What happened in the last two months? | Next - Issue #37

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Harshvardhan
Harshvardhan
Hi there,
One thing that did happen was that Next gained many subscribers. Thanks a lot to all of you; I’m grateful I could add value to your R journey. To recall: every eighth edition, I recall the top stories from the last seven editions of the newsletter.
Read on for the best nuggets!

Five Stories
Street maps of cities are beautiful — even though we never actually use them these days. They are an excellent gift to your friend moving to a new city. This tutorial teaches you how to build such maps using ggplot2. You can include highways, streets and even rivers. The tutorial is so simple that I spent my weekend exploring maps of different cities worldwide.
The mapping is based on data from the OpenStreetMaps package in R. Check out the tutorial and build the next decorative piece for your living room!
Sir David Spiegelhalter is a British statistician who, between 2007 and 2018, was the Professor of the Public Understanding of Risk at Cambridge University (what a cool title!). In this creative video, he discusses how little we understand about general risks.
Fun fact: When our “prize” is death, and its probability is tiny, a different scale was proposed in 1979 by R.A. Howard to convey the chance of dying. A micromort (MM) is a one-in-a-million probability of death.
3. R Screencasts
Greg Wilson once said:
Just before I left the University of Toronto, I asked several people to record their desktops while solving a small data analysis problem. The result shook me: the times ranged from just under four minutes to just under 38 minutes, and I would not have been able to tell which was which from the code they produced.
Screencasts are an excellent way to learn to code. Unlike following textual mediums, they allow viewers to understand how good coders think and arrive at solutions: what broken solutions they try till something sticks.
This website consists of a host of screencasts by David Robinson, accompanied by their codes and datasets. It is an amazing resource, all available for free. Dive in!
Last week (#32), I wrote about using APIs in R. Specifically, I talked about Radlibrary and APIs for Social Scientists. Today, I am listing a (very long) list of free APIs that we can use for software development. Some notable APIs:
  • MeowFacts: Get random facts about cats. Did you know of all the species of cats, the domestic cat is the only species able to hold its tail vertically while walking? All species of wild cats hold their tail horizontally or tucked between their legs while walking.
  • GoFile: Unlimited size file uploads for free
  • Oxford: Dictionary and thesaurus
  • mail.tm: Temporary email service
  • WolframAlpha: Provides specific answers to questions using data and algorithms
  • SkyBiometry: Face Detection, Face Recognition and Face Grouping
And many more. Now the question is, what are you building next with it?
Here are the last few issues that covered APIs in detail. (This book covers how to use them in R.)
Have you watched Hans Rosling explain statistics? A Swedish physician and academic, Prof Rosling, explains numbers and how we feel them. We believe we know the world, but do we? This talk shows how our beliefs don’t align with data anymore.
I’m not going to hold it against you if you leave now and go watch all his TED talks. Seriously.
Four Packages
Ever looked at a plot and wanted to get the data from it? juicr provides a GUI interface to tools for extracting data from scientific images like scatter or bar plots. See the vignette here.
Looking for free financial data in R? Here’s simfinapi — a package to get all kinds of financial information. You do need to register to get an API key. Check the vignette here.
pins package publishes data, models, and other R objects, making it easy to share them across projects and with your colleagues. Read the vignette here.
botmaker provides an interface to make your Twitter bots based on Github actions. For details, see the vignetteYouTube walkthrough.
Three Jargons
Micromort
Some probabilities are small but significant, like death by car accidents. For such rare occasions, we use a special metric. A micromort (MM) is a one-in-a-million probability of death. Learn more.
Regression to The Mean
The law of the averages or regression to the mean is a canonical law which states that most future events are likely to balance any past deviation from a presumed average.
Air force pilot trainers observed that pilots that performed amazing in their first training performed average in their second training, and pilots that performed average in their first training performed much better in their second training. This could be a classic example of regression to the means.
The same was also observed with companies. Those that showed high stock price rises in the initial years after an IPO had a more humble growth soon after — and vice versa (Malcolm Gladwell’s Outliers).
Interpreting P-values
Interpreting p-values can be tricky. There are two approaches: the Fisher’s way and Neyman-Pearson’s way.
Neyman-Pearson [NP] said you pick a cutoff and use it. It’s less than the cutoff(say, 0.05) or it’s not. There’s no other information to convey. In NP, 0.08 and 0.97 are the same.
Fisher said you take the p-value and treat it as the level of evidence that there is an effect. <0.2 is some evidence, but it’s pretty weak; <0.1 is a bit better, but still kind of weak. <0.05 is what Fisher said is often good enough (but he also wrote that one should change one’s significance level according to the situation, which no one does). Source.
Two Tweets
Patrick Weiss
Great opportunity to present #TidyFinance today @_useRconf. Slides are at: https://t.co/bUM5kZpFve.

Very inspiring presentations by @daxkellie, @cornelioid, and @brshallo! Thank you for your contributions to #rstats and #tidyverse.
One Meme
That's a wrap!
I hope you enjoyed today’s letter. Could you share it with a friend or a colleague? See you next week!
Harsh
Did you enjoy this issue? Yes No
Harshvardhan
Harshvardhan @harshbutjust

A short and sweet curated collection of R-related works. Five stories. Four packages. Three jargons. Two tweets. One Meme.

List of all packages covered in past issues: https://www.harsh17.in/nextpackages/.

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