![]() ![]() All elemements can be changed through the theme() function but there also are pre-configured. Ggplot2 theme manages how your graphic looks like. Besides, it’s better if you know how to create a R Markdown document and you know how to include R code in it (with a chunk).Labs(x = "Culmen Length (mm)", y = "Culmen depth (mm)", fill = "Species", color = "Species") Geom_smooth(method = "lm", formula = "y ~ x", alpha = 0.3) + P <- ggplot(penguins_raw, aes(x = culmen_length_mm, y = culmen_depth_mm, color = species, fill = species)) + To avoid iris data, I will use a data visualisation of Palmer penguins data recently included in a R package by Allison Horst (go see her illustrations too !). ![]() If not, you can have a look at this book freely available online. I assume you have already made a graphic with ggplot2 or at least seen some ggplot2 code. ![]() In this post, I share with you some tips found over time. Therefore, ggplot2 graphics are often included in my R Markdown documents.įeatures of both packages are highly flexible and you CAN always get what you want ! But if you are just starting out, getting what you want can be cumbersome. You’ll find quite a few R packages to build graphics but I have a preference for ggplot2 (I’m not alone!). Doing daily data analysis, I usually deliver outputs in report and R Markdown naturally became an essential tool of my workflow.ĭata analysis without data visualisation is like playing darts in the dark, there is a good chance you’ll miss the bullseye point. It is a real asset for analysis reproducibility as well as communication of methods and results. Writing R Markdown document makes possible to insert R code and its results in a report with a choosen output format (HTML, PDF, Word). ![]()
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