Using R in 3 Different Ways With Datazar Desktop

October 1, 2018

(This article was first published on R Language in Datazar Blog on Medium, and kindly contributed to R-bloggers)

With the increase in R usage over the past few years and the sheer diversity of industries employing data-science and analytics, the backgrounds of the people using R day-to-day has also changed. Different background, from hard-sciences to software-engineering are now converging to data-science in several industries. With them, these people bring diverse environments in terms of how they run projects.

A lot of professionals with backgrounds in software or engineering are used to scripts. Academics with backgrounds in hard-sciences (physics, biology, chemistry or mathematics) are used to programs like Mathematica which employ a notebook model. Others love the good-old, time-tested terminal.

That being said, these differences can make reproducibility and going from one job/environment to another a difficult feat.

At Datazar, we recognize this and we’re working to solve this all the time. To make research/data-science accessible to everyone, our platform supports all three interfaces for both R and Python.




Having this diversity means you can have people with diverse backgrounds working on the same project using whatever interface they’re comfortable with. This decreases training time so you can hit the ground running and makes working on projects that much fun.

Desktop Application

Using R in 3 Different Ways With Datazar Desktop was originally published in Datazar Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.

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