65-plotting.Rmd

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# Plotting {#plotting}

Whether you are doing EDA, or preparing your results for publication, you need plots.
R has many plotting mechanisms, allowing the user a tremendous amount of flexibility, while abstracting away a lot of the tedious details. 
To be concrete, many of the plots in R are simply impossible to produce with Excel, SPSS, or SAS, and would take a tremendous amount of work to produce with Python, Java and lower level programming languages. 

In this text, we will focus on two plotting packages. 
The basic __graphics__ package, distributed with the base R distribution, and the __ggplot2__ package. 

Before going into the details of the plotting packages, we start with some philosophy.
The __graphics__ package originates from the mainframe days. 
Computers had no graphical interface, and the output of the plot was immediately sent to a printer. 
Once a plot has been produced with the __graphics__ package, just like a printed output, it cannot be queried nor changed, except for further additions.

The philosophy of R is that __everyting is an object__.
The __graphics__ package does not adhere to this philosophy, and indeed it was soon augmented with the __grid__ package [@Rlanguage], that treats plots as objects. 
__grid__ is a low level graphics interface, and users may be more familiar with the __lattice__ package built upon it [@lattice].

__lattice__ is very powerful, but soon enough, it was overtaken in popularity by the __ggplot2__ package [@ggplot2].
__ggplot2__ was the PhD project of [Hadley Wickham](http://hadley.nz/), a name to remember...
Two fundamental ideas underlay __ggplot2__: (i) everything is an object, and (ii), plots can be described by a simple grammar, i.e., a language to describe the building blocks of the plot. 
The grammar in __ggplot2__ are is the one stated by @wilkinson2006grammar.
The objects and grammar of __ggplot2__ have later evolved to allow more complicated plotting and in particular, interactive plotting. 

Interactive plotting is a very important feature for EDA, and reporting. 
The major leap in interactive plotting was made possible by the advancement of web technologies, such as JavaScript and [D3.JS](https://en.wikipedia.org/wiki/D3.js).
Why is this? 
Because an interactive plot, or report, can be seen as a web-site. 
Building upon the capabilities of JavaScript and your web browser to provide the interactivity, greatly facilitates the development of such plots, as the programmer can rely on the web-browsers capabilities for interactivity. 



## The graphics System

The R code from the Basics Chapter \@ref(basics) is a demonstration of the __graphics__ package and plotting system. 
We make a quick review of the basics.

### Using Existing Plotting Functions

#### Scatter Plot

A simple scatter plot.
```{r}
attach(trees)
plot(Girth ~ Height)
```

Various types of plots.

```{r}
par.old <- par(no.readonly = TRUE)
par(mfrow=c(2,3))
plot(Girth, type='h', main="type='h'") 
plot(Girth, type='o', main="type='o'") 
plot(Girth, type='l', main="type='l'")
plot(Girth, type='s', main="type='s'")
plot(Girth, type='b', main="type='b'")
plot(Girth, type='p', main="type='p'")
par(par.old)
```

Things to note:

- The `par` command controls the plotting parameters. `mfrow=c(2,3)` is used to produce a matrix of plots with 2 rows and 3 columns.
- The `par.old` object saves the original plotting setting. It is restored after plotting using `par(par.old)`.
- The `type` argument controls the type of plot. 
- The `main` argument controls the title.
- See `?plot` and `?par` for more options.

Control the plotting characters with the `pch` argument, and size with the `cex` argument. 
```{r}
plot(Girth, pch='+', cex=3)
```

Control the line's type with `lty` argument, and width with `lwd`.

```{r}
par(mfrow=c(2,3))
plot(Girth, type='l', lty=1, lwd=2)
plot(Girth, type='l', lty=2, lwd=2)
plot(Girth, type='l', lty=3, lwd=2)
plot(Girth, type='l', lty=4, lwd=2)
plot(Girth, type='l', lty=5, lwd=2)
plot(Girth, type='l', lty=6, lwd=2)
```

