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tidyverse.org
https://ggplot2.tidyverse.org/
Create Elegant Data Visualisations Using the Grammar of ...
However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).
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r-graph-gallery.com
https://r-graph-gallery.com/ggplot2-package.html
Data visualization with R and ggplot2 | the R Graph Gallery
plotly: turn your ggplot interactive Another awesome feature of ggplot2 is its link with the plotly library. If you know how to make a ggplot2 chart, you are 10 seconds away to rendering an interactive version. Just call the ggplotly() function, and you’re done. Visit the interactive graphic section of the gallery for more.
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r-project.org
https://cran.r-project.org/package=ggplot2
CRAN: Package ggplot2
A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
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rdocumentation.org
https://www.rdocumentation.org/packages/ggplot2/ve…
ggplot2 package - RDocumentation
However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()).
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rstudio.github.io
https://rstudio.github.io/cheatsheets/html/data-vi…
Data visualization with ggplot2 :: Cheat Sheet - GitHub Pages
Geoms Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer. Graphical Primitives a <- ggplot(economics, aes(date, unemploy)) b <- ggplot(seals, aes(x = long, y = lat))
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datavizpyr.com
https://datavizpyr.com/ggplot2/
ggplot2 guide and cookbook (R)
A curated ggplot2 hub for R. Learn geoms, axes/scales, labels/annotations, themes, faceting, colors, and saving plots—each with working code and examples.
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geeksforgeeks.org
https://www.geeksforgeeks.org/r-language/data-visu…
Data visualization with R and ggplot2 - GeeksforGeeks
ggplot(data = mtcars, aes(x = hp, y = mpg, col = disp))+ labs(title = "MTCars Data Plot")