How to Use dplyr::filter Within a Plumber API

Learn how to integrate `dplyr::filter` in your Plumber API to return filtered values based on user input parameters.
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How to Use dplyr::filter Within a Plumber API

Plumber is an R package that makes it easy to create APIs by simply decorating your R scripts with special comments. One common task when developing an API is filtering data based on user input, especially when using the popular dplyr package. In this guide, we will discuss how to use dplyr::filter within a Plumber API and provide an illustrative example for better understanding.

The Problem

You may have created a Plumber API that needs to return specific rows from a data frame based on user input. You can typically use dplyr::filter to accomplish this in R. However, when working with APIs, input parameters might not be passed in the same way you are used to when programming directly in R. For instance, you would usually filter something like group == "a" in standard R code, but for APIs, you need to handle the input differently.

The Solution

Let’s go through the solution step-by-step to implement filtering using dplyr::filter in a Plumber API.

Step 1: Set Up Your Data Frame

First, you need to define the data you will work with. Here is a simple data frame with groups and associated values:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Define Your API Endpoint

Next, define an endpoint that will accept a parameter for filtering. You can pass this parameter from a URL or a POST request:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Making the API Call

After defining the endpoint, you can invoke this API using a URL. For example, you would use the following URL to get the value for group "a":

[[See Video to Reveal this Text or Code Snippet]]

Handling Parameter Named the Same as Data Column

If you would like to keep the parameter name the same as the column name in your data frame, you can use the following approach. This uses .data and .env to disambiguate between the function argument and the data frame column:

[[See Video to Reveal this Text or Code Snippet]]

Again, you can call this API using:

[[See Video to Reveal this Text or Code Snippet]]

Important Notes

GET vs. POST Requests: The examples provided use a GET request for simplicity. However, the approach will work with POST requests too.

Input Handling: The input parameters will automatically come through as strings, so you do not need to worry about quoting them as you normally would when using dplyr without an API.

Conclusion

Integrating dplyr::filter into your Plumber API does not have to be complicated. By following the structure outlined in this post, you can create an endpoint that filters your data based on user input efficiently. This opens up a whole new world of possibilities for creating dynamic APIs leveraging R's data manipulation capabilities.

By understanding these steps, you're now equipped to incorporate filtering techniques in your own Plumber API projects. Happy coding!

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