project.yaml

version: '3.0'

expectations:
  population_size: 1000

actions:

# study cohort

  generate_study_population_covid_primarycare:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_covid_primarycare
    outputs:
      highly_sensitive:
        cohort: output/input_covid_primarycare.csv
  
  generate_study_population_covid_SGSS:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_covid_SGSS
    outputs:
      highly_sensitive:
        cohort: output/input_covid_SGSS.csv

  generate_study_population_covid_admission:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_covid_admission
    outputs:
      highly_sensitive:
        cohort: output/input_covid_admission.csv

  process_1: 
    run: r:latest analysis/process_1.R
    needs: [generate_study_population_covid_primarycare, generate_study_population_covid_SGSS,generate_study_population_covid_admission]
    outputs:
      highly_sensitive:
        case: output/case_covid_hosp.csv 
        control: output/control_covid_infection.csv 

# matching

  matching: #R MatchIt  matching with replacement
    run: r:latest -e 'rmarkdown::render("analysis/matching.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
    needs: [process_1]
    outputs:
      moderately_sensitive:
        html: output/matching.html
      highly_sensitive: 
        rds1: output/matched_patients.rds
        rds2: output/unmatched_cases.rds
        csv: output/matched_patients_id.csv # unique patient ID
        
  check_unmatched:
    run: r:latest -e 'rmarkdown::render("analysis/check_unmatched.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
    needs: [matching]
    outputs:
      moderately_sensitive:
        html: output/check_unmatched.html

  extract_variables: # confounders
    run: cohortextractor:latest generate_cohort --study-definition study_definition_outcome --with-end-date-fix
    needs: [matching]
    outputs:
      highly_sensitive:
        cohort: output/input_outcome.csv

  process_Rmatching: #  confounders
    run: r:latest analysis/process_Rmatching.R
    needs: [extract_variables,matching]
    outputs:
      highly_sensitive:
        cohort1: output/matched_outcome.rds
        cohort2: output/matched_outcome_check.rds # filter died & de-regist again
        rds1: output/abtype79.rds
        rds2: output/comor17.rds

# extract ab for RF
  extract_variables_ab_time:   # exposure variables
    run: cohortextractor:latest generate_cohort --study-definition study_definition_ab_time --with-end-date-fix # unique matched patient ID
    needs: [matching]
    outputs:
      highly_sensitive:
        cohort: output/input_ab_time.csv

  process_ab_time: # exposures #merge ab time with mathced patients
    run: r:latest -e 'rmarkdown::render("analysis/process_ab_time.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
    needs: [extract_variables_ab_time,process_Rmatching]
    outputs:
      moderately_sensitive:
        html: output/process_ab_time.html
      highly_sensitive: 
         rds: output/matched_ab.rds

  model_RF_process: # merge 79 types of ab, split train and valid set
    run: r:latest -e 'rmarkdown::render("analysis/model_RF_process.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [process_ab_time,process_Rmatching]
    outputs:
      moderately_sensitive:
        html: output/model_RF_process.html
      highly_sensitive: 
        rds1: output/train_X.rds
        rds2: output/train_Y.rds
        rds3: output/valid_X.rds
        rds4: output/valid_Y.rds
        rds5: output/abtype.rds

  model_clogit: # coditional logistic regression for expo variables # create category variable
    run: r:latest -e 'rmarkdown::render("analysis/model_clogit.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest_decile,model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/model_clogit.html
        rds1: output/train_cat.rds
        rds2: output/valid_cat.rds 

  # classification_tree:  #decision tree check # category
  #   run: r:latest -e 'rmarkdown::render("analysis/classification_tree.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/classification_tree.html

  classification_tree_ind:  #decision tree check # category #uni+multi predictor #1:6 vs 1:1
    run: r:latest -e 'rmarkdown::render("analysis/classification_tree_ind.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/classification_tree_ind.html

  classification_tree_combined:  
    run: r:latest -e 'rmarkdown::render("analysis/classification_tree_combined.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/classification_tree_combined.html

  # classification_tree_all:  #decision tree check # category #all predictor
  #   run: r:latest -e 'rmarkdown::render("analysis/classification_tree_all.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/classification_tree_all.html

