Fix #236

Author: thomasp85

doc/get_started.qmd

@@ -117,3 +117,24 @@ If you prefer to build from source or need the latest development version:
    ggsql-jupyter --install
    ```
 
+## The grammar of graphics
+ggsql is a tool for visualising data, combining our 20 years of experience developing [ggplot2](https://ggplot2.tidyverse.org/) with a SQL-native syntax.
+Unlike most visualisation systems, ggsql has an underlying grammar, based on the [Grammar of Graphics](https://en.wikipedia.org/wiki/Wilkinson%27s_Grammar_of_Graphics) by Leland Wilkinson.
+This is a different approach than the one taken by most tools, which give you a set of pre-defined graphics, like scatterplots, bar charts, and pie charts.
+ggsql instead gives you a number of independent components that you can combine to solve a very wide set of problems.
+
+This means there's a learning curve to ggsql: you have to learn a few big ideas about the grammar before you can be productive.
+But we believe the payoff is worth it.
+The grammar gives you new vocabulary and new ways to think about visualizations.
+This allows you to create not only familiar graphics but also newer, better graphics.
+You'll gain the ability to look at a visualization that someone else has made, analyse the key components, then recreate it yourself.
+The hardest part of learning ggsql is likely to be unlearning the preconceptions that you bring over from previous visualization tools.
+
+We've tried to make the learning curve as easy as possible by keeping the grammar close to the SQL syntax that you're already familiar with.
+You'll start with a classic `SELECT` statement to get the data that you want.
+Then you'll use `VISUALIZE` (or `VISUALISE` 🇬🇧) to switch from creating a table of data to creating a plot of that data.
+Then you'll `DRAW` a layer that maps columns in your data to aesthetics (visual properties), like position, colour, and shape.
+Then you tweak the `SCALE`s, the mappings between the data and the visual properties, to make the plot easier to read.
+Then you `FACET` the plot to show how the relationships differ across subsets of the data.
+Finally you finish up by adding `LABEL`s to explain your plot to others.
+This allows you to produce graphics using the same structured thinking that you already use to design a SQL query.