How Do You Discover R Packages?

How Do You Discover ...

Like I mentioned in my last blog post, I am contributing to a session at userR 2017 this coming July that will focus on discovering and learning about R packages. This is an increasingly important issue for R users as we all decide which of the 10,000+ packages to invest time in understanding and then […]

On indexing operators and composition

On indexing operator...

In this article I will discuss array indexing, operators, and composition in depth. If you work through this article you should end up with a very deep understanding of array indexing and the deep interpretation available when we realize indexing is an instance of function composition (or an example of permutation groups or semigroups: some […]

Scraping CRAN with rvest

Scraping CRAN with r...

I am one of the organizers for a session at userR 2017 this coming July that will focus on discovering and learning about R packages. How do R users find packages that meet their needs? Can we make this process easier? As somebody who is relatively new to the R world compared to many, this […]

Fixing an infelicity in ‘leaps’

Fixing an infelicity...

The ‘leaps’ package for R is ancient – this is its tenth twentieth year on CRAN.  It uses old Fortran code by the Australian computational statistician Alan Miller. The Fortran 90 versions are on the web, but Fortran 90 compilation with R wasn’t portable back then, so I used the older Fortran 77 version. The main point […]

Useful Functions in R

Useful Functions in ...

I have listed some useful functions below: with() The with( ) function applys an expression to a dataset. It is similar to DATA= in SAS. # with(data, expression) # example applying a t-test to a data frame mydata with(mydata, t.test(y ~ group)) Please look at other examples here and here. by() The by( ) function […]

xda: R package for exploratory data analysis

xda: R package for e...

This package contains several tools to perform initial exploratory analysis on any input dataset. It includes custom functions for plotting the data as well as performing different kinds of analyses such as univariate, bivariate and multivariate investigation which is the first step of any predictive modeling pipeline. This package can be used to get a […]

Intro to Caret: Pre-Processing

Intro to Caret: Pre-...

Editor’s note: This is the third of a series of posts on the caret package. Creating Dummy Variables Zero- and Near Zero-Variance Predictors Identifying Correlated Predictors Linear Dependencies The preProcess Function Centering and Scaling Imputation Transforming Predictors Putting It All Together Class Distance Calculations caret includes several functions to pre-process the predictor data. It assumes that […]

Intro to caret: Visualizations

Intro to caret: Visu...

Editor’s note: This is the second of a series of posts on the caret package. The featurePlot function is a wrapper for different lattice plots to visualize the data. For example, the following figures show the default plot for continuous outcomes generated using the featurePlotfunction. For classification data sets, the iris data are used for illustration. […]

Decrypt Emotion with our Web App…and learn APIs in R

Decrypt Emotion with...

Editor’s Note: Jump to the emotion reader application, submit the img url and see what their face says. An API is a way for one piece of software to interact with another application.  API stands for “application program interface” allowing your application or R script to interact with an outside service.  The cool thing about APIs is […]