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If you’re serious and competitive when it comes to gaming (like you should be), then it’s in your best interest to drop the antiquated quips and platitudes and get with the program. With proximity chat becoming ever more popular in some of the latest Battle Royale and First-Person Shooters, it has never been more important to know how to verbally thrash your opponent into oblivion after a good PK and teabag. “Are you saying they’re abdicating their duty to scrutinize me? Are you saying that they don’t have the guts to put the questions to me? Great supine protoplasmic invertebrate jellies!” Boris Johnson, Mayor of London Unlike Mr. Johnson, you don’t need to graduate at the top of your class from Oxford University to have a rich gaming vocabulary and to properly be able to articulate to your team that you’re responsible for the friendly-fire; due in-part to your nagging girlfriend. All you have to do is memorize this guide by heart and then you’ll never have to worry about another immature keyboard warrior giving you any more watercooler banter. The Complete List Gaming Slang Terms The most comprehensive and unadulterated list of gaming slang terms below is in alphabetical order, and applies to traditional games as much as it does to the more novel . Finding any particular phrase or word that you may be looking for is a complete non-issue. Web3 games The alphabetically ordered gaming terminology list is also segmented by genres so you don’t have to spend to much of your time and attention on thoroughly understanding how to deal with scrubs. General Gaming Terms – a PC gaming technology that aims to deliver better image quality without adding any additional strain on a graphics card. This results in higher frame rates and a better overall quality of game. Deep Learning Super Sampling (DLSS) – a setting that determines how much of your in-game surroundings you can actually see. Field of View (FOV) – a genre of games that feature a top-down player view and was originally popularized by DOTA. Multiplayer Online Battle Arena (MOBA) – the added immersion and ability to interact with in-game, cinematic cut-scenes. Quick Time Event (QTE) First-Person Shooter Gaming Terminology – when a highly skilled player goes berserk and annihilates all members of an opposing team all by themselves. ACE – players refer to a ranged weapon’s (typically a gun but could be a bow and arrow) ADS when discussing how long (in seconds or milliseconds) it takes to aim down the sight of a weapon when right clicking or pressing the left bumper on a controller. Aim Down Sights (ADS) – what players refer to characters as in the FPS VALORANT. Agent (VALORANT) – The AWP is a military spec sniper rifle that’s used by the Arctic Warfare Police in CS:GO. Those who use the AWP are known as AWPers and are feared due to their ability to quick-scope headshots. AWPer (CS:GO) – some FPS games have a game mode known as search and destroy. The bombsite is where one of the teams has to plant and detonate a bomb in order to successfully win the round. Bombsite – Non-Player Characters (NPCs) that are automata and typically act entirely based on a predetermined ruleset that is programmed into the game. Advanced bots can be governed by an artificial intelligence that is less predictable. Bots – the act of rapidly jumping and crouching in rapid succession so as to avoid getting hit by the enemy bullets. Bunny Hopping – someone who hides after spawning in-game instead of heading to the frontlines like all of the other players. In some instances, camping may be advantageous to those who provide sniper support fire. Camper – if rounds in an require the use of resources, some teams may agree to play a few practice rounds and minimize resource spend in order to save the remaining resources for future rounds. Economy Round FPS game – someone who aggressively attacks and kills an enemy member of the opposing forces. Frag – this is short for “get wrecked”. This is an insulting term that is acceptable to use upon defeating your opponent. Get Rekt – means “Good Game. Well Played.” GGWP – means “Good Luck, Have Fun.” GLHF – any player who hacks to gain an advantage and violates a game’s terms of service is deemed a haxor. Such individuals are less-than-adequate and will typically ruin the game for everyone else playing with them. Haxor – stands for elite and usually refers to players who are highly skilled and well geared. Leet – players who use alphanumeric and special characters in rapid succession to substitute the original grammar (i.e. l337, h4xor, etc.). L33t Speak – a player who thinks they’re L337 but in fact is rather horrendous in practice. A noob will usually be on the south-end of a leaderboard and may ragequit if triggered. Noobxor (noob) – usually means “please”, but can be used to annoy other players (“noobxor plox”). Plox – when a player flawlessly destroys another player without any loss of health. Pwned – players who are noobs and can’t handle the mental anguish of constantly losing to those better than they are may ragequit. Ragequitting is the act of vehemently turning off the console and throwing controllers through walls while profusely yelling unintelligible expletives at those who may be in the player’s vicinity. Ragequit – unlike a n00b, a scrub is a player who has been playing for a long time yet their skills haven’t improved in the slightest. Scrub – some rounds limit all of the players’ choice of weapons to only shotguns. Shotties Only – this refers to a round in a game that is stacked entirely to one-side. A “stomp” is preclusion to a round that is unbalanced and won’t prove to be a challenge to the team that has all of the best players. Stomp – someone who crouches on a deceased enemies face over and over again after a kill for the kill cam is known as a teabagger; for obvious reasons. Teabag MMORPG Gaming Terminology – In Player-versus-Environment (PvE), the additional enemy NPCs that spawn in large numbers during a main fight or battle; typically during a complex boss fight. Adds – stands for “Away From Keyboard”. Players who are idle in-game and are distracted with other unimportant activities are considered to be Away From Keyboard. In some MMORPGs, if you’re AFK for too long, the server will automatically boot you. AFK – also known as the “Aggro Radius”; this is the invisible radius around your in-game character that will provoke NPC mobs to engage you and begin attacking you in battle. Aggro – short for “Area of Effect”. This refers to skills, spells, abilities, and buffs that effect a certain area as opposed to a specific targeted NPC or player. AoE – short for “Damage per Second”. The amount of damage that a player can inflict on NPCs or other players in a single battle is represented as the DPS metric. There are two forms of DPS; Melee and ranged (guns and spells) DPS. DPS – often resembling bullying, this is when a group of players team up against another single player to take them out and collect their loot. Gank – players request heals in combat from other friendly players who have the ability to remedy the loss of health. Heals may take the form of spells and buffs or resources such as potions, bandages, and med-kits. Heals – the ability for a player to keep a boss or mob aggroed at a distance while moving around in a pattern that avoids taking substantial damage. Kite – stands for “Looking for Group”. In MMORPGs, players will look for or announce in chat that they are seeking to either start of join a group of other players to complete a task that they alone cannot do; such as a quest, a dungeon, or a raid. LFG – stands for “Looking for Party”. A common phrase that is used interchangeably with Looking for Group (LFG). LFP – these are standard PvE enemy NPCs that exist within MMORPG worlds. Mobs – buffs are passive and temporary spells that boost player stats and skill sets. When a player requests to stack buffs, they are requesting that another player adds additional buffs to their character in order to multiply the positive effects caused the buffs. Stack Buffs – a tank is an in-game character that takes all of the damage in a party. Tank – provides the ability for a player to change their avatar’s appearance while retaining stats of the original gear. Transmog – common in-game loot that has such a large supply that the marketplace has deemed the item or resource in question practically worthless. Vendor Trash – a common tactic used by n00bs who find power in numbers. These are typically brutes and are far from being skilled tacticians as the only way for them to find solace is to overpower an enemy with a large group of people. Zerg Gaming Terminology Recap You’re now equipped with the latest and most commonly used gaming slang terms that the pros use on the daily. Whether you’re playing a riveting and fast-paced First-Person Shooter or you’re slowly grinding for experience points in an MMORPG, you’ll know how to respond to a fellow gamer without appearing like a complete noob. Use these gamer slang terms in combination with IRL chatter for best results. Also published . here

