Category: conference

rstudio::conf 2019 summary

rstudio::conf 2019 summary

Cool intro video!
Thanks to Amelia for pointing to it

Welcome to rstudio::conf 2019

Similar to last year, I was not able to attend rstudio::conf 2019.

Fortunately, so much of the conference is shared on Twitter and media outlets that I still felt included. Here are some things that I liked and learned from, despite the Austin-Tilburg distance.

All presentations are streamed

One great thing about rstudio::conf is that all presentations are streamed and later posted on the RStudio website.

Of what I’ve already reviewed, I really liked Jenny Bryan’s presentation on lazy evaluation, Max Kuhn’s presentation on parsnip, and teaching data science with puzzles by Irene Steves. Also, the gt package is a serious power tool! And I was already a gganimate fanboy, as you know from here and here.

One of the insights shared in Jenny Bryan’s talk that can be a life-saver

I think I’m going to watch all talks over the coming weekends!

Slides & Extra Materials

There’s an official rstudio-conf repository on Github hosting many materials in an orderly fashion.

Karl Broman made his own awesome GitHub repository with links to the videos, the slides, and all kinds of extra resources.

Karl’s handy github repo of rstudio::conf

All takeaways in a handy #rstudioconf Shiny app

Garrick Aden-Buie made a fabulous Shiny app that allows you to review all #rstudioconf tweets during and since the conference. It even includes some random statistics about the tweets, and a page with all the shared media.

Some random takeaways

Via this tweet about this rstudio::conf presentation
Some words of wisdom by Emily Robinson (whom we know from here)
You should consider joining #tidytuesday!

Extra: Online RStudio Webinars

Did you know that RStudio also posts all the webinars they host? There really are some hidden pearls among them. For instance, this presentation by Nathan Stephens on rendering rmarkdown to powerpoint will save me tons of work, and those new to broom will also be astonished by this webinar by Alex Hayes.

Analytics in HR case study: Behind the scenes

Analytics in HR case study: Behind the scenes

Past week, Analytics in HR published a guest blog about one of my People Analytics projects which you can read here. In the blog, I explain why and how I examined the turnover of management trainees in light of the international work assignments they go on.

For the analyses, I used a statistical model called a survival analysis – also referred to as event history analysis, reliability analysis, duration analysis, time-to-event analysis, or proporational hazard models. It estimates the likelihood of an event occuring at time t, potentially as a function of certain data.

The sec version of surival analysis is a relatively easy model, requiring very little data. You can come a long way if you only have the time of observation (in this case tenure), and whether or not an event (turnover in this case) occured. For my own project, I had two organizations, so I added a source column as well (see below).


sources = c("Organization Red","Organization Blue")
prob_leave = c(0.5, 0.5)
prob_stay = c(0.8, 0.2)
n = 60

    Tenure = sample(1:80, n*2, T),
    Source = sample(sources, n*2, T, prob_leave),
    Turnover = T
    Tenure = sample(1:85, n*25, T),
    Source = sample(sources, n*25, T, prob_stay),
    Turnover = F
) ->

sfit <- survfit(Surv(data_surv$Tenure, event = data_surv$Turnover) ~ data_surv$Source)

autoplot(sfit, censor = F, surv.geom = 'line', surv.size = 1.5, = 0.2) +
  scale_x_continuous(breaks = seq(0, max(data_surv$Tenure), 12)) +
  coord_cartesian(xlim = c(0,72), ylim = c(0.4, 1)) +
  scale_color_manual(values = c("blue", "red")) +
  scale_fill_manual(values = c("blue", "red")) +
  theme_light() +
  theme(legend.background = element_rect(fill = "transparent"),
        legend.justification = c(0, 0),
        legend.position = c(0, 0),
        legend.text = element_text(size = 12)
        ) +
  labs(x = "Length of service", 
       y = "Percentage employed",
       title = "Survival model applied to the retention of new trainees",
       fill = "",
       color = "")

The resulting plot saved with ggsave, using width = 8 and height = 6.

Using the code above, you should be able to conduct a survival analysis and visualize the results for your own projects. Please do share your results!

PyData, London 2018

PyData, London 2018

PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

April 2018, a PyData conference was held in London, with three days of super interesting sessions and hackathons. While I couldn’t attend in person, I very much enjoy reviewing the sessions at home as all are shared open access on YouTube channel PyDataTV!

In the following section, I will outline some of my favorites as I progress through the channel:

Winning with simple, even linear, models:

One talk that really resonated with me is Vincent Warmerdam‘s talk on “Winning with Simple, even Linear, Models“. Working at GoDataDriven, a data science consultancy firm in the Netherlands, Vincent is quite familiar with deploying deep learning models, but is also midly annoyed by all the hype surrounding deep learning and neural networks. Particularly when less complex models perform equally well or only slightly less. One of his quote’s nicely sums it up:

“Tensorflow is a cool tool, but it’s even cooler when you don’t need it!”

