In a nutshell, a Bland-Altman plot shows the differences between two measurements against their means. It's a powerful tool for quality control and validation, widely used in various industries, including healthcare.
Step 1: Load your data.
Step 2: Perform Principal Component Analysis (PCA).
Step 3: Calculate the variance explained.
Step 4: Create a stunning scree plot.
Step 5: Interpret the plot to find the "elbow."
Step 6: Decide how many components to retain.
Step 7: Apply your decision and get insights!
📊 Unlocking Insights with Added Variable Plots in R! 🚀
AV Plots are a game-changer for data enthusiasts. They allow you to visualize how one predictor variable affects the response variable while controlling for other factors. Here's a quick guide to get you started:
I'll give you a quick rundown on creating horizontal boxplots in R using both base R and ggplot2. We'll work with the "palmerpenguins" dataset to keep things interesting!
Are you ready to dive into the world of decision trees? They're a fantastic tool for making transparent, interpretable decisions based on data. In this quick post, we'll show you how to create and visualize a tree using R, specifically the rpart and rpart.plot packages, with the classic Iris dataset.
Ever wondered how to compare the distributions of two variables simultaneously? Look no further! In this post, we'll dive into the world of dual-variable histograms using R, a go-to language for data analysis and visualization.
Ready to embark on your own dual-variable histogram journey? The key to effective data visualization is in your hands. 🚀
Joint event by R-Ladies Cologne (@cosima_meyer and
Gabe Winter) and R-Ladies Bergen (Jonelle and me): we're thrilled to be hosting Mine Çetinkaya-Rundel who will teach us about Quarto 🥳 👩💻
This is a kick-off event for a brand-new book club, where we'll be going through the book "Building reproducible analytical pipelines with R". Come, join us!
🔥 Uncover Insights with Correlation Heatmaps in R! 🔍
Curious about exploring relationships within your data? 📊 Correlation heatmaps are the key! 🗝️ These vibrant visualizations use color intensity to showcase the strength of connections between variables, making complex insights a breeze to grasp. Let's dive into this exciting world using R!
✅ Gain a deeper understanding of mathematical functions.
✅ Visualize complex concepts with ease.
✅ Explore the versatility of R programming.
✅ Enhance your data analysis and visualization skills.
Compare vectors with ease using pmax(...). Pass multiple vectors, and voila! You have the element-wise maximum. With the optional na.rm parameter, handling missing values becomes a breeze.
pmin() 📉
Meet pmin(...), your shortcut to element-wise minimum computation. Similar to pmax(), but for minimum values! Clean and simple.
Imagine you have a bunch of data points and you want to know how many belong to different categories. This is where grouped counting comes in. We've got three fantastic methods for you to explore, each with its own flair: aggregate(), dplyr, and data.table.
Ever wondered how to tweak margins, jazz up colors, or adjust font sizes in your R plots? Look no further! With par(), you're in control. 🎉
🔥 Level up your data viz skills and stand out from the crowd. The par() function is your key to data storytelling like never before. Don't just read—dive in, code, and watch your visualizations come alive. 📊💡
✨ What is unlist()?
The unlist() function in R is like a magician that takes complex nested lists or vectors and transforms them into a simple atomic vector. It's a game-changer when dealing with intricate data structures, allowing you to flatten them with ease.
if you're ready to level up your data manipulation skills, give intersect() a spin and let your insights shine! 🌈 Embrace the world of R and keep growing as a data wizard! 🧙♂️ Happy coding! 🎉
🚀 Supercharge Your Text Manipulation in R with paste() and cat()!
Ready to amaze yourself? 🤩 Mix and match with other R functions and explore diverse text manipulations. Whether you're dealing with data or crafting beautiful displays, paste() and cat() have got your back!