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project.yaml
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/
project.yaml
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version: '3.0'
expectations:
population_size: 1000
actions:
generate_study_population:
run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2020-01-01 to 2020-06-01 by month" --skip-existing --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_*.csv.gz
generate_study_population_infection:
run: cohortextractor:latest generate_cohort --study-definition study_definition_infection_variables --index-date-range "2020-01-01 to 2020-06-01 by month" --skip-existing --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_infection_*.csv.gz
generate_study_population_elderly:
run: cohortextractor:latest generate_cohort --study-definition study_definition_elderly
--output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/input_elderly.csv.gz
generate_measures:
run: cohortextractor:latest generate_measures --study-definition study_definition --skip-existing --output-dir=output/measures
needs: [generate_study_population]
outputs:
moderately_sensitive:
measure_csv: output/measures/measure_*.csv
generate_measures_infection:
run: cohortextractor:latest generate_measures --study-definition study_definition_infection_variables --skip-existing --output-dir=output/measures
needs: [generate_study_population_infection]
outputs:
moderately_sensitive:
measure_csv: output/measures/measure_infec_*.csv
# describe_elderly_agedis:
# run: r:latest analysis/tables/gen_csv_age_check.R
# needs: [generate_study_population_elderly]
# outputs:
# moderately_sensitive:
# agetable: output/age_quant.csv
# describe:
# run: r:latest analysis/plot/overall_ab_prescribing.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# cohort: output/overall.png
# boxplot: output/overallbox.png
# describe_percentile:
# run: r:latest analysis/plot/overall_ab_prescribing_2575percentile.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# percentile: output/overall_25th_75th_percentile.png
# describe_starpu:
# run: r:latest analysis/plot/starpu_ab_prescribing.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# cohort: output/starpuline.png
# boxplot: output/starpubox.png
# generate_notebook_starpu:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/starpu.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# notebook: output/starpu.html
# figures: output/*
# #tables: output/tables/*
# #csvs: output/*/* # two possible subfolders
# #text: output/text/*
# describe_consultation_rate:
# run: r:latest analysis/plot/incident_consultation_age_stacked_barchart.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# bar1: output/consult_age_UTI.png
# bar2: output/consult_age_LRTI.png
# bar3: output/consult_age_URTI.png
# bar4: output/consult_age_sinusitis.png
# bar5: output/consult_age_ot_externa.png
# bar6: output/consult_age_otmedia.png
# bar7: output/consult_age_repeatedUTI.png
# describe_consultation_prescribed:
# run: r:latest analysis/plot/consultation_prescibed_percentage.R
# needs: [generate_study_population]
# outputs:
# moderately_sensitive:
# bar1: output/prescribed_percentage_UTI.png
# csvs: output/uti_prescrib_check.csv
#generate_notebook_starpu:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/starpu.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# notebook: output/hospitalisation_risk/starpu.html
# figures: output/hospitalisation_risk/*
#tables: output/tables/*
#csvs: output/*/* # two possible subfolders
#text: output/text/*
# generate_notebook_hospitalisation_analysis:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_analysis.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
# needs: [generate_study_population]
# outputs:
# moderately_sensitive:
# notebook: output/hospitalisation_risk/hospitalisation_analysis.html
# figures: output/hospitalisation_risk/*
# generate_notebook_hospitalisation_analysis:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_analysis.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
# needs: [generate_study_population]
# outputs:
# moderately_sensitive:
# notebook: output/hospitalisation_risk/hospitalisation_analysis.html
# figures: output/hospitalisation_risk/*
# describe_prior_ab_12mb4:
# run: r:latest analysis/plot/ab_1yb4_stackedbar_2.R
# needs: [generate_study_population]
# outputs:
# moderately_sensitive:
# plot: output/AB_1yb4_line.jpeg
# plot_sex: output/AB_1yb4_SEX.jpeg
# count_table: output/prior_ab_by_month.csv
describe_consultation_rate_all_incident:
run: r:latest analysis/plot/consultation_by_age_infection_incident.R
needs: [generate_measures_infection]
outputs:
moderately_sensitive:
plot1: output/consult_age_1_incident.jpeg
plot2: output/consult_age_2_incident.jpeg
plot3: output/consult_all_incident.jpeg
csv1: output/consultation_rate_incident.csv
csv2: output/consultation_GP_rate_incident.csv
describe_consultation_rate_all_prevalent:
run: r:latest analysis/plot/consultation_by_age_infection_prevalent.R
needs: [generate_measures_infection]
outputs:
moderately_sensitive:
plot1: output/consult_age_1_prevalent.jpeg
plot2: output/consult_age_2_prevalent.jpeg
plot3: output/consult_all_prevalent.jpeg
csv1: output/consultation_rate_prevalent.csv
csv2: output/consultation_GP_rate_prevalent.csv
describe_infection_prescribed_percent:
run: r:latest analysis/plot/infection_prescibed_percent.R
needs: [generate_measures_infection]
outputs:
moderately_sensitive:
plot1: output/infection_ab_precent_p1.jpeg
plot2: output/infection_ab_precent_p2.jpeg
plot3: output/infection_ab_precent_i1.jpeg
plot4: output/infection_ab_precent_i2.jpeg
plot5: output/infection_ab_precent_all.jpeg
csv1: output/prescribed_infection_prevalent.csv
csv2: output/prescribed_infection_incident.csv
describe_top10ABtypes_byInfection:
run: r:latest analysis/plot/abtypes_top10.R
needs: [generate_measures_infection]
outputs:
moderately_sensitive:
plot1: output/abtype_UTI.jpeg
plot2: output/abtype_URTI.jpeg
plot3: output/abtype_LRTI.jpeg
plot4: output/abtype_sinusitis.jpeg
plot5: output/abtype_ot_externa.jpeg
plot6: output/abtype_otmedia.jpeg
plot7: output/abtype_percent_UTI.jpeg
plot8: output/abtype_percent_URTI.jpeg
plot9: output/abtype_percent_LRTI.jpeg
plot10: output/abtype_percent_sinusitis.jpeg
plot11: output/abtype_percent_ot_externa.jpeg
plot12: output/abtype_percent_otmedia.jpeg
csv: output/abtype_top10_by_infection.csv
plot13: output/abtype_UTI_line.jpeg
plot14: output/abtype_URTI_line.jpeg
plot15: output/abtype_LRTI_line.jpeg
plot16: output/abtype_sinusitis_line.jpeg
plot17: output/abtype_ot_externa_line.jpeg
plot18: output/abtype_otmedia_line.jpeg
plot19: output/abtype_percent_UTI_line.jpeg
plot20: output/abtype_percent_URTI_line.jpeg
plot21: output/abtype_percent_LRTI_line.jpeg
plot22: output/abtype_percent_sinusitis_line.jpeg
plot23: output/abtype_percent_ot_externa_line.jpeg
plot24: output/abtype_percent_otmedia_line.jpeg
# describe_top10ABtypes_total:
# run: r:latest analysis/plot/types_ab_prescriptions.R
# needs: [generate_study_population]
# outputs:
# moderately_sensitive:
# plot1: output/abtype_all_Rx.jpeg
# plot2: output/abtype_all_Rx_percent.jpeg