Base Graphics
=============

Building Graphs
---------------

Incremental building of features into graphs.

`Less is more <http://i.imgur.com/W4BKCVU.gif>`__

`More visualization
tips <http://blog.plot.ly/post/123617968702/online-dashboards-eight-helpful-tips-you-should>`__

**References**

1. `Graphical
   parameters <http://www.statmethods.net/advgraphs/parameters.html>`__
2. `Colors <http://research.stowers-institute.org/efg/R/Color/Chart/index.htm>`__
3. Named colors - type ``colors()``
4. `Using color scales and plattes in
   R <http://thedata.co/sites/thedata.co/files/u1/VisThis_Tutorial_Using%20Color%20Scales%20and%20Palettes%20in%20R.pdf>`__
5. ``?plot``
6. ``?par``
7. Specific options e.g. ``?pch``

Basic plots
~~~~~~~~~~~

.. 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>




.. code:: python

    with(mtcars, plot(wt, mpg))

.. image:: BaseGraphics_files/BaseGraphics_7_0.png
   :width: 600

.. code:: python

    scatter.smooth(mtcars$wt, mtcars$mpg)

.. image:: BaseGraphics_files/BaseGraphics_8_0.png
   :width: 600




.. code:: python

    df <- mtcars[order(mtcars$wt),]
    with(df, plot(wt, mpg, type="b"))

.. image:: BaseGraphics_files/BaseGraphics_9_0.png
   :width: 600




.. code:: python

    with(mtcars, hist(mpg, breaks=10))


.. image:: BaseGraphics_files/BaseGraphics_10_0.png
   :width: 600



.. code:: python

    plot(density(mtcars$mpg), main="Density plot")

.. image:: BaseGraphics_files/BaseGraphics_11_0.png
   :width: 600




.. code:: python

    attach(mtcars)
    hist(mpg, breaks=10, probability = TRUE, main="")
    rug(mpg)
    x <- seq(min(mpg), max(mpg), length.out = 50)
    lines(x, dnorm(x, mean=mean(x), sd=sd(x)), col="red", lwd=2)
    detach(mtcars)

.. image:: BaseGraphics_files/BaseGraphics_12_0.png
   :width: 600




.. code:: python

    with(mtcars, pie(table(carb)))

.. image:: BaseGraphics_files/BaseGraphics_13_0.png
   :width: 600




.. code:: python

    with(mtcars, barplot(table(carb)))

.. image:: BaseGraphics_files/BaseGraphics_14_0.png
   :width: 600




.. code:: python

    with(mtcars, barplot(table(carb), horiz=TRUE))


.. image:: BaseGraphics_files/BaseGraphics_15_0.png
   :width: 600



.. code:: python

    attach(mtcars)
    (tbl <- table(carb, am))
    barplot(tbl, beside=TRUE, legend=rownames(tbl), col=heat.colors(carb))
    detach(mtcars)

.. image:: BaseGraphics_files/BaseGraphics_16_0.png
   :width: 600




.. parsed-literal::

        am
    carb 0 1
       1 3 4
       2 6 4
       3 3 0
       4 7 3
       6 0 1
       8 0 1





.. code:: python

    boxplot(log1p(mtcars))

.. image:: BaseGraphics_files/BaseGraphics_17_0.png
   :width: 600




.. code:: python

    df <- mtcars[order(-mtcars$mpg),]
    dotchart(df$mpg, labels=row.names(df))

.. image:: BaseGraphics_files/BaseGraphics_18_0.png
   :width: 600




.. code:: python

    dotchart(df$mpg, labels=row.names(df), groups=df$cyl, color=df$cyl, pch=19)

.. image:: BaseGraphics_files/BaseGraphics_19_0.png
   :width: 600




.. code:: python

    pairs(~mpg + drat + wt, data=mtcars)

.. image:: BaseGraphics_files/BaseGraphics_20_0.png
   :width: 600




Building plots
~~~~~~~~~~~~~~

-  Local customization of plots
-  Glaobl customization of plots
-  Adding more stuff to a plot

   -  points
   -  lines
   -  text
   -  mathematical expressions

-  Combining plots
-  Saving plots

Scatterplot
^^^^^^^^^^^

.. code:: python

    df <- mtcars[order(mtcars$wt),]
    attach(df)
    plot(wt, mpg)
    detach(df)

