Simplify and elevate your data visualization with GGally, an R package designed to extend ggplot2 by providing specialized tools for visualizing complex data relationships. Whether you're exploring data, comparing models, or analyzing correlations, GGally has you covered. Why

Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic

Heatmap in ggplot2 https://t.co/Y5jZlq5GYC I always use complexheatmap, but this seems to be a good alternative if you want to stay within the ggplot

🧵 Everyone is using ggplot2 to make graphs. But did you know you can turn ggplot2 into a full mapping engine? From polygons to rasters to wind fields, these 5 functions unlock geographic storytelling in R. Save this thread if you love maps 👇

Want to master bioinformatics data visualization? Learn ggplot2! 🧵👇

Unleash the power of ggraph, an innovative extension package for ggplot2 designed to simplify and enhance the visualization of network and graph structures in R. If you've ever struggled to make sense of complex relationships, ggraph turns those challenges into opportunities for

How to Align multiple ggplot2 plots by axis #rstats https://t.co/ZXE2oLjwFS

I recently stumbled over the tmap R package and was amazed by its capabilities! With tmap, you can craft dynamic, interactive maps with ease. The package offers a flexible syntax similar to ggplot2, but with a dedicated focus on maps. Key Features: ✅ Interactive Maps: Switch

tidyplotsがどんどん充実している．ggplot2よりもサイト情報も見やすくなってる．add関数を使えば「ggplotならできるのにー」も解決できるし，もうこっちを第一選択にしようかな https://t.co/rUPASOnoCu

Making your data analysis more insightful and informative is effortless with ggstatsplot. This powerful ggplot2 extension in R combines statistical analysis and data visualization in a single workflow, helping you generate plots that include statistical summaries directly on the

Prefer bar charts over pie charts for visualizing proportions? The ggbarstats() function from the ggstatsplot package is a great alternative, providing detailed insights with a similar syntax to ggplot2. ✔️ Effective Proportional Display: Creates grouped bar charts to show the

Today's artwork generated with #rstats and #ggplot2:

ggplot2 version 4 just hit CRAN

Track trends and transitions with ggbump, an R package that extends ggplot2 by providing tools to create elegant bump charts. Ideal for visualizing changes in rankings over time, ggbump helps you craft clear and impactful visualizations. Why use ggbump? ✔️ Specialized for

Bring your visualizations to life with see, a dynamic R package from the easystats ecosystem that extends ggplot2 to create modern and intuitive graphics. Whether you're visualizing statistical models or exploring data, see simplifies the process and enhances the presentation of

#30DayMapChallenge · Day 19 · Projections. Here's a visualisation of eight map projections. #ggplot2 adventures, an #rstats tale

The wide range of ggplot2 extensions for data visualization in R is truly impressive. Even better, these extensions usually work seamlessly together, making it easy to enhance your plots. Below is an example of an animated ggplot2 plot created using the gganimate and ggblend

ggplot2 in R is one of the most powerful tools for data visualization, offering unparalleled flexibility and ease of use. Its functionality can be further expanded with a variety of extensions, making it suitable for everything from simple plots to complex, customized

Choosing the right plot to visualize your data can be challenging. That’s why my online course, Data Visualization in R Using ggplot2 & Friends, features an entire section with 9 modules dedicated to different plot types and their applications. Here’s an overview of the plot