A Fine Slice Of SQL Server
Steven Sanderson shows us the ways:
If you often work with Excel files and are looking to streamline your data import and export processes, R and Python offer some powerful packages to help you. Here, I’ll introduce you to some essential tools in both R and Python that will make handling Excel files a breeze.
Read on for three options in R and two in Python.
Comments closedColin Gillespie introduces an R package for MLOps:
Most R users are familiar with the classic workflow popularised by R for Data Science. Data scientists begin by importing and cleaning the data, then iteratively transform, model, and visualise it. Visualisation drives the modeling process, which in turn prompts new visualisations, and periodically, they summarise their work and report results.
Click through for a demonstration of how to create and deploy a model using vetiver.
Comments closedSteven Sanderson covers a package:
This article will introduce you to the
healthyRpackage.healthyRis a package that provides functions for analyzing and visualizing health-related data. It is designed to make it easier for health professionals and researchers to work with health data in R. It is an experimental package that is still under active development, so some functions may change in the future along with the package structure and scope.Unfortunately, the package needs some love and attention. Which I am trying to give it. Given that information, I will be updating the package to include more functions and improve the existing ones. I will also be updating the documentation and adding more examples to help users get started with the package.
So let’s get started!
Read on for that overview, including an explanation of why the package exists and several examples of how to use it.
Comments closedSteven Sanderson looks for a sub-string:
When working with text data in R, one common task is to check if a character or substring is present within a larger string. R offers multiple ways to accomplish this, ranging from base R functions to packages like
stringrandstringi. In this post, we’ll explore how to usegrepl()from base R,str_detect()fromstringr, andstri_detect_fixed()fromstringito achieve this.
Click through for three separate ways of doing this.
Comments closedSteven Sanderson introduces a new set of functions:
Greetings, fellow data enthusiasts! Today, we’re thrilled to unveil a fresh wave of functionalities in the ever-evolving TidyDensity package. Buckle up, as we delve into the realm of distribution statistics!
This update brings a bounty of new functions that streamline the process of extracting key parameters from various probability distributions. These functions adhere to the familiar naming convention
util_distribution_name_stats_tbl(), making them easily discoverable within your R workflow.
Read on for the list and an example of how to use them.
Comments closedTomaz Kastrun pulls out the color swatches:
A simple, yet effective way to set your colour palette in R using ggplot library.
Click through for the demonstration. Tomaz keeps the text very light in this post, so I’ll do a little vamping of my own. Creating a custom palette is neat, but do make sure that your custom palette works for users with color vision deficiency (CVD). Taking Tomaz’s bar chart into Coblis (an amazing tool I continue to use quite regularly), here’s what it looks like for people with protanopia—that is, no red cones in their eyes:
It’s not awful, particularly because Tomaz changed the fill but not the border color, so you get a funky striation effect.
But the real kicker is if you switch to the monochromatic option in Coblis.
Granted, I know of exactly one person with monochromacy, so if you want to be fair, this isn’t one I’d check for on a webpage. But the large majority of technical books have grayscale images because it saves money on printing, so if this were your sweet-looking color scheme and you’re adding the image into a book, readers would need to focus particularly hard on the bars to figure anything out.
Comments closedTomaz Kastrun builds a visual:
Calculating cumulative percentage or percentage per group for each time can sometimes be a task with a slight twist. Let’s check this with ggplot2 and tidyverse.
Click through for three separate ways of doing this.
Comments closedSteven Sanderson has a round of updates to share:
util_negative_binomial_aic(): Calculate the Akaike Information Criterion (AIC) for the negative binomial distribution. This function aids in model selection, helping you determine the best-fitting model for your data.
Read on for utility functions for a dozen or so distributions, as well as a few other small fixes.
Comments closedSteven Sanderson has an update for us:
I’m excited to share the latest updates to the healthyR.data R package! This release brings new functionality and minor improvements, all aimed at making your data management tasks easier and more efficient. Here’s a breakdown of what’s new:
Read on for information on four new functions and a couple of bugfixes.
Comments closedSteven Sanderson does a bit of splitting:
Splitting numbers into individual digits can be a handy trick in data analysis and manipulation. Today, we’ll explore how to achieve this using base R functions, specifically
gsub()andstrsplit(). Let’s walk through the process step by step, explain the syntax of each function, and provide some examples for clarity.
Click through for a pair of examples.
Comments closed