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Bring your ggplot2 visualizations into 3D with rayshader! This extension adds powerful 3D plotting capabilities to R, making it easy to transform standard visuals into interactive and visually engaging data representations.

The visualizations shown here are taken from the rayshader package website: rayshader.com/

Click this link for detailed information: statisticsglobe.com/online-cou

Standard deviation is one of the most important concepts in statistics, providing a way to measure how spread out data points are around the mean. It helps in understanding data variability, which is critical for interpreting trends and patterns.

Image credit to Wikipedia: en.wikipedia.org/wiki/Standard

Interested in learning further? Check out my online course on Statistical Methods in R. Check out this link for more details: statisticsglobe.com/online-cou

Basic boxplots are often not the best way to visualize your data! They can hide important information, such as the distribution of individual data points or group-specific differences.

The attached visual showcases several ways to enhance boxplots.

All of these examples were created using ggplot2 and extensions in R.

Click this link for detailed information: statisticsglobe.com/online-cou

Multivariate interpolation estimates unknown values based on multiple variables, making it useful in physics, finance, and machine learning. It allows for smoother approximations and more accurate predictions in multidimensional spaces.

Visualization: en.wikipedia.org/wiki/Multivar

Want to expand your knowledge of Statistics, Data Science, R, and Python? Subscribe to my newsletter for more insights! Link: eepurl.com/gH6myT

Combining Principal Component Analysis (PCA) with k-means Clustering in R can significantly enhance your data analysis by reducing dimensionality and improving clustering performance.

Check out my article created with Cansu Kebabci: statisticsglobe.com/pca-before

I've also created a video: youtube.com/watch?v=nzhSjOKSGC8

Furthermore, I offer an extensive online course on PCA: statisticsglobe.com/online-cou

Principal Component Analysis (PCA) before Linear Regression can greatly enhance your data analysis process.

By incorporating PCA before performing linear regression, you can streamline your analysis pipeline and build more robust models that capture the essential relationships within your data.

I've developed an in-depth course on PCA theory and its application in R programming.

Further details: statisticsglobe.com/online-cou

Heterogeneous Treatment Effects (HTE) are a game-changer in A/B testing that often gets overlooked.

The visualization recently shared by Leihua Ye highlights that some segments of users, like the top and bottom 5%, might show vastly different outcomes.

For regular updates on data science, statistics, Python, and R programming, subscribe to my free email newsletter! Check out this link for more details: eepurl.com/gH6myT

Did you know that the top 10 economies in the world together account for over two-thirds of the global GDP? 🌍 These countries play a pivotal role in shaping the global economic landscape.

I have created an extensive article on this topic for those who want to dive deeper into the GDP comparison among the top 10 economies. More information: statisticsglobe.com/gdp-worldw

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Hey, I've created a new website called Data Hacks containing 1000 quick R & Python tutorials. Solutions are shown without unnecessary talk, so you may use it to quickly find code snippets for specific problems: data-hacks.com/

Data HacksData Hacks - Learn How to Handle DataAll tutorials on Data Hacks - Learn how to handle data - Reproducible example codes - Programming examples & instructions