I have to merge two datasets that include onsets and offsets of two events - when person A did X and when person B did y. I want to merge them on the onsets and offsets per pairs to see what they were doing at the same times.

DF1

ID | X_onset | X_offset | X_activity

001 | 120 | 135 | watering plants

001 | 137 | 143 | reading

DF2

ID | X_onset | X_offset | X_activity

001 | 139 | 150 | talking

001 | 170 | 189 | watching TV

Only the ones that temporally overlap should be in the merged dataset.

Originally the app would crash immediately, with "Main window closed unexpectedly" showing up in rdesktop.log, plus an IpcError: getStartupErrorInfo about setting innerHTML on null. There's also an older, possibly unrelated error in rsession log about discoverR failing with "system error 2 (No such file or directory)" but that timestamp is weeks stale and doesn't update on new launch attempts, so it might not be the real issue.
Every launch also throws a GLib-GIO-CRITICAL warning about g_dbus_proxy_new and G_IS_DBUS_CONNECTION failing, but that seems to be harmless noise unrelated to the crash.
What I've tried so far: confirmed R is installed and on PATH (which R, R --version, R RHOME all work fine). Set RSTUDIO_WHICH_R=/usr/bin/R, no change. Ran rstudio --verbose-init, got nothing extra in the output besides the GLib warning. Ran rstudio --disable-gpu and that actually changed something — the window now opens and stays open instead of crashing, but it's still not functioning correctly once open.
Has anyone run into this with RStudio Desktop 2026.06.0 on Ubuntu, either the Electron/GPU crash or the discoverR error? Trying to figure out if this is a known bug with this release or something specific to my setup.

I keep hitting the same decision: a one-off analysis is fine as a Quarto report, but the

moment stakeholders start asking "what if we change X?", it's usually time for a Shiny

app so they can explore it themselves — on a reproducible R backbone so the app and the

analysis don't drift apart.

Some interactive R examples I've collected: https://rverseanalytics.com/

Where do you draw the line between report and app?

also is this too much for an econ major? total newbie btw
if not then what courses might you suggest?

https://www.coursera.org/specializations/r

Hi! I've got some experience with R, but haven't dealt with a dataset like the one I'm working on at the moment before. I've got long form data consisting of subject ID, questionnaire name, questionnaire score & date questionnaire was answered. The address multiple responses to 2 different questionnaires over time. I need to summarise the scores for specific time windows, for example, how many people scored 1 or 0 in Qu1 and less than 3 in Qu2 by week 4, week 12, week 26.

Probably a silly question, how do I do that with this data? I've added windows but I get multiple responses in each window when I pivot the data to wide form for analysis. How do I select the lowest score for each questionnaire, occurring at the same date as each other within the window?

Sorry for the essay and their very much.

EDIT: SOLVED

I figured before importing my data, I'd follow along a tutorial to get some basics down at least. I added up numbers, worked. Then tried to assign values to variables, and the programme just crashes every time as seen in my screenshot. Tried reinstalling R-studio, tried deleting some of the folders which I found might be a solution, but it keeps happening.

I'm just a beginner and maybe there's a very simple solution to this, but either way I'd appreciate any pointers on how to solve this, as for now I see no way to even work with this tool like this.

I am using R studio and tried different themes such as Dracula... etc.. . The thing is I don't get a feel similar to VScodium or Jupyter. Most themes are messing with graphs pane or the fonts colour palletes are not good. I need suggestions for both dark and light themes for latest versions of R studio.

I am also curious about alternatives for R studio, which are similar to Jupyterlab or something that has clean ui.

I put together a free beginner R course: 15 short video lessons that go from using R as a calculator to data frames, reading a CSV, writing functions, packages, dplyr and the pipe.

What might make it useful vs. the usual videos: each lesson pairs the screencast with the exact code, a downloadable .R script, and a live webR console right on the page — so you can edit and run everything in the browser, nothing to install. All lessons have English subtitles.

The videos are going up on YouTube, and the full course with the interactive consoles is on the site:

- Course: https://rverseanalytics.com/courses/r-foundations.html

- YouTube: https://youtube.com/@rverseanalytics

It's all free and open. Happy to hear feedback on the lessons — and what topics you'd want covered next.

Curious what others prefer. I’ve actually been trending towards base R graphics lately.

