الدوري الممتاز الهندي 2020

Intro Setup Fundamentals Extensions Endgame Wrap-up Intro The tidyverse “many models” approach was formally introduced in the first edition of R for Data Science (R4DS) in 2017. Since then, the tidyverse has evolved significantly, and along with it, the way we can harness the many models approach. This blog post ... Continue reading: Mastering the Many Models Approach http://dlvr.it/Swp6s5

What if you could 100X your coding productivity? Well you can with ChatGPT. One of the areas I’m most excited about is speeding up the development process of R Shiny web apps. And in this tutorial I’m going to show you how I built an app in 10 minutes... Continue reading: ChatGPT: Made this Shiny App in 10 Minutes http://dlvr.it/Swp6cg

Introduction Boxplots are a great way to visualize the distribution of a dataset. However, sometimes the default ordering of boxplots may not be ideal for the data being presented. In this blog post, we will explore how to reorder boxplots in R ... Continue reading: How to Reorder Boxplots in R: A Comprehensive Guide http://dlvr.it/SwlhD5

I learned a great deal throughout this journey. In the second part, I gained knowledge about implementing logistic regression in Stan. I also learned the significance of data type declarations for obtaining accurate estimates, how to use posterior to ... Continue reading: An Educational Stroll With Stan – Part 2 http://dlvr.it/Swlh8z

In the following we show how it is possible to obtain parallelizable, unbiased and computationally cheap estimates of Crossed random effects models with a linear cost in the number of datapoints (and paramaters) exploiting couplings. Crossed random effects models (CREM) CREM model a continuous response variables \(Y\) as depending on ... Continue reading: Linear-cost unbiased estimator for large crossed random effect models via couplings http://dlvr.it/Swj2nm

This post has been cross-posted on the Epiverse-TRACE blog. In a previous post, we discussed a package dependency that goes slightly beyond the normal R package ecosystem dependency: R itself. Today, we step even further and discuss dependencies outside of R: system dependencies. This happens when packages rely on external ... Continue reading: System Dependencies in R Packages & Automatic Testing http://dlvr.it/SwfbqG

Javier Orraca-Deatcu of the Southern California R User Group (SoCal RUG) highlighted his work at a health insurance company for quality of life improvements through data science models. He uses... The post Empowering Healthcare with R: Javier Orraca-Deatcu’s Journey from Finance to Predictive Health Models appeared first on R ... Continue reading: Empowering Healthcare with R: Javier Orraca-Deatcu’s Journey from Finance to Predictive Health Models http://dlvr.it/SwcCGG

(Photo by Erik Cats) Yet another Blood Bowl post! If you don’t know about Blood Bowl and/or FUMBBL, See my previous blog posts on Blood Bowl for more background and stats. This one is to introduce the Super League, where top (and not so top) coaches compete with ... Continue reading: The Super League: Tournament Blood Bowl online http://dlvr.it/SwbBfR

My dqrng package has some quite old issues, one is “More distribution functions” where I brought forward the idea to support additional distribution functions within dqrng, which currently only supports uniform, normal and exponential distributions.... Continue reading: Using dqrng as user-supplied RNG http://dlvr.it/SwYZRn

Rite-Aid closed 60+ stores in 2021. They said they’d nuke over 1,000 of them over three years, back in 2022. And, they’re now about to close ~500 due to bankruptcy. FWIW Heyward Donigan, Former President and CEO — in 2023 — took home $1,043,713 in cash, $7,106,993 in equity, and $617,105 in “other” (total $8,767,811)... Continue reading → Continue reading: Tracking Rite-Aid Store Closures http://dlvr.it/SwXKp1

So this tweet came across my feed. To save you going there it is about a selection exercise for a job for (I think) an IT start up, described proudly by its author as “insanely hard”; and the job is to find the radius of the brown circle in this ... Continue reading: Finding a circle in a chart by @ellis2013nz http://dlvr.it/SwX6TW

Introduction Linear regression is a fundamental statistical technique used to model the relationship between a dependent variable and one or more independent variables. While fitting a linear model is relatively straightforward in R, it’s also e... Continue reading: Creating Confidence Intervals for a Linear Model in R Using Base R and the Iris Dataset http://dlvr.it/SwVWFJ

Dear rOpenSci friends, it’s time for our monthly news roundup! You can read this post on our blog. Now let’s dive into the activity at and around rOpenSci! rOpenSci HQ WIP: WebAssembly support in R-universe! Thanks to some help from George Stagg, we added experimental support for building ... Continue reading: rOpenSci News Digest, September 2023 http://dlvr.it/SwVW5v

