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How To: Work With Custom R and Python Models in Alteryx Designer

Alteryx Community Team
Alteryx Community Team

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?


For Python, use pickle or joblib to move the model in and out of Python. Here's an example that fits a decision tree model to the iris dataset and dumps the model to disk as a joblib file:


from ayx import Package

from ayx import Alteryx
from sklearn import tree
from joblib import dump

train ="#1")
clf = tree.DecisionTreeClassifier()
y_train = train.pop('Species').values
clf =, y_train)
dump(clf, 'C:/Users/nryan/Desktop/treeModel.joblib')


Use RDS files in R:



train <- read.Alteryx("#1", mode="data.frame")
treeFit <- rpart(Species ~ ., data=train, method = 'class')
saveRDS(treeFit, "C:/Users/nryan/Desktop/treeModel.rds")


Bringing the joblib file back into Alteryx is straightforward. Here we bring the decision tree model back into the Python tool in Designer to make predictions on new data:


from ayx import Package

from ayx import Alteryx
import pandas as pd
from joblib import load

test ="#1")
clf = load('C:/Users/nryan/Desktop/treeModel.joblib')
prediction = clf.predict(test)
Alteryx.write(pd.DataFrame({'Py_Prediction':prediction}), 1)


And the R tool equivalent:


treeModel <- readRDS("C:/Users/nryan/Desktop/treeModel.rds")
test <- read.Alteryx("#1", mode="data.frame")
R_Prediction <- predict(treeModel, newdata=test, type = 'class')
write.Alteryx(, 1)


Fit the modelFit the model


Make predictionsMake predictions

See attached example workflows. To run these workflows you will need to edit the file paths to reflect a location on your machine. For help installing the joblib package, please see the article How To: Use Alteryx.installPackages() in Python tool