Add line by slope and intercept with `abline`.

```{r}
plot(Girth)
abline(v=14, col='red') # vertical line at 14.
abline(h=9, lty=4,lwd=4, col='pink') # horizontal line at 9.
abline(a = 0, b=1) # linear line with intercept a=0, and slope b=1.
```

```{r}
plot(Girth)
points(x=1:30, y=rep(12,30), cex=0.5, col='darkblue')
lines(x=rep(c(5,10), 7), y=7:20, lty=2 )
lines(x=rep(c(5,10), 7)+2, y=7:20, lty=2 )
lines(x=rep(c(5,10), 7)+4, y=7:20, lty=2 , col='darkgreen')
lines(x=rep(c(5,10), 7)+6, y=7:20, lty=4 , col='brown', lwd=4)
```

Things to note:

- `points` adds points on an existing plot.
- `lines` adds lines on an existing plot.
- `col` controls the color of the element. It takes names or numbers as argument. 
- `cex` controls the scale of the element. Defaults to `cex=1`.


Add other elements.

```{r}
plot(Girth)
segments(x0=rep(c(5,10), 7), y0=7:20, x1=rep(c(5,10), 7)+2, y1=(7:20)+2 ) # line segments
arrows(x0=13,y0=16,x1=16,y1=17) # arrows
rect(xleft=10, ybottom=12,  xright=12, ytop=16) # rectangle
polygon(x=c(10,11,12,11.5,10.5), y=c(9,9.5,10,10.5,9.8), col='grey') # polygon
title(main='This plot makes no sense', sub='Or does it?') 
mtext('Printing in the margins', side=2) # math text
mtext(expression(alpha==log(f[i])), side=4)
```

Things to note:

- The following functions add the elements they are named after: `segments`, `arrows`, `rect`, `polygon`, `title`. 
- `mtext` adds mathematical text, which needs to be wrapped in `expression()`. For more information for mathematical annotation see `?plotmath`.

Add a legend.
```{r}
plot(Girth, pch='G',ylim=c(8,77), xlab='Tree number', ylab='', type='b', col='blue')
points(Volume, pch='V', type='b', col='red')
legend(x=2, y=70, legend=c('Girth', 'Volume'), pch=c('G','V'), col=c('blue','red'), bg='grey')
```


Adjusting Axes with `xlim` and `ylim`.

```{r}
plot(Girth, xlim=c(0,15), ylim=c(8,12))
```

Use `layout` for complicated plot layouts.

```{r}
A<-matrix(c(1,1,2,3,4,4,5,6), byrow=TRUE, ncol=2)
layout(A,heights=c(1/14,6/14,1/14,6/14))

oma.saved <- par("oma")
par(oma = rep.int(0, 4))
par(oma = oma.saved)
o.par <- par(mar = rep.int(0, 4))
for (i in seq_len(6)) {
	plot.new()
	box()
	text(0.5, 0.5, paste('Box no.',i), cex=3)
}
```


Always detach.

```{r}
detach(trees)
```

### Exporting a Plot

The pipeline for exporting graphics is similar to the export of data. 
Instead of the `write.table` or `save` functions, we will use the `pdf`, `tiff`, `png`, functions. Depending on the type of desired output.


Check and set the working directory.

```{r, eval=FALSE}
getwd()
setwd("/tmp/")
```

Export tiff.

```{r, eval=FALSE}
tiff(filename='graphicExample.tiff')
plot(rnorm(100))
dev.off()
```

Things to note:

- The `tiff` function tells R to open a .tiff file, and write the output of a plot.
- Only a single (the last) plot is saved.
- `dev.off` to close the tiff device, and return the plotting to the R console (or RStudio).

If you want to produce several plots, you can use a counter in the file's name. The counter uses the [printf](https://en.wikipedia.org/wiki/Printf_format_string) format string.

```{r, eval=FALSE}
tiff(filename='graphicExample%d.tiff') #Creates a sequence of files
plot(rnorm(100))
boxplot(rnorm(100))
hist(rnorm(100))
dev.off()
```

To see the list of all open devices use `dev.list()`. 
To close __all__ device, (not only the last one), use `graphics.off()`.


See `?pdf` and `?jpeg` for more info.







