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THE TIDYVERSE IN A TABLE

Dec 29th, 2020

This was my submission to the 2020 RStudio Table Contest. For many good reasons
it didn’t qualify, you can check out all those good reasons here: Winners of the
2020 RStudio Table Contest.

Some tables are beautiful. And yes, I’m talking about the stats-and-numbers kind
of tables and not the ones you get at IKEA. Some tables show carefully selected
statistics, with headers in bold and spacious yet austere design; the numbers
rounded to just the right number of decimal places.

But here we’re not going to make a beautiful table, instead we’re making a
useful table. In this tutorial, I’m going show you how to take all the
documentation, for all the functions in the tidyverse core packages, and
condense it into one single table. Why is this useful? As we’re going to use the
excellent DT package the result is going to be an interactive table that makes
it easy to search, sort, and explore the functions of the tidyverse.

Actually, let’s start with the finished table and then I’ll show you how it’s
made. Or a screenshot of it, at least. To read on and to try out the interactive
table check out my full submission here.



Read on →


IMAGE DITHERING IN R

Jan 22nd, 2019

This January I played the most intriguing computer game I’ve played in ages: The
Return of the Obra Dinn. Except for being a masterpiece of murder-mystery
storytelling it also has the most unique art-style as it only uses black and
white pixels. To pull this off Obra Dinn makes use of image dithering: the
arrangement of pixels of low color resolution to emulate the color shades in
between. Since the game was over all too quickly I thought I instead would
explore how basic image dithering can be implemented in R. If old school
graphics piques your interest, read on! There will be some grainy looking ggplot
charts at the end.



(The image above is copyright Lucas Pope and is the title screen of The Return
of the Obra Dinn)

Read on →


YET ANOTHER VISUALIZATION OF THE BAYESIAN BETA-BINOMIAL MODEL

Dec 13th, 2018

The Beta-Binomial model is the “hello world” of Bayesian statistics. That is,
it’s the first model you get to run, often before you even know what you are
doing. There are many reasons for this:

 * It only has one parameter, the underlying proportion of success, so it’s easy
   to visualize and reason about.
 * It’s easy to come up with a scenario where it can be used, for example: “What
   is the proportion of patients that will be cured by this drug?”
 * The model can be computed analytically (no need for any messy MCMC).
 * It’s relatively easy to come up with an informative prior for the underlying
   proportion.
 * Most importantly: It’s fun to see some results before diving into the theory!
   😁

That’s why I also introduced the Beta-Binomial model as the first model in my
DataCamp course Fundamentals of Bayesian Data Analysis in R and quite a lot of
people have asked me for the code I used to visualize the Beta-Binomial. Scroll
to the bottom of this post if that’s what you want, otherwise, here is how I
visualized the Beta-Binomial in my course given two successes and four failures:



Read on →


MY INTRODUCTORY COURSE ON BAYESIAN STATISTICS

Dec 12th, 2018

So, after having held workshops introducing Bayes for a couple of years now, I
finally pulled myself together and completed my DataCamp course: Fundamentals of
Bayesian Data Analysis in R! 😁



Read on →


A STAN CASE STUDY, SORT OF: THE PROBABILITY MY SON WILL BE STUNG BY A BUMBLEBEE

Aug 14th, 2017

The Stan project for statistical computation has a great collection of curated
case studies which anybody can contribute to, maybe even me, I was thinking. But
I don’t have time to worry about that right now because I’m on vacation, being
on the yearly visit to my old family home in the north of Sweden.

What I do worry about is that my son will be stung by a bumblebee. His name is
Torsten, he’s almost two years old, and he loves running around barefoot on the
big lawn. Which has its fair share of bumblebees. Maybe I should put shoes on
him so he wont step on one, but what are the chances, really.

Well, what are the chances? I guess if I only had

 * Data on the bumblebee density of the lawn.
 * Data on the size of Torsten’s feet and how many steps he takes when running
   around.
 * A reasonable Bayesian model, maybe implemented in Stan.

I could figure that out. “How hard can it be?”, I thought. And so I made an
attempt.


GETTING THE DATA

To get some data on bumblebee density I marked out a 1 m² square on a
representative part of the lawn. During the course of the day, now and then, I
counted up how many bumblebees sat in the square.



