ggplot2
=======

ggplot2 is a visualization package included in the tidyverse. It follows
the Grammar of Graphics (GoG), which involves building graphs from the
following components:



.. figure:: /_static/images/r/data-visualization/grammarofgraphics.png
   :alt: Image from *The Grammar of Graphics by Leland Wilkinson*

Image from *The Grammar of Graphics by Leland Wilkinson* 

Check out the `ggplot
cheatsheet <https://www.rstudio.com/resources/cheatsheets/#ggplot2>`__.

Elements in ggplot2 funtions
----------------------------

The vocabulary of ggplot can be difficult to parse at first. Here are
the essential components:

-  Aesthetics: Visual properties of the objects in your plot, e.g. axis
   of data, size, shape, color, pattern, fill of variables, alpha
-  Geoms: Geometric objects representing data, such as lines, bars,
   points
-  Facets: Subgrouping of data
-  Statistics: Additional functions like regression lines
-  Scales: Legends and labels
-  Coordinate System: Cartesian, polar, etc.
-  Themes: Background visuals

Install/load tidyverse
----------------------

The very first time you want to use a package you first need to install
it.

.. tab:: R

   .. code:: r

      # if you have never downloaded tidyverse uncomment the line below and run to install it
      install.packages('tidyverse')
      # load tidyverse
      library(tidyverse)

We will do a similar step with our penguin data that we will be
visualizing.

.. tab:: R

   .. code:: r

      install.packages("palmerpenguins")
      library(palmerpenguins)

We use the ``View()`` function to look at the data frame and check that
we have tidy data: each variable is a column and each observation is a
row.

.. tab:: R

   .. code:: r

      View(penguins)

Building layers
---------------

Let’s start by laying the foundations of our plot with the ``ggplot()``
function. We add in our data, letting us create a blank plot.

.. tab:: R

   .. code:: r

      ggplot(data=penguins)

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/firstlayer.png

Now we need to add aesthetics and geometric objects. Aesthetics are what
you plot (x, y, size, color, fill, shape), and geoms are how you plot
aesthetics (point, line, bar, boxplot). We specify aesthetics in
``aes()``.

Here, we set up axes to show the relationship between the variables
``bill_length_mm`` and ``bill_depth_mm``. The data will still not be
visualized, but the axes show the range of the data.

.. tab:: R

   .. code:: r

      ggplot(data=penguins,aes(x=bill_length_mm,y=bill_depth_mm))

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/Adding\ aes()-1.png

Now we can decide what kind of plot to make. Let’s start with a simple
scatter plot. We need to add the geom (geometry), which here is
``geom_point()``.

.. tab:: R

   .. code:: r

      ggplot(data=penguins,aes(x=bill_length_mm,y=bill_depth_mm))+
             geom_point()

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/adding\ geom-1.png

We can now see our data! However, it is difficult to see any pattern at
the moment.

Let’s group together data from each species. We can do this by adding
``color=species`` to the ``aes()``, which gives each species its own
color. This will also create a legend.

.. tab:: R

   .. code:: r

      ggplot(data=penguins,aes(x=bill_length_mm,y=bill_depth_mm, color=species))+
               geom_point()

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/Adding\ color-1.png

In addition to color, you also add other aesthetics: fill, shape,
linewidth, and alpha (transparency).

.. tab:: R

   .. code:: r

      ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, shape = species)) +
         geom_point()

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/Adding\ shape-1.png

If we specify a color outside of aesthetics, such as within
``geom_point()``, every data point will be that color. We pick the
specific color in quotes.

.. tab:: R

   .. code:: r

      ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
         geom_point(color = "red")

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/adding\ color\ to\ geom-1.png

Let’s try making another type of plot. Here, we make a boxplot of bill
depth by species with ``geom_boxplot()``.

.. tab:: R

   .. code:: r

      ggplot(data = penguins, aes(x = species, y = bill_depth_mm)) +
         geom_boxplot()

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/boxplot-1.png

We can make a histogram of bill depth with ``geom_histogram``.

.. tab:: R

   .. code:: r

      ggplot(data = penguins, aes(x = bill_depth_mm)) +
         geom_histogram()

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/histogram-1.png

Like with our scatter plot, we can separate out species with color, here
specified with ``fill``.

