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Below is a walk-through of some of the basics of customizing plot in R. These are all based on the graphics package that comes in the base installation of R. Let’s start by making a basic plot in R. In the code snippets below, green text behind a... [Read more...]

How to use Eclipse and StatET to create a handy-dandy development environment for R. Using the Eclipse environment provides a nice colored-syntax script editor, an R console, and an object browser to let you peruse the variables you've put into memory.... [Read more...]

Conditional inference trees (ctree) in package party allows weighting which is useful when one classification outcome is more important than another. Useful examples are not difficult to imagine: in a marketing direct mailing, a false positive (non-res... [Read more...]

Say you’ve got a bunch of survival/mortality data from an experiment. Maybe you exposed batches of snails to various high temperatures for a few hours, and recorded the number alive and dead in each batch at the end. Now you’d like to repor... [Read more...]

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Interaction variables introduce an additional level of regression analysis by allowing researchers to explore the synergistic effects of combined predictors. This tutorial will explore how interaction models can be created in R.Tutorial Files Before we... [Read more...]

Regression models can become increasingly complex as more variables are included in an analysis. Furthermore, they can become exceedingly convoluted when things such as polynomials and interactions are explored. Thankfully, once the potential independe... [Read more...]

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Do you need a way to find out which individual variables in R consume the most memory? # create dummy variables for demonstration x [Read more...]

This tutorial demonstrates to the R novice how to create five machine learning models for classification and compare the performance graphically with ROC curves in one chart. For a simpler introduction, start with Plot ROC curve and lift chart in R. # ... [Read more...]

This tutorial with real R code demonstrates how to create a predictive model using cforest (Breiman’s random forests) from the package party, evaluate the predictive model on a separate set of data, and then plot the performance using ROC curves ... [Read more...]

An important aspect of regression involves assessing the tenability of the assumptions upon which its analyses are based. This tutorial will explore how R can help one scrutinize the regression assumptions of a model via its residuals plot, normality h... [Read more...]

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Named rjson, appropriately. It’s quite basic just now, but contains methods for interconversion between R objects and JSON. Something like this: __ library(rjson) __ data json json [1] "{\"a\":1,\"b\":2,\"c\":3}" __ cat(json, file="data.json") Use cases? I wonder if RApache could be used to build an API that serves ... [Read more...]

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Welcome to part two of the Introduction to The R Project for Statistical Computing tutorial. If you missed part one, it can be found here. In this segment, we will explore the following topics.Importing DataVariablesWorkspace FilesConsole FilesFinding ... [Read more...]