Category: 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.

Sarah Dutkiewicz tidies up a data set in R:

Looking at this data, the first thing I thought was untidy. There has to be a better way. When I think of tidy data, I think of the tidyr package, which is used to help make data tidy, easier to work with. Specifically, I thought of the spread() function, where I could break things up. Once data was spread into appropriate columns, I figure I can operate on the data a bit better.

Sarah has also made the data set available in case you’re interested in following along.

Steph Locke shows how to use the params section of a YAML header to enable parameter reuse:

You may already know the trick about making the date dynamic to whatever date the report gets rendered on by using the inline R execution mode of rmarkdown to insert a value.

---
title: "My report"
date: "`r Sys.Date()`"
output: pdf_document
---

What you may not already know is that YAML fields get evaluated sequentially so you can use a value created further up in the params section, to use it later in the block.

Click through to see how it’s done.

Steph Locke shows how to create a package in R:

Then we need to add github repository to our project. I use the git command line for this:

git remote add origin git@github.com:stephlocke/datasauRus.git
git push --set-upstream origin master

With just these things, I have a package that contains the unit test framework, documentation stubs, continuous integration and test coverage, and source control.

That is all you need to do to get things going!

This is great timing for me, as I’m starting to look at packaging internal code.  Also, it’s great timing because it includes dinosaurs.

Steph Locke explains how to use lapply() and fread() to validate and combine multiple uploaded files in R:

In shiny, you can use the fileInput with the parameter multiple = TRUE to enable you to upload multiple files at once. But how do you process those multiple files in shiny and consolidate into a single dataset?

The bit we need from shiny is the input$param$fileinputpath value.

Read the whole thing.