John Mount is a bit jazzed when it comes to a new package:

I recently got back from Strata West 2017 (where I ran a very well received workshop on R and Spark). One thing that really stood out for me at the exhibition hall was Bokeh plus datashader from Continuum Analytics.

I had the privilege of having Peter Wang himself demonstrate datashaderfor me and answer a few of my questions.

I am so excited about datashader capabilities I literally will not wait for the functionality to be exposed in R through rbokeh. I am going to leave my usual knitr/rmarkdown world and dust off Jupyter Notebook just to use datashader plotting. This is worth trying, even for diehard R users.

For the moment, it looks like datashader is only available for Python, but it’s coming to R.

Related Posts

Executing ML Services Scripts From Jupyter Notebooks

Kyle Weller has an inception moment with Python and SQL Server Machine Learning Services: While this example is trivial with the Iris dataset, imagine the additional scale, performance, and security capabilities that you now unlocked. You can use any of the latest open source R/Python packages to build Deep Learning and AI applications on large […]

Read More

Using ggpairs To Find Correlations Between Variables In R

Akshay Mahale shows how to use the ggpairs function in R to see the correlation between different pairs of variables: From the above matrix for iris we can deduce the following insights: Correlation between Sepal.Length and Petal.Length is strong and dense. Sepal.Length and Sepal.Width seems to show very little correlation as datapoints are spreaded through out the plot area. Petal.Length and Petal.Width also shows strong correlation. Note: The […]

Read More


March 2017
« Feb Apr »