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library(DataQualityDashboard) | |
library(dplyr) | |
if (Sys.getenv("DONT_DOWNLOAD_JDBC_DRIVERS", "") == "TRUE") { | |
jdbcDriverFolder <- Sys.getenv("DATABASECONNECTOR_JAR_FOLDER") | |
} else { | |
jdbcDriverFolder <- tempfile("jdbcDrivers") | |
dir.create(jdbcDriverFolder) | |
downloadJdbcDrivers("postgresql", jdbcDriverFolder) | |
downloadJdbcDrivers("sql server", jdbcDriverFolder) |
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# eunomia based test script for achilles and ares | |
backupOptions <- options() | |
options(connectionObserver = NULL) | |
options(useFancyQuotes = FALSE) | |
Sys.setenv(EUNOMIA_DATA_FOLDER = "D:/OHDSI/EunomiaDatasets") | |
aresDataRoot <- "D:/git/Ares/public/data" | |
datasetOptions <- list( |
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options(connectionObserver = NULL) | |
library("dplyr") | |
library("tibble") | |
# This script leverages an existing WebAPI installation to compare two versions of the standard vocabulary and their impact on concept set resolution across a set of cohort definitions. | |
# base url of the WebAPI installation | |
baseUrl <- "https://epi.jnj.com:8443/WebAPI" | |
# if security is enabled authorize use of the webapi |
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library(httr) | |
library(tibble) | |
options(warn=-1) | |
# create data frame to store results | |
unitData <- tibble( | |
ResultBaseUnit = character(), | |
ResultQuantity = numeric(), | |
SourceUnit = character() |
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library("DBI") | |
library("Eunomia") | |
library("dplyr") | |
library("readr") | |
library("dbplyr") | |
connection = DBI::dbConnect(duckdb::duckdb(), dbdir=":memory:") | |
Eunomia::exportToCsv("D:/OHDSI/EunomiaCsv") | |
DBI::dbWithTransaction(connection, { |
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# TODO | |
# Potentially create a version with just Sqlite | |
# create_source_to_standard_vocab_map.sql - need to improve performance | |
# include additional measurement units in the vocabulary shard | |
# abstract creation of concepts.csv to create synthea concepts from concept extraction jar at execution | |
# add scan report to loop (whiterabbit) | |
options(connectionObserver = NULL) | |
options(useFancyQuotes = FALSE) |
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library(tidyr) | |
library(ggplot2) | |
library(dplyr) | |
# pull data from live updated repository | |
data <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv") | |
# pivot data format and convert dates | |
narrowData <- data %>% pivot_longer(cols = starts_with("X"), names_prefix = "X", names_to = "date", values_to = "count", values_drop_na = TRUE) | |
narrowData$date <- as.Date(narrowData$date, "%m.%d.%y") |
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<!doctype html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"> | |
<title>population pyramid integration</title> | |
</head> | |
<body> | |
</body> |