Data acquisition Data loading Sampling Modelling Prediction Single process Multiple processes Post-processing [view raw Rmd] Summary: Prediction on large datasets can be time-consuming, but with enough computing... Continue reading: Parallel raster processing in stars http://dlvr.it/SwVVxJ

Election polls play a crucial role in predicting the outcome of elections and shaping public opinion. However, it’s important to understand that the results of any single poll should be taken with a grain of salt. Many polls only ask about 1,000 people about their political preferences, which is quite ... Continue reading: Confidence Intervals in Election Polling: Understanding the Uncertainty of Political Forecasting http://dlvr.it/SwSFJ7

Warning: I highly recommend you read this blog post first, which will explain how to run a pipeline inside Nix in detail. This blog post will assume that you’ve read that one, and it would also help if you’re familiar with Github Actions, if not, read this other ... Continue reading: Reproducible data science with Nix, part 6 — CI/CD has never been easier http://dlvr.it/SwSF6Z

There is a lot to learn about Bayesian statistics, but it’s fun, exciting, and flexible! I thoroughly enjoyed the beginning of this journey. There will be learning curves, but there are so many great people and resources out there to help us get closer to understanding the Bayesian way. ... Continue reading: An Educational Stroll With Stan – Part 1 http://dlvr.it/SwQCNL

Algorithmic Fairness Tuesday, October 3rd, 2023, 7:30 PT / 10:30 ET / 15:30 CET 2nd joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: Tuesday, October 3rd, 20... Continue reading: Algorithmic Fairness http://dlvr.it/SwMgc6

As mentioned in the first post in this series, this post will cover how to build a Mastodon bot. This can be fairly straightforward, but - as always - there are multiple ways to “make it work”. Fortunately, there are already some fantastic tutorials out there that can help you ... Continue reading: Building Mastodon Bots and Promoting the Community – Part 2 http://dlvr.it/SwL437

Introduction Histograms are a fundamental tool in data analysis and visualization, allowing us to explore the distribution of data quickly and effectively. While creating a histogram in R is straightforward, specifying breaks appropriately can m... Continue reading: Mastering Histogram Breaks in R: Unveiling the Power of Data Visualization http://dlvr.it/SwGKP2

Project Euler 7 delves into the wonderful world of prime numbers. These numbers are interesting because they don't follow a predictable pattern. There is no algorithm to calculate primes, which is what makes them valuable in cryptography. As the numbers get larger, the gaps between consecutive primes also increase. There are, ... Continue reading: Project Euler 7: 10,001st Prime Number http://dlvr.it/SwCvGs

You can find all the posts on this series under the tag maps-app (including the Spanish versions). You can also find the current state of the project under my GitHub repo mapic. Scope of this post We are creating maps of data showing changes over a spa... Continue reading: Map any region in the world with R – Part IV: Object Oriented Programming in R with S3 http://dlvr.it/Sw9wYt

Here are four representations of the Klein j-invariant function: The Klein j-invariant function is a complex function defined on the upper half-plane of the complex nu... Continue reading: The pretty Klein j-invariant function http://dlvr.it/Sw7Jt3

Introduction Data visualization is a crucial aspect of data analysis. In R, the flexibility and power of its plotting capabilities allow you to create compelling visualizations. One common scenario is the need to display multiple plots on the sa... Continue reading: How to Plot Multiple Plots on the Same Graph in R http://dlvr.it/Sw4Zmy

Introduction If you’re an R enthusiast looking to take your data visualization to the next level, you’re in for a treat. In this blog post, we’re going to dive into the world of 3D plotting using R’s powerful persp() function. Whether you’re vis... Continue reading: Exploring the Third Dimension with R: A Guide to the persp() Function http://dlvr.it/Sw1pJy

The github branch of my Github repository cgalMeshes has a vignette explaining how to use parameterizations of surface meshes. A parameterization allows to map a texture on a mesh. Some of them ar... Continue reading: Enneper surface with rotating checkerboard http://dlvr.it/Sw0KbG

The R Consortium is pleased to announce that on August 28, 2023, the R Submissions Working Group successfully submitted an R-based test submission pilot 3 package through the FDA eCTD... The post First Publicly Available R-Based Submission Package Submitted to FDA (Pilot 3) appeared first on R Consortium. Continue reading: First Publicly Available R-Based Submission Package Submitted to FDA (Pilot 3) http://dlvr.it/Svz2kT