Read on →


VIDEO INTRODUCTION TO BAYESIAN DATA ANALYSIS, PART 3: HOW TO DO BAYES?

May 8th, 2017

This is the last video of a three part introduction to Bayesian data analysis
aimed at you who isn’t necessarily that well-versed in probability theory but
that do know a little bit of programming. If you haven’t watched the other parts
yet, I really recommend you do that first: Part 1 & Part 2.

This third video covers the how? of Bayesian data analysis: How to do it
efficiently and how to do it in practice. But covers is really a big word,
briefly introduces is really more appropriate. Along the way I will then briefly
introduce Markov chain Monte Carlo, parameter spaces and the computational
framework Stan:

Read on →


VIDEO INTRODUCTION TO BAYESIAN DATA ANALYSIS, PART 2: WHY USE BAYES?

Feb 27th, 2017

This is video two of a three part introduction to Bayesian data analysis aimed
at you who isn’t necessarily that well-versed in probability theory but that do
know a little bit of programming. If you haven’t watched part one yet, I really
recommend you do that first, here it is. This second video covers the why? of
Bayesian data analysis: Why (and when) use it instead of some other method of
analyzing data?

Read on →


VIDEO INTRODUCTION TO BAYESIAN DATA ANALYSIS, PART 1: WHAT IS BAYES?

Feb 13th, 2017

This is video one of a three part introduction to Bayesian data analysis aimed
at you who isn’t necessarily that well-versed in probability theory but that do
know a little bit of programming. I gave a version of this tutorial at the UseR
2015 conference, but I didn’t get around doing a screencast of it. Until now,
that is! I should warn you that this tutorial is quite handwavey (but it’s also
pretty short), and if you want a more rigorous video tutorial I can really
recommend Richard McElreath’s YouTube lectures.

This first video covers the what? of Bayesian data analysis with part two and
three covering the why? and the how?. I expect to be able to record part two and
three over the next couple of weeks but, for now, here is part one:

Read on →


BEGINNERS EXERCISE: BAYESIAN COMPUTATION WITH STAN AND FARMER JÖNS

Jan 15th, 2017

Over the last two years I’ve occasionally been giving a very basic tutorial to
Bayesian statistics using R and Stan. At the end of the tutorial I hand out an
exercise for those that want to flex their newly acquired skills. I call this
exercise Bayesian computation with Stan and Farmer Jöns and it’s pretty cool!
Now, it’s not cool because of me, but because the expressiveness of Stan allowed
me to write a small number of data analytic questions that quickly takes you
from running a simple binomial model up to running a linear regression.
Throughout the exercise you work with the same model code and each question just
requires you to make a minimal change to this code, yet you will cover most
models taught in a basic statistics course! Well, briefly at least… :) If you
want to try out this exercise yourself, or use it for some other purpose, you
can find it here:

Beginners Exercise: Bayesian computation with Stan and Farmer Jöns (R-markdown
source)
Solutions to Bayesian computation with Stan and Farmer Jöns (R-markdown source)

My friend and colleague Christophe Carvenius also helped me translate this
exercise into Python:

Python Beginners Exercise: Bayesian computation with Stan and Farmer Jöns
Python Solutions to Bayesian computation with Stan and Farmer Jöns

Now, this exercise would surely have been better if I’d used real data, but
unfortunately I couldn’t find enough datasets related to cows… Finally, here is
a depiction of farmer Jöns and his two lazy siblings by the great master
Hokusai.




A FUN GASTRONOMICAL DATASET: WHAT’S ON THE MENU?

Sep 21st, 2016

I just found a fun food themed dataset that I’d never heard about and that I
thought I’d share. It’s from a project called What’s on the menu where the New
York Public Library has crowdsourced a digitization of their collection of
historical restaurant menus. The collection stretches all the way back to the
19th century and well into the 1990’s, and on the home page it is stated that
there are “1,332,271 dishes transcribed from 17,545 menus”. Here is one of those
menus, from a turn of the (old) century Chinese-American restaurant:



The data is freely available in csv format (yay!) and here I ‘ll just show how
to the get the data into R and I’ll use it to plot the popularity of some foods
over time.

Read on →
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