A Comprehensive List of Gaming Slang Terms That You’ll Ever Need

This paper is available on arxiv under CC 4.0 license. Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References Abstract Protein-protein interactions (PPIs) are critical for various biological processes, and understanding their dynamics is essential for decoding molecular mechanisms and advancing fields such as cancer research and drug discovery. Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Predicting the impact of mutations on binding affinity is valuable but experimentally challenging. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. Machine learning-based methods, fueled by extensive PPI datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in predicting binding affinity changes. However, accurate predictions and generalization of these models across different datasets remain challenging. Geometric graph learning has emerged as a powerful approach, combining graph theory and machine learning, to capture structural features of biomolecules. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored geometric subgraphs to extract informative features, demonstrating superior performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased predictions of GGLPPI for both direct and reverse mutations. The findings underscore the potential of GGL-PPI in accurately predicting binding free energy changes, contributing to our understanding of PPIs and aiding drug design efforts. — geometric graph, machine learning, protein-protein interactions, mutation, binding free energy changes Keywords 1 Introduction Protein-protein interactions (PPIs) play a fundamental role in numerous biological processes, including cell signaling, metabolic pathways, and immune responses [1;2;3]. Understanding PPIs and their dynamics is crucial for unraveling the intricate mechanisms underlying these processes and holds significant implications for various fields, such as cancer research, drug discovery, and personalized medicine [3;4]. The effects of mutations on PPIs have drawn substantial attention due to their potential impact on protein function and cellular behavior [5;6;7;8]. Missense mutations, which involve single amino acid substitutions, can disrupt the binding affinity between proteins and their partners [9;10]. Such alterations can lead to malfunctioning PPI networks, resulting in diseases, drug resistance, or other molecular disorders [11;12;13;14;15;16]. Therefore, accurate prediction of the impact of mutations on binding affinity holds significant importance in understanding disease mechanisms, facilitating therapeutic interventions, and enabling the design of innovative biopharmaceuticals. One of the key parameters used to assess the impact of mutations on PPIs is the binding free energy change (∆∆G). This thermodynamic parameter quantifies the difference in binding affinity between the wild-type and mutant protein complexes. Experimental determination of ∆∆G values, while accurate, can be tedious and costly. Consequently, there has been a surge in the development of computational methods to predict these energy changes. Broadly, these computation approaches fall into two main categories: physics-based and machine learning-based methods. The former, rooted in biophysical principles, delves into protein conformations and offers a rigorous approach [17;18;19]. However, they often demand significant computational resources and are not always scalable. On the other hand, machine learning-based methods have gained popularity due to their scalability and rapid prediction capabilities. Leveraging the wealth of data from PPI datasets such as ASEdb [20], PINT[21], ProTherm[22], SKEMPI [23;24], and others [25;26;27], machine learning models like mCSM28, BindProf 6 , iSEE29, MutaBind [7] , and several others [30;31;32;33] have been developed. These models have shown significant potential in predicting ∆∆Gs. However, challenges such as imbalanced training datasets, generalization across different PPI datasets, and the intricacy of capturing complex sequence-structure-function relationships remain obstacles [34;35;36;37]. This underscores the need for further research to enhance machine learning methodologies, ensuring accurate and efficient ∆∆G predictions. In recent years, geometric graph learning has emerged as a promising approach for analyzing complex biomolecular systems [38;39;40]. By representing proteins and their interactions as graphs, this methodology leverages the power of graph theory and machine learning to capture essential structural and spatial features of the biomolecular complexes. Specifically, the use of geometric subgraphs, which encode local interactions between atoms and residues, offers a rich representation. This not only sheds light on intricate molecular details but also provides insights into their impact on binding affinity [39]. This work presents a novel method, called GGL-PPI (Geometric Graph Learning for Protein-Protein Interactions), which combines the principles of geometric graph learning and machine learning to predict mutation-induced binding free energy changes. The workflow of GGL-PPI is depicted in Figure 1. Central to its methodology, GGL-PPI utilizes atom-level graph coloring and multi-scale weighted colored geometric subgraphs, enabling the extraction of informative features from protein structures and their interactions. These features serve as inputs to a gradient-boosting tree model, which facilitates precise and consistent predictions of binding free energy change upon mutations. When compared with existing models, GGL-PPI consistently outperforms state-of-the-art approaches across all datasets. Further addressing its generalizability, GGL-PPI was evaluated on a blind test set, Ssym dataset [36]. This evaluation was conducted using a homology-reduced balanced training set to avert data leakage, showcasing GGL-PPI’s robust performance and ability to produce unbiased predictions for both direct and reverse mutations. This paper is available on under CC 4.0 license. Arxiv

GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes

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Welcome to my blog! As a seasoned software developer with extensive experience in Python, data science, and machine learning, I'm excited to share with you 20 essential libraries that will help elevate your skills in data mining, data visualization, and data processing. Let's dive right in! Python 1. Pandas is a powerful, open-source library that provides data manipulation and analysis tools for Python. It is particularly well-suited for handling structured data. Description: Pandas Pandas offers , a convenient way to manipulate and analyze tabular data, similar to tables in a spreadsheet. Feature: DataFrames From data cleaning and preprocessing to simple data exploration and analysis, Pandas is widely used in various data science projects. Application: Pros: Intuitive syntax Excellent performance Extensive documentation Cons: High memory usage for large datasets Limited scalability Link to a project to learn better: 10 Minutes to Pandas 2. NumPy is a fundamental library for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices and includes a collection of mathematical functions. Description: NumPy NumPy's core feature is the , which is a powerful and versatile n-dimensional array object. Feature: ndarray NumPy is used extensively in data science, machine learning, and scientific computing for linear algebra, Fourier analysis, and more. Application: Pros: Efficient array operations Widely supported by other libraries Large community and ecosystem Cons: Steeper learning curve for beginners Less intuitive than Pandas for data manipulation Link to a project to learn better: NumPy Quickstart Tutorial 3. Scikit-learn Scikit-learn is a widely-used, open-source machine learning library that provides simple and efficient tools for data mining and data analysis. Description: Scikit-learn offers a comprehensive collection of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction. Feature: Scikit-learn is widely used in industry and academia for building machine learning models and developing data-driven applications. Application: Pros: Easy-to-use API Extensive documentation and examples Active development and community support Cons: Not optimized for deep learning Limited GPU support Link to a project to learn better: Scikit-learn Tutorial: Wine Classifier 4. Matplotlib Matplotlib is a popular Python library for creating static, interactive, and animated visualizations in a variety of formats. Description: Matplotlib provides a high-level interface for drawing attractive and informative graphs, plots, and charts. Feature: Matplotlib is used for creating visualizations in data exploration, data analysis, and presentation of results. Application: Pros: Highly customizable Wide range of supported plots and charts Active community and development Cons: Complex API for advanced use-cases Less interactive compared to other libraries Link to a project to learn better: Matplotlib Pyplot Tutorial 5. Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating statistical graphics. Description: Seaborn Seaborn comes with built-in themes, color palettes, and advanced functions for creating beautiful, easy-to-read plots with fewer lines of code. Feature: Seaborn is ideal for exploring and understanding data through visualization, particularly for statistical analysis and visualizing complex datasets. Application: Pros: Easy to use and intuitive Integrates well with Pandas DataFrames Excellent support for categorical data visualization Cons: Less customizable than Matplotlib Slower performance for large datasets Link to a project to learn better: Seaborn Tutorial: Visualizing the Titanic Dataset 6. Plotly Plotly is a powerful, interactive graphing library for Python, R, and JavaScript, allowing users to create visually stunning, web-based data visualizations. Description: Plotly supports a wide range of chart types, such as scatter plots, bar charts, and heatmaps, with interactive features like zoom, pan, and hover tooltips. Feature: Plotly is ideal for creating interactive dashboards, web applications, and sharing visualizations online. Application: Pros: Highly interactive Wide range of supported chart types Cross-platform compatibility Cons: Requires an internet connection for some features Can be slow for large datasets Steeper learning curve compared to other libraries Link to a project to learn better: Plotly Getting Started Guide 7. TensorFlow is an open-source machine learning library developed by Google, designed for high-performance numerical computation and deep learning. Description: TensorFlow TensorFlow provides an extensive and flexible ecosystem of tools, libraries, and community resources for building and deploying machine learning models. Feature: TensorFlow is widely used in research and production for deep learning applications, such as image and speech recognition, natural language processing, and reinforcement learning. Application: Pros: Scalable and high-performance GPU support for faster computation Active development and large community Cons: Steeper learning curve for beginners Can be complex for simple use-cases Link to a project to learn better: TensorFlow Quickstart for Beginners 8. Keras Keras is a user-friendly, high-level neural networks API, written in Python and capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, and others. Description: Keras provides a simple, modular, and extensible interface for building and training deep learning models with minimal code. Feature: Keras is widely used for prototyping and building deep learning models for various applications, such as computer vision, natural language processing, and more. Application: Pros: Easy to use and intuitive Supports multiple backends Large community and support Cons: Less flexible than TensorFlow for advanced use-cases Performance may be slower than lower-level libraries Link to a project to learn better: Keras Getting Started Guide 9. PyTorch is an open-source machine learning library developed by Facebook, offering a flexible deep learning framework with strong GPU acceleration. Description: PyTorch PyTorch provides an intuitive and dynamic interface for building, training, and deploying deep learning models, along with extensive support for tensor computation. Feature: PyTorch is popular in research and industry for deep learning, computer vision, natural language processing, and reinforcement learning applications. Application: Pros: Dynamic computation graph Strong GPU support Active development and large community Cons: Steeper learning curve than Keras Less mature ecosystem compared to TensorFlow Link to a project to learn better: PyTorch 60 Minute Blitz 10. Dask Dask is a parallel computing library for Python that enables users to harness the power of multi-core processors and distributed computing clusters. Description: Dask provides a flexible and efficient way to parallelize operations on large datasets, offering parallelized versions of NumPy arrays, Pandas DataFrames, and more. Feature: Dask is well-suited for out-of-core and distributed computing tasks, such as big data processing, machine learning, and advanced analytics. Application: Pros: Scales seamlessly from single-core to multi-core and distributed systems Integrates well with existing Python libraries Highly customizable and extensible Cons: Steeper learning curve for advanced features Some limitations compared to native Pandas and NumPy functionality Link to a project to learn better: Dask Tutorial 11. Scrapy Scrapy is an open-source web crawling framework for Python that allows you to extract data from websites easily and efficiently. Description: Scrapy provides a robust and extensible architecture for building web spiders, with built-in support for handling common web scraping tasks like logging in and handling cookies. Feature: Scrapy is ideal for web scraping, data mining, and extracting structured data from websites for further processing and analysis. Application: Pros: Flexible and powerful Extensible through plugins Comprehensive documentation Cons: Steeper learning curve for beginners Overkill for small-scale scraping projects Link to a project to learn better: Scrapy Tutorial: Quotes Spider 12. Beautiful Soup is a Python library designed for web scraping purposes to pull data out of HTML and XML files. Description: Beautiful Soup Beautiful Soup provides an easy-to-use interface for parsing HTML and XML documents, making it simple to navigate, search, and modify the parse tree. Feature: Beautiful Soup is widely used for web scraping tasks, such as extracting information from websites, cleaning and preprocessing text data, and more. Application: Pros: Easy to use and beginner-friendly Robust parsing capabilities Handles malformed HTML gracefully Cons: Slower parsing compared to other libraries Limited support for advanced web scraping tasks Link to a project to learn better: Beautiful Soup Quickstart Guide 13. XGBoost XGBoost (eXtreme Gradient Boosting) is a highly efficient and scalable implementation of gradient-boosted decision trees, designed for speed and performance. Description: XGBoost provides a flexible and parallelized boosting library, with support for various loss functions, regularization, and early stopping. Feature: XGBoost is widely used in machine learning competitions and real-world applications for its exceptional performance in classification, regression, and ranking problems. Application: Pros: Fast and efficient Handles large datasets with ease Regularization and early stopping features Cons: Can be prone to overfitting Requires careful tuning of hyperparameters Link to a project to learn better: XGBoost Tutorial: Boston Housing Dataset 14. OpenCV OpenCV (Open Source Computer Vision Library) is a powerful and widely-used library for real-time computer vision, image processing, and machine learning. Description: OpenCV provides a rich set of functions and algorithms for image and video processing, feature extraction, object detection, and machine learning. Feature: OpenCV is extensively used in applications such as robotics, self-driving cars, augmented reality, facial recognition, and more. Application: Pros: Efficient and optimized performance Wide range of supported algorithms Active community and development Cons: Steeper learning curve for beginners Limited high-level API compared to other libraries Link to a project to learn better: OpenCV Python Tutorials 15. ggplot ggplot is a Python data visualization library based on R's ggplot2 and the Grammar of Graphics. It aims to provide a simple and concise way to create beautiful, customizable plots. Description: ggplot offers a declarative approach to visualization, allowing you to build complex plots by adding layers, scales, and themes to a base plot object. Feature: ggplot is ideal for creating static, publication-quality visualizations for data exploration, analysis, and presentation. Application: Pros: Elegant and expressive syntax Highly customizable Consistent and coherent plotting system Cons: Less mature than Matplotlib and Seaborn Limited interactivity compared to other libraries May have a learning curve for those unfamiliar with the Grammar of Graphics ggplot: Getting Started Link to a project to learn better: Conclusion These 15 essential Python libraries will help you tackle various tasks in data science and machine learning, from data mining and visualization to data processing. With a solid foundation in these tools, you'll be well on your way to becoming a successful data scientist or machine learning engineer. So, start exploring these libraries, work on the linked projects, and expand your skill set! Happy coding!

15 Essential Python Libraries for Data Science and Machine Learning

You’re been working on a project in RStudio for days and are knee-deep in code and loaded libraries. Things are going great…until they aren’t anymore 🙄. Suddenly, the dreaded error messages pop up when you try to utilize a library you’ve been using effortlessly so far. You decide to ignore it for now and worry about it later. But the next time you open your RStudio session, another problem arises. It’s time to update your RStudio software. You reason with yourself and decide to take care of it now because you know all too well, these issues and indicators ONLY KEEP COMING 😱. These problems can be scary and intimidating when you’re in the middle of a workflow, even for the most experienced programmers. For one, you have to disrupt the ‘ ’ that you have magically achieved. No one wants to stop what they’re doing so they can jump into troubleshooting mode. flow state Second, programming can be finicky and there are many moving pieces. Once you’ve found a working method, you just don’t want to mess with it. Updating your software leads to updating your libraries and it’s impossible to know if those updates may lead to bugs in your code. Table of Contents: What is R and RStudio? Why you should keep your software updated. How to check your current version of R and RStudio. Sidetrack - Updating R packages. How to update R. How to update RStudio. Troubleshooting. R and RStudio - What’s the Difference? R is the open-source programming language itself, and is the integrated development environment (IDE) that allows users to and have easy access to tools, data, and visualizations in a tidy console-like environment. Unfortunately, you have to update them independently. Although, you can get away with only updating RStudio most of the time and leaving R alone. RStudio code with R Why You Should Stay up to Date A common reaction to this scenario is to put off the updates and “think about it later”. That might work for a little while, but here’s why you should just deal with it right away and keep your software updated. Benefit from the latest new features of RStudio. Repair issues that you’re having. Remedy underlying bugs that RStudio programmers have updated, even if they haven’t affected your workflow (yet). Eliminate those pesky warning messages. Maintain R and RStudio in a happy state of being together. How To Check Your Current Version of R and RStudio There are a few methods to check your current software version(s). It’s good to take note of the version(s) you are using now, just in case you run into any issues and decide you want to revert backward. This is unlikely, but you might as well be cautious. RStudio - Method 1: Mostly, you’ll see a pop-up window like the one below when you open RStudio. This tells you your current version and which version is available. RStudio - Method 2: If you’re not receiving these update indications, you can still check for updates manually. First, you navigate to the and select (highlighted in yellow below). Help tab Check for Updates Clicking this will prompt the same window as before to pop up. R - Method 1: To check your current version of R, type into the console (seen in blue font below). Running the command will yield the following results. The results show that R version 4.2.1 is currently in use. sessionInfo() R - Method 2: Things are getting redundant now, but the easiest method to check your version of R (and the funniest) is to simply open RStudio, look to the console, and read the text displayed.You’ll notice the project team that developed this version gave it a humorous name. In this case, R version 4.2.1 is also known as “Funny-Looking-Kid” (highlighted in yellow below). 😭 A Sidetrack You might not be ready to update RStudio altogether, but you’re receiving error messages and bugs related to packages. Keep in mind you can try updating the troublesome package and see if that remedies the error. Navigate to your where you can see a list of RStudio packages. Click the (see image below). Packages tab Update button After clicking on the , a new window will appear like the image shown below. The new window informs you of the package version you have installed currently and which version is available to be installed. Update Packages tab You can also click on the for the package of interest to see what has been updated. Clicking this button will open a browser with a list of information. For example, clicking reveals numerous changes and bug fixes in the new version (see the image below). NEWS button ggplot2’s NEWS button Updating Packages: Method 1: Update Every Package Click at the bottom to select all packages. Select All Click . Install Updates Method 2: Update Only Select Packages Click the box next to the package(s) you wish to update. Select . Install Updates A warning prompt requesting to restart R will appear, click . Yes After the update is complete, you will notice the changes listed in the console. Your package version should be updated within the list of packages. How to Update R If you’re planning on updating both R and RStudio, it’s more logical to update R first but you can update them in either order. If you’re only planning on updating RStudio, proceed to the next section. Navigate to and look up the latest version of R. To download the latest version, you’ll have to choose your which is sorted regionally. CRAN is a network of servers that store the latest versions of R. They ask to choose the mirror “nearest to you to minimize network load”. Download and install the file. r-project CRAN mirror Confirm that you have successfully installed the latest version and move on to updating RStudio. The image below shows that R has been updated to version 4.2.3 “Shortstop Beagle”. How to Update RStudio Now you know what version you are running, what next? From the updater popup window, click on . This action will close your RStudio session and will open automatically in your browser. Quit and Download RStudio Desktop - Posit Download the latest version, open the installation file, and you will be greeted with the RStudio Setup window. Next, you can select your Start Menu Folder, and which version of R you would like to use. If you have installed the latest R, select the specific version. After updating, open a new session of RStudio and check for updates. You should see the following window. Troubleshooting Both R and RStudio should be updated now. But your console session in RStudio reads that you are still using the old version of R. What gives? You may have to do one last step to select the updated version of R. In RStudio, under the , select . Tools tab Global Options On the next window, under the section, select the correct directory for your updated R version by clicking the button. R General Change On the next window that appears, select the updated version of R. Finally, close your RStudio session, reopen it, and inspect the console. It should read the latest version of R installed. In my case, I am now using R 4.2.3 or “Shortstop Beagle”. Final Thoughts Now you have learned how to update R, RStudio, and your packages. Things should be smooth sailing now and you can get back into working in the zone. Note: This tutorial was performed on a Windows computer. Lead image source: stable diffusion v2.1 with prompt “Beagles playing baseball”.

