Programming Leftovers

posted by Roy Schestowitz on Nov 27, 2022

• 5 NeoVim GUI Editors You Could Try If You are Not a Total Terminal Junkie - It’s FOSS

  Vim is awesome. NeoVim is newer and even more awesome. Both Vim and NeoVim are terminal-based text editors with similar features.

• RObservations #42: Using the jinjar and tidyRSS packages to make a simple newsletter template - bensstats

  Jinja is a powerful templating engine that is useful in a variety of contexts. Recently, I discovered how its possible to use the power of Jinja syntax in R with the jinjar package written by David C Hall. With jinjar and the tidyRSS package by Robert Myles it is possible to make an email template that can provide short and informative updates. In his blog, I’m going to share how the jinjar and tidyRSS packages work and show how to combine them to make a simple daily email newsletter.

• XGBoost’s assumptions - finnstats

  XGBoost’s assumptions, First will provide an overview of the algorithm before we dive into XGBoost’s assumptions.

  Extreme Gradient Boosting, often known as XGBoost, is a supervised learning technique that belongs to the family of machine learning algorithms known as gradient-boosted decision trees (GBDT).

• 6 Best Python IDE(s) and Code Editor(s) [Ed: This list unfortunately starts with Microsoft's proprietary software that spies on users; this isn't a goof recommendation at all.]

  We list the six best Python code editor(s) for Ubuntu and other Linux distros and Windows in 2022.

  Python is everywhere today, and it is arguably the C programming language of the modern era. You can find Python everywhere, from websites, apps, data science projects, and AI to IoT devices. So being a popular programming language of this decade, it is essential to know the development environment of Python, where developers create applications, especially if you are starting afresh.

• Tree Based Methods: Exploring the Forest

  I was recently reading my copy of “An Introduction to Statistical Learning” (my Amazon affiliate link) and got the chapter about the different tree based methods. I am pretty familiar with Random Forest, but a few of the other methods are new to me. Let’s explore these different techniques.

  For these examples, I will explore the glass dataset from the openml site. This dataset has 9 different features used to predict the type of glass. The dataset has 214 observations.

  The dataset is downloaded with the following code. This requires the farff package to open the arff files used on the openml site.

• Saturn Elephant - The lazy numbers in R: correction

  Because of a change I did in the lazyNumbers package, I have to post a correction to my previous post.

  The as.double function, called on a lazy number, was not stable.

• Top 10 Data Visualisation Tools

  Top 10 Data Visualisation Tools, Data Science, one of the most established areas of study and practice in the IT sector, has been in the spotlight for almost a decade.

  It has proven to be beneficial in numerous industry verticals as well. This technology involves deriving essential insights from data, from top-notch approaches to market analysis.

  Following the data collection, it is processed by data analysts who further examine the data to identify patterns and then forecast user behavior using those patterns.

  Tools for data visualization are used in this stage. The top data visualization tools for data scientists to try, as well as several, will be covered in this post.

• Layton R blog - Introducing formatdown

  Convert the elements of a numerical vector or data frame column to character strings in which the numbers are formatted using powers-of-ten notation in scientific or engineering form and delimited for rendering as inline equations in an rmarkdown document.

• Analyzing Projected Calculations Using R - R Views

  Nicolas Nguyen works in the Supply Chain industry, in the area of Demand and Supply Planning, S&OP and Analytics, where he enjoys developing solutions using R and Shiny. Outside his job, he teaches data visualization in R at the Engineering School EIGSI and Business School Excelia in the city of La Rochelle, France.

• Codegen in Hare v2

  I spoke about code generation in Hare back in May when I wrote a tool for generating ioctl numbers. I wrote another code generator over the past few weeks, and it seems like a good time to revisit the topic on my blog to showcase another approach, and the improvements we’ve made for this use-case.

  In this case, I wanted to generate code to implement IPC (inter-process communication) interfaces for my operating system. I have designed a DSL for describing these interfaces — you can read the grammar here. This calls for a parser, which is another interesting topic for Hare, but I’ll set that aside for now and focus on the code gen. Assume that, given a file like the following, we can parse it and produce an AST