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Job request: 6946

Organisation:
University of Bristol
Workspace:
covid-ve-change-over-time--previous-infection
ID:
piolh5j3wlcjsxwi
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Elsie Horne requested one or more actions to be run against real patient data in the project, within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level various outputs were written to. Researchers can never directly view outputs marked as highly_sensitive ; they can only request that code runs against them. Outputs marked as moderately_sensitive can be viewed by an approved researcher by logging into a highly secure environment. Only outputs marked as moderately_sensitive can be requested for release to the public, via a controlled output review service.

Jobs

Pipeline

Show project.yaml 
version: '3.0'

expectations:

  population_size: 100000

actions:

  ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 
  ## DO NOT EDIT project.yaml or study_definition_1-6.py DIRECTLY 
  ## These files are created by create-project.R 
  ## Edit and run create-project.R to update the project.yaml 
  ## Edit study_definition_k.py and run create-project.R to update 
  ## study_definition_1-6.py 
  ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 
  ## #################################### 
  ## study definition 
  ## #################################### 
  ## generate dummy data for study_definition 

  dummy_data_vax:
    run: r:latest analysis/dummy_data_vax.R
    outputs:
      moderately_sensitive:
        dummy_data: analysis/dummy_data_vax.feather

  ## study definition 

  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_vax
      --output-format feather
    dummy_data_file: analysis/dummy_data_vax.feather
    needs:
    - dummy_data_vax
    outputs:
      highly_sensitive:
        cohort: output/input_vax.feather

  ## #################################### 
  ## preprocessing 
  ## #################################### 
  ## process data from study_definition 

  data_input_process:
    run: r:latest analysis/preprocess/data_input_process.R
    needs:
    - dummy_data_vax
    - generate_study_population
    outputs:
      highly_sensitive:
        data_wide_vax_dates: output/data/data_wide_vax_dates.rds
        data_processed: output/data/data_processed.rds
      moderately_sensitive:
        data_properties: output/tables/data_*_tabulate.txt

  ## apply eligiblity criteria from boxes a and b 

  data_eligible_ab:
    run: r:latest analysis/preprocess/data_eligible_ab.R
    needs:
    - data_input_process
    outputs:
      highly_sensitive:
        data_eligible_a: output/data/data_eligible_a.rds
        data_eligible_b: output/data/data_eligible_b.rds
      moderately_sensitive:
        eligibility_count_ab: output/tables/eligibility_count_ab.csv
        group_age_ranges: output/lib/group_age_ranges.csv

  ## #################################### 
  ## second_vax_period 
  ## #################################### 
  ## identify second vaccination time periods 
  ## create dataset for identifying second vaccination time periods 

  data_2nd_vax_dates:
    run: r:latest analysis/second_vax_period/data_2nd_vax_dates.R
    needs:
    - data_input_process
    - data_eligible_ab
    outputs:
      highly_sensitive:
        data_vax_plot: output/second_vax_period/data/data_vax_plot.rds
        second_vax_period_dates_rds: output/second_vax_period/data/second_vax_period_dates.rds
      moderately_sensitive:
        second_vax_period_dates_txt: output/second_vax_period/tables/second_vax_period_dates.txt

  ## plot second vaccination time periods 

  plot_2nd_vax_dates:
    run: r:latest analysis/second_vax_period/plot_2nd_vax_dates.R
    needs:
    - data_eligible_ab
    - data_2nd_vax_dates
    outputs:
      moderately_sensitive:
        plots_by_region: output/second_vax_period/images/plot_by_region_*.png

  ## apply eligiblity criteria from boxes c, d and e 

  data_eligible_cde:
    run: r:latest analysis/preprocess/data_eligible_cde.R
    needs:
    - data_input_process
    - data_eligible_ab
    - data_2nd_vax_dates
    outputs:
      highly_sensitive:
        data_eligible_e_vax: output/data/data_eligible_e_vax.rds
        data_eligible_e_unvax: output/data/data_eligible_e_unvax.rds
        data_eligible_e: output/data/data_eligible_e.csv
      moderately_sensitive:
        eligibility_count_cde: output/tables/eligibility_count_cde.csv

  ## #################################### 
  ## study definition ever and k 
  ## #################################### 
  ## study definition ever 

  generate_ever_data:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_ever
      --output-format feather
    needs:
    - data_eligible_cde
    outputs:
      highly_sensitive:
        cohort: output/input_ever.feather

  ## explore positive tests around COVID-19 hospital admissions 

  covidadmitted_postest_eda:
    run: r:latest analysis/eda/covidadmitted_postest.R
    needs:
    - generate_ever_data
    outputs:
      moderately_sensitive:
        covidadmitted_postest_report: output/eda/covidadmitted_postest.html

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 01:33:02

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job information

Status
Succeeded
Backend
TPP
Workspace
covid-ve-change-over-time--previous-infection
Requested by
Elsie Horne
Branch
previous-infection
Force run dependencies
No
Git commit hash
d3a3003
Requested actions
  • generate_ever_data

Code comparison

Compare the code used in this Job Request