These functions simplify the process for reading and writing datasets. Two functions from this package are included in the default recipe: dkuReadDataset() and dkuWriteDataset(). The first line loads the dataiku R package, which includes functions for interacting with Dataiku objects, such as datasets and folders. Let’s break down the default R code recipe. Name the output dataset churn_prepared_r. That being said, an R recipe grants you the freedom to code as you wish.įrom the churn_copy dataset, add an R recipe from the Actions sidebar on the right. For routine data preparation, a visual recipe is an excellent choice since a wider pool of colleagues can more easily understand the actions in the Flow. If you look at the Prepare recipe that creates the churn_prepared dataset, you’ll see it contains only a few simple steps. ![]() ![]() Pre/post Filter step in many visual recipesįold multiple columns processor in Prepare recipe Compute and Resource Quotas on Dataiku CloudĮven if primarily an R user, it will be helpful for you to familiarize yourself with the available set of visual recipes and what they can achieve.Īlthough the table below is far from 1-1 matching, it suggests a Dataiku recipe that performs a similar operation for some of the most common data preparation functions in base R or the tidyverse.Preferred Connections and Format for Dataset Storage.Deploying Dataiku Instances to Cloud Stacks.Examples of Plugin Component Development.
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