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Add a scripted action to the pipeline

Every study starts with a dataset definition like the one you just edited. When executed, a dataset definition generates a compressed CSV (.csv.gz) of patient data.

A real analysis will have several further steps after this. Each step is defined in a separate file, and can be written in any of the programming languages supported in OpenSAFELY.

Create a new action🔗

In this tutorial, we're going to draw a histogram of ages, using either four lines of Python or just a few more lines of R.

  1. Right-click on the analysis folder in the editor's Explorer and select "New file". Type "report.py" as the filename and press Enter.
  2. Add the following to report.py:. 
    import pandas as pd

data = pd.read_csv("output/dataset.csv.gz")

fig = data.age.plot.hist().get_figure()
fig.savefig("output/report.png")
  1. Right-click on the analysis folder in the editor's Explorer and select "New file". Type "report.R" as the filename and press Enter.
  2. Add the following to report.R:. 
    library('tidyverse')

df_input <- read_csv(
  here::here("output", "dataset.csv.gz"),
  col_types = cols(patient_id = col_integer(),age = col_double())
)

plot_age <- ggplot(data=df_input, aes(df_input$age)) + geom_histogram()

ggsave(
  plot= plot_age,
  filename="report.png", path=here::here("output"),
)

This code reads the CSV of patient data, and saves a histogram of ages to a new file.

Add the action to the pipline🔗

  1. Open project.yaml in the editor. This file will be near the end of the list of files and folders. This file describes how each step in your analysis should be run. It already defines a single generate_dataset action which defines the output that we've generated so far. This file is in a format called YAML: the way it's indented matters, so be careful to copy and paste the following carefully.
  2. Add a generate_report action to the file, so the entire file looks like this: 
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version: "4.0"

actions:
  generate_dataset:
    run: ehrql:v1 generate-dataset analysis/dataset_definition.py --output output/dataset.csv.gz
    outputs:
      highly_sensitive:
        dataset: output/dataset.csv.gz

  generate_report:
    run: python:v2 python analysis/report.py
    needs: [generate_dataset]
    outputs:
      moderately_sensitive:
        chart: output/report.png

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version: "4.0"

actions:
  generate_dataset:
    run: ehrql:v1 generate-dataset analysis/dataset_definition.py --output output/dataset.csv.gz
    outputs:
      highly_sensitive:
        dataset: output/dataset.csv.gz

  generate_report:
    run: r:latest analysis/report.R
    needs: [generate_dataset]
    outputs:
      moderately_sensitive:
        chart: output/report.png
  • Line 14 tells the system we want to create a new action called generate_report.
  • Line 15 says how to run the script (using the python or R runner).
  • Line 16 tells the system that this action depends on the outputs of the generate_dataset being present.
  • Lines 17-19 describe the files that the action creates. Line 18 says that the items indented below it are moderately sensitive, which means they may be released to the public after a careful review (and possible redaction). Line 19 says that there's one output file, which will be found at output/report.png.

At the command line, type opensafely run generate_report and press Enter. This should end by telling you a file containing the histogram has been created. Open the output folder — you can do this via Visual Studio Code's Explorer — and check that it contains report.png.

Double click on report.png to display the image, or right-click on report.png and select Download to download the image.

Warning

Changes you make to files are automatically saved on GitHub. However, changes will not persist outside of the GitHub codespace unless you commit and push them to GitHub, as described in the next section.