Plotting in R

1 native

Basic R plotting via the plot command is functional.

2 ggplot2

More tidyverse! Probably the favourite plotting system for R these days.

Kieran Healy advises the following setup for visualisation in the tidyverse style:

my_packages <- c("tidyverse", "broom", "coefplot", "cowplot",
    "gapminder", "GGally", "ggrepel", "ggridges", "gridExtra",
    "interplot", "margins", "maps", "mapproj", "mapdata",
    "MASS", "quantreg", "scales", "survey", "srvyr",
    "viridis", "viridisLite", "devtools")

install.packages(my_packages,
    repos = "http://cran.rstudio.com")

• yonicd/ggedit: Interactively edit ggplot layer aesthetics and theme definitions.

  ggedit is a package that helps users bridge the gap between making a plot and getting all of those pesky plot aesthetics just right, all while keeping everything portable for further research and collaboration.

  ggedit is powered by a Shiny gadget where the user inputs a ggplot plot object or a list of ggplot objects. You can run ggedit directly from the console or from the Addin menu within RStudio.

  See The ggedit gitbook

• The ggplot2 reference is thorough but inscrutable without knowing the philosophy of the thing.

• Fancy features are best discovered via the extension gallery.

• The cheat sheet has graphical references, which is the appropriate way to consider this thing.

• Max Woolf’s simple ggplot tutorial

• The ggplot book is available free if you build it yourself

• The ggplot chapter of R for data science.

• Want arbitrary lines and stuff to appear in the legends? E.g. a mean line or multiple model fits.

• diagrammer emits your graphs as JavaScript widgets. V Web2.0 wow.

• ggvis is a planned next iteration of the Hadley Wickham’s ggplot family, AFAICT, but currently on hiatus

• animating is sort of possible using ggplot2 but it is not fun.

• gggedit is an interactive RStudio extension for dynamically adjusting plots

• [Plotly graphing library for ggplot2 in ggplot2](https: