QBits Workspace: A New Online Editor to Share and Deploy R Code
Today we are excited to announce the QBits Workspace to run and deploy R code in the browser. QBits enable you to run R in a serverless cloud environment and provide an easy and cost-effective way to develop, run, deploy and share data science projects at scale without the need to manage servers, software setup and package installations. They start up instantly, have very quick deployment times and can handle all sorts of data science projects. In fact, QBits already power our online course platform and even more exciting use cases will follow soon.
We created QBits to make the deployment experience for data scientists easier. Too many projects fail because data scientists struggle to deploy their results. Think of a simple ggplot2 example to reproduce the
gapminder plots from Hans Rosling’s excellent presentation:
library(ggplot2) library(dplyr) library(gapminder) gapminder_2007 <- filter(gapminder, year == 2007) gapminder_2007$pop <- gapminder_2007$pop/1e6 ggplot(gapminder_2007) + geom_point(aes(x = gdpPercap, y = lifeExp, color = continent, size = pop), alpha = 0.7) + scale_size_area(max_size = 15)
This plot runs fine locally. However, to reproduce the plot in some interactive web application allowing users to filter the dataset by e.g.
year == 1952 we need to
- Create a docker container choosing the right operating system.
- Install the correct language runtime, e.g. R 4.0.0.
- Install all package dependencies (e.g. ggplot2, dplyr, gapminder)
- Create a Shiny application or Plumber API for interactive or programmatic use.
You see that even for this simple example the deployment overhead is considerable. This leads to a deployment bottleneck leaving many data science projects unfinished and frustrated data scientists behind. The big difference with QBits is that they already provide the correct container, language runtime and packages. The only thing you have to do is to put your code on top. That’s it.
The QBits Workspace provides a development environment to rapidly develop your custom QBits. Since you are already working within your custom container the final deployment is then only a matter of a second—not weeks.
Check out the previous example Reproduce Gapminder scatter plot within the QBits Workspace here.
We are hard at work to expand the editor to fit more workflows and implement new features. Further updates will introduce the possibility to
- Create your own QBits
- Add and remove packages (all 15,000+ CRAN packages are available)
- QBit deployment including versioning
- … and more (yes, Python is coming as well)
For now, head over to our playgrounds and give them a try.
We would love to hear your feedback and feature requests:
Your Quantargo Team