.. tab:: R

   .. code:: r

      ggplot(data = penguins, aes(x=bill_depth_mm, fill=species)) +
         geom_histogram(binwidth = 0.25)

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/unnamed-chunk-3-1.png
      
Facets
------

Another way to separate out groups is with **facets**. Facets are
essentially panels showing each group individually. We specify the
facets as their own layer in ``facet_wrap()``.

.. tab:: R

   .. code:: r

      ggplot(data = penguins, aes(x = bill_depth_mm)) +
         geom_histogram(binwidth = 0.25) +
         facet_wrap(~ species)

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/creating\ multiple\ plots-1.png

Customizing our plot
--------------------

ggplot has many options for customizing plots. We will go into the very
basics of those options here.

We will start by saving a simple colored box plot to a variable named
``myplot``.

.. tab:: R

   .. code:: r

      myplot<- ggplot(data = penguins, aes(x = species, y = bill_depth_mm, color = species)) +
         geom_boxplot()
      myplot

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/unnamed-chunk-4-1.png

Once the plot is saved as a variable, we can add axes labels with ``xlab()`` and ``ylab()``.

.. tab:: R

   .. code:: r

      myplot+
         xlab("Species")+
         ylab("Bill Depth")

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/adding\ adding\ labels-1.png

We can also change the title of the legend. Depending on various
factors, such as how you are distinguishing groups, there are different
functions for this. For this specific case, we use the function
``scale_color_discrete()``.

.. tab:: R

   .. code:: r

      myplot+
         xlab("Species")+
         ylab("Bill Depth")+
         scale_color_discrete(name="Species of Penguin")

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/Legends-1.png

Themes
------

The default theme in ggplot has a light gray background with a faint
grid. There are many other themes you can use in ggplot, such as
``theme_minimal``.

.. tab:: R

   .. code:: r

      myplot+
         xlab("Species")+
         ylab("Bill Depth")+
         scale_color_discrete(name="Species of Penguin")+
            theme_minimal()

.. tab:: Output
   :new-set:

   .. figure:: /_static/images/r/data-visualization/Themes-1.png

This is one of many pre-built themes available. It is also possible to
`make a custom
theme <https://ggplot2.tidyverse.org/reference/theme.html>`__.

If you would like to play with other pre-built themes, try the ggthemes
package!

.. tab:: R

   .. code:: r

      install.packages('ggthemes')
      library(ggthemes)
      +theme_tufte()
      +theme_fivethirtyeight()
      +theme_economist()
      +theme_wsj()
      +theme_solarized()

Saving plots
------------

Finally, to save a plot, you can use the ``ggsave()`` function,
specifying the desired file name.

.. tab:: R

   .. code:: r

      penguins_plot<-myplot+
         xlab("Species")+
         ylab("Bill Depth")+
         scale_color_discrete(name="Species of Penguin")+
            theme_minimal()

      ggsave("penguins_plot.pdf", penguins_plot, device="pdf")

.. tab:: Output
   
   .. code:: none

      ## Saving 7 x 5 in image

Challenge
---------

Try to recreate the following plot from the penguins data set:

.. figure:: /_static/images/r/data-visualization/challenge.png


.. collapse:: Solution

   .. container::

      .. tab:: R

         .. code:: r

               ggplot(data = penguins, aes(x = flipper_length_mm,y = body_mass_g)) +
                  geom_point(aes(color = species, 
                                 shape = species),
                                 size = 3,
                                 alpha = 0.8) +
               scale_color_manual(values = c("darkorange","purple","cyan4")) +
               labs(title = "Penguin size, Palmer Station LTER",
                     subtitle = "Flipper length and body mass for Adelie, Chinstrap and Gentoo Penguins",
                     x = "Flipper length (mm)",
                     y = "Body mass (g)",
                     color = "Penguin species",
                     shape = "Penguin species") +
               theme(legend.position = c(0.2, 0.7),
                     plot.title.position = "plot",
                     plot.caption = element_text(hjust = 0, face= "italic"),
                     plot.caption.position = "plot")