Training + Business. Get your 2-for-1 deal to ODSC
East & CxO Summit before it expires on Friday.

This deal has timed out, but the next deal might just around the corner, or find a way to contact us about writing a blog and we'll talk. See you at ODSC East!

Use code: BUSINESS for an extra 20% Off

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 […]

The caret Package

The caret Package...

Editor’s note: This is the first of a long series of posts on the caret package. Introduction The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: data splitting pre-processing feature selection model tuning using resampling […]

heatmaply: interactive heat maps (with R)

heatmaply: interacti...

I am pleased to announce heatmaply, my new R package for generating interactive heat maps, based on the plotly R package. tl;dr By running the following 3 lines of code: install.packages("heatmaply") library(heatmaply) heatmaply(mtcars, k_col = 2, k_row = 3) %>% layout(margin = list(l = 130, b = 40)) You will get this output in your […]

Choroplethr v3.5.3 is now on CRAN

Choroplethr v3.5.3 i...

A new version of choroplethr is now on CRAN. You can get it by typing the following: 1 2 3 4 5 install.packages("choroplethr") packageVersion("choroplethr") [1] '3.3.1' Motivation This new version was motivated by a few warnings that had started appearing since the latest update to ggplot2: 1 2 3 4 5 6 7 8 library(choroplethr) data(df_pop_state) […]