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How to Speak to Executives? | Next - Issue #40

Harshvardhan
Harshvardhan
Hi there!
What do you expect from a city? Happiness? Satisfaction? Economic well-being? After all, what is a good goal? Singapore designed itself in a way where everyone had equity of access. Small decisions in cities can have a massive impact over time. But what about abolishing traffic lights altogether?
Learn more about urban planning in “Bonus”. But before that, let’s learn R.

Five Stories
1. Quarto in Two Hours
Tom Mock, RStudio
Many of us missed the rstudio::rconf(2022). Quarto was the new kid in town, as I wrote last week. Here’s a two-hour version of the two-day-long workshop. This session, led by Tom Mock, showcases how you can use Quarto for the data analysis you already do.
Quarto will be R Markdown’s successor, with new features being continuously added. It comes “batteries included”, which means it doesn’t require any additional packages. All workshop materials are available on RStudio Cloud if you want to experiment without downloading new stuff.
2. A Beginner’s Guide to Shiny for Python
Shiny apps are powerful tools for interactive data science applications. For example, consider when T-Mobile wanted to automate customer engagement based on text queries. A live demonstration with a Shiny app drove the management’s buy-in.
Now, it is available for Python. Hugh relief! It is in Alpha, but you can experiment with what is possible now. This guide by Winston Chang will help you get started.
3. How to Speak to Executives?
This playlist is designed for our TikTok generation. In eight two-minute videos, different executives give quick advice on how to work on data science communication. Simple questions: How to speak to executives? They don’t care about the 30 days you spent perfecting your analysis. What’s the dollar impact?
You can watch the entire playlist while waiting for your coffee. Jump in!
4. Don’t repeat yourself, talk to yourself!
Sharla Gelfand
If you’re responsible for analyses that need updating or repeating on a semi-regular basis, you might find yourself doing the same work over and over again. The principle of “don’t repeat yourself” from software engineering motivates us to use functions and packages, the core of repetition in the R universe. For analyses, it can be difficult to know how to use this principle and move beyond “copying and pasting scripts and changing the data file and the object names and updating the dates and results in RMarkdown”, especially when there’s some element of human intervention required, whether it be for validating assumptions or cleaning artisanal data.
Sharla focuses on how to do such a “repeatable” analysis in this engaging talk. This will prepare you to be a better data scientist, going beyond data examination!
5. People analytics for getting to the moon
David Meza, NASA
David Meza is AIML R&D Lead of People Analytics at NASA. For NASA, people are essential. They need great talent and want to retain them. How do they identify cool people suitable for the work?
Here’s him:
We need to identify the various skills and competencies that we have in our individuals. From a human perspective, that’s very labour intensive. We’re trying to use some of the concepts I’ve mentioned [natural language processing, graph databases and algorithms] to be able to extract NASA-specific competencies from individuals or people applying for jobs to see how they align with our workforce.
Four Packages
highcharter comes with hchart(x) . With a single function, you can create scatter plots, time series plots, candle stick plots, choropleth maps and more. It also comes with themes specific to The Economist, Financial Times, Google and more. Vignette.
pbapply is a lightweight package for adding progress bars to apply functions in R. Something similar to tqdm for Python. Github.
dendextend is a ggplot2 extension for dendrograms. It works with almost any tree-producing object in R (random forest, trees, agnes, diana and more). Vignette.
ggthemes is your one-stop shop for ggplot2 themes. Check out the available options!
Three Jargons
Bohr bug: A repeatable bug; one that manifests reliably under a possibly unknown but well-defined set of conditions.
Heisenbug: A bug that disappears or alters its behaviour when one attempts to probe or isolate it.
Mandelbug: A bug whose underlying causes are so complex and obscure as to make its behaviour appear chaotic or even non-deterministic. This term implies that the speaker thinks it is a Bohr bug rather than a heisenbug.
Two Tweets
Noah Greifer
Been very into @psolymos's {pbapply} #Rstats package lately. Provides a drop-in for `lapply()` with a progress bar and support for parallel computation. Progress bar displays estimated time to completion, too.
One Meme
Live from my office at HP, Vancouver.
Live from my office at HP, Vancouver.
Bonus
A few days ago, a friend introduced me to the interesting world of urban planning. If you manage to save some time this week, check out “The Most Miserable City in America” and other cool videos from City Beautiful.
That's a wrap!
If you liked today’s letter, why not share it? Feedback is greatly appreciated. See you next week!
Harsh
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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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