ggplot2

How to use your favorite fonts in R charts

brian-diggs — Mon, 24 Sep 2012 17:23:11 -0400

How to use your favorite fonts in R charts:

Winston Chang has a guest post on the Revolution Analytics blog about embedding fonts into PDFs using the extrafont package which, not surprisingly, works very well with ggplot2.

ggbio

hollybeale — Sun, 02 Sep 2012 12:40:16 -0400

ggbio (package, publication) is an extension and specialization of ggplot designed for visualizing genomics annotations and high-throughput data

A frequent requirement for plots of genomic data is indicating the relationship of measurements on different scales. For example, the x-axis of the lower plots in this image is the linear chromosome position, but the upper plot shows the expression levels of each exon (which are indicated in the lower image by vertical lines). In the past I’ve written code to generate the diagonal lines between the images that show the relationship of the two scales, and I’ll be happy if ggbio makes that unnecessary.

Defining a new transformation for ggplot2/scales - Part II

brian-diggs — Tue, 14 Aug 2012 17:48:46 -0400

In my previous blog post, I explored what was needed to create a new transformation for the scales package and gave an example of a mathematical transformation. In this post, I want to show an additional example related to the other mentioned use case (mapping a continuous like variable with specific structure and formatting) and extend the example into creating new scales functions which integrate into ggplot even more directly.

Time

Dates and times are tricky to work with because they have detailed external constraints and conventions. Within the R ecosystem, several packages exist solely to deal with dates and times (chron, lubridate, date, mondate, timeDate, TimeWarp, etc.), and an article has appeared in R News on the topic (Brian D. Ripley and Kurt Hornik. Date-time classes. R News, 1(2):8-11, June 2001.).

There is already support for dates (using the Date class, via date_trans in scales and scale_*_date in ggplot2) and datetimes (using the POSIXt class, via time_trans in scales and scale_*_datetime in ggplot2). The piece that is missing is for time, separate from any date; “clock time”, if you will.

Existing solutions

Exercising the first of the three great virtues of a programmer, laziness, it is worth seeing what has already been done (classes and functions) to deal with clock time.

The chron package has a class times which can specify times of day, independent of a date. Additionally, there are many supporting functions for this class:

> methods(class="times")
 [1] [.times*             [[.times*            [<-.times*          
 [4] as.character.times*  as.data.frame.times* axis.times*         
 [7] Axis.times*          c.times*             diff.times*         
[10] format.times*        hist.times*          identify.times*     
[13] is.na.times*         lines.times*         Math.times*         
[16] mean.times*          Ops.times*           plot.times*         
[19] points.times*        pretty.times*        print.times*        
[22] quantile.times*      summary.times*       Summary.times*      
[25] trunc.times*         unique.times*        xtfrm.times*        

   Non-visible functions are asterisked

Following the pattern of the previous post, each of the parts of the transformation can be determined.

`transform` and `inverse`

When dealing with variable that is a class, transform must take the specific representation and convert it to a simple numeric representation (map to [part of] the real line in mathematical terms); inverse does the opposite functional mapping. Generally, this requires delving into the structure of the class to see how it is really put together. To do that, let’s create some data. The times documentation says it can convert a character vector (by default in 24-hour, minute, second format, separated by colons) to times.

Time <- times(c("18:37:11", "16:51:34", "15:05:57", "13:20:20",
                "11:34:43", "09:49:06", "08:03:29", "06:17:52",
                "04:32:15", "02:46:38", "01:01:01"))

which if printed gives

> Time
 [1] 18:37:11 16:51:34 15:05:57 13:20:20 11:34:43 09:49:06
 [7] 08:03:29 06:17:52 04:32:15 02:46:38 01:01:01

So far, so good. But what does this object/class really look like?