### Fancy graphics Examples {#fancy}

#### Line Graph

```{r}
x = 1995:2005
y = c(81.1, 83.1, 84.3, 85.2, 85.4, 86.5, 88.3, 88.6, 90.8, 91.1, 91.3)
plot.new()
plot.window(xlim = range(x), ylim = range(y))
abline(h = -4:4, v = -4:4, col = "lightgrey")
lines(x, y, lwd = 2)
title(main = "A Line Graph Example",
		xlab = "Time",
		ylab = "Quality of R Graphics")
axis(1)
axis(2)
box()
```

Things to note:

- `plot.new` creates a new, empty, plotting device.
- `plot.window` determines the limits of the plotting region.
- `axis` adds the axes, and `box` the framing box. 
- The rest of the elements, you already know.

#### Rosette

```{r}
n = 17
theta = seq(0, 2 * pi, length = n + 1)[1:n]
x = sin(theta)
y = cos(theta)
v1 = rep(1:n, n)
v2 = rep(1:n, rep(n, n))
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1), asp = 1)
segments(x[v1], y[v1], x[v2], y[v2])
box()
```



#### Arrows

```{r}
plot.new()
plot.window(xlim = c(0, 1), ylim = c(0, 1))
arrows(.05, .075, .45, .9, code = 1)
arrows(.55, .9, .95, .075, code = 2)
arrows(.1, 0, .9, 0, code = 3)
text(.5, 1, "A", cex = 1.5)
text(0, 0, "B", cex = 1.5)
text(1, 0, "C", cex = 1.5)
```

#### Arrows as error bars

```{r}
x = 1:10
y = runif(10) + rep(c(5, 6.5), c(5, 5))
yl = y - 0.25 - runif(10)/3
yu = y + 0.25 + runif(10)/3
plot.new()
plot.window(xlim = c(0.5, 10.5), ylim = range(yl, yu))
arrows(x, yl, x, yu, code = 3, angle = 90, length = .125)
points(x, y, pch = 19, cex = 1.5)
axis(1, at = 1:10, labels = LETTERS[1:10])
axis(2, las = 1)
box()
```

#### Histogram

A histogram is nothing but a bunch of rectangle elements.

```{r}
plot.new()
plot.window(xlim = c(0, 5), ylim = c(0, 10))
rect(0:4, 0, 1:5, c(7, 8, 4, 3), col = "lightblue")
axis(1)
axis(2, las = 1)
```


##### Spiral Squares

```{r}
plot.new()
plot.window(xlim = c(-1, 1), ylim = c(-1, 1), asp = 1)
x = c(-1, 1, 1, -1)
y = c( 1, 1, -1, -1)
polygon(x, y, col = "cornsilk")
vertex1 = c(1, 2, 3, 4)
vertex2 = c(2, 3, 4, 1)
for(i in 1:50) {
	x = 0.9 * x[vertex1] + 0.1 * x[vertex2]
	y = 0.9 * y[vertex1] + 0.1 * y[vertex2]
	polygon(x, y, col = "cornsilk")
}
```

#### Circles

Circles are just dense polygons.

```{r}
R = 1
xc = 0
yc = 0
n = 72
t = seq(0, 2 * pi, length = n)[1:(n-1)]
x = xc + R * cos(t)
y = yc + R * sin(t)
plot.new()
plot.window(xlim = range(x), ylim = range(y), asp = 1)
polygon(x, y, col = "lightblue", border = "navyblue")
```


#### Spiral

```{r}
k = 5
n = k * 72
theta = seq(0, k * 2 * pi, length = n)
R = .98^(1:n - 1)
x = R * cos(theta)
y = R * sin(theta)
plot.new()
plot.window(xlim = range(x), ylim = range(y), asp = 1)
lines(x, y)
```





## The ggplot2 System


The philosophy of __ggplot2__ is very different from the __graphics__ device. 
Recall, in __ggplot2__, a plot is a object.
It can be queried, it can be changed, and among other things, it can be plotted. 

__ggplot2__ provides a convenience function for many plots: `qplot`. 
We take a non-typical approach by ignoring `qplot`, and presenting the fundamental building blocks. 
Once the building blocks have been understood, mastering `qplot` will be easy.

The following is taken from [UCLA's idre](http://www.ats.ucla.edu/stat/r/seminars/ggplot2_intro/ggplot2_intro.htm).


