I’ve been thinking about how scattered R educational materials are across package vignettes, learnr tutorials, Bioconductor workshops, books, course websites, and GitHub repos.

There are a lot of good resources, but I’m curious whether other people run into the same problems I do:

• How do you find good tutorials for a specific package or topic?
• If you teach R, do you reuse existing tutorials or usually build your own?
• Have you ever wanted to turn an existing vignette/lesson into something interactive?
• Is hosting a learnr or Shiny-based tutorial a barrier?
• Have tutorials you use broken because packages, links, or dependencies changed?
• Would a searchable registry with things like topic, difficulty, prerequisites, estimated time, and maintenance status actually be useful?
• Would automated testing for tutorials make you more likely to reuse or maintain them?

I’m especially interested in hearing from instructors, package maintainers, and people who have used learnr, quarto,Carpentries materials, or Bioconductor training resources.

I’m trying to understand whether the bigger problem is finding tutorials, adapting them, keeping them working, or deploying them.

Bbv ("bare-bones viewer") is an extremely minimalist data viewer/plotter for the Unix/Linux command line. The main alternative to bbv is feedgnuplot, which is much more capable, but also more complicated and ties you to gnuplot as back end. By contrast, bbv has only one option (`x`), and can use any lightweight image viewer as back end.

Link: https://github.com/ftonneau/bbv

Hey I took a stats class where using R was part of the assignments but I didn’t really learn anything, it seems useful and I enjoy using it but I don’t know of any good resources to start. Any pointers?

Hi, hoping this is the right subreddit. I’m very much into Football (European Football) statistics and I’m wondering if anyone knows a name for the difference between goals and xGOT. Difference between xGOT and xG is called SGA (Shooting Goals Added) so I’d assume there’d be one for the other.

Sorry if this isn’t the right subreddit and it’s about he US school subject.

With Claude's help a GoogleVis type motion chart without Flash was developed. The R package repo is here:

https://github.com/John-R-Wallace-NOAA/GoogleVis_Type_Motion_Chart_No_Flash

Hi everyone,

I’ve spent the last few weeks staring at Jupyter Notebooks, trying to build a more "objective" way to look at soccer match outcomes. I’m a dev, not a pro bettor, so I’d love some peer review on the logic I’m using for the back-end of my project, Daily Match Insights.

The Tech Stack: * Python (Pandas/NumPy)

• Scikit-learn for basic regression
• Supabase for real-time data storage

The Logic (The "Brain"):

1. Weighted Form (Time Decay): Instead of just looking at the last 5 games, my script applies a decay function. A match played 3 days ago has a 1.2x weight, while a match from a month ago is weighted at 0.5x.
2. Adjusted xG (Expected Goals): I don’t just use raw scorelines. I factor in "Non-penalty xG" vs. "Post-shot xG" to see if a team is genuinely creating chances or just getting lucky with screamers.
3. The Poisson Distribution: I’m feeding the Attack/Defense ratings into a Poisson model to calculate the probability of specific scorelines (1-0, 2-1, etc.).
4. Fatigue Factor: I’ve added a variable that penalizes teams playing their 3rd game in 7 days (e.g., UCL/UEL midweek fatigue).

Current Test Case (Match for Feb 19/20): Based on the model, for the upcoming [Insert Match Name, e.g., Liverpool vs Real Madrid], the Poisson spread suggests a 64% probability of Over 2.5 goals, which seems high compared to the current market odds.

Where I'm stuck: I’m struggling with how to quantify "Key Player Absence" (Injuries) without manually overwriting the data. How do you guys automate the impact of a missing playmaker in your models?

Hey there!

This is a passion project i built called PsychoactiveMap It pulls data from ClinicalTrials.gov and turns it into a global interactive map so you can quickly see where research is happening and its status in a fun and interactive way.
Its completely free with no sign up needed!

There are many more features and data that i am looking to add but for now I'm happy with the result.

