Blog Archives

New screencast: using R and RStudio to install and experiment with Apache Spark

March 15, 2017
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I have new short screencast up: using R and RStudio to install and experiment with Apache Spark. More material from my recent Strata workshop Modeling big data with R, sparklyr, and Apache Spark can be found here.

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Practical Data Science with R errata update: Java SQLScrewdriver replaced by R procedures and article

March 11, 2017
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Practical Data Science with R errata update: Java SQLScrewdriver replaced by R procedures and article

We have updated the errata for Practical Data Science with R to reflect that it is no longer worth the effort to use the Java version of SQLScrewdriver as described. We are very sorry for any confusion, trouble, or wasted effort bringing in Java software (something we are very familiar with, but forget not everybody … Continue...

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Some Win-Vector R packages

March 9, 2017
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Some Win-Vector R packages

This post concludes our mini-series of Win-Vector open source R packages. We end with WVPlots, a collection of ready-made ggplot2 plots we find handy. Please read on for list of some of the Win-Vector LLC open-source R packages that we are pleased to share. For each package we have prepared a short introduction, so you … Continue...

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sigr: Simple Significance Reporting

March 7, 2017
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sigr: Simple Significance Reporting

sigr is a simple R package that conveniently formats a few statistics and their significance tests. This allows the analyst to use the correct test no matter what modeling package or procedure they use. Model Example Let’s take as our example the following linear relation between x and y: library('sigr') set.seed(353525) d <- data.frame(x= rnorm(5)) … Continue...

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Step-Debugging magrittr/dplyr Pipelines in R with wrapr and replyr

March 6, 2017
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In this screencast we demonstrate how to easily and effectively step-debug magrittr/dplyr pipelines in R using wrapr and replyr. Some of the big issues in trying to debug magrittr/dplyr pipelines include: Pipelines being large expressions that are hard to line-step into. Visibility of intermediate results. Localizing operations (in time and code position) in the presence … Continue...

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replyr: Get a Grip on Big Data in R

March 5, 2017
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replyr: Get a Grip on Big Data in R

replyr is an R package that contains extensions, adaptions, and work-arounds to make remote R dplyr data sources (including big data systems such as Spark) behave more like local data. This allows the analyst to more easily develop and debug procedures that simultaneously work on a variety of data services (in-memory data.frame, SQLite, PostgreSQL, and … Continue...

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vtreat: prepare data

March 3, 2017
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vtreat: prepare data

This article is on preparing data for modeling in R using vtreat. Our example Suppose we wish to work with some data. Our example task is to train a classification model for credit approval using the ranger implementation of the random forests method. We will take our data from John Ross Quinlan's re-processed "credit approval" … Continue...

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wrapr: for sweet R code

March 1, 2017
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wrapr: for sweet R code

This article is on writing sweet R code using the wrapr package. The problem Consider the following R puzzle. You are given: a data.frame, the name of a column that you wish to find missing values (NA) in, and the name of a column to land the result. For instance: d <- data.frame(x = c(1, … Continue...

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Iteration and closures in R

February 26, 2017
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Iteration and closures in R

I recently read an interesting thread on unexpected behavior in R when creating a list of functions in a loop or iteration. The issue is solved, but I am going to take the liberty to try and re-state and slow down the discussion of the problem (and fix) for clarity. The issue is: are references … Continue...

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The Zero Bug

February 21, 2017
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The Zero Bug

I am going to write about an insidious statistical, data analysis, and presentation fallacy I call “the zero bug” and the habits you need to cultivate to avoid it. The zero bug Here is the zero bug in a nutshell: common data aggregation tools often can not “count to zero” from examples, and this causes … Continue...

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