E (additive presence/absence)

For each experiment I need to know:

1. Whether each factor has a significant main effect
2. Whether the two factors interact
3. Which specific groups differ from each other (pairwise)

Essentially, which treatment performed best in certain categories.

Questions:

1. Given this structure (small number of replicates, two repeated/within-subject factors), is repeated-measures ANOVA appropriate, or should I be using a linear mixed model (e.g. lme4/nlme) with replicate as a random effect? What's the practical difference for a dataset this size?
2. How do I set this up correctly in R, what does the model formula look like for two within-subject factors (treatment × time) with a random effect for replicate?
3. For checking normality, should I test raw data per group, or the model residuals? What's the correct R workflow?
4. How do I check homogeneity of variance / sphericity for repeated measures in R, and how much do violations matter with only 3 replicates?
5. If assumptions are violated, what's an appropriate alternative given the small sample size (e.g. non-parametric repeated-measures test, or is a mixed model still preferred)?
6. For post-hoc pairwise comparisons (which treatments/time points differ), what's appropriate here, emmeans with a mixed model, Tukey, or something else given the repeated structure?
7. With only 3 replicates, is there anything I should be especially cautious about in terms of statistical power or overinterpreting results?

Any pointers, code snippets, or common pitfalls for this kind of repeated-measures/mixed factorial design in R would be hugely appreciated. Thanks!

I'm very new to R so might be making some very obvious mistakes, any help is appreciated! I know at least the numbers work because I manually entered them into an actual 4PL calculator.

I have this code:

fit <- nls(OD ~ D + (A - D)/(1 + (Concentration/C)^B),
           data = stand_mean,
           start = list(A = max(stand_mean$OD), D = min(stand_mean$OD), C = median(stand_mean$Concentration), B = 1))

giving this error

Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
Missing value or an infinity produced when evaluating the model

My Professor - knowing this is the only required course for the Graduate Certificate - Business Analytics - Is making us use this program and in his syllabus he states:

"This course gives students an introduction to forecasting methods using R."

"It would be ideal to have a class that teaches R programming, but this is not possible due to the MBA program structure. This class is not required to teach R programing; however, the instructor will be pleased to help students with learning R."

THEN!!!

THE Required Text - Is "Hyndman, R.J., and Athanasopoulos, G. 2021. Forecasting: Principles and practice. 3rd edition. OTexts: Melbourne, Australia. OTexts.com/fpp3."

AND ON THE FIRST DAY - WE ARE JUMPING INTO CHAPTER 3!!!

Learning R: The course introduces students to using R. The supplemental texts explain the R code used in the required text. One way to learn R programming is to run the chapters’ programs, use the supplemental texts as reference, ask questions to the instructor, and invest enough time on it. The ideal scenario would be to take an R programming class first and then take this class, but there is no time for this. Therefore, students must invest enough time into learning R programming.

The student must read a chapter a week as shown in the course outline below, run and understand the chapter R-program, and submit assignments on D2L weekly. The student is responsible for reading each assigned chapter and any additional class material. Class materials will be posted on D2L. The students can ask questions on any point that is not understood.

WHAT IN THE ABSOLUTE FUCK DID I GET MYSELF INTO?

IM ABOUT TO SAY F U TO THIS SCHOOL BECAUSE...

How are you going to say "The ideal scenario would be to take an R programming class first and then take this class, but there is no time for this."

THEN! say "The student must read a chapter a week as shown in the course outline below, run and understand the chapter R-program, and submit assignments on D2L weekly."

HOW CAN I LEARN AND UNDERSTAND R IN A WEEK WORKING FULL TIME AND A PARENT?!

I've been working on a statistical analysis framework called MVS Analyzer, and I'm interested in the statistical methodology behind it rather than promoting the software itself.

The problem I'm trying to address is fairly simple:

Suppose I have several candidate metrics for describing or detecting an effect in a dataset. Instead of choosing one based only on convention, intuition, or which one gives the most convenient result, can I empirically evaluate how each metric behaves under simulated conditions?

My current approach creates controlled null and effect scenarios and evaluates metrics using several properties:

- false-positive rate

- statistical power

- robustness to perturbations/outliers

- repeatability across runs

- stability across simulated conditions

- coverage

These properties are then combined into an MVS Score intended to help compare candidate metrics.

The framework also keeps the simulation seeds, input/configuration hashes, metric-definition versions, and analysis history so that results can be reproduced and audited.

GitHub (implementation and detailed methodology):

https://github.com/d1d2dopamine/MVS-analyzer

What I'm most interested in is whether the underlying idea makes statistical sense.

For example:

1. Is simulation-based evaluation a reasonable way to compare the practical behavior of different descriptive/inferential metrics?

2. What important properties of a metric am I missing?

3. Could combining things such as power, false-positive rate, robustness and repeatability into one score introduce misleading conclusions?

4. Are there established frameworks or literature for evaluating statistical procedures in this way that I should look at?

5. Are there obvious forms of circularity or simulation-design bias in this approach?

I'm particularly interested in criticism of the methodology. If the premise itself is flawed, I'd rather find that out now than optimize the implementation around it.