This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
To assist you in your R adventures in Alteryx, we've developed a R Tool Cheat Sheet which you can download to have as your very own. This article reviews and explains the functions included in the Alteryx - R cheat sheet.
R is an open-source programming language and software environment, specifically intended for statistical computing and graphics. The Alteryx Predictive Tools install includes an installation of R, along with a set of R Packages used by the Predictive Tools. This article describes how to determine which R packages (and versions) are installed for used with your Alteryx R Tool, as well as a few Alteryx-specific packages on Github.
Neural Networks are frequently referred to as "black box" predictive models. This is because the actual inner workings of why a Neural Network sorts data the way it does are not explicitly available for interpretation. A wide variety of work has been conducted to make Neural Networks more transparent, ranging from visualization methods to developing a Neural Network model that can “show it’s work”. This article demonstrates how to leverage the NeuralNetTools R package to create a plot of the Neural Network trained by the Alteryx Neural Net tool.
With the introduction of the Predictive Analytics Starter Kit, you can enhance your analytic skills through an interactive, guided starter kit that teaches core predictive modeling techniques (A/B testing, linear regression, and logistic regression)
Time series forecasting is using a model to predict future values based on previously observed values. In a time series forecast, the prediction is based on history and we are assuming the future will resemble the past. We project current trends using existing data.
Predictive Grouping is an approach that allows users to assess and create the appropriate number of clusters (groups) for their data to be assigned based on their similarity to each other in the same cluster and dissimilar to other data assigned to other clusters. K-Centroids represent a class of algorithms for doing what is known as partitioning cluster analysis. These methods work by taking the records in a database and dividing (partitioning) them into the best K groups based on some criteria. The purpose of creating clusters is to assist you in the business decision-making process as it relates to the clustered data.
Alteryx Designer comes with tools (based on both R and Python) to create and use predictive models without needing to write any code. But what if you've got custom models written in R or Python outside of Designer that you want to use in Designer, or vice versa?
We were recently approached by a concerned client with "Help! I have a model object in a .yxdb but my computer crashed and I need to document the predictor variables!" This naturally led to a discussion on how we can pull these variables back for the client, and what kind of scenarios would lead to this. The first scenario is the most obvious (the case of the client). The model object was created using Alteryx and was stored in a .yxdb. During another process, my computer crashed and I lost all of my data! Luckily, I still had the model object in a shared location, but I need to document the variables and the model object looks like this: Unfortunately, this does not give us any information about the data or more importantly, the predictor variables. Luckily, a simple script can break down this model object and fill you in on all of the details. Within Alteryx, attach an R Tool to your data stream (I am using the Forest Model Object that is created from an Alteryx Sample):
Next, copy and paste the following script into your R Tool code builder:
model.data <- read.Alteryx("#1")
the.obj <- unserializeObject(as.character(model.data$Object))
print(the.obj$call) This script states to take the data coming in from Input #1 and label it "model.data". Next, unserialize (break down) the data in the field Object (specified by "model.data%Object"). Finally, print the results in the Alteryx Output window. The final results for this particular object are then printed out, as shown. As you can see, the output clearly states that my predictor variables are Chk_Bal, Duration, Credit_Hist, Purpose, etc. The end result is quick, clean, and can really help get you out of a jam if you lose your data.
Alteryx has a full set of integrated predictive tools but even with developers working at full speed, it is hard to keep up with the R community. Sometimes users want to install and utilize their favorite R packages. This post demonstrates how to use and install additional R packages.