.. _r_ch02_graphical_summaries:

Chapter 2 Companion Script: Graphical Summaries
===============================================

This page accompanies a single, self-contained R script that reproduces every kind of graph
in Chapter 2—frequency tables, pie charts, bar graphs, histograms, and density/normal
overlays—using the same conventions you are expected to follow on the computer assignments.

:download:`Download stat350_ch02_graphical_summaries.R <RCodes/stat350_ch02_graphical_summaries.R>`

**How to use the script**

1. Set up R and RStudio first if you have not already: see :ref:`r_setup_guide`.
2. Open the script in RStudio and run it **top to bottom** once. Part 0 loads ggplot2 (the
   only package required), sets a course-wide theme, and defines the bin-rule helper; after
   that, every block is self-contained, so you can re-run any single plot later without
   restarting.
3. Blocks marked ``[TRAP]`` are deliberate: they show a common mistake first, then the fix.
   Run both and compare the plots—these are the mistakes that actually cost points on
   assignments.

**How the script maps onto the webbook**

.. list-table::
   :header-rows: 1
   :widths: 12 63 25

   * - Part
     - Topic
     - Webbook section
   * - Part 0
     - Setup: ggplot2, a course-wide ``theme_set()``, and the bin rule as a function
     - —
   * - Part 1
     - The structure of a data set (aggregated vs. raw data, ``geom_bar`` vs. ``geom_col``)
     - 2.1
   * - Part 2
     - Frequency tables (counts, relative frequencies, percentages)
     - 2.2
   * - Part 3
     - Pie charts (and why ``theme_void()`` is mandatory)
     - 2.2
   * - Part 4
     - Bar graphs: simple, dodged, stacked; three ``[TRAP]`` blocks
     - 2.2
   * - Part 5
     - Bar graph or histogram? A ``[TRAP]`` where ``table()`` destroys the number line
     - 2.3
   * - Part 6
     - Histograms and the bin rule of thumb
     - 2.3
   * - Part 7
     - Density and normal overlays (the course histogram recipe)
     - 2.3
   * - Part 8
     - Shape: modality, skewness, and outliers; faceting
     - 2.4

Six practice exercises at the end of the script modify specific parts and ask you to explain
what changes and why.

Two conventions worth knowing
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**The bin rule, written once.** The script encodes the Chapter 2.3 rule of thumb
:math:`b = \max(\text{round}(\sqrt{n}) + 2,\ 5)` as a function, so the formula in your code
visibly matches the formula in the webbook:

.. literalinclude:: RCodes/stat350_ch02_graphical_summaries.R
   :language: r
   :start-at: n_bins <- function
   :end-at: n_bins(100)

**The course histogram recipe.** From Part 7 on, every single-variable histogram carries a
density scale, a red kernel density curve, and a blue normal curve—the same layering required
on the computer assignments:

.. literalinclude:: RCodes/stat350_ch02_graphical_summaries.R
   :language: r
   :start-at: ggplot(furnace, aes(x = Consumption)) +
   :end-at: x = "BTU", y = "Density")

For a deeper treatment of the plotting layers used throughout the script, see
:ref:`r_ggplot2_guide`; for the datasets it loads, see :ref:`r_datasets`.