This tutorial will illustrate how to use the cohortExplorer package created by the OHDSI communitiy and described here: https://ohdsi.github.io/CohortExplorer/. This package retrieves person-level data for persons included in a specific cohort and displays it in a user-friendly Shiny application. Because this package displays person-level data, any outputs should not be shared with individuals who are not active OHDSI Lab Users (with the exception of outputs based on Synpuf synthetic data).
We’ll need to install one new package: CohortExplorer
renv::install("OHDSI/CohortExplorer")We’ll generate a cohort from ATLAS as we did in vignette 3.
# Packages =====================================================================
library(CohortExplorer)
library(DatabaseConnector)
library(ohdsilab)
library(DatabaseConnector)
library(keyring)
library(CohortGenerator)
library(tidyverse)
# DB and ATLAS Connections =====================================================
atlas_url = "https://atlas.roux-ohdsi-prod.aws.northeastern.edu/WebAPI"
synpuf_schema = "omop_cdm_synpuf_110k_531"
write_schema = paste0("work_", keyring::key_get("db_username"))
# Setting connectionDetails for later use
Sys.setenv("DATABASECONNECTOR_JAR_FOLDER" = "Insert path to JDBC driver")
connectionDetails <- createConnectionDetails(
dbms = "redshift",
server = "ohdsi-lab-redshift-cluster-prod.clsyktjhufn7.us-east-1.redshift.amazonaws.com/ohdsi_lab",
port = 5439,
user = keyring::key_get("db_username"),
password = keyring::key_get("db_password"))
# Connecting to the database for the purpose of setting the following options
con = DatabaseConnector::connect(connectionDetails)
options(con.default.value = con)
options(schema.default.value = synpuf_schema)
options(write_schema.default.value = write_schema)
# Connecting to ATLAS
ROhdsiWebApi::authorizeWebApi(
atlas_url,
authMethod = "db",
webApiUsername = keyring::key_get("atlas_username"),
webApiPassword = keyring::key_get("atlas_password"))
# Choosing an ATLAS cohort definition
cohortId <- 4675
# Pulling the cohort definition from ATLAS
cohortDefinitionSet <- ROhdsiWebApi::exportCohortDefinitionSet(
baseUrl = atlas_url,
cohortIds = cohortId)
# Setting naming convention for cohort tables
cohortTableNames <- getCohortTableNames(cohortTable = "cohort")
# Creating cohort tables based on naming convention
createCohortTables(
connectionDetails = connectionDetails,
cohortTableNames = cohortTableNames,
cohortDatabaseSchema = write_schema)
# Generating the cohort population from the Synpuf dataset
cohortsGenerated <- generateCohortSet(
connectionDetails = connectionDetails,
cdmDatabaseSchema = synpuf_schema,
cohortDatabaseSchema = write_schema,
cohortTableNames = cohortTableNames,
cohortDefinitionSet = cohortDefinitionSet)Now, we build the CohortExplorer app.
CohortExplorer::createCohortExplorerApp(
connectionDetails = connectionDetails,
cohortDatabaseSchema = write_schema,
cdmDatabaseSchema = synpuf_schema,
cohortTable = "cohort",
cohortDefinitionId = cohortId,
exportFolder = "Insert path to folder you want application files created in",
databaseId = "SynPUF"
)The above code will generate several files in the export folder you designated. Navigate to that folder and open the README.md file. Follow the contained instructions to launch the Shiny application.
Note: If you run into errors when running renv::restore() within the CohortExplorer R project, run the following code instead, then move on to runApp().
# Install required packages
renv::install("lattice", "MASS", "Matrix", "mgcv", "nlme", "renv", "anytime",
"askpass", "base64enc", "BH", "bslib", "cachem", "cli",
"colorspace", "commonmark", "cpp11", "crayon", "crosstalk",
"curl", "data.table", "digest", "dplyr", "ellipsis", "evaluate",
"fansi", "farver", "fastmap", "fontawesome", "fs", "generics",
"ggplot2", "glue", "gtable", "highr", "htmltools", "htmlwidgets",
"httpuv", "httr", "isoband", "jquerylib", "jsonlite", "knitr",
"labeling", "later", "lazyeval", "lifecycle", "magrittr",
"memoise", "mime", "munsell", "openssl", "pillar", "pkgconfig",
"plotly", "promises", "purrr", "R6", "rappdirs", "RColorBrewer",
"Rcpp", "reactable", "reactR", "rlang", "rmarkdown", "sass",
"scales", "shiny", "shinycssloaders", "shinyWidgets",
"sourcetools", "stringi", "stringr", "sys", "tibble", "tidyr",
"tidyselect", "tinytex", "utf8", "vctrs", "viridisLite", "withr",
"xfun", "xtable", "yaml")
# Load the required packages
packages <- c("lattice", "MASS", "Matrix", "mgcv", "nlme", "renv", "anytime",
"askpass", "base64enc", "BH", "bslib", "cachem", "cli",
"colorspace", "commonmark", "cpp11", "crayon", "crosstalk",
"curl", "data.table", "digest", "dplyr", "ellipsis", "evaluate",
"fansi", "farver", "fastmap", "fontawesome", "fs", "generics",
"ggplot2", "glue", "gtable", "highr", "htmltools", "htmlwidgets",
"httpuv", "httr", "isoband", "jquerylib", "jsonlite", "knitr",
"labeling", "later", "lazyeval", "lifecycle", "magrittr",
"memoise", "mime", "munsell", "openssl", "pillar", "pkgconfig",
"plotly", "promises", "purrr", "R6", "rappdirs", "RColorBrewer",
"Rcpp", "reactable", "reactR", "rlang", "rmarkdown", "sass",
"scales", "shiny", "shinycssloaders", "shinyWidgets",
"sourcetools", "stringi", "stringr", "sys", "tibble", "tidyr",
"tidyselect", "tinytex", "utf8", "vctrs", "viridisLite", "withr",
"xfun", "xtable", "yaml")
lapply(
packages,
library,
character.only = TRUE)