R config: How to Manage Environment-Specific Configuration Files

Estimated time:
time
min

How many times have you encountered a file path issue when running your code on a different environment? Probably far too many. It doesn't need to be the case anymore - The R config package is here to allow you to manage environment-specific configuration files. The best part? You'll learn all about it today.

In this article, we'll go through a basic introduction to R config, explain how to use different configuration environments, and also show you how to use the package in R Shiny applications. Let's get started.
<blockquote>Want to start a career as an R Shiny Developer? <a href="https://appsilon.com/how-to-start-a-career-as-an-r-shiny-developer/" target="_blank" rel="noopener">Here's our how-to guide for 2022 and beyond</a>.</blockquote>
Table of contents:
<ul><li><a href="#introduction">Introduction to R config</a></li><li><a href="#different-env">How to Change Environment for the Configuration File</a></li><li><a href="#shiny">R config in R Shiny - How to Get Started</a></li><li><a href="#summary">Summing up R config</a></li></ul>

<hr />

<h2 id="introduction">Introduction to R config</h2>
The <a href="https://cran.r-project.org/web/packages/config/vignettes/introduction.html" target="_blank" rel="nofollow noopener">config package for R</a> makes it easy for developers to manage environment-specific configuration values. That's useful when you want to use specific values for development, testing, and production environments.

For example, maybe you're reading a dataset from different locations in different environments, or storing runtime logs differently. Maybe you're developing an app on a Windows machine and deploying it to a server that runs Linux. Or perhaps you're deploying in different environments (e.g. development and production), where you'll have different credentials and ways to access data.

The reasons are long, and R config is here to save you from headaches.

To get started, you'll have to install the package. It's available on CRAN, so the installation can't be any simpler:
<pre><code class="language-r">install.packages("config")</code></pre>
R config wants to read a <code>config.yml</code> file by default. You can change that by specifying a different value to the <code>file</code> parameter, but we'll stick to the convention today.

Start by creating a <code>config.yml</code> file and specifying values for default and production environments:
<pre><code class="language-yml">default:
 dataset: "/Users/dradecic/Desktop/iris.csv"
 n_rows_to_print: 10
 
production:
 dataset: "iris.csv"
 n_rows_to_print: 5</code></pre>
The config file just stores a path to the <a href="https://gist.github.com/netj/8836201" target="_blank" rel="nofollow noopener">Iris dataset</a>. For demonstration purposes, let's say it's stored on the Desktop on by default, and in the same folder as <code>config.yml</code> in a production environment.

The question is, <b>how can you access this file path in R?</b>

The process can't be any simpler, just read the configuration file and access a property with R's traditional <code>$</code> notation:
<pre><code class="language-r">config &lt;- config::get()
<br>print(config$dataset)
print(config$n_rows_to_print)</code></pre>
Here's the output:

<img class="size-full wp-image-16143" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b29f4b5d4048c02f96f0ab_1-3.webp" alt="Image 1 - Contents of the default environment" width="427" height="130" /> Image 1 - Contents of the default environment

For reference, if your config file wasn't named <code>config.yml</code>, you'd read it by running <code>config::get(file = "path/to/file")</code>.

You now know how to read a default environment, but what about others? Let's cover that next.
<h2 id="different-env">How to Change Environment for the Configuration File</h2>
The <code>R_CONFIG_ACTIVE</code> environment variable controls which environment is currently active. It's typically set either in <code>Renviron</code> or <code>Rprofile</code> file, but you can manually override it in your R script.

The best practice is to configure either of the two mentioned files on a production machine so you don't have to add anything to R scripts.

