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https://github.com/r-dbi/odbc

Connect to ODBC databases (using the DBI interface)
https://github.com/r-dbi/odbc

database odbc r

Last synced: 6 days ago
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Connect to ODBC databases (using the DBI interface)

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README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-",

  eval = as.logical(Sys.getenv("ODBC_EVAL_README", "false"))

)

```



# odbc odbc website



[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/odbc)](https://cran.r-project.org/package=odbc)

[![R-CMD-check](https://github.com/r-dbi/odbc/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/r-dbi/odbc/actions/workflows/R-CMD-check.yaml)

[![Codecov test coverage](https://codecov.io/gh/r-dbi/odbc/branch/main/graph/badge.svg)](https://app.codecov.io/gh/r-dbi/odbc?branch=main)



The goal of the odbc package is to provide a [DBI](https://dbi.r-dbi.org/)-compliant interface to [ODBC](https://learn.microsoft.com/en-us/sql/odbc/microsoft-open-database-connectivity-odbc) drivers. This makes it easy to connect databases such as [SQL Server](https://www.microsoft.com/en-us/sql-server/), Oracle, [Databricks](https://www.databricks.com/), and Snowflake.



The odbc package is an alternative to

[RODBC](https://cran.r-project.org/package=RODBC) and [RODBCDBI](https://cran.r-project.org/package=RODBCDBI) packages, and is typically much faster. See `vignette("benchmarks")` to learn more.



## Overview



The odbc package is one piece of the R interface to databases with support for ODBC:



```{r whole-game, eval = TRUE, echo = FALSE}

#| fig-alt: > 

#|   A diagram containing four boxes with arrows linking each pointing left to 

#|   right. The boxes read, in order, "R interface," "driver manager," 

#|   "ODBC driver," and "DBMS." The left-most box, R interface, contains 

#|   three smaller components, labeled "dbplyr," "DBI," and "odbc."

knitr::include_graphics("man/figures/whole-game.png")

```



Support for a given DBMS is provided by an **ODBC driver**, which defines how to interact with that DBMS using the standardized syntax of ODBC and SQL. Drivers can be downloaded from the DBMS vendor or, if you're a Posit customer, using the [professional drivers](https://docs.posit.co/pro-drivers/).



Drivers are managed by a **driver manager**, which is responsible for configuring driver locations, and optionally named **data sources** that describe how to connect to a specific database. Windows is bundled with a driver manager, while MacOS and Linux require installation of [unixODBC](https://www.unixodbc.org/). Drivers often require some manual configuration; see `vignette("setup")` for details.



In the **R interface**, the [DBI package](https://dbi.r-dbi.org/) provides a front-end while odbc implements a back-end to communicate with the driver manager. The odbc package is built on top of the

[nanodbc](https://nanodbc.github.io/nanodbc/) C++ library. To interface with DBMSs using R and odbc:



```{r r-interface, eval = TRUE, echo = FALSE}

#| fig-alt: >

#|   A high-level workflow for using the R interface in 3 steps. 

#|   In step 1, configure drivers and data sources, the functions 

#|   odbcListDrivers() and odbcListDataSources() help to interface with the 

#|   driver manager. In step 2, the dbConnect() function, called with the 

#|   first argument odbc(), connects to a database using the specified ODBC 

#|   driver to create a connection object "con." Finally, in step 3, that 

#|   connection object can be passed to various functions to retrieve 

#|   information on database structure, iteratively develop queries, and 

#|   query data objects.

knitr::include_graphics("man/figures/r-interface.png")

```



You might also use the [dbplyr package](https://dbplyr.tidyverse.org/) to automatically generate SQL from your dplyr code.



## Installation 



Install the latest release of odbc from CRAN with the following code:



```r

install.packages("odbc")

```



To get a bug fix or to use a feature from the development version, you can install the development version of odbc from GitHub:



```r

# install.packages("pak")

pak::pak("r-dbi/odbc")

```



## Usage



To use odbc, begin by creating a database connection, which might look something like this:



```{r}

#| eval: false

library(DBI)



con <- dbConnect(

  odbc::odbc(),

  driver = "SQL Server",

  server = "my-server",

  database = "my-database",

  uid = "my-username",

  pwd = rstudioapi::askForPassword("Database password")

)

```



(See `vignette("setup")` for examples of connecting to a variety of databases.)



`dbListTables()` is used for listing all existing tables in a database.



```{r}

#| eval: false

dbListTables(con)

```



`dbReadTable()` will read a full table into an R `data.frame()`.



```{r}

#| eval: false

data <- dbReadTable(con, "flights")

```



`dbWriteTable()` will write an R `data.frame()` to an SQL table.



```{r}

#| eval: false

dbWriteTable(con, "iris", iris)

```



`dbGetQuery()` will submit a SQL query and fetch the results:



```{R}

#| eval: false

df <- dbGetQuery(

  con,

  "SELECT flight, tailnum, origin FROM flights ORDER BY origin"

)

```



It is also possible to submit the query and fetch separately with `dbSendQuery()` and `dbFetch()`. This allows you to use the `n` argument to `dbFetch()` to iterate over results that would otherwise be too large to fit in memory.