In the second episode of our tidymodels series, Ellis Hughes and I cover splitting your data into test and training sets and then setting up recipes for pre-processing your data.

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.

Ellis Hughes and I just wrapped up our series on using SQL in R and have decided to move on to doing a series on tidymodels.

For those that don’t know, tidymodels is an approach to building machine learning models in R using tidyverse principles. Up until this point, most of our model building has been in either the native package for the given model or using the caret package (which tidymodels has now replaced). So, we are super excited to get into the tidymodels framework and learn along with you! Each week we will try and build on a different component of modeling within tidymodels.

This first week is a basic introduction to tidymodels and the broom package (which is automatically loaded with tidymodels). We cover how to set up the model and obtain the model outputs in a nice tidy manner.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

Ellis Hughes and I wrap up our SQL database/shiny series by taking a question from one of our viewers.

In TidyX 75, we built a {shiny} app that allowed the user to update a table and save the results back to the database. One of the viewers asked if we could address the issue of multiple users editing the table simultaneously, ultimately canceling out the notes that they are both writing to the database. So, we have addressed this in our recent episode.

To watch the screen cast, CLICK HERE.

To access the code, CLICK HERE.

This week on TidyX, Ellis Hughes and I discuss how to create an interactive table using the {DT} package in {shiny} that will allow the user to write comments, update the data, and then send that new information back to the database.

To watch the screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I continue our SQL in R series by discussing an important database task, joining two tables. Some of the things we cover:

• The different types of joins (LEFT JOIN, RIGHT JOIN, FULL JOIN, INNER JOIN, and ANTI JOIN)
• Doing a JOIN using SQL versus the local R environment and when you might choose one over the other
• Using the {microbenchmark} package to test which query is faster and performing optimally
• Finally, if you’d like more explicit info on creating JOINS in R, I wrote a blog post a little over a year ago that covers this topic in more detail, CLICK HERE.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.