Table of contents
ggplot2
Application and Data, Libraries, and Charting Libraries.
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Elegant Data Visualization
ggplot2 offers a high level of abstraction for creating complex multi-layered graphics using a coherent and consistent syntax.
Grammar of Graphics
It is built around the Grammar of Graphics, allowing users to build plots by combining different components in a clear and structured manner.
Extensibility
ggplot2 is highly extensible, enabling users and developers to create their own geoms, stats, and themes.
Integration with Tidyverse
It is part of the tidyverse, thus integrating smoothly with other tools and packages for data manipulation and cleaning, like dplyr and tidyr.
Wide Range of Plot Types
Users can create a diverse array of plots from simple bar graphs to complex multi-panel plots seamlessly.
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Check the traffic stats of ggplot2 on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of ggplot2 on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of ggplot2's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of ggplot2 on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about ggplot2 on Reddit. This can help you find out how popualr the product is and what people think about it.
Plotnine is heavily inspired by the ggplot2 library, which uses the + operator in the same way: https://ggplot2.tidyverse.org/#usage. - Source: Hacker News / about 2 months ago
For random, quick and dirty, ad-hoc plotting tasks my default is GNUPlot[1]. Otherwise I tend to use either Python with matplotlib, or R with ggplot2. I keep saying I'm going to invest the time to properly learn D3[4] or something similar for doing web-based plotting, but somehow never quite seem to find time to do it. sigh [1]: http://www.gnuplot.info/ [2]: https://matplotlib.org/ [3]:... - Source: Hacker News / about 3 years ago
I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package. Source: about 3 years ago
Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2. Source: about 3 years ago
I used the ggplot2 package in R to create these figures. Source: about 3 years ago
This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and... - Source: dev.to / about 3 years ago
Sorry about the slow reply, but the auto-moderator seems to be deleting my comments (for some unknown reason). I will try once more: the geom_tile function in ggplot2. Source: about 3 years ago
Visual cues are a much better idea, thanks! Unfortunately, I don't know how to do that in ggplot2, either (I created these figures in R). Source: about 3 years ago
This should get you started. Here's another good cheat sheet. Ggplot2 is pretty logical once you get the jist. Putting the legend on the right side is absolutely possible. Source: about 3 years ago
I highly suggest checking out some ggplot resources online, such as this documentation. I understand that you're getting your desired plots, but your syntax is a little wonky. Source: over 3 years ago
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: over 3 years ago
For more information on how to use ggplot2 and create charts consult the ggplot2 official page or the ggplot2 cheat graphic. - Source: dev.to / over 3 years ago
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