https://i.redd.it/2zd8qeolt8bh1.gif

I posted v1.5 of ggsketch here a while back. Short version if you missed it: the sketchy hand-drawn look as real ggplot2 geoms - pure R, no JavaScript, no browser, clean PDF/SVG export, and all the wobble is seeded so plots stay reproducible.

2.0 ended up much bigger than I intended.

The main change is that lines now have a medium: pen, ink, brush, pencil, charcoal, chalk, marker, highlighter, crayon and a few others, 13 in all. Set it as a constant or map it with aes(medium = ) like anything else. Getting there meant making strokes variable-width, which grid won't do with a polyline, so each stroke is now a filled ribbon built around a re-roughened centreline. That's what lets ink pool at the end of a stroke and a brush swell through the middle.

I also fell down two rabbit holes. geom_sketch_engrave() shades by hatch density the way old engravings build shadow - the relief map in the images is just volcano. And there's a watercolor fill now: the violins in the images are the exact example from my last post with fill_style = "watercolor" added.

Beyond that: a pile of new chart types (waterfall, gantt, calendar heatmap, treemap, chord, radar, dendrogram, and more - the geom count is at 89, which is getting silly), repelled labels with hand-drawn leader lines, and animate_sketch(), which either "boils" a plot or draws it on stroke by stroke. Both GIFs attached are a single function call.

Same caveat as last time: I build this working with AI, with me driving the design and checking the behaviour.

The example from last post, one argument later:

library(ggplot2)
library(ggsketch)

ggplot(mpg, aes(class, hwy, fill = class)) +
  geom_sketch_violin(fill_style = "watercolor", show.legend = FALSE, seed = 1) +
  scale_fill_sketch() +
  labs(title = "Highway mpg by class") +
  theme_sketch(rough_frame = TRUE)

2.0.0 is submitted to CRAN. Until it lands:

pak::pak("orijitghosh/ggsketch")

Gallery of every geom: https://orijitghosh.github.io/ggsketch/articles/gallery.html

Docs: https://orijitghosh.github.io/ggsketch/

As before, feedback is very welcome - especially if the API feels off somewhere, or there's a medium or chart type you'd want. And if you make something with it, I'd love to see it.

Hello everyone I have been assigned the work of formulating a proposal for a course on advance statistics , I was hoping if you all had any pointers in what should it cover and what not,

My objective is to make it in a manner that it doesn't get too tough and is rather accessible and easy to comprehend for people from all walks of life, but at the same time it also covers enough and should be made keeping in mind that it should make the learner more employable or more practically skilled than they were before it. Any help or advice would be greatly appreciated

Why that when I start Positron, It takes more than a minute for the R session to start. While when I open R from the windows terminal or the Rgui it starts instantly?

Windows 11, R 4.6.1, latest Positron version

i've been building some simple medical dashboards in shiny.... mostly for filtering data and making interactive plots. I’m not a web developer, but I’m trying to slowly understand how better data visualization tools work together

recently saw a developer influencer talking about D3.js, HTML, CSS, and JavaScript, and got me wondering bout it

guys who use Shiny: when does D3.js actually become useful?

is it mainly for making very custom visuals that normal R packages can’t handle well? Or is it usually too much work unless you are building a serious web app? not gonna replace my r workflow but i’m just wondering whether D3.js is useful

Would love to hear simple examples of when D3 was worth it, and when it was not.

My work laptop (Intel Ultra 7 268V, 32GB RAM) is hitting its ceiling running a furrr pipeline — RAM maxes out while CPU is only at ~50%, sometimes it crashes at the end. Scaling up soon so need a real solution.

Torn between two options:

1. Azure VM (E16s_v5, 16 vCPU / 128GB RAM) — pay per hour, deallocate when done, data syncs via OneDrive. Worried about complicated initial setup and whether pick_workers() behaves sanely against availableCores() on a VM.
2. Dedicated workstation (9950X, 64GB DDR5) — build here. Went AMD specifically because furrr uses a synchronization barrier and Intel's hybrid P/E core architecture creates straggler workers. 128 GB RAM would have been nice, but the prices are currently insane.

Leaning toward the desktop for now but the VM scales better as data size grows. Curious if anyone's running furrr workloads in either setup and what your worker count / RAM usage looks like in practice. If there are better alternatives, please share them as well.

Hi everyone, more stats puzzles I can't quite figure out because of conflicting information I've read.

Essentially as the title says, I have a large sample size and I am creating 2x2 contigency tables where one cell often has counts <5 or even 0 sometimes. My understanding is that I be running Fishers on these tables right?

I wrote a follow-up article after my previous post about benchmarking a Shiny dashboard pipeline for blog analytics.

The previous article was somewhat polarizing. Most feedback was positive, but some people disagreed with the benchmarking methodology. That criticism was useful, so I tried to make this new benchmark more transparent and reproducible.

The pipeline is still based on my real blog analytics use case:

• read NGINX TSV logs
• filter bot traffic
• apply time-window filters
• do ASN / Geo enrichment
• compute read-time metrics

This time I benchmarked 6 pipeline configurations:

• readr::read_tsv() + dplyr
• vroom::vroom() + dplyr
• fread() + dplyr
• readr::read_tsv() + data.table
• vroom::vroom() + data.table
• fread() + data.table

The benchmark uses 20 generated log files, from 1× to 20× the original size, with increasing timestamp and IP cardinality instead of simply appending the same file repeatedly.

Methodology:

• 20 log files, from ~557k rows to ~11.1M rows

• 10 consecutive runs per file and per configuration (we will distinguish the cold-starts from the others runs)

• execution time measured per pipeline step with proc.time(), so elapsed, user, and system time recorded

• memory tracked with gc() counters: current/max NCells and VCells (more on that in the article)

A few things I found interesting:

• fread + data.table is still the most predictable path when the pipeline eventually works on fully materialized data.