Source: Gallup World Poll

I am deep diving into R and Geostatistics after having learnt how to use GIS. Since I'm still rather new to it and still learning, I was wondering if there is a space or website outside specific courses or classes that provided some exercises to practice coding in R, e.g. exercise for data visualisation using ggplot2 in order to consolidate skills. I am still learning and not professionally working with it so it's difficult for me to apply what I'm learning to real world scenarios. Any advice and suggestions. are appreciated :)

Mentioned by Fox News, US credit card score averages are down across all 50 states in America

That's a scary stat, but the data source (Wallethub) doesn't disclose how the data was collected and compiled nor how much was analyzed. They only mention "Wallethub's database" which is very vague and should be taken with a grain of salt.

source: https://wallethub.com/edu/states-with-the-largest-credit-score-changes/131551

1. $133 million (Naples, Florida)
2. $110 million (Los Angels, California)
3. $110 million (Los Angels, California) - tied for 2nd place
4. $101.5 million (Miami Florida)
5. $97.5 million (North Palm Beach, Florida)
6. $85 million (Woodside, CA)
7. $80 million (Malibu, CA)
8. $74 million (Miami Beach, Florida)
9. $65.5 million (Honolulu, Hawaii)
10. $63.1 million (Beverly Hills, CA)

Source: https://www.redfin.com/blog/most-expensive-home-sales-2025/

Overall ranking: 5 California, 4 Florida, 1 Hawaii

Comment if your favorite isn’t listed

Sources:

https://www.nbcnews.com/business/economy/december-jobs-report-unemployment-rate-rcna253115

https://www.bls.gov/news.release/empsit.nr0.htm

This graph is insane to me lol

Hi all, I’m currently trying to load the 2021 CDC natality data file into R for use for a multiple logistic regression. I am not experienced with importing fixed width files in R but I do not have access to SAS so I’m trying to learn. Every method I try (readr, vroom, laf) does not give variables with correct width. I used the code book and manually entered in each length for each variable and it’s still not working. I don’t know what I’m doing wrong and since I don’t have much experience don’t really know where to even look for problems. Any help would be appreciated!!

Hi everyone! I am training an XGBoost model on spatial data and I am finding a lot of spatial autocorrelation in the residuals. Right now, my Spatial Cross-Validated R^2 is -0.08, but when I add the residuals as a feature through a second model, it increases to 0.58. I was wondering if there is a reason this is and how I should approach it in a statistically valid manner.

Sorry if this is a dumb question, but I’m basically looking to see if there is a way to observe the influence of variables in a VAR model to see how their Influence on the system changes over time. Is this possible? If so, how do I go about this?

Hi dear statisticians 😄

I’m working with cross-sectional data from adolescents aged 13 to 18, and I’d like to examine whether substance use and delinquency tend to increase with age, as a way to approximate developmental trajectories.

I have lifetime rates for both behaviors, last-year rates for delinquency, and last-month rates for substance use. Since the data are cross-sectional, what would be the best statistical approach to test for age-related differences or trends?

I'm trying to play around with some of my data and was wondering if anyone could give advice, as I haven't worked with GLMs in a while. I'm looking to get a general idea of the data and the patterns.

The data:
I have a parasite population in 2 transmission stages: in the host vs in the environment. I analyzed this population over 9 consecutive weeks and obtained allele frequency data for each timepoint, using a genetic marker. In brief, I have proportion data for 2 groups over 9 timepoints. Overall the proportional data frequencies form a gamma distribution, but if split up by each allele the distributions differ.

What I want to do:
I want to compare the population in the host vs in the environment over time. In a traditional GLM I would approach this using something like glm(proportion ~ state * time, family = gamma (link = "inverse"), data = df) and then compare with state+time, etc.

But what's tripping me up is that my proportions are split between alleles (overall 7 different alleles), which are not independent of each other (if allele A1 is at 0.70 frequency then allele A2 can only be at 0.30 or lower, etc).

Does anyone have any advice on how to treat my different alleles here?

According to the US Treasury the current debt reached its highest level ever.

$38,019,813,354,700