  # classification_tree_ind_rpart:  #decision tree check # category #per predictor
  #   run: r:latest -e 'rmarkdown::render("analysis/classification_tree_ind_rpart.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/classification_tree_ind_rpart.html

  # classification_tree_contd:  #decision tree check # contd
  #   run: r:latest -e 'rmarkdown::render("analysis/classification_tree_contd.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_RF_process]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/classification_tree_contd.html

  # model_tuneRF: #mtry, 
  #   run: r:latest -e 'rmarkdown::render("analysis/model_tuneRF.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_RF_process]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_tuneRF.html

  # model_RF_training: #
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RF_training.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_RF_process]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RF_training.html

  model_RandomForest: # pick variables for model training # contd
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest.html
        rds: output/model_RandomForest.rds
      highly_sensitive:
        rds1: output/development.rds 
        rds2: output/validation.rds 
        
  model_RandomForest_check: # check performance
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_check.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest_check.html

  # model_RandomForest_cat: # pick variables for model training # category #1:1 vs 1:6
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_cat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RandomForest_cat.html
  #     highly_sensitive:
  #       rds: output/model_RandomForest_cat.rds
  #      # train: output/train_6_cat.rds
  #      # valid: output/valid_6_cat.rds

  model_RandomForest_tune: # check prob
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_tune.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest_tune.html

  model_RandomForest_weighted:
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_weighted.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest_weighted.html

  model_RandomForest_recode:
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_recode.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest_recode.html
  


  # python

  # python RF
  pre_generate_notebook_RF: # rds to csv
    run: r:latest -e 'rmarkdown::render("analysis/python/pre_generate_notebook_RF.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_clogit]
    outputs:
      moderately_sensitive:
        html: output/pre_generate_notebook_RF.html
      highly_sensitive: 
        csv1: output/train_cat.csv
        csv2: output/valid_cat.csv

  generate_notebook_RF:
    run: jupyter:latest jupyter nbconvert /workspace/analysis/python/RF.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400
    needs: [pre_generate_notebook_RF]
    outputs:
      moderately_sensitive:
        notebook: output/RF.html 

  generate_notebook_boost:
    run: jupyter:latest jupyter nbconvert /workspace/analysis/python/boost.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400
    needs: [pre_generate_notebook_RF]
    outputs:
      moderately_sensitive:
        notebook: output/boost.html 

  # model_RF_ranger: # pick variables for model training # category 
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RF_weighted.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RF_weighted.html
  #     highly_sensitive:
  #       rds: output/model_RF_weighted.rds
       

  # model_RandomForest_check_cat: # check performance
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_check_cat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_RandomForest_cat]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RandomForest_check_cat.html

  # model_RandomForest_cat_ind: # pick variables for model training # category #6:6 # individual variables
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_cat_ind.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RandomForest_cat_ind.html
  
  # model_RandomForest_decile_cat: # create decile groups for probabilities # get counfounders  #6:6
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_decile_cat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_clogit,model_RandomForest]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RandomForest_decile_cat.html
  #     highly_sensitive:
  #       rds1: output/development_cat.rds 
  #       rds2: output/validation_cat.rds 

  model_cat: # coditional logistic regression for decile groups
    run: r:latest -e 'rmarkdown::render("analysis/model_cat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest]
    outputs:
      moderately_sensitive:
        html: output/model_cat.html    

  RF_descriptive_stat_cat: 
    run: r:latest -e 'rmarkdown::render("analysis/RF_descriptive_stat_cat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest]
    outputs:
      moderately_sensitive:
        html: output/RF_descriptive_stat_cat.html   
  # model_RF_clust: # use proximity
  #   run: r:latest -e 'rmarkdown::render("analysis/model_RF_clust.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_RF_process]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/model_RF_clust.html
  # #      csv1: output/var_tree.csv
  # #      rds: output/model_RandomForest.rds



  model_RandomForest_tree: # check tree
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_tree.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,model_RandomForest]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest_tree.html

  model_RandomForest_decile: # create decile groups for probabilities # get counfounders
    run: r:latest -e 'rmarkdown::render("analysis/model_RandomForest_decile.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,model_RandomForest,process_ab_time,process_Rmatching]
    outputs:
      moderately_sensitive:
        html: output/model_RandomForest_decile.html
        # rds1: output/development.rds
        # rds2: output/validation.rds