— Vincent Warmerdam, PyData 2018

In only 40 minutes, Vincent goes to show the finesse of much simpler (linear) models in all different kinds of production settings. Among others, Vincent shows:

  • how to solve the XOR problem with linear models
  • how to win at timeseries with radial basis features
  • how to use weighted regression to deal with historical overfitting
  • how deep learning models introduce a new theme of horror in production
  • how to create streaming models using passive aggressive updating
  • how to build a real-time video game ranking system using mere histograms
  • how to create a well performing recommender with two SQL tables
  • how to rock at data science and machine learning using Python, R, and even Stan

Predicting Employee Turnover at SIOP 2018

The 2018 annual Society for Industrial and Organizational Psychology (SIOP) conference featured its first-ever machine learning competition. Teams competed for several months in predicting the enployee turnover (or churn) in a large US company. A more complete introduction as presented at the conference can be found here. All submissions had to be open source and the winning submissions have been posted in this GitHub repository. The winning teams consist of analysts working at WalMart, DDI, and HumRRO. They mostly built ensemble models, in Python and/or R, combining algorithms such as (light) gradient boosted trees, neural networks, and random forest analysis.

rstudio::conf 2018 summary

rstudio::conf 2018 summary

rstudio::conf is the yearly conference when it comes to R programming and RStudio. In 2017, nearly 500 people attended and, last week, 1100 people went to the 2018 edition. Regretfully, I was on holiday in Cardiff and missed out on meeting all my #rstats hero’s. Just browsing through the #rstudioconf Twitter-feed, I already learned so many new things that I decided to dedicate a page to it!

Fortunately, you can watch the live streams taped during the conference:

Two people have collected the slides of most rstudio::conf 2018 talks, which you can acces via the Github repo’s of matthewravey and by simecek. People on Twitter have particularly recommended teach the tidyverse to beginners (by David Robinson), the lesser known stars of the tidyverse (by Emily Robinson), the future of time series and financial analysis in the tidyverse (by Davis Vaughan of, Understanding Principal Component Analysis (by Julia Silge), and Deploying TensorFlow models (by Javier Luraschi). Nevertheless, all other presentations are definitely worth checking out as well!

One of the workshops deserves an honorable mention. Jenny Bryan presented on What they forgot to teach you about R, providing some excellent advice on reproducible workflows. It elaborates on her earlier blog on project-oriented workflows, which you should read if you haven’t yet. Some best pRactices Jenny suggests:

  • Restart R often. This ensures your code is still working as intended. Use Shift-CMD-F10 to do so quickly in RStudio.
  • Use stable instead of absolute paths. This allows you to (1) better manage your imports/exports and folders, and (2) allows you to move/share your folders without the code breaking. For instance, here::here("data","raw-data.csv") loads the raw-data.csv-file from the data folder in your project directory. If you are not using the here package yet, you are honestly missing out! Alternatively you can use fs::path_home()normalizePath() will make paths work on both windows and mac. You can usebasename instead of strsplit to get name of file from a path.
  • To upload an existing git directory to GitHub easily, you can usethis::use_github().
  • If you include the below YAML header in your .R file, you can easily generate .md files for you github repo.
#' ---
#' output: github_document
#' ---
  • Moreover, Jenny proposed these useful default settings for knitr:
collapse = TRUE,
comment = "#>",
out.width = "100%"

Another of Jenny Bryan‘s talks was named Data Rectangling and although you might not get much out of her slides without her presenting them, you should definitely try the associated repurrrsive tutorial if you haven’t done so yet. It’s a poweR up for any useR!

Here’s a Shiny dashboard made by Garrick Aden-Buie including all the #rstudioconf tweets so you can browse the posts yourself. If you want to download the tweets, Mike Kearney (author of rtweet) shares the data here on his Github. Some highlights:

These probably only present a minimal portion of the thousands of tips and tricks you could have learned by simply attending rstudio::conf. I will definitely try to attend next year’s edition. Nevertheless, I hope the above has been useful. If I missed out on any tips, presentations, tweets, or other materials, please reply below, tweet me or pop me a message!

A/B testing and Statistics at Etsy, by Emily Robinson

A/B testing and Statistics at Etsy, by Emily Robinson

Generating numbers is easy; generating numbers you should trust is hard!

Emily Robinson is a data scientist at Etsy, an e-commerce website for handmade and vintage products. In the #rstats community, Emily is nearly as famous as her brother David Robinson, whom we know from the tidytext R-package.

Like any large tech company, Etsy relies heavily on statistics to improve their way of doing business. In their case, data from real-life experiments provide the business intelligence that allow effective decision-making. For instance, they experiment with the layout of their buttons, with the text shown near products, or with the suggestions made after a search query. To detect whether such changes have (ever so) small effects on Etsy’s KPI’s (e.g., conversion), data scientists such as Emily rely on traditional A/B testing.

In a 40-minute presentation, Emily explains how statistical issues such as skewed distributions, outliers, and power are dealt with at Etsy, among others using bootstrapping and simulations. Moreover, 30 minutes in Emily shares her lessons when it comes to working with (less stats-savvy) business stakeholders. For instance, how to help identify and transform business questions into data questions back into business solutions, or how to deal with the desire to peek at the results of experiments early.

Overall, I can the presentation below, the slides of which you find on Emily’s GitHub.