What are some good resources to learn Financial Risk Analysis techniques while at the same time learn how to apply the same in R.

Any specific libraries, books, tutorials that can help a someone like me (knowing nothing in Financial Risk Analysis but competent in R) to learn the aforementioned.

Thanks!

I'm trying to restart Rstudio on my new laptop the different options are greyed out. I don't know how to get it to run.

If I click the 64-bit version I get this error:

An error occurred while attempting to load the selected version of R. Please select a different R installation.

But it won't let me click the 32-bit version and I don't know what to scroll down otherwise as it's empty.

I tried restarting my laptop and reinstalling R.

Im in my first year of my masters and have all of my data now. Im doing wildlife biology and working with 20 years of GPS collar data (looking at movement/migration).

Im overwhelmed with where to start. I know the basics of R like uploading data, adding new columns, deleting columns, changing names... basic things like that.

But what is a typical workflow?

Data cleaning ---> data prep (getting your data set ready for models?) --> statically analysis/running models?

I guess I just get confused on the most efficient workflow.

What has yours been? How have you organized your scripts? Do you use R projects?

I guess im looking for any tips for someone who is in the beginning of their analysis and doesn't want to get stuck wasting time on things that can be quick to figure out.

Thank you!

i recently did an R project analysing the LG ipo. it's a very basic project since I'm only familiar with base R. But i don't know how exactly a project is supposed to be made. do i write in the first person? do i explain my code? i have formatting and wording doubts.

If anyone can share a project/link to show me what these kind of projects generally look like I'd be truly very grateful.

Thank you!

i’ve been learning R for about three months using R4DS textbook, I covered a good portion but i still don’t feel like i’ve mastered it, at least not to the level where i can add it to my resume. what’s the best way to practice R and what are the most important skills/packages that i should focus on. for context, i’m learning it for my masters thesis and for a potential PhD in a few months.

Can y'all help me with the best packages for data visualization and statistical analysis? thank you.

Hey guys, is there any free online courses that offer RStudio lesson? I wanna learn like from the basic. Goin on for my masters degree and I cannot just copy and paste chunks like what I did during my bachelor years. (Ps: I really dont have any coding skills, I've learned R coz my friend wants me to go with her to a "statistical workshop")

I write both R and Python in the same week most weeks, and I got tired of not having seaborn in R for rapid prototyping.

So over a few weekends I ported seaborn to R.

reaborn keeps seaborn's function names, argument names, and defaults. Load it and you get the sns. prefixed aliases plus True, False and None bound to their R equivalents, so pasted seaborn mostly just runs.

The part I'm most excited to share is that everything comes back as a ggplot:

library(reaborn)
penguins <- load_dataset("penguins")

sns.scatterplot(data = penguins, x = "bill_length_mm", y = "bill_depth_mm", hue = "species") +
  ggplot2::facet_wrap(~island)

...which isn't as straightforward to do on the Python side.

v1.0.1 is live on CRAN. Would love to get more folks using it (and breaking it) so I can continue improving it!

install.packages("reaborn")

Docs and a side-by-side gallery: https://reaborn.org

If a plot doesn't match seaborn, please open an issue on GitHub (https://github.com/shawntz/reaborn) 🙏

(My package. BSD-3, same as seaborn. Fun fact: the creator of seaborn and I received our PhD's from the same lab 🚀)

Hi guys,

Been trying to visualize some data using ggplot2, but some of my boxplots look weird. Is this normal? Compare the two plots in the middle to the other two.

TIA!

Edit: just decided to use the regular bar graphs as someone suggested and everything now looks cool. Thanks, y’all!

I'm attempting to deploy a Shiny app to Posit Connect Cloud and running into some problems. Everything runs fine locally, but one package I'm using (terra) is being built from source on the cloud platform and the build is failing. Part of the error says that a common cause for failed builds is insufficient memory, which is possible since I'm on the free tier.

My questions: I know that locally I could specify binary packages and not have to build, but I don't know if that's possible with Posit Connect? Do I need to know what OS the server is running in order to specify binaries? If it is a memory issue, do packages in Connect Cloud work like local packages where it'll only have to build from source initially and at upgrades?

Just curious about everyone's thoughts on the comparison between Positron vs RStudio! I have had no reason to switch, and I only see people complain about all the bugs they encounter with Positron.