The book can be downloaded for free but you will need a Leanpub account, same if you buy it. The Hitchhiker’s Guide to Linear Models is finally complete. It took me a while to finish it but I’m happy with the result. I hope you enjoy it as ... Continue reading: The Hitchhiker’s Guide to Linear Models is now complete http://dlvr.it/SvwPFR

Introduction Are you interested in visualizing demographic data in a unique and insightful way? Population pyramids are a fantastic tool for this purpose! They allow you to compare the distribution of populations across age groups for different ... Continue reading: Creating Population Pyramid Plots in R with ggplot2 http://dlvr.it/SvtTNC

The visualization of a rgl mesh is rather shiny by default. We’ll see how to make it dull and shadowed. Take for instance the Barth sextic: ## Barth sextic is the isosurface f=0 #### phi Continue reading: A dull and shadowed ‘rgl’ mesh http://dlvr.it/SvrV1M

This post is the third one of a series of post about webR: Using webR in an Express JS REST API The Old Faithful Geyser Data shiny app with webR, Bootstrap & ExpressJS Preloading your R packages in webR in an Express JS API Note: the first post of this series ... Continue reading: Preloading your R packages in webR in an Express JS API http://dlvr.it/SvrTnl

Rhino 1.5 Release We are pleased to announce that Rhino 1.5 is now available on CRAN! This update brings a range of new features and enhancements that aim to make your R development workflow even more efficient. With Rhino’s new addins, you can seamlessly integrate essential tasks into your work and ... Continue reading: Rhino 1.5.0 Update on CRAN: Streamlining Your R Development Workflow with New Addins http://dlvr.it/Svny98

Introduction Data visualization is a powerful tool for gaining insights from your data. In R, you have a plethora of libraries and functions at your disposal to create stunning and informative plots. One common task is to plot a subset of your d... Continue reading: Mastering Data Visualization in R: How to Plot a Subset of Data http://dlvr.it/SvnxvY

I have been off from the blog lately due to a big load of personal projects. Just lately I got a few days off and found time to work on my personal website, to be ready soon. That made me get more into Nginx configuration, where I consider myself a ... Continue reading: Adding a website next to your Shiny server http://dlvr.it/Svnxhr

Introduction ‘Space is to place as eternity is to time.’ Joseph Joubert Greetings, humanists, social and data scientists! In the realm of data science, the ability to visualize geospatial data is paramount. This is particularly true when wo... Continue reading: Mapping the Past – Geospatial Visualization in R http://dlvr.it/SvlDdd

Intro/Overview to TidyTuesday 36: Union Membership in the United States This week’s TidyTuesday presents data taken from the Union Membership and Coverage Database from the CPS (Unionstats.com) created by Barry T. Hirsch, David A. Macpherson, and William E. Even. This database contains data about the wages of union ... Continue reading: TidyTuesday 36: Visualizing Worker Demographic Information with Treemaps http://dlvr.it/SvhTHZ

Interaction adventures through simulations and gradient boosting trees using the S-learner approach. I hadn’t realized that lightGBM and XGBoost could reveal interaction terms without explicit specification. Quite intriguing! picture resembles interaction 🤣 Objectives: What is interaction? Simulate interaction Visualize interaction True Model ✅ Wrong Model ❌ What is S Learner? What is ... Continue reading: Exploring Interaction Effects and S-Learners http://dlvr.it/Svgk6z

Introduction Data With ties Without ties Correlation coefficients by hand Pearson With and without ties Spearman With ties Without ties Kendall Without ties With ties Verification in R Conclusion Introduction In statistics, a correlation is used to evaluate the relationship between two variables. In a previous post, we showed how ... Continue reading: Pearson, Spearman and Kendall correlation coefficients by hand http://dlvr.it/Svdn7G

I’m delighted that R users can have access to the incredible Hugging Face pre-trained models. In this demonstration, we provide a straightforward example of how to utilize them for sentiment analysis using GPT-generated synthetic data from evaluation comments. Let’s go! Interesting Problem 😎 What if you’re faced with ... Continue reading: Hugging Face 🤗, with a warm embrace, meet R️ ❤️ http://dlvr.it/SvcQ45

My sticky note is full! And luckily all functions on it can be squeezed into a similar topic: making things smaller! Make lists smaller with purrr::compact(), purrr::keep(), purrr::discard() Once upon a time there was a list (isn’t this the begin... Continue reading: R functions that shorten/filter stuff: less is more http://dlvr.it/SvT4l1