A __ggplot2__ object will have the following elements:

- __Data__ the data frame holding the data to be plotted.
- __Aes__ defines the mapping between variables to their visualization.
- __Geoms__ are the objects/shapes you add as layers to your graph.
- __Stats__ are statistical transformations when you are not plotting the raw data, such as the mean or confidence intervals.
- __Faceting__ splits the data into subsets to create multiple variations of the same graph (paneling).

The `nlme::Milk` dataset has the protein level of various cows, at various times, with various diets.
```{r}
library(nlme)
data(Milk)
head(Milk)
```

```{r}
library(ggplot2)
ggplot(data = Milk, aes(x=Time, y=protein)) +
  geom_point()
```

Things to note:

- The `ggplot` function is the constructor of the __ggplot2__ object. If the object is not assigned, it is plotted.
- The `aes` argument tells R that the `Time` variable in the `Milk` data is the x axis, and protein is y.
- The `geom_point` defines the __Geom__, i.e., it tells R to plot the points as they are (and not lines, histograms, etc.).
- The __ggplot2__ object is build by compounding its various elements separated by the `+` operator.
- All the variables that we will need are assumed to be in the `Milk` data frame. This means that (a) the data needs to be a data frame (not a matrix for instance), and (b) we will not be able to use variables that are not in the `Milk` data frame.


Let's add some color. 

```{r}
ggplot(data = Milk, aes(x=Time, y=protein)) +
  geom_point(aes(color=Diet))
```

The `color` argument tells R to use the variable `Diet` as the coloring. 
A legend is added by default. 
If we wanted a fixed color, and not a variable dependent color, `color` would have been put outside the `aes` function.

```{r}
ggplot(data = Milk, aes(x=Time, y=protein)) +
  geom_point(color="green")
```

Let's save the __ggplot2__ object so we can reuse it. 
Notice it is not plotted.

```{r}
p <- ggplot(data = Milk, aes(x=Time, y=protein)) +
  geom_point()
```

We can change^{In the Object-Oriented Programming lingo, this is known as [mutating](https://en.wikipedia.org/wiki/Immutable_object)} existing plots using the `+` operator.
Here, we add a smoothing line to the plot `p`.

```{r}
p + geom_smooth(method = 'gam')
```

Things to note:

- The smoothing line is a layer added with the `geom_smooth()` function.
- Lacking arguments of its own, the new layer will inherit the `aes` of the original object, x and y variables in particular. 


To split the plot along some variable, we use faceting, done with the `facet_wrap` function.

```{r}
p + facet_wrap(~Diet)
```

Instead of faceting, we can add a layer of the mean of each `Diet` subgroup, connected by lines.

```{r}
p + stat_summary(aes(color=Diet), fun.y="mean", geom="line")
```

Things to note:

- `stat_summary` adds a statistical summary.
- The summary is applied along `Diet` subgroups, because of the `color=Diet` aesthetic, which has already split the data.
- The summary to be applied is the mean, because of `fun.y="mean"`.
- The group means are connected by lines, because of the `geom="line"` argument.

What layers can be added using the __geoms__ family of functions?

- `geom_bar`: bars with bases on the x-axis.
- `geom_boxplot`: boxes-and-whiskers.
- `geom_errorbar`: T-shaped error bars.
- `geom_histogram`: histogram.
- `geom_line`: lines.
- `geom_point`: points (scatterplot).
- `geom_ribbon`: bands spanning y-values across a range of x-values.
- `geom_smooth`: smoothed conditional means (e.g. loess smooth).


To demonstrate the layers added with the `geoms_*` functions, we start with a histogram.

```{r}
pro <- ggplot(Milk, aes(x=protein))
pro + geom_histogram(bins=30)
```

A bar plot.

```{r}
ggplot(Milk, aes(x=Diet)) +
  geom_bar()
```

A scatter plot.

```{r}
tp <- ggplot(Milk, aes(x=Time, y=protein))
tp + geom_point()
```

A smooth regression plot, reusing the `tp` object.

```{r}
tp + geom_smooth(method='gam')
```

And now, a simple line plot, reusing the `tp` object, and connecting lines along `Cow`.

```{r}
tp + geom_line(aes(group=Cow))
```

The line plot is completely incomprehensible. 
Better look at boxplots along time (even if omitting the `Cow` information).
```{r}
tp + geom_boxplot(aes(group=Time))
```

