A Fine Slice Of SQL Server
Steven Sanderson goes for a walk on the stock exchange:
In the world of time series analysis, Random Walks, Brownian Motion, and Geometric Brownian Motion are fundamental concepts used in various fields, including finance, physics, and biology. Today, we’ll explore these concepts using functions from the
healthyR.tspackage.
Click through to learn about each of these concepts and some examples of how you can generate time series datasets following each of them.
Comments closedSteven Sanderson toes a bit of tag replacement:
Hello, R enthusiasts! Today, we’re jumping into a common text processing task: extracting strings between specific characters. This is a great skill for data cleaning and manipulation, especially when working with raw text data. I’m going to show you how to achieve this using base R, the
stringrpackage, and thestringipackage. Let’s go!
Read on for examples.
Comments closedSteven Sanderson explains the purpose of a package:
The ultimate goal really is to make it easier to do data analysis and machine learning in R. The package is designed to be easy to use and to provide a wide range of functionality for data analysis. The package is also meant to help and provide some easy boilerplate functionality for machine learning. This package is in its early stages and will be updated frequently.
It also keeps with the same framework of all of the
healthyversepackages in that it is meant for the user to be able to use the package without having to know a lot of R. Many rural hospitals do not have the resources to perform this sort of work, so I am working hard to build these types of things out for them for free.
Read on to see how it works, including several examples of the package in action.
Comments closedSteven Sanderson saves some changes:
When working with data, exporting your results to an Excel file can be very handy. Today, I’ll show you how to write the iris dataset to an Excel file using R and Python. We will explore three R packages:
writexl,openxlsx, andxlsx, and theopenpyxllibrary in Python. Let’s dive in!
Click through for these four examples.
Comments closedSteven Sanderson provides some examples:
Today I am going to go over some quick yet practical examples of ways that you can use the
healthyR.tspackage. This package is designed to help you analyze time series data in a more efficient and effective manner.Let’s just jump right into it!
Read on for a few common time series activities, such as testing for stationarity, extracting tends from noise, and performing lagged correlation.
Comments closedColin Gillespie continues a series on vetiver:
In our previous blog, we provided an overview of MLOps and the {vetiver} package, creating and deploying a simple model locally. In this post, we’ll show you how to deploy a model to production using Posit Connect, SageMaker, and Docker.
Read on for those instructions and a demonstration.
Comments closedDario Radecic shows off a neat library:
The main criticism people have when it comes to ggplot2 is the static nature of the charts it has to offer. Truth be told, it will never be an interactive visualization king like Highcharts, but it doesn’t mean animation is out of the picture.
Meet R gganimate – a natural extension of ggplot2 that allows you to visualize your data change through time or some other variable, and then render and export the chart as a set of PNGs, or a single GIF/MP4.
Click through to learn more about it. I remembered the original gganimate and was going to say, “Wow, I hadn’t heard of that library in forever.” But it turns out that Thomas Lin Pedersen built a newer version of the library and has added in quite a bit of functionality since the last time I looked. H/T R-Bloggers.
Comments closedSteven 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 closed