• readr + dplyr is closer than I expected in several parts of the pipeline.

• data.table becomes more clearly advantageous in grouped / index-like filtering steps.

• vroom + dplyr is very interesting: it looks extremely cheap at ingestion because columns stay lazy / ALTREP-backed.

But vroom does not make parsing free; it moves the cost to later operations that force materialization.

Nevertheless, in this specific pipeline, that can still be a big memory win (and elapsed_time not dramatic), because the dataframe is filtered before all columns are fully materialized so later materialization happens on a smaller subset compared to the other configurations.

• gc()-based memory results are useful, but they are not perfect per-operation allocation measurements because the R process is reused across reloads.

Article:

https://julienlargetpiet.tech/articles/where-r-data-pipelines-pay-their-cost-data-table-dplyr-fread-readr-and-vroom.html

I would be curious to hear how people here would improve the methodology further.

(PS: Thanks for the comments! It’s pretty late here, so I’m going to sleep now. I’ll come back and answer questions in around 8 hours.)

I have spent a considerable amount of time debugging other package managers and the time has come to create my own!

Now I can tell you of the cool features I have like:

• blazingly fast dependency resolution and installs
• a pinned lockfile
• optional system dependency management (with lockfile support!)
• recorded R version
• virtual environments
• native support for private repositories

but let's be honest, that's the bare minimum.

My thesis is that all your issues with package installs are because of CRAN.

You see every package manager uses PACKAGES the index of the latest versions of all available packages and what dependencies they need. Whenever you install a package, R or any other package manager retrieves the index and checks what transitive dependencies you might need.

You might ask what would happen if the package you wanted said it required dplyr less than 1.0.0 because according to SemVer it contains a breaking change. Well now dependency resolution get's spotty. rv and uvr fail completely, renv falls back to crandb, an additional service most private repositories don't implement.

Thankfully you will never see this problem as no one in the R ecosystem actually sets upper bounds in their package dependencies. They all rely on CRAN testing the new package with all its dependencies. I don't think that's the right way to handle breaking changes, especially if you have private packages that don't get tested.

I made a complete solution to the problem.

rrepo.org hosts a public package repository mirroring CRAN in it's contents but with a completely revised api structure. It allows listing packages, and retrieving DESCRIPTION files directly. It also supports private repositories

rpx defaults to the rrepo CRAN mirror. It also set's version boundaries of current < x < next major by default so you don't have to think about it! For dependency resolution it uses pubgrub so the new but expected resolution failures always tell you exactly how to fix them. I also reluctantly support CRAN-like url's for compatibility purposes, though with limited usability due to its design.

I'd love to hear what the community thinks of my approach. Please try it and let me know how I can make it even better!

Hi everyone,

I'm currently using Statistical Computing with R (2nd Edition) and have been trying to find solutions to the exercises.

https://preview.redd.it/05yb6xp82h9h1.png?width=818&format=png&auto=webp&s=6bdac598040da3ca1b8a1a7507bb04bf3ecc51a2

I managed to find what appears to be an instructor's solutions manual online, but the exercise numbers and questions don't seem to match the ones in the 2nd edition textbook. My guess is that the manual might be for an earlier edition, or perhaps an incomplete version.

I'm wondering if a complete instructor's solutions manual exists for the 2nd edition, or if anyone knows where the exercise solutions can be found.

I've searched around quite a bit but haven't had much luck so far. If any former students, instructors, or anyone familiar with this book could point me in the right direction, I'd really appreciate it.

Thanks in advance!

We have a shiny server where we can deploy our shiny apps, this has a system/default library path (/usr/lib64/R/library) which is only writable for the admins, not me.

I use my own library paths for the apps I make, setting them using .libPaths()

However, when accessing the shiny app through the browser it needs (and loads) the shiny package residing in the system path before getting to my .libPaths() method. This on it's turn loads rlang but the rlang version is too old for one of my packages.

So once my packages start loading (using the path I set using .libPAths()) I get an error:

Error: package or namespace load failed for ‘ggplot2’ in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]):
 namespace ‘rlang’ 1.1.2 is already loaded, but >= 1.1.7 is required

Execution halted

I have tried reloading and also detaching (unloading) the old package, but this gives other issue due to the package being used by shiny (and others)

Error in detach(c("ellipsis", "lifecycle", "htmltools", "shiny"), unload = T) :
  invalid 'name' argument

Calls: <Anonymous> ... sourceUTF8 -> eval -> eval -> ..stacktraceon.. -> detach

Execution halted

Is there something I can do about this? Or does the admin really need to update the system wide packages.

Reason for using a library folder per shiny app is to prevent conflicts so I would really like to be able to somehow force it that way instead of needing to install/update system wide packages.

If I manually launch it from the terminal giving the .libPaths() command as an extra argument it seems to launch fine (can't access it through the browser though) but I can't do this when accessing it through the browser:

R -e ".libPaths('/srv/shiny-server/library/myapp/'); shiny::runApp('myapp')"

Any ideas?

I'm using the Pedtools package in R to recreate pedigree charts from a set of handmade records for analysis and so I can generate chart images at different resolutions, colors, etc. I'm running into an issue where I want to make a pedigree with individuals who are married without kids and Pedtools locks out any individual that doesn't contribute to the family genetically.

I can see why this design choice makes sense for actual analysis of genetics and relatedness, but I've been specifically asked to illustrate the prevalence of a condition while including literally all members of the family where records are available, including those without kids.

Is there a different R package I should use for this task or is there a way to override the Pedtools plotting method that I missed?