We can do some statistics for each subgroup.
The following will compute the mean and standard errors of `protein` at each time point.

```{r}
ggplot(Milk, aes(x=Time, y=protein)) +
  stat_summary(fun.data = 'mean_se')
```

Some popular statistical summaries, have gained their own functions:

- `mean_cl_boot`: mean and bootstrapped confidence interval (default 95%).
- `mean_cl_normal`: mean and Gaussian (t-distribution based) confidence interval (default 95%).
- `mean_dsl`: mean plus or minus standard deviation times some constant (default constant=2).
- `median_hilow`: median and outer quantiles (default outer quantiles = 0.025 and 0.975).

For less popular statistical summaries, we may specify the statistical function in `stat_summary`. The median is a first example.

```{r}
ggplot(Milk, aes(x=Time, y=protein)) +
  stat_summary(fun.y="median", geom="point")
```

We can also define our own statistical summaries.
```{r}
medianlog <- function(y) {median(log(y))}
ggplot(Milk, aes(x=Time, y=protein)) +
  stat_summary(fun.y="medianlog", geom="line")
```



__Faceting__ allows to split the plotting along some variable.
`face_wrap` tells R to compute the number of columns and rows of plots automatically. 

```{r}
ggplot(Milk, aes(x=protein, color=Diet)) +
  geom_density() +
  facet_wrap(~Time)
```

`facet_grid` forces the plot to appear allow rows or columns, using the `~` syntax.

```{r}
ggplot(Milk, aes(x=Time, y=protein)) +
  geom_point() +
  facet_grid(Diet~.) # `.~Diet` to split along columns and not rows.
```

To control the looks of the plot, __ggplot2__ uses __themes__.

```{r}
ggplot(Milk, aes(x=Time, y=protein)) +
  geom_point() +
  theme(panel.background=element_rect(fill="lightblue"))
```

```{r}
ggplot(Milk, aes(x=Time, y=protein)) +
  geom_point() +
  theme(panel.background=element_blank(),
        axis.title.x=element_blank())
```


Saving plots can be done using `ggplot2::ggsave`, or with `pdf` like the __graphics__ plots:
```{r, eval=FALSE}
pdf(file = 'myplot.pdf')
print(tp) # You will need an explicit print command!
dev.off()
```

```{remark}
If you are exporting a PDF for publication, you will probably need to embed your fonts in the PDF. 
In this case, use `cairo_pdf()` instead of `pdf()`.
```



Finally, what every user of __ggplot2__ constantly uses, is the (excellent!) online documentation at http://docs.ggplot2.org.



### Extensions of the ggplot2 System

Because __ggplot2__ plots are R objects, they can be used for computations and altered. 
Many authors, have thus extended the basic __ggplot2__ functionality. 
A list of __ggplot2__ extensions is curated by Daniel Emaasit at  [http://www.ggplot2-exts.org](http://www.ggplot2-exts.org/gallery/).
The RStudio team has its own list of recommended packages at [RStartHere](https://github.com/rstudio/RStartHere).




## Interactive Graphics

As already mentioned, the recent and dramatic advancement in interactive visualization was made possible by the advances in web technologies, and the [D3.JS](https://d3js.org/) JavaScript library in particular. 
This is because it allows developers to rely on existing libraries designed for web browsing, instead of re-implementing interactive visualizations.
These libraries are more visually pleasing, and computationally efficient, than anything they could have developed themselves.  

The [htmlwidgets](http://www.htmlwidgets.org/) package does not provide visualization, but rather, it facilitates the creation of new interactive visualizations. 
This is because it handles all the technical details that are required to use R output within JavaScript visualization libraries. 


For a list of interactive visualization tools that rely on __htmlwidgets__ see [their (amazing) gallery](http://gallery.htmlwidgets.org/), and the [RStartsHere](https://github.com/rstudio/RStartHere) page. 
In the following sections, we discuss a selected subset.


