Dplyr summarize issues with list code#
Dplyr summarize issues with list download#

Dplyr summarize issues with list code#

Let’s import code from a git repository into our project library so it can be used in the current recipe.įrom the code menu of the top navigation bar, select Libraries, or use the shortcut G+L. Perhaps we want to reuse the same parameters or hyperparameter settings found in models elsewhere. Imagine, however, that we want to reuse some code already developed outside of Dataiku. We now have the correct code environment, input, and output to build our model.

Replace the existing R script with the new code below. Let’s edit it to mimic the action of the visual Split recipe. The previously empty R script should now be filled with the same R code found on the Dataiku instance.

Dplyr summarize issues with list download#

Alternatively, you can also skip this step, and directly edit the R recipe within Dataiku.įrom the Addins menu, select “Dataiku: download R recipe code”. If you followed the setup in the section above, there are no additional configuration steps needed. Now that you have created the recipe, let’s edit it in RStudio, and save the new version back to the Dataiku instance. Select the churn_prepared_r dataset, and add a new R recipe.Īdd two output datasets, train_r and test_r, and click Create Recipe. In addition to the RStudio integration used here, some users may also prefer to write R code in the RStudio Server IDE through a Code Studio template. While the distribution for many variables is quite similar, a few variables like CustServ_Calls, Day_Charge, and Day_Mins follow different patterns.

The code above visualizes the distribution for all numeric variables in the dataset among churning and returning customers. If you wish, you can publish it to a dashboard like any other insight such as native charts or model reports. Library ( dataiku ) library ( dplyr ) library ( tidyr ) library ( ggplot2 ) # These lines are unnecessary if running within Dataiku dkuSetRemoteDSS ( 'http(s)://DSS_HOST:DSS_PORT/', 'Your API Key' ) dkuSetCurrentProjectKey ( 'DKU_TUT_R_USERS' ) # Replace with your project key if different # Read the dataset as a R dataframe in memory df % select ( - c ( State, Area_Code, Intl_Plan, VMail_Plan )) %>% gather ( 'metric', 'value', - Churn ) %>% ggplot ( aes ( x = value, color = Churn )) + facet_wrap ( ~ metric, scales = 'free' ) + geom_density () # Save visualization above as a static insight dkuSaveGgplotInsight ( 'density-plots-by-churn' )Īfter running the code above, return to Dataiku, and navigate to the Insights page (G+I) to confirm the insight has been added. You’ll want to replace this with your own logic to define a new output dataset based on the input. The line churn_prepared_r <- churn_copy assigns the input dataset as the output dataset. However, if the Sync recipe were instead moving the CSV file to an SQL database or an HDFS cluster, the syntax in the R recipe would be exactly the same. The churn_copy dataset, in this case, is a managed filesystem dataset, resulting from the original uploaded CSV file. 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.

Users, Profiles & Groups on Dataiku Cloud.Preferred Connections and Format for Dataset Storage.Deploying Dataiku Instances to Cloud Stacks.Examples of Plugin Component Development.