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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = TRUE,
echo = TRUE,
message = TRUE,
warning = TRUE,
fig.width = 8,
fig.height = 6,
dpi = 200,
fig.align = "center",
fig.path = "man/figures/README-"
)
library(magrittr)
library(cohortBuilder)
library(shinyCohortBuilder)
library(cohortBuilder.db)
set.seed(123)
options(tibble.width = Inf)
iris <- tibble::as.tibble(iris)
options("tibble.print_max" = 5)
options("tibble.print_min" = 5)
```
# cohortBuilder.db <img src="man/figures/logo.png" align="right" width="120" />
cohortBuilder.db is database layer for [cohortBuilder](https://r-world-devs.github.io/cohortBuilder/) and [shinyCohortBuilder](https://r-world-devs.github.io/shinyCohortBuilder/).
The package allows you to operate with `cohortBuilder` API on remote tables stores within a database.
All of the operations are performed directly on the database server side!
[](https://github.io/r-world-devs/cohortBuilder.db/)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
## Installation
```{r, eval = FALSE}
# CRAN version
install.packages("cohortBuilder.db")
# Latest development version
remotes::install_github("https://github.com/r-world-devs/cohortBuilder.db")
```
## Overview
`cohortBuilder.db` allows you to use `cohortBuilder` and `shinyCohortBuilder` features working with database connection.
Comparing to the standard workflow (working on list of tables) you just need to create database tables configuration using `dbtables` where you provide database connection, schema name and table names.
Than pass the created object to `set_source` method:
```{r, eval = FALSE}
library(cohortBuilder)
library(cohortBuilder.db)
db_tables_conf <- dbtables(
connection = <db-connection>,
schema = <schema-name>,
tables = <vector-of-table-names-to-use>
)
librarian_db_source <- set_source(
db_tables_conf
)
```
and configure Cohort using the same rules known from `cohortBuilder`:
```{r, eval = FALSE}
librarian_db_cohort <- cohort(
librarian_source,
filter(
"discrete", id = "author", dataset = "books",
variable = "author", value = "Dan Brown"
),
filter(
"range", id = "copies", dataset = "books",
variable = "copies", range = c(5, 10)
),
filter(
"date_range", id = "registered", dataset = "borrowers",
variable = "registered", range = c(as.Date("2010-01-01"), Inf)
)
)
```
Now feel free to use all the known `cohortBuilder` features.
You can also use `shinyCohortBuilder` to use `cohortBuilder` features in shiny.
Just use db-based Cohort with the known approach:
```{r, eval = FALSE}
library(shiny)
library(shinyCohortBuilder)
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
cb_ui("librarian")
),
mainPanel(
verbatimTextOutput("cohort_data")
)
)
)
server <- function(input, output, session) {
cb_server("librarian", librarian_db_cohort)
output$cohort_data <- renderPrint({
input[["librarian-data-updated"]]
get_data(librarian_cohort)
})
}
shinyApp(ui, server)
```
## Acknowledgement
Special thanks to:
- [Kamil Wais](mailto:[email protected]) for highlighting the need for the package and its relevance to real-world applications.
- [Adam Foryś](mailto:[email protected]) for technical support, numerous suggestions for the current and future implementation of the package.
- [Paweł Kawski](mailto:[email protected]) for indication of initial assumptions about the package based on real-world medical data.
## Getting help
In a case you found any bugs, have feature request or general question please file an issue at the package [Github](https://github.com/r-world-devs/cohortBuilder.db/issues).
You may also contact the package author directly via email at [[email protected]]([email protected]).