### Plotly {#plotly}

You can create nice interactive graphs using `plotly::plot_ly`:

```{r}
library(plotly)
set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]
```

```{r, eval=FALSE}
plot_ly(data = d, x = ~carat, y = ~price, color = ~carat, size = ~carat, text = ~paste("Clarity: ", clarity))
```


More conveniently, any __ggplot2__ graph can be made interactive using `plotly::ggplotly`:

```{r}
p <- ggplot(data = d, aes(x = carat, y = price)) +
  geom_smooth(aes(colour = cut, fill = cut), method = 'loess') + 
  facet_wrap(~ cut) # make ggplot
ggplotly(p) # from ggplot to plotly
```

How about exporting __plotly__ objects?
Well, a __plotly__ object is nothing more than a little web site: an HTML file.
When showing a __plotly__ figure, RStudio merely servers you as a web browser. 
You could, alternatively, export this HTML file to send your colleagues as an email attachment, or embed it in a web site.
To export these, use the `plotly::export` or the `htmlwidgets::saveWidget` functions.


For more on __plotly__ see https://plot.ly/r/.








## Other R Interfaces to JavaScript Plotting

Plotly is not the only interactive plotting framework in R that relies o JavaScript for interactivity. 
Here are some more interactive and beautiful charting libraries.

- [Highcharts](https://www.highcharts.com/), like Plotly [\@ref(plotly)], is a popular collection of JavaScript plotting libraries, with great emphasis on aesthetics. 
The package [highcharter](https://cran.r-project.org/package=highcharter) is an R wrapper for dispatching plots to highcharts. 
For a demo of the capabilities of Highcarts, see [here](https://www.highcharts.com/demo).

- [Rbokeh](http://hafen.github.io/rbokeh/) is a R wrapper for the popular [Bokeh](https://bokeh.pydata.org/en/latest/) JavaScript charting libraries.

- [r2d3](https://rstudio.github.io/r2d3/): a R wrapper to the [D3](https://d3js.org/) plotting libraries. 

- [trelliscope](https://hafen.github.io/trelliscopejs/#trelliscope): for beautiful, interactive, plotting of [small multiples](https://www.juiceanalytics.com/writing/better-know-visualization-small-multiples); think of it as interactive faceting. 


- [VegaWidget](https://vegawidget.github.io/vegawidget/). An interfave to the [Vega-lite](https://vega.github.io/vega-lite/) plotting libraries. 

## Bibliographic Notes
For the __graphics__ package, see @Rlanguage.
For __ggplot2__ see @ggplot2.
For the theory underlying __ggplot2__, i.e. the Grammar of Graphics, see @wilkinson2006grammar.
A [video](https://www.youtube.com/watch?v=9Objw9Tvhb4&feature=youtu.be) by one of my heroes,  [Brian Caffo](http://www.bcaffo.com/), discussing __graphics__ vs. __ggplot2__.



## Practice Yourself

1. Go to the Fancy Graphics Section \@ref(fancy). Try parsing the commands in your head. 

1. Recall the `medianlog` example and replace the `medianlog` function with a [harmonic mean](https://en.wikipedia.org/wiki/Harmonic_mean). 
    ```{r}
medianlog <- function(y) {median(log(y))}
ggplot(Milk, aes(x=Time, y=protein)) +
  stat_summary(fun.y="medianlog", geom="line")
```
``` 
1. Write a function that creates a boxplot from scratch. See how I built a line graph in Section \@ref(fancy). 

1. Export my plotly example using the RStudio interface and send it to yourself by email. 

ggplot2:

1. Read about the “oats” dataset using `? MASS::oats `.
    1. Inspect, visually, the dependency of the yield (Y) in the Varieties (V) and the Nitrogen treatment (N).
    1. Compute the mean and the standard error of the yield for every value of Varieties and Nitrogen treatment.
    1. Change the axis labels to be informative with `labs` function and give a title to the plot with `ggtitle` function.

1. Read about the “mtcars” data set using `? mtcars`.
    1. Inspect, visually, the dependency of the Fuel consumption (mpg) in the weight (wt) 
    1. Inspect, visually, the assumption that the Fuel consumption also depends on the number of cylinders. 
    1. Is there an interaction between the number of cylinders to the weight (i.e. the slope of the regression line is different between the number of cylinders)?  Use `geom_smooth`.

See DataCamp's [Data Visualization with ggplot2](https://www.datacamp.com/courses/data-visualization-with-ggplot2-1) for more self practice.