Transformations

How do I run an R transformation?

Create, run, and develop an R transformation in Keboola — write the script, map input and output CSV files, run it, confirm the result, and debug it in a workspace or locally.

You want to run advanced statistical or vector/matrix computations with R. An R transformation reads your mapped input tables as CSV files, runs your script, and writes CSV outputs back to Storage. This page gets you from nothing to a successful run, then shows how to develop and debug. For limits, packages, and CSV rules, see the reference.

Time: ~10 minutes · You will need: a Keboola project and one table in Storage (or the sample CSV file).

Step 1 — Create the transformation

Open Components → Transformations, click New Transformation, and choose R Transformation.
Name it and confirm.

Step 2 — Map input and output

Upload the sample CSV file to Storage as a table.
In Input Mapping, add it and set its Destination to source (the script reads in/tables/source.csv).
In Output Mapping, map result.csv to a new Storage table, for example out.c-main.result.

Step 3 — Write the script

data <- read.csv(file = "in/tables/source.csv")

df <- data.frame(
  col1 = paste0(data$first, 'ping'),
  col2 = data$second * 42
)
write.csv(df, file = "out/tables/result.csv", row.names = FALSE)

Always write outputs with row.names = FALSE — see row index in output tables. You can split the script into blocks.

Step 4 — Run it and confirm the result

Click Run.
Wait for the job to finish with a success status.
Open Storage, find your output table, and confirm col1 has the ping suffix and col2 is second × 42.

Develop and debug

The fastest way to iterate is an R workspace with the same input mapping. While developing, read fewer rows to catch issues quickly:

mydata <- read.csv("in/tables/mydata", nrows=500)

To develop locally, install R (preferably the same version as Keboola) and recreate the directory structure (in/tables/, out/tables/, and in/user/ for extension-less binary files) with your input files. A ready example is in data.zip; the same script then runs unchanged as a transformation. For an exact environment, use the Keboola Docker image.

Make it faster (backend size)

For large data, raise the Backend size in the configuration (XSmall → Small → Medium → Large); see backend sizes. This affects time-credit consumption.

Troubleshooting

Symptom	Likely cause	Fix
Transformation fails on a harmless warning	Warnings are converted to errors	Fix the cause, or wrap the code in `tryCatch(..., warning = function(w) {})` (reference).
Extra unnamed column / import fails	Row index written to the CSV	Write with `row.names = FALSE`.
`cannot open file 'in/tables/source.csv'`	Input destination doesn’t match the path	Set the input Destination to `source`.
Output table empty / not created	Output Source doesn’t match the file the script writes	Map `result.csv`.

R transformation reference — limits, packages, CSV, logging.
In-depth examples: array splitting · charts & graphs · binary files.
Workspaces · Input and output mapping.

✨ How do I run an R transformation?