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If you’re not familiar with the basics of dplyr (how to apply these verbs to data frames in local storage), you should take the time to do so through this introductory vignette). The dplyr package is a very popular data manipulation package that aims to provide a function for each basic verb of data manipulation: The easiest way to hook up to an external database from within your Shiny app is to use dplyr.

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It is, however, outside of the scope of these articles to explain further when you do need to connect to a database we will just assume that you’ve thought through your options and concluded that this is the best way to go.įinally, this series will only cover relational databases – in particular, relational databases whose drivers are DBI-compliant (which means that the R interface must have followed these steps these steps): SQLite, MySQL and PostgreSQL. (If your data fits in memory there is no advantage to putting it in a database: it will only be slower and more frustrating).

You have so much data that it does not all fit into memory simultaneously and you need to use some external storage engine.

This is particularly useful in two scenarios: To quote Hadley on when to use dplyr with databases vs in-memory data:Īs well as working with local in-memory data stored in data frames, dplyr also works with remote on-disk data stored in databases. Note that it isn’t always ideal to link up to an external database, as it may break and it is certainly more computationally expensive than dealing with local data.

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How to integrate the pool package with dplyr. How to manage connections, prevent leaks and ensure the best performance using the pool package How to use the DBI package to hook up to an external database How to use the dplyr package to read data from an external database The goal of this series of articles is to demystify this process and to introduce some brand new features that should make it easier. While this is already possible, so far it’s been mostly up to the app authors to figure out the appropriate database driver for R and how to manage the database connections within the app itself. Here are the installation instructions, so your code runs smoothly: # get shiny, DBI, dplyr and dbplyr from CRAN install.packages ( "shiny" ) install.packages ( "DBI" ) install.packages ( "dplyr" ) install.packages ( "dbplyr" ) # get pool from GitHub, since it's not yet on CRAN devtools :: install_github ( "rstudio/pool" ) OverviewĪs Shiny apps grow and become more complex, a recurring issue has been that of integrating an external database into an app. There are four packages that you need throughout this series of articles.

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If you want to continue using pool with an older version of dplyr, please install ( Installation Note (6/22/17): pool is now compatible with dplyr 0.7.0 and the brand new dbplyr.