To manually set (or override) an environment variable in R, use <code>Sys.setenv()</code> function:
<pre><code class="language-r">Sys.setenv(R_CONFIG_ACTIVE = "production")
config &lt;- config::get()
<br>print(config$dataset)
print(config$n_rows_to_print)</code></pre>
<img class="size-full wp-image-16145" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b29ff6afe4df21c48ac220_2-3.webp" alt="Image 2 - Contents of the production environment" width="490" height="155" /> Image 2 - Contents of the production environment

You can pass these configuration values straight into any R code. For example, the snippet below reads the Iris dataset and prints the head with the specified number of rows. Both are set in the configuration file:
<pre><code class="language-r">config &lt;- config::get()
<br>df &lt;- read.csv(config$dataset)
head(df, config$n_rows_to_print)</code></pre>
<img class="size-full wp-image-16147" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b29ff75ec24573082e9cdf_3-3.webp" alt="Image 3 - Head of the Iris dataset" width="652" height="181" /> Image 3 - Head of the Iris dataset

You now know how to use R config and how to change environments. Next, you'll learn how to use it in R Shiny.
<h2 id="shiny">R config in R Shiny - How to Get Started</h2>
Using configuration files in R shiny applications is a straightforward process, but needed for the reasons we listed earlier. You're likely to be deploying your Shiny apps to a Linux environment. It uses a different file system than, let's say, Windows, so a dedicated config file is a good way to perform <i>file path translation</i>.

There are other obvious benefits to using config files, but you get the gist.

Onto the Shiny app. We'll keep it simple - two dropdown menus allowing you to change columns for the X and Y axes of a scatter plot. The configuration file is loaded right after the library import.

Here's the full code snippet for the app:
<pre><code class="language-r">library(shiny)
library(ggplot2)
<br># Configuration
config &lt;- config::get()
df_iris &lt;- read.csv(config$dataset)
features_iris &lt;- c("sepal.length", "sepal.width", "petal.length", "petal.width")
<br># Shiny app UI
ui &lt;- fluidPage(
 headerPanel("Iris dataset visualizer"),
 sidebarLayout(
   sidebarPanel(
     selectInput(inputId = "xcol", label = "X Axis Variable", choices = features_iris, selected = features_iris[1]),
     selectInput(inputId = "ycol", label = "Y Axis Variable", choices = features_iris, selected = features_iris[2])
   ),
   mainPanel(
     plotOutput("plot")
   )
 )
)
<br># Server logic
server &lt;- function(input, output) {
 output$plot &lt;- renderPlot({
   ggplot(df_iris, aes(x=.data[[input$xcol]], y=.data[[input$ycol]])) +
     geom_point(size = 5, aes(color = variety)) +
     theme(legend.position = "top")
 })
 
}
<br>
shinyApp(ui, server)</code></pre>
Let's see it in action:

<img class="size-full wp-image-16149" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b29f0dc94ac8aeb3b36f2b_4.gif" alt="Image 4 - R Shiny app that uses the R config package" width="958" height="562" /> Image 4 - R Shiny app that uses the R config package

There's nothing special or groundbreaking about the app, but it perfectly demonstrates how to use R config in R Shiny. Let's wrap things up next.

<hr />

<h2 id="summary">Summing up R config</h2>
Long story short, configuration files make your life as a developer easier and can alleviate (some) headaches. It's a good idea to set up a configuration file at the beginning of the project, so you don't have to change a large code base at once.

Configuration files are also a lifesaver in R Shiny apps, as they tend to rely on I/O and database - and you don't want to hardcode these.

<i>What are your thoughts on R config? Is it your preferred method for managing configuration files, or do you use something else?</i> Please let us know in the comment section below. Also, feel free to move the discussion to Twitter - <a href="http://twitter.com/appsilon" target="_blank" rel="noopener">@appsilon</a>. We'd love to hear from you.
<blockquote>Did you know Shiny is also available for Python? <a href="https://appsilon.com/shiny-for-python-introduction/" target="_blank" rel="noopener">Read our first impressions and getting started guide</a>.</blockquote>


Contact us!
Damian's Avatar
Damian Rodziewicz
Head of Sales
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Have questions or insights?
Engage with experts, share ideas and take your data journey to the next level!
Join Slack
Explore Possibilities

Take Your Business Further with Custom Data Solutions

Unlock the full potential of your enterprise with our data services, tailored to the unique needs of Fortune 500 companies. Elevate your strategy—connect with us today!

Talk to our Experts
r
shiny
tutorials