Jonas’ original blog uses R programming to visually show how the tests work, what the linear models look like, and how different approaches result in the same statistics.
Another pearl of a resource on Twitter is this thread by Madison on 10 of fundamentalal concepts of Javascript — and programming in general for that matter.
For your convience, I copied the links below. Just click them to browse to the resource and learn more about the concept
If you're learning JavaScript, you've likely heard people tell you how important it is to learn the fundamentals.
But what are they? And where do you learn them?
Here's a list of JavaScript fundamentals and my favorite free resources for learning them. 👇
In most (observational) research papers you read, you will probably run into a correlation matrix. Often it looks something like this:
In Social Sciences, like Psychology, researchers like to denote the statistical significance levels of the correlation coefficients, often using asterisks (i.e., *). Then the table will look more like this:
Regardless of my personal preferences and opinions, I had to make many of these tables for the scientific (non-)publications of my Ph.D..
I remember that, when I first started using R, I found it quite difficult to generate these correlation matrices automatically.
Yes, there is the cor function, but it does not include significance levels.
Then there the (in)famous Hmisc package, with its rcorr function. But this tool provides a whole new range of issues.
What’s this storage.mode, and what are we trying to coerce again?
Soon you figure out that Hmisc::rcorr only takes in matrices (thus with only numeric values). Hurray, now you can run a correlation analysis on your dataframe, you think…
Yet, the output is all but publication-ready!
You wanted one correlation matrix, but now you have two… Double the trouble?
[UPDATED] To spare future scholars the struggle of the early day R programming, Laura Lambert and I created an R package corrtable, which includes the helpful function correlation_matrix.
This correlation_matrix takes in a dataframe, selects only the numeric (and boolean/logical) columns, calculates the correlation coefficients and p-values, and outputs a fully formatted publication-ready correlation matrix!
For instance, you can use only 2 decimals. You can focus on the lower triangle (as the lower and upper triangle values are identical). And you can drop the diagonal values:
Or maybe you are interested in a different type of correlation coefficients, and not so much in significance levels:
Cascading Stylesheets — or CSS — is the first technology you should start learning after HTML. While HTML is used to define the structure and semantics of your content, CSS is used to style it and lay it out. For example, you can use CSS to alter the font, color, size, and spacing of your content, split it into multiple columns, or add animations and other decorative features.
I was personally encoutered CSS in multiple stages of my Data Science career:
When I started using (R) markdown (see here, or here), I could present my data science projects as HTML pages, styled through CSS.
When I got more acustomed to building web applications (e.g., Shiny) on top of my data science models, I had to use CSS to build more beautiful dashboard layouts.
When I was scraping data from Ebay, Amazon, WordPress, and Goodreads, my prior experiences with CSS & HTML helped greatly to identify and interpret the elements when you look under the hood of a webpage (try pressing CTRL + SHIFT + C).
I know others agree with me when I say that the small investment in learning the basics behind HTML & CSS pay off big time:
ok listen……. i finally took a few hours to learn some CSS basics and big time recommend to any and all #rstats people who have always felt absolutely clueless looking up CSS stuff on stack overflow
I read that Mozilla offers some great tutorials for those interested in learning more about “the web”, so here are some quicklinks to their free tutorials:
Kevin Markham shares his tips and tricks for the most common data handling tasks on twitter. He compiled the top 100 in this one amazing overview page. Find the hyperlinks to specific sections below!
🐼🤹♂️ pandas trick:
Want to plot a DataFrame? It's as easy as: df.plot(kind='…')
You can use: line 📈 bar 📊 barh hist box 📦 kde area scatter hexbin pie 🥧
paulvanderlaken.com 