R Is Bad For You?

Kevin Feasel

2017-05-18

R

Bill Vorhies lays out a controversial argument:

I have been a practicing data scientist with an emphasis on predictive modeling for about 16 years.  I know enough R to be dangerous but when I want to build a model I reach for my SAS Enterprise Miner (could just as easily be SPSS, Rapid Miner or one of the other complete platforms).

The key issue is that I can clean, prep, transform, engineer features, select features, and run 10 or more model types simultaneously in less than 60 minutes (sometimes a lot less) and get back a nice display of the most accurate and robust model along with exportable code in my selection of languages.

The reason I can do that is because these advanced platforms now all have drag-and-drop visual workspaces into which I deploy and rapidly adjust each major element of the modeling process without ever touching a line of code.

I have almost exactly the opposite thought on the matter:  that drag-and-drop development is intolerably slow; I can drag and drop and connect and click and click and click for a while, or I can write a few lines of code.  Nevertheless, I think Bill’s post is well worth reading.

Related Posts

Scatterplots For Multivariate Analysis

Neil Saunders declutters a complicated visual with a simple scatterplot: Sydney’s congestion at ‘tipping point’ blares the headline and to illustrate, an interactive chart with bars for city population densities, points for commute times and of course, dual-axes. Yuck. OK, I guess it does show that Sydney is one of three cities that are low density, […]

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

Categories