Be a Shortstop Beagle: Learn How to Update R and RStudio to the Latest Version

শর্টস্টপ বিগল হও: সর্বশেষ সংস্করণে R এবং RStudio আপডেট করতে শিখুন

Seien Sie ein Shortstop Beagle: Erfahren Sie, wie Sie R und RStudio auf die neueste Version aktualisieren

Conviértase en un campocorto Beagle: aprenda cómo actualizar R y RStudio a la última versión

Soyez un Beagle d'arrêt-court : apprenez à mettre à jour R et RStudio vers la dernière version

शॉर्टस्टॉप बीगल बनें: R और RStudio को नवीनतम संस्करण में अपडेट करना सीखें

ショートストップ ビーグルになる: R と RStudio を最新バージョンに更新する方法を学ぶ

유격수 비글이 되어 보세요: R 및 RStudio를 최신 버전으로 업데이트하는 방법 알아보기

Seja um Shortstop Beagle: Aprenda como atualizar R e RStudio para a versão mais recente

Kısa Stoper Beagle Olun: R ve RStudio'yu En Son Sürüme Nasıl Güncelleyeceğinizi Öğrenin

Trở thành một Beagle Shortstop: Tìm hiểu cách cập nhật R và RStudio lên phiên bản mới nhất

成为游击手比格犬：了解如何将 R 和 RStudio 更新到最新版本

If you’re seeing this interview draft, it means you’ve recently published on HackerNoon . For this reason, we would like to help the community get to know you better as well as find out some writing tips from you. a story that the community found interesting and/or valuable While this template is automatic, our interest in the answers below is genuine and our human editors (and some cyborg wannabes) will review it before publishing. So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Hello techsters and netizens! I am the Editor of a tech blog, The Tech Panda, and a writer who accidentally fell into the tech world and now can’t stop gawping at all the new-fangled stuff that happens every day. Interesting! What was your latest Hackernoon Top story about? My was about ChatGPT breaking bad after it was released onto the playground that’s the Internet, reflecting humanity’s bias and deep-seated flaws. latest story Do you usually write on similar topics? If not, what do you usually write about? Well, ChatGPT is the hottest topic right now, and like all other techs, it was baffling enough to pique my interest. Usually, AI is one of my fav topics, along with crypto, robotics, esports, and any other tech that impacts society. I am also working on a sci-fi fantasy novel right now, fiction being my original passion. Great! What is your usual writing routine like (if you have one?) Copious amounts of reading news, books, and blogs help me keep up. While reading so much, thoughts are bound to rush in, and that’s when I put them into words. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Tech is indeed a challenge for me, coz I am from a literature background. My biggest challenge is making sense of the tech enough to be able to connect it with real life. At such times, an interview with an expert helps. What is the next thing you hope to achieve in your career? I am always aiming for that story that breaks the internet. Also, I, one day, want to be interviewed for having written the best book. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Cozying up with a book with cuppa Joe and a box of chocolates is my fav pass time. Other than that, I enjoy goofing around with my family a lot. My teenage son and I often read aloud books assuming different characters. I cherish those moments. Do you have a non-tech-related hobby? If yes, what is it? When it comes to hobbies, I am quite versatile; apart from writing stories and poetry, I love to sew, cook, craft, paint, and have a few strange collections. What can the Hacker Noon community expect to read from you next? There are definitely more ChatGPT stories coming up. Also, I am working on a piece on social media. What’s your opinion on HackerNoon as a platform for writers? HackerNoon is a boon for writers like me who aren’t that good with social media and spreading the word. The platform is easy to use, and their interactions are fun. Thanks for taking time to join our “ ” series. It was a pleasure. Do you have any closing words? Meet the writer This has been fun. I hope to keep audiences thrilled with my future writing.