  RF_descriptive_stat: 
    run: r:latest -e 'rmarkdown::render("analysis/RF_descriptive_stat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest_decile]
    outputs:
      moderately_sensitive:
        html: output/RF_descriptive_stat.html

  model: # coditional logistic regression for decile groups
    run: r:latest -e 'rmarkdown::render("analysis/model.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest_decile]
    outputs:
      moderately_sensitive:
        html: output/model.html
  


  model_clogit_adjusted: # coditional logistic regression for expo variables
    run: r:latest -e 'rmarkdown::render("analysis/model_clogit_adjusted.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest_decile,model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/model_clogit_adjusted.html

  model_logistic: #  logistic regression for expo variables
    run: r:latest -e 'rmarkdown::render("analysis/model_logistic.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RandomForest_decile,model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/model_logistic.html


## updated method
  RF_model: # pick variables for model training #distinct # ab users # merge
    run: r:latest -e 'rmarkdown::render("analysis/RF_model.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/RF_model.html
        rds1: output/RF_model.rds
        rds2: output/RF_model_decile.rds

  RF_model_develop: # pick variables for model training #distinct # ab users # development
    run: r:latest -e 'rmarkdown::render("analysis/RF_model_develop.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,RF_model]
    outputs:
      moderately_sensitive:
        html: output/RF_model_develop.html
        rds1: output/RF_model_develop.rds
        rds2: output/RF_model_decile_develop.rds

  RF_model_valid: # pick variables for model training #distinct # ab users # validation
    run: r:latest -e 'rmarkdown::render("analysis/RF_model_valid.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,RF_model_develop]
    outputs:
      moderately_sensitive:
        html: output/RF_model_valid.html
        rds1: output/RF_model_decile_valid.rds

  RF_classification_check: 
    run: r:latest -e 'rmarkdown::render("analysis/RF_classification_check.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,RF_model_develop,RF_model_valid]
    outputs:
      moderately_sensitive:
        html: output/RF_classification_check.html
  
  descriptive_stat: 
    run: r:latest -e 'rmarkdown::render("analysis/descriptive_stat.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,RF_model_develop,RF_model_valid]
    outputs:
      moderately_sensitive:
        html: output/descriptive_stat.html

# # main analysis
 
  table1_round: 
    run: r:latest analysis/table1.R
    needs: [process_1,process_Rmatching]
    outputs:
      moderately_sensitive:
        csv1: output/table1_unmatched.csv
        csv2: output/table1_matched.csv
        csv3: output/table1_random.csv

  table2_round: 
    run: r:latest analysis/table2.R
    needs: [process_Rmatching]
    outputs:
      moderately_sensitive:
        csv1: output/table2_matched.csv
        csv3: output/table2_random.csv
  
  table3_round: # baseline table of exposure variables/ training &validation 
    run: r:latest analysis/table3.R
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        csv1: output/table3_train.csv
        csv2: output/table3_valid.csv
        csv3: output/table3_all.csv

# variables check
  check_variables: # check input
    run: r:latest -e 'rmarkdown::render("analysis/check_variables.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/check_variables.html


###### random 1 control #####
  classification_check: # RF # total_ab #1000trees used to compared with 6controls
    run: r:latest -e 'rmarkdown::render("analysis/classification_check.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/classification_check.html
        rds1: output/train_1control.rds
        rds2: output/valid_1control.rds
  

  
  classification_check_1_control: # RF # all # 1 control
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_1_control.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,classification_check]
    outputs:
      moderately_sensitive:
        html: output/classification_check_1_control.html

  classification_check_6_control: # RF # total_ab #1:6
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_6_control.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/classification_check_6_control.html

  classification_check_logi: #logistic #6 controls 
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_logi.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/classification_check_logi.html

  classification_check_logi_1_control: #logistic # single control # decile
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_logi_1_control.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/classification_check_logi_1_control.html        

