More ``ggplot2``
================

Using ``ggplot2`` (Grammar of Graphics)
---------------------------------------

In addition to the ``base`` plotting facilites we have been using, R
also has the ``ggplot2`` package that can be used to generate beutfiul
graphs. We will only touch on a small subset of ``ggplot2`` capabiliites
here.

**References**

-  `R Graphics Cookbook <http://www.cookbook-r.com/Graphs/>`__

.. code:: python

    library(ggplot2)
    library(grid)
    library(gridExtra)

.. code:: python

    head(mtcars)




.. raw:: html

    <table>
    <thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>
    <tbody>
    	<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.62</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>
    	<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>
    	<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108</td><td>93</td><td>3.85</td><td>2.32</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>
    	<tr><th scope=row>Hornet 4 Drive</th><td>21.4</td><td>6</td><td>258</td><td>110</td><td>3.08</td><td>3.215</td><td>19.44</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>
    	<tr><th scope=row>Hornet Sportabout</th><td>18.7</td><td>8</td><td>360</td><td>175</td><td>3.15</td><td>3.44</td><td>17.02</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>
    	<tr><th scope=row>Valiant</th><td>18.1</td><td>6</td><td>225</td><td>105</td><td>2.76</td><td>3.46</td><td>20.22</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>
    </tbody>
    </table>




Chaining plotting functions
---------------------------

-  ggplot()
-  aes()
-  geom\_xxx()
-  annotationa

.. code:: python

    ggplot(data=mtcars, aes(x=wt, y=mpg)) +
    geom_point() +
    labs(title="Simple scatter plot", x="Weight", y="Miles per gallon")

.. image:: GGPlot2Graphics_files/GGPlot2Graphics_5_0.png
   :width: 600

.. code:: python

    ggplot(data=mtcars, aes(x=wt, y=mpg)) +
    geom_point(color="blue", size=5) +
    geom_smooth(method="loess", color="orange") +
    labs(title="Fitting a loess", x="Weight", y="Miles per gallon")

.. image:: GGPlot2Graphics_files/GGPlot2Graphics_6_0.png
   :width: 600



.. code:: python

    ggplot(data=mtcars, aes(x=wt, y=mpg, color=factor(cyl),, shape=factor(am))) +
    geom_point(size=5) +
    labs(title="Use shape and color", x="Weight", y="Miles per gallon")


.. image:: GGPlot2Graphics_files/GGPlot2Graphics_7_0.png
   :width: 600


.. code:: python

    p <- ggplot(mtcars, aes(x=wt, y=mpg))
    p + 
    geom_point(aes(size=hp, color=disp)) + 
    ggtitle("Use color and size") + 
    scale_colour_gradientn(colours=heat.colors(10)) +
    scale_size(range=c(2, 10))

.. image:: GGPlot2Graphics_files/GGPlot2Graphics_8_0.png
   :width: 600



.. code:: python

    ggplot(data=mtcars, aes(x=hp, y=mpg, color=factor(cyl))) +
    geom_point(size=5) +
    facet_grid(am ~ vs, labeller = label_both) +
    labs(title="Split plots with conditioning", x="Horsepower", y="Miles per gallon")


.. image:: GGPlot2Graphics_files/GGPlot2Graphics_9_0.png
   :width: 600


More examples
~~~~~~~~~~~~~

.. code:: python

    p4 <- ggplot(mtcars, aes(x=factor(gear), y=wt)) + 
          geom_boxplot()
    p5 <- ggplot(data.frame(x=seq(0, 2*pi, length.out = 50)), aes(x=x)) + 
          stat_function(fun=sin, geom="line")
    p6 <- ggplot(mtcars, aes(x=mpg, alpha=0.5, fill=factor(gear))) + 
          geom_density() +
          guides(alpha=FALSE, fill=FALSE)
    grid.arrange(p4, p5, p6, ncol = 1)


.. image:: GGPlot2Graphics_files/GGPlot2Graphics_11_0.png
   :width: 600


Plot aesthetics
~~~~~~~~~~~~~~~

.. code:: python

    ggplot(mtcars, aes(x=wt, y=mpg)) +
    geom_point(colour="black", size = 4.5, show_guide = TRUE) + 
    geom_point(colour="pink", size = 4, show_guide = TRUE) +
    geom_point(aes(shape = factor(cyl))) +
    theme_bw(base_size=18) +
    theme(aspect.ratio=1)

.. image:: GGPlot2Graphics_files/GGPlot2Graphics_13_0.png
   :width: 600



Adding fitted lines
~~~~~~~~~~~~~~~~~~~

.. code:: python

    p <- ggplot(mtcars, aes(x=wt, y=mpg))
    p1 <- p + 
          geom_point() + 
          stat_smooth(method=lm, se=FALSE)
    p2 <- p + 
          geom_point() + 
          stat_smooth(method=lm, level=0.95)
    p3 <- p + 
          geom_point() + 
          stat_smooth(method=loess, color='red')
    p4 <- ggplot(mtcars, aes(x=wt, y=mpg, color=factor(am))) + 
          geom_point() + 
          geom_smooth(method='loess') + 
          guides(color=FALSE)
    grid.arrange(p1, p2, p3, p4, ncol = 2)

.. image:: GGPlot2Graphics_files/GGPlot2Graphics_15_0.png
   :width: 600



Using existing model fits
^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: python

    m1 <- lm(mpg ~ wt, data=mtcars)
    pred1 <- data.frame(wt=seq(min(mtcars$wt), max(mtcars$wt), length.out=100))
    pred <- predict(m1, pred1, se.fit=TRUE)
    pred1$mpg <- pred$fit
    pred1$low <- pred1$mpg - 1.96*pred$se.fit
    pred1$high <- pred1$mpg + 1.96*pred$se.fit

.. code:: python

    m2 <- loess(mpg ~ wt, data=mtcars)
    pred2 <- data.frame(wt=seq(min(mtcars$wt), max(mtcars$wt), length.out=100))
    pred2$mpg <- predict(m2, pred2)

.. code:: python

    p <- ggplot(mtcars, aes(x=wt, y=mpg))
    p1 <- p + 
          geom_point(size=4, color='gray40') + 
          geom_line(data=pred1)
    p2 <- p + 
          geom_point(size=4, color='gray40') + 
          geom_line(data=pred1) +
          annotate("text", label="r^2 == 0.75", parse=TRUE, x=4.8, y=32)
    p3 <- p + 
          geom_point(size=4, color='gray40') + 
          geom_line(data=pred1) +
          geom_ribbon(data=pred1, aes(ymin=low, ymax=high), alpha=0.3) +
          annotate("text", label="r^2 == 0.75", parse=TRUE, x=4.8, y=32)
    p4 <- p + 
          geom_point(size=4, color='blue', alpha=0.5) + 
          geom_line(data=pred2, color='red', size=1)
    grid.arrange(p1, p2, p3, p4, ncol = 2)

.. image:: GGPlot2Graphics_files/GGPlot2Graphics_19_0.png
   :width: 600



Fitting a lgoistic
~~~~~~~~~~~~~~~~~~

.. code:: python

    ggplot(mtcars, aes(x=mpg, y=am)) +
    geom_point(position=position_jitter(width=.3, height=.08), shape=21, alpha=0.6, size=3) +
    stat_smooth(method=glm, family=binomial, color="red") 




.. image:: GGPlot2Graphics_files/GGPlot2Graphics_21_0.png
   :width: 600