Meet the Writer: HackerNoon's Contributor Navanwita Bora Sachdev, Editor

Python and R are two of the most popular programming languages used for statistical analysis, data analysis and machine learning. Both languages offer a rich collection of tools and libraries for working with data and are widely used in academia, research and industry. R is more focused on statistical analysis and has a simpler syntax, while Python has versatility, which enables it to have a broader range of applications and libraries. In this article, we will focus on the , including classification, regression analysis, clustering and time series analysis. Best Pre-Installed R Datasets Commonly Used for Statistical Analysis Pre-installed datasets are datasets that come with a piece of software or a platform. In R, these datasets provide a convenient way for users to get started with statistical analysis and machine learning without having to spend time searching for or creating their datasets. Ultimate List of Pre-Installed R Datasets 1. Mtcars This dataset includes information about various car models and their performance characteristics. The mtcars dataset is also derived from the 1974 Motor Trend US magazine and comprises 32 observations on 11 variables. The variables include: Mpg - The mpg variable represents miles per gallon (mpg) for the car model. Cyl - The cyl variable represents the number of cylinders in the engine of the car model. Disp - The disp variable represents the displacement of the car model, which is the volume of air and fuel mixture that the engine can compress and burn in one cycle, in cubic inches. Hp - The hp variable represents the gross horsepower of the car model. Drat - The drat variable represents the rear axle ratio of the car model. Wt - The wt variable represents the weight of the car model, in thousands of pounds. Qsec - The qsec variable represents the time taken to cover a quarter-mile distance from a standing start. Vs - The vs variable represents the type of engine (0 = V-shaped, 1 = straight). Am - The am variable represents the type of transmission (0 = automatic, 1 = manual). Gear - The gear represents the number of forward gears in the car model. Carb - The carb variable represents the number of carburettors in the engine of the car model. The can be loaded into R by typing data(mtcars), or it can be downloaded by clicking . Mtcars dataset here . 2 ChickWeight The ChickWeight, or chickwts, dataset includes information about the weight of chickens over time. The dataset also has 578 observations on 4 variables. The variables include: Weight - The weight variable represents the weight of the chicken in grams. Time - The time variable represents the time in days at which the weight was measured. Chick - The chick variable represents the ID of the chicken. Diet - The diet variable represents the type of diet that the chicken was fed during the experiment. The dataset can be downloaded into R by typing data(ChickWeight), or it can be downloaded by clicking . ChickWeight here 3. CO2 The CO2 dataset includes measurements of atmospheric carbon dioxide (CO2) concentrations at the Mauna Loa Observatory in Hawaii, taken from March 1958 to December 2001. The dataset also has 468 observations on 2 variables. The variables include: CO2 - This variable represents the atmospheric CO2 concentration in ppm, as measured at the Mauna Loa Observatory in Hawaii. Plant - This variable represents the type of plant on which the CO2 concentration was measured. There are two types of plants: "Quebec" and "Mississippi." The can be downloaded into R by typing data(CO2), or it can be downloaded by clicking . CO2 dataset here 4. Iris This dataset includes measurements of the sepal length, sepal width, petal length and petal width of 150 iris flowers, which belong to 3 different species: setosa, versicolor and virginica. The iris dataset has 150 rows and 5 columns, which are stored as a dataframe, including a column for the species of each flower. The variables include: Sepal.Length - The sepal.length represents the length of the sepal in centimetres. Sepal.Width - The sepal.width represents the width of the sepal in centimetres. Petal.Length - The petal.length represents the length of the petal in centimetres. Species - The species variable represents the species of the iris flower, with three possible values: setosa, versicolor and virginica. The can be loaded into R by typing data(iris), or it can be downloaded by clicking . Iris dataset here 5. Boston Housing The Boston Housing dataset includes housing prices and related factors in the Boston area. The dataset was obtained from information collected by the U.S. Census Service concerning housing in the area of Boston, Massachusetts. The dataset also comprises 506 observations on 14 variables. The variables include: Crim - The crim variable represents the per capita crime rate by town. Zn - The zn variable represents the proportion of residential land zoned for lots over 25,000 sq.ft. Indus - The indus variable represents the proportion of non-retail business acres per town. Chas - The chas variable represents whether the property is located along the Charles River or not. Nox - The nox variable represents the nitrogen oxides concentration (parts per 10 million) in the air. Rm -The rm variable represents the average number of rooms per dwelling. age: This variable represents the proportion of owner-occupied units built prior to 1940. Dis -The dis variable represents the weighted distances to five Boston employment centres. Rad - The rad variable represents the index of accessibility to radial highways. Tax - The tax variable represents the full-value property-tax rate per $10,000. Ptratio - The ptratio variable represents the pupil-teacher ratio by town. Black - The black variable represents the proportion of blacks by town. Lstat - The lstat variable represents the percentage of the lower status of the population. Medv - The medv variable represents the median value of owner-occupied homes in $1000s. The can be loaded into R by typing data(Boston), or it can be downloaded by clicking . Boston Housing dataset here 6. Airquality This dataset includes daily air quality measurements in New York, between May to September 1973. It also consists of 153 observations on 6 variables. The variables include: Ozone - The ozone variable represents the maximum daily ozone concentration in parts per billion (ppb) measured at a monitoring station in New York. Solar - R: The solar.R variable represents the daily solar radiation in Langleys (a measure of solar energy) measured at the same monitoring station. Wind - The wind variable represents the average daily wind speed in miles per hour (mph). Temp - The temp variable represents the average daily temperature in degrees Fahrenheit. Month - The month variable represents the month in which the observation was made (a value between 5 and 9). Day - The day variable represents the day of the month on which the observation was made (a value between 1 and 31). The can be loaded into R by typing data(air quality), or it can be downloaded by clicking . Airquality dataset here 7. Titanic The Titanic dataset includes information about the passengers aboard the Titanic, which includes whether they survived or not. The dataset also contains 891 rows and 12 variables and is also based on the passenger list of the ill-fated maiden voyage of the Titanic, which sank in the North Atlantic Ocean on April 15th,1912, after colliding with an iceberg. The variables include: PassengerId - This variable represents the unique identifier for each passenger on the Titanic. Survived - This variable represents whether the passenger survived or not. A value of 0 indicates that the passenger did not survive, while a value of 1 indicates that the passenger did survive. Pclass - This variable represents the passenger's class of travel. There were three classes on the Titanic: first, second, and third. Name - This variable represents the passenger's name. Sex - This variable represents the passenger's gender. Age - This variable represents the passenger's age. SibSp - This variable represents the number of siblings/spouses that the passenger had on board the Titanic. Parch - This variable represents the number of parents/children that the passenger had on board the Titanic. Ticket - This variable represents the ticket number of the passenger. Fare - This variable represents the fare that the passenger paid for their ticket. Cabin - This variable represents the cabin number of the passenger. Embarked - This variable represents the port of embarkation for the passenger. There were three ports of embarkation on the Titanic: Cherbourg, Queenstown, and Southampton. The can be loaded into R by typing data(titanic), or it can be downloaded by clicking . Titanic dataset here 8. Faithful This dataset includes measurements of the eruption and waiting times between eruptions for the Old Faithful geyser in Yellowstone National Park. The Faithful dataset also contains 272 observations on 2 variables. The variables include: Eruption time - The eruption time variable represents the duration of the current eruption in minutes. Waiting time - The waiting time variable represents the length of time between the current and the previous eruption in minutes. The can be loaded into R by typing data(faithful), or it can be downloaded by clicking . Faithful dataset here 9. Orange The Orange dataset includes growth measurements of orange trees. The dataset also contains 35 observations on 3 variables. The variables include: Tree - A numeric vector that represents the tree number (1-5). Age - A numeric vector that represents the age of the tree (in years). Circumference - A numeric vector that represents the trunk circumference of the tree (in mm). The can be loaded into R by typing data(Orange), or it can be downloaded by clicking . Orange dataset here 10. PlantGrowth This dataset includes the results of an experiment on the effect of fertilizer on plant growth. The dataset also contains 30 observations on 2 variables. The variables include: Weight - A numeric vector that represents the weight of plants (in grams). Group - A factor variable that represents the fertilizer treatment group (either "ctrl" for the control group or "trt" for the treated group). The can be loaded into R by typing data(PlantGrowth), or it can be downloaded by clicking . PlantGrowth dataset here 11. Swiss The Swiss dataset includes socio-economic data for 47 French-speaking provinces of Switzerland during the early 1880s. It also consists of 47 observations across 6 variables. The variables include: Fertility - This variable represents the fertility rate (number of live births per 1000 women) in each canton. Agriculture - This variable represents the percentage of males in the canton classified as farmers. Examination - This variable represents the percentage of males in the canton who took the military examination. Education - This variable represents the percentage of people in the canton who were educationally qualified to attend university. Catholic - This variable represents the percentage of the population in the canton who are Catholic. Infant.Mortality - This variable represents the infant mortality rate (number of deaths of children under 1 year of age per 1000 live births) in each canton. The can be loaded into R by typing data(swiss), or it can be downloaded by clicking . Swiss dataset here 12. Women This dataset includes the heights of mothers and their daughters for American women aged 30-39. The dataset also consists of 15 observations on 2 variables. The variables include: Mother - A numeric vector that represents the height of the mother (in inches). Daughter - A numeric vector that represents the height of the daughter (in inches). The can be loaded into R by typing data(women), or it can be downloaded by clicking . Women dataset here Common Use Cases for Pre-Installed R Datasets - The Mtcars dataset is used for regression analysis and exploratory data analysis to study the relationship between car specifications and fuel efficiency. Mtcars - The ChickWeight dataset is used for analyzing longitudinal growth data, such as the effects of diet on the growth of chicks. ChickWeight - The CO2 dataset is used for analyzing the relationship between atmospheric CO2 concentration and plant growth. CO2 - The Iris dataset is used for exploratory data analysis and classification analysis to study the relationship between iris flower species and their physical attributes. Iris - The Boston Housing dataset is used for regression analysis to study the relationship between housing prices and various factors, such as crime rate and accessibility to public transportation. Boston Housing - The Airquality dataset is used for exploratory data analysis and regression analysis to study the relationship between air pollution and various weather factors. Airquality - The Titanic dataset is used for classification analysis and survival analysis to study the factors that influenced the survival of passengers on the Titanic. Titanic - The Faithful dataset is used for exploratory data analysis and modelling to study the patterns of eruption time and waiting time of the Old Faithful geyser in Yellowstone National Park. Faithful - The Orange dataset is used for regression analysis and growth modelling to study the growth of orange trees. Orange - The PlantGrowth dataset is used for hypothesis testing and ANOVA analysis to study the effect of different types of fertilizer on plant growth. PlantGrowth - The Swiss dataset is used for exploratory data analysis and hypothesis testing to study the relationship between fertility and socio-economic indicators in the provinces of Switzerland. Swiss - The Women dataset is used for exploratory data analysis and hypothesis testing to study the relationship between the height and weight of women. Women Final Thoughts These datasets can also be accessed through R packages like "tidyverse", "ggplot2" and "data.table” They are also available in public repositories such as Kaggle, GitHub and the UCI Machine Learning Repository. The lead image of this article was generated via HackerNoon's AI Stable Diffusion model using the prompt 'R Programming'. More Dataset Listicles: Hugging Datasets PyTorch Datasets Power BI Datasets

