Using Scoped dplyr verbs

Introduction Over the past several months, I have really started to increase the amount that I have been using scoped dplyr verbs. For those of you who don’t know about these functions, they are handy variants to the normal dplyr verbs, such as filter, mutate, and summarize, that allow you to target multiple columns or all of your columns. These functions allow for you to save yourself time and typing when you want to apply either one or multiple functions to more than one column, a group of columns, or to all of your columns.

Calculating quantiles for groups with dplyr::summarize and purrr::partial

Recently, I was trying to calculate the percentiles of a set of variables within a data set grouped by another variable. However, I quickly ran into the realization that this is not very straight forward when using dplyr’s summarize. Before I demonstrate, let’s load the libraries that we will need. library(dplyr) library(purrr) If you don’t believe me when I say that it is not straight forward, go ahead and try to run the following block of code.

Create a dynamic number of UI elements in Shiny with purrr

Introduction purrr is an incredibly powerful package that has greatly enhanced my R programming abilities. purrr has applications in pretty much any situation. One of the most useful situations, IMHO, is in the creation of a dynamic number of shiny UI elements. This can be extremely useful if you want to be able to create a dynamic number of ui elements (whether that be inputs or outputs) based on either user selection or the data being used.

PCA in a tidy(verse) framework

Introduction The other day, a question was posted on RStudio Community about performing Principal Component Analysis (PCA) in a tidyverse workflow. Conveniently, I had literally just worked through this process the day before and was able to post an answer. While most questions and answers are good as they are on forum sites, I thought this one might be worth exploring a little more since using the tidyverse framework makes PCA much easier, in my opinion.

Creating, Writing, Querying, and Modifying SQL Database from R using odbc, dbplyr, and DBI

Introduction Recently, I have been building shiny apps for work. The app that I am currently working on is an interface to a database for storing information about laboratory samples being collected. In addition to building the shiny app for my coworkers to interact with the database, I also was tasked with creating and building the database. I have never build a SQL database from scratch, but luckily the odbc and the DBI packages make it fairly straight foreward.