CodeToRun.R

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################## READ ALL THE STEPS AND RUN LINE BY LINE #####################
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# Install dependencies -----
# install.packages('renv') # if not already installed, install renv from CRAN
# seperate lockfiles are povided for ipci and sidiap.
# the sidiap lockfile is also valid for R version 4.4.0
renv::restore(lockfile = 'renv_sidiap_r_4_3_2.lock') # this should prompt you to 
# install the various packages required for the study

# Load packages ------
library(DBI)
library(here)
library(CDMConnector)
library(dplyr)
library(CodelistGenerator)
library(DrugUtilisation)
library(IncidencePrevalence)
library(PatientProfiles)
library(CohortCharacteristics)
library(stringr)
library(RPostgres)

# Database connection details -----
# In this study we also use the DBI package to connect to the database
# set up the dbConnect details below (see https://dbi.r-dbi.org/articles/dbi for more details)
# make the db connection, example:

# dbiConnection <- DBI::dbConnect(RPostgres::Postgres(),
#                                 dbname = '...',
#                                 host = '...',
#                                 port = '...',
#                                 user = '...',
#                                 password = '...')
dbiConnection <- DBI::dbConnect('....')

# The name of the schema that contains the OMOP CDM with patient-level data
cdm_database_schema<-'....'

# The name of the schema where results tables will be created 
results_database_schema<-'....'

# make the CDM object, use connection details from previously
cdm <- CDMConnector::cdm_from_con(
  con = dbiConnection,
  cdm_schema = cdm_database_schema,
  write_schema = results_database_schema
)

# check database connection
# running the next line should give you a count of your person table
cdm$person %>% 
  tally()

# Run the study ------
source(here('r_scripts','000_RunStudy.R'))
# after the study is run you should have a folder called Macrolides in 
# the root of this project to be shared