At CompassRed, we primarily work in R for client projects. Whether it’s creating models, custom data viz, or something in between, a static slide deck or HTML file only goes so far (more than once we’ve heard “I’d like to click into that” when presenting a static slide deck). Over the last year our team has invested in the R Shiny application framework to bring those static outputs to life in an interactive medium.
When I first learned how to use RMarkdown, I was in absolute awe. Moving from copy/pasting SPSS output into Word documents, to being able to automatically generate a rich analysis report full of text and visualizations at the press of a button was game-changing. RMarkdown files can also be rendered into a variety of formats which not only covers HTML, but also PDFs, Word documents, and others. While sharing an output file is easy and works great for an analysis that is conducted once, what if there are frequent updates that you want to share with clients/colleagues/others?
In the first part of our exploration of data manipulation tools in the tidyverse, we concentrated on some of the basic dplyr and tidyr verbs. These tools allowed us to subset, transform, pivot, and join data in our quest to understand StackOverflow’s Annual Developer Survey and the 2020 World Happiness Report.
In this article, we will continue exploring data manipulation tools in the tidyverse, building on our learnings from Part 1.
In this article I’ll explore different tools for data manipulation using tidyverse functions. This article assumes that you have a beginner’s understanding of R and how the pipe operator (%>%) works. This article can serve as scaffolding as you grow your data manipulation toolkit. There are many more patterns that are useful to learn and starting with some of these techniques puts you in a position for more success.
Over the past 3 years (2017, 2019, 2020), I have been writing open letters to the Google Data Studio development team with feature requests, and it appears that some of my wishes have been granted. During a recent community livestream, the lid was lifted on the product roadmap for the rest of the year and beyond. Here are the top 3 features that I’m looking forward to the most.
Wilmington, Delaware --CompassRed, Inc. today announced its Summer 2020 Intern Class consisting of students from across the greater Philadelphia region. This year’s intern class was selected from the widest and deepest pool of talent in CompassRed’s history with several hundred resumes submitted for a limited number of positions. The students selected for CompassRed’s highly coveted internship program are Michal Monselise, a PhD candidate from Drexel University; Aayush Dua, a MS candidate from the University of Pennsylvania; and Albert Opher, a rising senior at Sanford High School.
If you have been using Data Studio for any length of time, it’s very likely that you have run into limitations around date comparisons. By default, you get some decent options for comparing to a previous period, previous year, or customer period, but the way these comparisons are visualized isn’t always ideal.
Managing stress and coping during the COVID-19 pandemic is important for everyone. Whether you’re at home or on the frontlines, everyone is experiencing heightened levels of uncertainty; self-care might not be a priority right now. The CDC’s Daily Stress and Coping Resource recommends these four activities (among others) to best take care of oneself:
A couple of weeks ago, a collaboration of various research groups released a huge dataset of literature which can be used to gain insights into the current COVID-19 pandemic. Additionally, Johns Hopkins updates a dataset that tracks confirmed cases/fatalities/recoveries daily. A call to action to the data community was given to help join the fight against the virus.
When I first learned about the team here at CompassRed, the article below from Data Analytics Team Lead Patrick Strickler stood out. Creating a custom click heatmap using Google Analytics, Google Tag Manager, and Google Data Studio was (and still is) an innovative approach to extending Google Analytics capabilities.
We at CompassRed were able to sit on two panels and participate in discussions with some of these leaders and provide insights into what we see having large impacts in this space. We found the conference fascinating and telling of the changes starting to take place that demonstrates some of the mind-blowing advances in what some may perceive as an industry slow to make changes.
What’s becoming an annual tradition (see 2017, 2019), the list below encompasses the top 3 features I want from Google Data Studio in 2020. As a reminder, this list comes from a place of love — Data Studio has become my tool-of-choice over the past few years, and these are all improvements that would make it that much easier to recommend.
Last year I wrote about how I keep up with machine learning research. ICYMI, here was the short list:
Read the Seminal Papers
Take Advantage of arXiv Sanity
Subscribe to Papers with Code
One year later there is still a Sisyphean amount of machine learning research to keep up with. This calls for more tools to help curate the papers that are valuable to take a deep dive into.
Here are 3 tools you might use to do that:
At the beginning of this year, we looked back to reassess our dreams, our industry, and revitalize our vision for the future. We love reading the plethora of articles from those who are re-examining their predictions from last year and why they were “right on” or so far from reality. The year 2020 is extra special; we have the opportunity to look at the decade that’s passed and dream of the next one. Read more.
