LabPlot: Free, open source and cross-platform Data Visualization and Analysis • Hacker News Board

Re: LabPlot: Free, open source and cross-platform Data Visualization and Analysis

#41

Post by nxobject » Sun, Aug 24, 2025, 7:21 PM UTC

SubiculumCode wrote: post #35

nxobject wrote: post #26

Earlier quoted context omitted.

I just needed to stop and say: as a biostatistician, boy do I love a beautiful complex longitudinal design: I remember my old professor asking us how at this point we would decompose into cross-sectional and longitudinal effects, Lord's paradox, etc... and I still don't fully understand Lord's paradox as well as I should.

This is a very important idea. For example, one issue with accelerated longitudinal designs, see image [1], is that while they efficiently cover a larger age range, the fixed effects of age are largely driven by cross-sectional differences between who is samples are younger and older ages. One method that can be used to test whether the pattern seen in the fixed effects represents the pattern within subjects is to de…

Oh wow, thanks for pointing me to Lesa Hoffman's work! Back in my day I learned from Fitzmaurice, Laird, and Ware, more geared towards the health sciences (i.e. clinical trial data etc.) which I wasn't too fond of. [1]

[1] https://onlinelibrary.wiley.com/doi/book/10.1002/97811195134...

Re: LabPlot: Free, open source and cross-platform Data Visualization and Analysis

#42

Post by Breza » Fri, Aug 29, 2025, 7:37 PM UTC

MostlyStable wrote: post #34

REDS1736 wrote: post #33

Earlier quoted context omitted.

Sorry for the off-topic question but would you mind to elaborate on why you can't stand ggplot? I personally haven't spent too much time with the r base functions but have come to absolutely adore ggplot for graphing and am thus very interested in learning about potential reasons to use r base plotting functions instead!

I think it's just too different from base R, and I had spent too long in base R before tidyverse/ggplot became a thing. By the time it came around, I was already good enough to do basically all my plotting without it, and having to learn an entirely new set of syntax just annoyed me. My reaction is much more emotional than rational.

I guess I'm lucky to have learned it in 2012 when base, ggplot, and lattice were all being taught at the same time. I love ggplot, but I dip into base from time to time when hist(students$age) is faster then ggplot(students, aes(x = age)) + geom_histogram()

Re: LabPlot: Free, open source and cross-platform Data Visualization and Analysis

#43

Post by Breza » Fri, Aug 29, 2025, 7:41 PM UTC

analog31 wrote: post #37

jtrueb wrote: post #7

Obviously there is a lot of work here, but I am a bit confused. If you already have lab code in Julia, Matlab, R, Python, Excel, etc., what is the motivation to use this tool? Is this hot in a specific community?

In my experience, there are people out there who don't program, or who don't feel that it's a productive way of doing things. I'm firmly in the Python camp, but recognize that my workplace has several JMP licenses, and the majority of engineers are satisfied with Excel. And I never let anybody see how long it takes me to do things. ;-) However, those people also belong to the most-of-the-world who are still leery of…

What Python libraries do you prefer? Even after doing this for years, I have trouble making anything remotely complicated in matplotlib without at least one look at the documentation.

For data viz, I'm absolutely smitten with R and ggplot. It works the same way as my brain, "OK I want to use the students dataset, specifically the age variable, I want to make a histogram, and I'd like to label the axes." You build the viz in that order, with one function call for each thought.

Re: LabPlot: Free, open source and cross-platform Data Visualization and Analysis

#44

Post by analog31 » Sat, Aug 30, 2025, 1:16 PM UTC

Breza wrote: post #43

analog31 wrote: post #37

Earlier quoted context omitted.

In my experience, there are people out there who don't program, or who don't feel that it's a productive way of doing things. I'm firmly in the Python camp, but recognize that my workplace has several JMP licenses, and the majority of engineers are satisfied with Excel. And I never let anybody see how long it takes me to do things. ;-) However, those people also belong to the most-of-the-world who are still leery of…

What Python libraries do you prefer? Even after doing this for years, I have trouble making anything remotely complicated in matplotlib without at least one look at the documentation. For data viz, I'm absolutely smitten with R and ggplot. It works the same way as my brain, "OK I want to use the students dataset, specifically the age variable, I want to make a histogram, and I'd like to label the axes." You build the…

I actually use straight matplotlib, or for quick-and-dirty, pyplot. Every notebook starts with the same boilerplate, turning on auto-reload, then numpy, pyplot, asdf. And then my own weird libraries, or those shared with colleagues. Occasionally OpenCV, pyserial, sympy, and other odds and ends.

I have a Python "wrapper" for every piece of lab equipment that I touch.

I'm a physicist, and I work on developing measurement equipment. My graphing needs tend to be simplistic, with a big factor being the ability to visualize something quickly and then plan the next step (or realize I screwed up and start over). I'm often the only reader of my graphs.

My work is all secret, so I don't publish, except an occasional patent. The graphing needs for patents are their own beast, arcane, and perhaps a bit repulsive.

I noticed your comment suggests a more "life science" interest, and I think those fields may place a heavier burden on visualization. So I wouldn't be shocked if the physical and life sciences had different graphing needs. I suspect pyplot has a closer vibe to what you're using, than straight matplotlib, but maybe not close enough. There have been attempts to wrap mpl in a ggplot-like interface, but I don't know how successfully.