Programming Leftovers
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Rlang ☛ The Ultimate Guide to Creating Lists in R: From Basics to Advanced Examples
Lists are fundamental data structures in R programming that allow you to store multiple elements of different types in a single object. This comprehensive guide will walk you through everything you need to know about creating and working with lists in R.
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Michał Sapka ☛ Cool things I didn't knew: multiple Git repositories in a single directory
This tricks allows us to have any number of git repositories co-exist in the same directory. Those can share files (just git add them), or be completely different.
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Chris ☛ Probability-Generating Functions
I have long struggled with understanding what probability-generating functions are and how to intuit them. There were two pieces of the puzzle missing for me, and we’ll go through both in this article.
There’s no real reason for anyone other than me to care about this, but if you’ve ever heard the term pgf or characteristic function and you’re curious what it’s about, hop on for the ride!
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Rlang ☛ PAMLj: The new Power Analysis Module for jamovi
We’re excited to introduce a new power analysis module for jamovi, designed to simplify and enhance your research planning. This module supports a broad range of statistical tests, making it a useful tool for researchers across various fields.
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Rlang ☛ Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
Gradient boosting with any regression algorithm in Python and R package mlsauce.
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Medevel ☛ 9 Best Free Open-Source Tools to Convert Large CSV Files to JSON Efficiently
Converting CSV files is straightforward with many tools available. However, handling large data files can be challenging without reliable tools or scripts.
Here, we present the best open-source tools for converting large CSV files into JSON.
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Education
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Rlang ☛ 10 New Books added to Big Book of R + R community on BlueSky
There’s been a sudden jump in RStats peeps joining BlueSky, and some very familiar faces from the twitter days. You can find me here, and there’s a very handy “starter pack” which is a list of accounts to jumpstart your feed with!
We’ve got a bumper edition in this latest update of Big Book of R. 10 new books in total. Many thanks to Peter Kemp, Isabella Velásquez, David Díaz Rodríguez and the ever-mysterious “Gary” for their contributions.
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K Desktop Environment/KDE SC/Qt
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Akademy 2024 Experience
Getting There!
Wow! What a trip! 20 hours across 2 flights, 2 hours on the train with travel buddies Nate Graham and Bhushan Shah, and several bus "adventures" with Nate Graham to our hotel. The hotel... Let's not go into too much detail, suffice to say it was an absolute mess.
This being my first Akademy in person it was a very anxious experience getting there, but with global roaming on my phone to keep communication flowing and a few travel buddies it was certainly made much better!
But once we were settled in and unpacked, it was off to the first event!
The Welcome Event!
Wow, was it chaos once all the people showed up, but amazing to see so many KDE users and developers! A few locals even popped their head in, confused by the packed out venue. We thankfully managed to get a ride in Adriaan de Groot's smooth E.V to the venue and found a few others after parking.
The place had a great vibe and the free drinks and snack courtesy of KDE went down a treat! I quickly connected to the free Wi-Fi, spun up some translations of the menu and grabbed the Mexican Fries with guacamole, tomatoes and onions. It was amazing with a few drinks to wash it all down!
On to the main event!
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Python
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Linux Journal ☛ Unlocking Data Science Potential Understanding Machine Learning and Data Analysis with JupyterLab
In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development environment (IDE) provides a flexible and interactive workspace for performing data analysis, machine learning, and visualization, making it indispensable for professionals and enthusiasts alike.
In this guide, we will explore what makes JupyterLab so essential for data analysis and machine learning. We’ll look at its strengths and unique features, walk through the setup process, delve into its core functionalities, and explore best practices that will streamline workflows and maximize productivity. By the end, you’ll have a robust understanding of how JupyterLab can become an integral part of your data science journey.
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