The internet is awash in Venn diagrams of SQL JOINs and explanations of why they're necessary. Though it would be nice if we could answer every data question with a single table, more often than not, to get a complete picture, we need to combine different data sets.

As analysts, we know that each question we ask of our data can be answered with multiple potential languages and toolkits. Each language has its strengths and we've often pondered the distinctions. We've previously examined Group By, window functions and a general framework for thinking in SQL and Python.

Before starting Mode, I was an analyst. It's embarrassing to admit, but at the time, I thought I had all the answers. I thought that being the one with access to data made me the arbiter of truth, and that I was right by default when talking with someone who wasn't using quantitative information to back up their ideas.

For many of Mode's employees, this is their first job at an analytics company. Those team members often assume that Mode's internal analytics processes mirror the way analysis is commonly conducted within other organizations. In some ways, that assumption holds true. As CEO Derek Steer puts it, "I wish I had a more mind-blowing story for how we do internal reporting... But mostly, it's funnel analysis." That said, we approach analysis from some uncommon angles.

Plotly lets analysts and developers create powerful interactive visualizations, whether offline or right in your browser. Plotly.R is now available in Mode’s new R Notebooks. This means that it’s easier than ever to build all kinds of custom, interactive R-powered visualizations right into your Mode reports and dashboards.

Among data scientists and analysts, R has long held its own against Python in the battle to determine the “best” analytical programming language. Statisticians and numerically-savvy academics of all stripes have typically preferred R to Python, so lots of R&D - both inside the ivory tower and in various industries - gets done in R.