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#datavisualization

10 posts10 participants2 posts today

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

Replied to datatofu

(pandas 📋) For #rstats users, the DataFrame name will be familiar, as the object was named after the similar R data.frame object. Unlike Python, data frames are built into the R programming language and its standard library. As a result, many features found in pandas are typically either part of the R core implementation or provided by add-on packages

matplotlib. The most popular Python library for producing plots and other two-dimensional #datavisualization or #dataviz

IPython & Jupyter

gganimate is a powerful extension for ggplot2 that transforms static visualizations into dynamic animations. By adding a time dimension, it allows you to illustrate trends, changes, and patterns in your data more effectively.

The attached animated visualization, which I created with gganimate, showcases a ranked bar chart of the top 3 countries for each year based on inflation since 1980.

More information: statisticsglobe.com/online-cou

🦷💰 Did you know that untreated dental disease costs the U.S. $45 billion in lost productivity each year?📊 Explore more eye-opening stats in Stefan Marinic’s award-winning visualization from the undergraduate category of University of Arizona Libraries’ 2023 #DataVisualization Challenge! Check it out: doi.org/10.25422/azu.data.2272. Image: Marinic (2023). CC-BY 4.0.
#OpenData #OpenScience #DataViz #DentalHealth #UniversityofArizona

Understanding probability distributions is key to making informed decisions in statistics and data science. Probability distributions describe how the values of a variable are expected to behave, making them crucial for interpreting data and predicting outcomes.

The visualization shown in this post illustrates the distributions.

Further details: statisticsglobe.com/online-cou