I'll give you a quick rundown on creating horizontal boxplots in R using both base R and ggplot2. We'll work with the "palmerpenguins" dataset to keep things interesting!
Reminder that optimizations which work wonders on small amounts of data may not work on large amounts, and vice versa. #dataanalysis#datascience#sql#database
@jessie is a lover of #languages and helps run #CommonVoice, @mozilla 's open #voice#data set, which now supports over 100 languages. She also teaches #WebDev and loves #hiking. She's awesome you should follow her 🇬🇧
That's all for now, please do share your own lists so we can create deeper connections, and a tightly-connected community here
I'm reminded here of @maryrobinette's short story - "Red Rockets" - "She built something better than fireworks. She built community."
Don't think it isn't relevant to you if you don't live there, because it probably is. It certainly reflects trends many people have posted about in the #USA and the #UK, and I would bet it applies elsewhere, as well.
The #supermarket store chains use #marketing tricks of on-site #discounts, #coupons, and frequent shopper reward schemes, making it nearly impossible to know the real prices of items over time, and allowing companies to creep prices upwards unchecked. But this #DataAnalysis method clarifies how so many corporate profits increased so much post-Covid.
Take the time to read the whole thread. It's fascinating-- and eye opening.
Bon qui en #suisse s’y connaît assez en #dataanalysis pour répertorier les #prix des grandes enseignes.
Ce type l’a fait pour l’Autriche et il a vu des trucs pas joli joli. Il partage ce qu’il a construit pour nous aider. Je vous encourage à lit son thread!
Ever wondered how to compare the distributions of two variables simultaneously? Look no further! In this post, we'll dive into the world of dual-variable histograms using R, a go-to language for data analysis and visualization.
Ready to embark on your own dual-variable histogram journey? The key to effective data visualization is in your hands. 🚀
Two #Coding questions, from restarting #Python after doing mostly Matlab for a while.
I really liked Tables in Matlab - what’s the best (fastest, simplest) equivalent of it in Python nowadays? #Pandas?
with Matlab you can use ‘webread’ to one-line load the contents of a public google spreadsheet, as a table - very cool! What’s the simplest equivalent in Python?
Morning! I’ve been into town to get fresh bread, had breakfast and I’m ready for work. All four of us are WFH today, it’s going to be hard to keep out of each other’s way. More #DataAnalysis for me, I think I’ll work on category of offence and how that correlates with participants’ scoring of satisfaction with different areas of their life #criminology. Oh wait, Miss Cinnamon has just arrived and says that we must have cuddle first :blobcatreach: Have a great day everyone!
I've been trying to evaluate which is the better statistical model to analyze my data: an example of data that I could have to analyze is the firing rate of a neuron recorded in 60 trials belonging to two conditions (30 for the Go and 30 for the NoGo). The point of interest would be, for example, the effect of two different task epochs(appearance of cue and appearance of object) on modulating the firing rate across the two conditions.
I was wondering whether this reasoning, beyond what concerns the general adequacy of ANOVA ( which I would use for simplicity at the moment) is correct or not. #statistics#ANOVA#dataanalysis#neuroscience
✅ Gain a deeper understanding of mathematical functions.
✅ Visualize complex concepts with ease.
✅ Explore the versatility of R programming.
✅ Enhance your data analysis and visualization skills.
Compare vectors with ease using pmax(...). Pass multiple vectors, and voila! You have the element-wise maximum. With the optional na.rm parameter, handling missing values becomes a breeze.
pmin() 📉
Meet pmin(...), your shortcut to element-wise minimum computation. Similar to pmax(), but for minimum values! Clean and simple.
Imagine you have a bunch of data points and you want to know how many belong to different categories. This is where grouped counting comes in. We've got three fantastic methods for you to explore, each with its own flair: aggregate(), dplyr, and data.table.