12 Best Pre-Installed R Datasets Commonly Used for Statistical Analysis

Requesting and receiving data from internet sources is one of the basics of programming but isn’t taught  by most ‘Learn to Code <Programming Language>’ courses. For self-taught programmers, this is one of the skills that will get you moving further along your learning journey and make you better than 90% of the others. What is an API? Short for Application Programming Interface, API helps a program fetch information from various internet sources. Let’s say you’re building a basic currency conversion program in python. It takes an input amount of currency X and converts it into currency Y Sample Code for reference: def currency_converter ():

    currency_a = input ("Enter the currency you want to convert: ")

    currency_b = input ("Enter the currency you want to convert to: ")

    amount = float (input ("Enter the amount you want to convert: "))

    exchange_rate = float (input ("Enter the exchange rate: "))

    print(f"{amount} {currency_a} is equal to \
        {amount * exchange_rate} {currency_b}")
    
currency_converter () This program in its current form is no more than a glorified calculator - not of much use as a program. This is where APIs come in!! With a few lines of code, this program can be made so powerful that it does not need to ask for the exchange rate. Instead, with APIs, the code would be able to fetch the current exchange rate and provide it to the program. Need to make a program to give the country code for an input country? There’s an API for that. Inventory in your warehouse? Data in your spreadsheet? Payment confirmation? Email notification? All done with APIs! To put it simply, APIs enable communications between computing devices in a standardized manner. For example, if you go to Germany and use an interpreter to help translate for you, the interpreter is the API. Components of an API . An API has 3 elements - Request, Response, and Resource Request - You asking the interpreter to say ‘Good Day’ to a German is the request Response - What the German said back is the response Resource - The data within the response (‘Guten Tag’) is the resource In our program above, let’s see what the API needs to do: """

Request -- Our program requests for the currency_a -> currency_b exchange rate

Response -- API endpoint reads our request, fetches that information, and responds back