#decile check
  classification_check_logi_1_control_decile: #logistic # single control # total ab decile
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_logi_1_control_decile.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/classification_check_logi_1_control_decile.html 

  classification_check_1_control_decile: # RF # all # 1 control # total ab decile
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_1_control_decile.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,classification_check]
    outputs:
      moderately_sensitive:
        html: output/classification_check_1_control_decile.html

# remove outlier
  classification_check_1_control_0.9: # RF # all # 1 control # remove90th outlier
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_1_control_0.9.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,classification_check]
    outputs:
      moderately_sensitive:
        html: output/classification_check_1_control_0.9.html

  classification_check_logi_1_control_0.9: #logistic # single control remove90th outlier
    run: r:latest -e 'rmarkdown::render("analysis/classification_check_logi_1_control_0.9.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/classification_check_logi_1_control_0.9.html  


  # classification_check_logi_1_decile: #logistic # single control # decile group
  #   run: r:latest -e 'rmarkdown::render("analysis/classification_check_logi_1_control_decile.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
  #   needs: [model_RF_process]
  #   outputs:
  #     moderately_sensitive:
  #       html: output/classification_check_logi_1_control_decile.html     











# distinct  
  model_RF_distinct: # pick variables for model training # distinct patients
    run: r:latest -e 'rmarkdown::render("analysis/model_RF_distinct.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/model_RF_distinct.html
        rds: output/model_RF_distinct.rds

  model_RF_distinct_check: 
    run: r:latest -e 'rmarkdown::render("analysis/model_RF_distinct_check.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,model_RF_distinct]
    outputs:
      moderately_sensitive:
        html: output/model_RF_distinct_check.html

# random 1 control 
  model_RF_random_1_control: # random pick one control in subclass
    run: r:latest -e 'rmarkdown::render("analysis/model_RF_random_1_control.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process]
    outputs:
      moderately_sensitive:
        html: output/model_RF_random_1_control.html
        rds: output/model_RF_random_1_control.rds

  model_RF_random_1_control_check: 
    run: r:latest -e 'rmarkdown::render("analysis/model_RF_random_1_control_check.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
    needs: [model_RF_process,model_RF_random_1_control]
    outputs:
      moderately_sensitive:
        html: output/model_RF_random_1_control_check.html


#   #######
#   model_tuneRF: #
#     run: r:latest -e 'rmarkdown::render("analysis/model_tuneRF.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [model_RF_process]
#     outputs:
#       moderately_sensitive:
#         html: output/model_tuneRF.html

#   check_ab_time:  
#     run: r:latest -e 'rmarkdown::render("analysis/check_ab_time.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
#     needs: [process_ab_time]
#     outputs:
#       moderately_sensitive:
#         html: output/check_ab_time.html
#       # highly_sensitive: 
#       #   rds: output/matched_patients_monthly_ab.rds


#   check_RF_grid: 
#     run: r:latest -e 'rmarkdown::render("analysis/check_RF_grid.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [process_ab_time]
#     outputs:
#       moderately_sensitive:
#         html: output/check_RF_grid.html

#   check_RF: 
#     run: r:latest -e 'rmarkdown::render("analysis/check_RF.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [process_ab_time]
#     outputs:
#       moderately_sensitive:
#         html: output/check_RF.html

#   model_RF: 
#     run: r:latest -e 'rmarkdown::render("analysis/model_RF.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [process_ab_time]
#     outputs:
#       moderately_sensitive:
#         html: output/model_RF.html

#   model_RF_process_subclass: # random sampling by subclass
#     run: r:latest -e 'rmarkdown::render("analysis/model_RF_process_subclass.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [process_ab_time]
#     outputs:
#       moderately_sensitive:
#         html: output/model_RF_process_subclass.html

#   model_RF_process_check_sample: # check sample method
#     run: r:latest -e 'rmarkdown::render("analysis/model_RF_process_check_sample.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [process_ab_time, process_Rmatching]
#     outputs:
#       moderately_sensitive:
#         html: output/model_RF_process_check_sample.html