.. image:: BaseGraphics_files/BaseGraphics_23_0.png
   :width: 600




Combining plots
~~~~~~~~~~~~~~~

.. code:: python

    par(mfrow=c(2,2))
    plot(rnorm(10))
    plot(rnorm(10))
    plot(rnorm(10))
    plot(rnorm(10))


.. image:: BaseGraphics_files/BaseGraphics_25_0.png
   :width: 600



.. code:: python

    (m <- matrix(c(1,1,2,3), 2, 2, byrow=TRUE))




.. raw:: html

    <table>
    <tbody>
    	<tr><td>1</td><td>1</td></tr>
    	<tr><td>2</td><td>3</td></tr>
    </tbody>
    </table>




.. code:: python

    layout(m)
    plot(rnorm(10))
    plot(rnorm(10))
    plot(rnorm(10))

.. image:: BaseGraphics_files/BaseGraphics_27_0.png
   :width: 600




.. code:: python

    layout(m, widths=c(1,2), heights=c(2,1))
    plot(rnorm(10))
    plot(rnorm(10))
    plot(rnorm(10))


.. image:: BaseGraphics_files/BaseGraphics_28_0.png
   :width: 600



.. code:: python

    orig <- par(no.readonly=TRUE)
    x <- rnorm(10)
    y <- rnorm(10)
    par(fig = c(0, 0.8, 0, 0.8))
    plot(x, y)
    par(fig = c(0, 0.8, 0.6, 1), new=TRUE)
    plot(density(x), axes=FALSE, main="", xlab="")
    par(fig = c(0.6, 1, 0, 0.8), new=TRUE)
    boxplot(y, axes=FALSE)
    par(orig)

.. image:: BaseGraphics_files/BaseGraphics_29_0.png
   :width: 600




Plotting Graphcs and Heatmpas
----------------------------------------

Graphs
~~~~~~

It may be necessary to install first

.. code:: r

    install.packages("igraphdata", repos = "http://cran.r-project.org")

.. code:: python

    library(igraph)
    library(igraphdata)

.. code:: python

    data(macaque)

.. code:: python

    plot(macaque, layout=layout.auto, vertex.shape="circle",
         vertex.size=8, edge.arrow.size=0.5, vertex.label=NA)

.. image:: MiscGraphics_files/MiscGraphics_4_0.png
   :width: 600



Heatmap
~~~~~~~

.. code:: python

    heatmap(as.matrix(mtcars), Rowv = NA, Colv = NA, scale = "column")


.. image:: MiscGraphics_files/MiscGraphics_6_0.png
   :width: 600
	   
Heatmap with dendrogram
~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    heatmap(as.matrix(mtcars), scale = "column")

.. image:: MiscGraphics_files/MiscGraphics_8_0.png
   :width: 600
	   



Fancy heatmaps
~~~~~~~~~~~~~~

Also see `d3heatmaps <https://github.com/rstudio/d3heatmap>`__ if you
want an interactive heatmap. This does not seem to work within the
notebook but will work in RStudio or an R script.

.. code:: python

    library(pheatmap)

.. code:: python

    pheatmap(as.matrix(mtcars), scale = "column")



.. image:: MiscGraphics_files/MiscGraphics_11_0.png
   :width: 600


.. code:: python

    library(gplots)

.. code:: python

    heatmap.2(as.matrix(mtcars), scale = "column", col=redgreen)

.. image:: MiscGraphics_files/MiscGraphics_13_0.png
   :width: 600




Work!
^^^^^

Here we will try to replicate the noise discovery heatmap shown in the
statistics class.

.. code:: python

    suppressMessages(library(genefilter))

Perform noise discovery
~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    set.seed(123)
    
    n <- 20 # number of subjects
    m <- 20000 # number of genes 
    alpha <- 0.005 # significance level
    
    # create a matrix of gene expression values with m rows and 2*n columns
    M <- matrix(rnorm(2*n*m), m, 2*n)
    
    # give row and column names
    rownames(M) <- paste("G", 1:m, sep="")
    colnames(M) <- paste("id", 1:(2*n), sep="")
    
    # assign subjects inot equal sized groups
    grp <- factor(rep(0:1, c(n, n)))
    
    # calculate p-value using t-test for mean experession value of each gene
    pvals <- rowttests(M, grp)$p.value
    
    # extract the genes which meet the specified significance level
    hits <- M[pvals < alpha,]

-  Use pheatmap to plot a heatmap
-  Remove the row names (Google or use R's built-in help to figure out
   to do this)
-  Use this color palette to map expression values to a red-blakc-green
   scale

``colorRampPalette(c("red3", "black", "green3"))(50)``