[Read more...]

Introduction Yesterday I posted on performing a benchmark on reading in a compressed .csv.gz file of a 2,000 by 2,000 data.frame. It was brought to my attention by someone on Mastadon (@[email protected] - https://fediscience.org/@marivi... [Read more...]

Introduction I received an email over the weekend in regards to my last post not containing the reading in of gz compressed file(s) for the benchmarking. While this was not an over site per-se it was a good reminder that people would probably be... [Read more...]

Introduction I will demonstrate how to generate a 1,000 row and column matrix with random numbers in R, and then save it in different file formats. I will also show how to get the file size of each saved object and benchmark how long it takes to... [Read more...]

[Read more...]

Introduction The CCI30 Crypto Index is a cryptocurrency index that tracks the performance of the top 30 cryptocurrencies by market capitalization. It was created in 2017 by a team of researchers and analysts from the CryptoCompare and MVIS indic... [Read more...]

[Read more...]

Introduction In this post I will talk about the use of the R functions apply(), lapply(), sapply(), tapply(), and vapply() with examples. These functions are all designed to help users apply a function to a set of data in R, but they differ in t... [Read more...]

Introduction I had just recently posted on making an attempt to speedup computations with my package {TidyDensity} using a purely data.table solution, yes of course I can use {dtplyr} or {tidytable} but that not the challenge put to me. My origi... [Read more...]

Introduction If you live in New York and rely on heating oil to keep your home warm during the colder months, you know how important it is to keep track of heating oil prices. Fortunately, with a bit of R code, you can easily access the latest heating oil prices ... [Read more...]

Introduction So I was challanged by Adrian Antico to learn data.table, so yesterday I started with a single function from my package {TidyDensity} called tidy_bernoulli(). So let’s see how I did (hint, works but needs a lot of improvement, so I’... [Read more...]

Introduction The imap() function is a powerful tool for iterating over a list or a vector while also keeping track of the index or names of the elements. This function applies a given function to each element of a list, along with the name or in... [Read more...]

[Read more...]

[Read more...]

Introduction Are you tired of manually manipulating text data in R? Do you find yourself frequently needing to extract substrings from long strings or to grab just the first few characters of a string? If so, you’re in luck! The {healthyR} libra... [Read more...]

Inroduction When writing a function, it is possible that you may want to ask the user where they want the data stored and if they want to open the file folder after the download has taken place. Well we can do this in R by invoking the shell.exe... [Read more...]

Introduction There are many approaches to modeling time series data in R. One of the types of data that we might come across is a nested time series. This means the data is grouped simply by one or more keys. There are many methods in which to a... [Read more...]

Introduction There are many different methods that one can choose from in order to model their data. This brings with it a fundamental issue of how to prepare your data for the specified algorithm. With the [{healthyR.ai}] package there are many... [Read more...]

Introduction In time series analysis, it is common to split the data into training and testing sets to evaluate the accuracy of a model. However, it is important to ensure that the model is calibrated on the training set before evaluating its pe... [Read more...]

Introduction The {tidyAML} package is an R package that provides a set of tools for building regression/classification models on the fly with minimal input required. In this post we will discuss the create_workflow_set() function. The create_wor... [Read more...]