Resource -- The exchange rate data sent back is the resource

""" API Endpoint? What’s that? As we know by now (as self-taught programmers) that computers need very precise inputs in a very precise manner. Any deviation or any expectation from the computer to ‘understand things as we do’ is just going to lead to chaos! In that vein, we must understand that even API requests need to be structured in a very precise way. This is what the general structure of an API looks like: -- This is the URL that receives the API Endpoint Request. -- This is the type of API request and is of several types such as: Method GET -- indicates that the request is looking to fetch info from the server. POST -- indicates that the request is looking to send some info to a server. DELETE -- indicates that the request is looking to delete some info from the server. PUT/PATCH -- indicates that the request is looking to update existing info on the server. -- This provides some context about the Requested. Header Resource -- This is the main part of the request that triggers the Body Response. CRUD Responses CRUD stands for Create, Read, Update, and Delete and is performed by using the relevant API method in the API Request: API Method Response Description GET Read fetches existing info from the server POST Create adds info to the server DELETE Delete deletes info on the server PUT/PATCH Update updates info on the server Fetching Realtime Currency Rates into our Program Now we’re sufficiently ready to fetch the real-time currency rates into our sample program given earlier. We’ll be using the requests module for this exercise which you can get via pip3 install requests in your code editor’s (like VSCode) terminal. APIs that provide data come in Free and Paid forms - depending on the ease of use. I found a free source and shall be using it because it does not force me to sign up as long as I’m just testing here. ExchangeRateAPI API data usually comes in the form of a JSON (very similar to a dictionary in python) for easy interpretation by the programmers fetching that data. Here’s the code for fetching the current price of the US Dollar via the API. import requests
url =  "https://open.er-api.com/v6/latest/USD"
r = requests.get(url)
data = r.json()
print (data) Running this code will output the following: {'result': 'success', 
'provider': 'https://www.exchangerate-api.com', 
'documentation': 'https://www.exchangerate-api.com/docs/free', 
'terms_of_use': 'https://www.exchangerate-api.com/terms', 
'time_last_update_unix': 1666828952, 
'time_last_update_utc': 'Thu, 27 Oct 2022 00:02:32 +0000', 
'time_next_update_unix': 1666916222, 
'time_next_update_utc': 'Fri, 28 Oct 2022 00:17:02 +0000', 
'time_eol_unix': 0, 
'base_code': 'USD', 
'rates': {'USD': 1, 'AED': 3.6725, 'AFN': 87.825832, 'ALL': 118.121569, 
'AMD': 400.945057, 'ANG': 1.79, 'AOA': 469.341287, 'ARS': 155.541325, 'AUD': 1.541873,
 'AWG': 1.79, 'AZN': 1.703727, 'BAM': 1.945846, 'BBD': 2, 'BDT': 101.652423, 
'BGN': 1.946569, 'BHD': 0.376, 'BIF': 2061.89311, 'BMD': 1, 'BND': 1.40599, 
'BOB': 6.931975, 'BRL': 5.314395, 'BSD': 1, 'BTN': 81.988812, 'BWP': 13.376498, 
'BYN': 2.887549, 'BZD': 2, 'CAD': 1.355432, 'CDF': 2080.191892, 'CHF': 0.986916, 
'CLP': 967.707002, 'CNY': 7.181598, 'COP': 4992.818412, 'CRC': 624.374829, 'CUP': 24, 
'CVE': 109.702113, 'CZK': 24.428037, 'DJF': 177.721, 'DKK': 7.422296, 'DOP': 53.948303,
 'DZD': 140.691654, 'EGP': 19.734057, 'ERN': 15, 'ETB': 53.10939, 'EUR': 0.994984, 
'FJD': 2.280573, 'FKP': 0.862476, 'FOK': 7.422296, 'GBP': 0.862495, 'GEL': 2.780813, 
'GGP': 0.862476, 'GHS': 14.477856, 'GIP': 0.862476, 'GMD': 60.196663, 
'GNF': 8658.722933, 'GTQ': 7.851475, 'GYD': 210.222337, 'HKD': 7.848858, 
'HNL': 24.756699, 'HRK': 7.496037, 'HTG': 127.760311, 'HUF': 407.402111, 
'IDR': 15518.182729, 'ILS': 3.502567, 'IMP': 0.862476, 'INR': 81.991335, 
'IQD': 1466.589463, 'IRR': 42121.422024, 'ISK': 142.934149, 'JEP': 0.862476, 
'JMD': 153.171604, 'JOD': 0.709, 'JPY': 146.478104, 'KES': 121.814832, 
'KGS': 83.278965, 'KHR': 4159.804229, 'KID': 1.541816, 'KMF': 489.456326, 
'KRW': 1414.443423, 'KWD': 0.29963, 'KYD': 0.833333, 'KZT': 470.670656, 
'LAK': 17297.850805, 'LBP': 1507.5, 'LKR': 362.205766, 'LRD': 154.022883, 
'LSL': 17.964161, 'LYD': 5.043916, 'MAD': 10.860577, 'MDL': 19.373927, 
'MGA': 4262.390442, 'MKD': 61.999441, 'MMK': 2425.598271, 'MNT': 3414.010756, 
'MOP': 8.084315, 'MRU': 38.145372, 'MUR': 43.95057, 'MVR': 15.474793, 
'MWK': 1033.698319, 'MXN': 19.911183, 'MYR': 4.709804, 'MZN': 64.481751, 
'NAD': 17.964161, 'NGN': 437.991325, 'NIO': 36.062907, 'NOK': 10.27411, 
'NPR': 131.1821, 'NZD': 1.717982, 'OMR': 0.384497, 'PAB': 1, 'PEN': 3.994793, 
'PGK': 3.535979, 'PHP': 58.319524, 'PKR': 220.984121, 'PLN': 4.728217, 
'PYG': 7249.701869, 'QAR': 3.64, 'RON': 4.844272, 'RSD': 116.906933, 
'RUB': 61.346183, 'RWF': 1075.383068, 'SAR': 3.75, 'SBD': 8.07199, 'SCR': 13.126076, 
'SDG': 572.688874, 'SEK': 10.875131, 'SGD': 1.405948, 'SHP': 0.862476, 
'SLE': 17.636956, 'SLL': 17636.95595, 'SOS': 571.245973, 'SRD': 29.467417, 
'SSP': 611.916602, 'STN': 24.374931, 'SYP': 2514.92413, 'SZL': 17.964161, 
'THB': 37.747911, 'TJS': 10.200292, 'TMT': 3.504191, 'TND': 3.085688, 
'TOP': 2.388017, 'TRY': 18.607129, 'TTD': 6.77608, 'TVD': 1.541816, 
'TWD': 32.049926, 'TZS': 2335.14976, 'UAH': 37.234725, 'UGX': 3826.845217, 
'UYU': 41.283595, 'UZS': 11139.838897, 'VES': 8.4508, 'VND': 24821.263771, 
'VUV': 122.039898, 'WST': 2.787278, 'XAF': 652.608434, 'XCD': 2.7, 'XDR': 0.774448, 
'XOF': 652.608434, 'XPF': 118.722827, 'YER': 251.106608, 'ZAR': 17.964381, 
'ZMW': 16.127703, 'ZWL': 644.87339}} Looking at this JSON (Imma call it a dictionary from hereon for pythonic reasons), we see that the currency rates against that of the USD as base currency are given in the internal dictionary which can be accessed with the key. rates The actual conversion rate to a particular currency can be accessed by using the desired currency’s 3-letter currency code. For example, to get the conversion rate to INR (Indian Rupee), I will first need to use the key and then the key within the key. rates INR rates Case-sensitivity is important in most APIs so we’ll need to plan for that in our code as well. The code to get the USD to INR conversion rate, the code in python would look like this - inr_rate = data['rates'].get('INR', 'Not Found') Let’s now update our original code to become less of a calculator and more of a computer: import requests

def currency_converter ():
    currency_a = input ("Enter the currency you want to convert: ")
    currency_a = currency_a.upper() 
    #accepts user input for base currency name and converts it to uppercase
    
    currency_b = input ("Enter the currency you want to convert to: ")
    currency_b = currency_b.upper() 
    #accepts user input for final currency name and converts it to uppercase
    
    currency_converter_api = "https://open.er-api.com/v6/latest/" + currency_a
    #makes currency_a the base currency
    
    api_response = requests.get(currency_converter_api)
    #fetches the api response - the R from CRUD
    
    api_response_json = api_response.json()
    #Converts the response object into a JSON object
    
    amount_to_convert = float (input (f"Enter the amount of {currency_a} \
      you want to convert to {currency_b}: "))
    #accepts user input for the amount to convert as a float
    
    try:
        exchange_rate = api_response_json['rates'].get(currency_b, \
          'Currency Not Supported by API')

        if exchange_rate == 'Currency Not Supported by API':

            print ("Final Currency Not Supported by API")

        else:

            converted_amount = amount_to_convert * exchange_rate

            print (f"{amount_to_convert} {currency_a} is \
              equal to {converted_amount} {currency_b}")
    
    except KeyError:
        print ("Base Currency Not Supported by API")
    # for catching errors is the base currency name is not supported by the API
    
currency_converter () There you go - now it does a little better: Converting 5000 INR to EUR (successful conversion calculation): """
Enter the currency you want to convert: inr
Enter the currency you want to convert to: eur
Enter the amount of INR you want to convert to EUR: 5000
5000.0 INR is equal to 60.72 EUR
""" Converting unsupported currency (error-handling): """
Enter the currency you want to convert: utsav
Enter the currency you want to convert to: inr
Enter the amount of UTSAV you want to convert to INR: 45
Base Currency Not Supported by API
""" Converting TO Unsupported currency (error-handling): """
Enter the currency you want to convert: inr
Enter the currency you want to convert to: utsav
Enter the amount of INR you want to convert to UTSAV: 89
Final Currency Not Supported by API
""" Some Final Notes: While we've used print statements here for illustrative reasons, if you’re building an app, avoid print statements and use return instead Some premium APIs (paid) offer better sources, the ability to take full-name inputs (Indian Rupee) or just country as input (USA) and respond with the desired response Most APIs require the use of API keys for authentication and DDoS prevention features. Read more about them . here Resource Attributions: RapidAPI #API on HackerNoon Recommended Readings: Low Code APIs Error-Handling in React APIs Using Google Sheets API in Python Securing your API data GraphQL API

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