# # check

#   process_filter_ab: # filter ab users
#     run: r:latest -e 'rmarkdown::render("analysis/process_filter_ab.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
#     needs: [process_Rmatching]
#     outputs:
#       moderately_sensitive:
#         html: output/process_filter_ab.html
#       highly_sensitive: 
#         csv: output/matched_patients_id_ab.csv

#   extract_variables_ab_yr1: 
#     run: cohortextractor:latest generate_cohort --study-definition study_definition_ab_yr1 --with-end-date-fix
#     needs: [process_filter_ab]
#     outputs:
#       highly_sensitive:
#         cohort: output/input_ab_yr1.csv

#   extract_variables_ab_yr2: 
#     run: cohortextractor:latest generate_cohort --study-definition study_definition_ab_yr2 --with-end-date-fix
#     needs: [process_filter_ab]
#     outputs:
#       highly_sensitive:
#         cohort: output/input_ab_yr2.csv

#   extract_variables_ab_yr3: 
#     run: cohortextractor:latest generate_cohort --study-definition study_definition_ab_yr3 --with-end-date-fix
#     needs: [process_filter_ab]
#     outputs:
#       highly_sensitive:
#         cohort: output/input_ab_yr3.csv

#   extract_variables_ab_yr3_15d: 
#     run: cohortextractor:latest generate_cohort --study-definition study_definition_ab_yr3_15d --with-end-date-fix
#     needs: [process_filter_ab]
#     outputs:
#       highly_sensitive:
#         cohort: output/input_ab_yr3_15d.csv


#   process_merge_ab: # merge 1-2-3 year ab 
#     run: r:latest -e 'rmarkdown::render("analysis/process_merge_ab.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
#     needs: [process_Rmatching,extract_variables_ab_yr3_15d, extract_variables_ab_yr3,extract_variables_ab_yr2,extract_variables_ab_yr1]
#     outputs:
#       moderately_sensitive:
#         html: output/process_merge_ab.html
#       highly_sensitive: 
#         rds: output/matched_patients_monthly_ab.rds

#   check_ab_yr1:
#     run: r:latest -e 'rmarkdown::render("analysis/check_ab_yr1.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
#     needs: [extract_variables_ab_yr1,matching,process_Rmatching]
#     outputs:
#       moderately_sensitive:
#         html: output/check_ab_yr1.html

#   check_ab_yr3:
#     run: r:latest -e 'rmarkdown::render("analysis/check_ab_yr3.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
#     needs: [process_Rmatching]
#     outputs:
#       moderately_sensitive:
#         html: output/check_ab_yr3.html
 
#   check_abtype:
#     run: r:latest -e 'rmarkdown::render("analysis/check_abtype.Rmd", knit_root_dir = "/workspace", output_dir="/workspace/output")'
#     needs: [process_Rmatching]
#     outputs:
#       moderately_sensitive:
#         html: output/check_abtype.html

#   check_process_1: 
#     run: r:latest -e 'rmarkdown::render("analysis/check_process_1.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [generate_study_population_covid_primarycare,generate_study_population_covid_SGSS,generate_study_population_covid_admission]
#     outputs:
#       moderately_sensitive:
#         html: output/check_process_1.html

#   # check_RF: 
#   #   run: r:latest -e 'rmarkdown::render("analysis/check_RF.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#   #   needs: [process_Rmatching]
#   #   outputs:
#   #     moderately_sensitive:
#   #       html: output/check_RF.html
  
#   # check_RF_grid: 
#   #   run: r:latest -e 'rmarkdown::render("analysis/check_RF_grid.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#   #   needs: [process_Rmatching]
#   #   outputs:
#   #     moderately_sensitive:
#   #       html: output/check_RF_grid.html
  
#   check_RF_yr1: 
#     run: r:latest -e 'rmarkdown::render("analysis/check_RF_yr1.Rmd", knit_root_dir = "/workspace", output_dir = "output")'
#     needs: [extract_variables_ab_yr1,matching,process_Rmatching]
#     outputs:
#       moderately_sensitive:
#         html: output/check_RF_yr1.html