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Illustration by the author

As an information designer, I’m charged with summarizing data. But even the simplest of questions, like “How big is a typical case?” presents choices about what to do; about what kind of summary to use. An “average” is supposed to describe something like a typical case, or the “central tendency” of the data. But there are many kinds of averages, as you might know. Here I’ll give a quick overview of two familiar averages, median and arithmetic mean, and compare them to a third, the geometric mean — which I think should get a lot more use than it does.


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A Generic Class System for Fantasy Games

Flexibility with constraints. That’s what I find fun and to generate the most creative ideas. So I’ve created a generic system for fantasy classes. (Just remember, half the fun of old D&D games was making your character!)

Each character has three slots to use, assigning each a class archetype from the nine below. How you interpret these combinations is where the fun comes in. There are a lot of possibilities, and I’ve provided class specializations to shade each selection as well (each slot gets its own specialization). …


A First Lesson in R Commands For the Non-Programmer

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Photo by minkewink on Pixabay

If you haven’t done a lot of programming, learning R can be pretty intimidating.

But it’s easier if you focus on fundamentals, and slowly build up your skills through practice. Here I’ll give a short lesson on the most basic things you can do in R.

This post is adapted from my online course So You Need to Learn R. It assumes you have a copy of RStudio installed, which is free for individuals.

Let’s start by looking at the very basics of how we enter commands in R, to tell R what we want to do. …


So Students Can Face Real-World Data Problems

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Photo by Mikael Kristenson on Unsplash

Last year I started teaching a six-week R programming course at a university, and I have another available online on Udemy, So You Need to Learn R. I thought long and hard about how to teach R. Here’s what I came up with.

Beginners

I wanted a course that beginners could take. Most people learning R via a course aren’t computer scientists or experienced hackers, but doctoral students in the sciences, analysts who currently use Excel for their work, or those just starting to pursue career in data science. They might have basic coding experience, but maybe on shaky foundations.

(I…


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This isn’t me of course, but it could have been in a very different reality. (CC0)

Let me tell you about how to succeed. Obviously, I haven’t yet done this myself, or I’d have better things to do than write about it. Unless you pay me — my speaking rates are on the high end of very affordable, I assure you. But I digress. What was I talking about? Success! Because what else is there in life, or at least on Medium, anyway?

This particular brand of success will give you ten orders of magnitude better results than your current brand. 99.9% of all successful people do it. I have, of course, defined success to be…


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Photo by Charlie/SANY0012 (CC-SA 2.0)

You find your data, load it, model it — and get garbage out. Must have been garbage in, as they say. Of course, you made sure to have a nice rectangular files with consistently named columns, and you deleted the random comments people typed into the Excel document. So what gives?

Here are six common, real-world data irregularities you will have to deal with sooner or later.

1. More Rows Than You Expected

One expects one record per row. And in 95% of a file, that’s what you have. But then suddenly a join gives a warning, and you realize there are extras. Why? …


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Photo by Marco Verch, CC 2.0

There is now a major dialect of R, loudly proclaimed and apparently in the ascendant: the Tidyverse, promulgated by RStudio and largely the effort of one man, Hadley Wickham. Should we adopt it? Should students learn it? Is even having R dialects a good idea?

I have some strong opinions on this, being someone who’s used R extensively in academic and professional contexts — for everything from scientific research and simulation modeling to custom visualization and predictive modeling— and who now teaches a course in R.

Mostly, I am skeptical of this “tidy” fad: it does not sit well with…


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Or: How To Annoy Your Data Analyst/Scientist, Even If That’s You

At least half of data analytics work is cleaning, wrangling, and tidying — kind of like being a parent, actually. Just storing data in a usable form can make things go much smoother later, when you actually need to get real work done. Getting the basics wrong, on the other hand, can cause a lot of headaches.

Don’t do these things:

Don’t: Combine data types in one column

Most commonly seen as: “51 (22%)”. Stop. Make a second column for percentages, or put them in another table, or just calculate them on the fly when you need them. …


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Nuclear fuel rods | Foro Nuclear

Although all paid work involves toil and difficulty, it would be nice if we didn’t hate our jobs; if our strengths aligned somewhat with what we were doing; if we had something of the right personality to succeed there — or at least not fail.

Often, we focus too much on strengths, and should focus more on weaknesses: don’t go into a field that hinges critically on something you hate or are bad at, even if you love some other aspect of it. …


What pay increase do you get for putting “UX” on the top of your resume? Will you gain much by becoming “senior”? What about transitioning to a director role? Based on data from payscale.com, I made this chart to show designers’ median pay, based on title:

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Of course, the humble old “Graphic Designer” is the last in the pack. Making your designs move grants you an extra $7k per year, while making them “visual” commands a good $22k bonus. Interfaces, interactions, and experiences are better still, but not as good as products. …

Jasper McChesney

Data, graphics, games. So You Need to Learn R.

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