> str(Time)
Class 'times'  atomic [1:11] 0.776 0.702 0.629 0.556 0.482 ...
  ..- attr(*, "format")= chr "h:m:s"
> dput(Time)
structure(c(0.775821759259259, 0.702476851851852, 0.629131944444444, 
0.555787037037037, 0.48244212962963, 0.409097222222222, 0.335752314814815, 
0.262407407407407, 0.1890625, 0.115717592592593, 0.0423726851851852
), format = "h:m:s", class = "times")

times are just vectors with an attribute and a class. A little more digging and testing can show that the numeric part is just the fraction of a day that that time represents.

> str(times(c("00:00:00","6:00:00","12:00:00","23:59:59")))
Class 'times'  atomic [1:4] 0 0.25 0.5 1
  ..- attr(*, "format")= chr "h:m:s"
> dput(times(c("00:00:00","6:00:00","12:00:00","23:59:59")))
structure(c(0, 0.25, 0.5, 0.999988425925926), format = "h:m:s", class = "times")

Most of the work of creating a mapping to numeric values is already done; all that is needed is to strip off the class and attributes. as.numeric() does that nicely.

> as.numeric(Time)
 [1] 0.77582176 0.70247685 0.62913194 0.55578704 0.48244213
 [6] 0.40909722 0.33575231 0.26240741 0.18906250 0.11571759
[11] 0.04237269

That is only half the mapping. We also need to go from this representation to a times object. Looking at the constructor for times, it can take a numeric vector representing “number of days since an origin.” It’s not stated, but maybe times are then just fractions of a day?

> times(as.numeric(Time))
 [1] 18:37:11 16:51:34 15:05:57 13:20:20 11:34:43 09:49:06
 [7] 08:03:29 06:17:52 04:32:15 02:46:38 01:01:01

Sure looks like it.

> identical(Time, times(as.numeric(Time)))
[1] TRUE

So transform is just the as.numeric function and inverse is the times function.

breaks

Getting breaks on time right is important; an axis where the ticks are every 7 seconds is going to look odd (unless there is a really compelling reason), as would 25 seconds. In base graphics, the generic function pretty has the responsibility to find “nice” breaks. Looking at the methods for times, there is a pretty.times. Does it work (well enough)?

> pretty(Time)
[1] 03:00:00 06:00:00 09:00:00 12:00:00 15:00:00 18:00:00
attr(,"labels")
[1] 03:00 06:00 09:00 12:00 15:00 18:00

That’s pretty reasonable. Checking under the hood to see what is going on, chron::pretty.times calls chron::pretty.chron which calls grDevices::pretty.POSIXt which calls grDevices::prettyDate. Looking at the code for prettyDate, the allowed (sub-day) breaks are 1 second, 2 seconds, 5 seconds, 10 seconds, 15 seconds, 30 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 1 hour, 3 hours, 6 hours, and 12 hours. I might have added a 2 hour option, but it is not worth throwing away others’ work because of. pretty_breaks already wraps pretty in the format expected by scales, so we can just use pretty_breaks() as the breaks function.

> pretty_breaks()(range(Time))
   03:00    06:00    09:00    12:00    15:00    18:00 
03:00:00 06:00:00 09:00:00 12:00:00 15:00:00 18:00:00 
attr(,"labels")
[1] 03:00 06:00 09:00 12:00 15:00 18:00

format

Here, we almost catch another break. When format is not defined, the names that are associated with what breaks returns in principle are used. Unfortunately, in practice, this is not the case because inside ggplot code, the breaks get transformed back and forth between data spaces and lose their attributes (names). If we want the default formatting (full hour minute and second), then this can simply be format. If we only want seconds to appear when they are not all 0 (when the increment is less than 1 minute), then we have to write our own function that passes the appropriate flag (simplify) as to whether the seconds should be suppressed.

fmt <- function(x) {
    format(x, simplify = !any(diff(x) < 1/(24*60)))
}

domain

Since times is defined in terms of a fraction of a day, it is only meaningful in the range 0 to 1 (inclusive on the left, exclusive on the right). domain does not have a way of defining inclusivity or exclusivity of the endpoints, so the domain is just c(0,1)

name

The transform object for datetime (POSIXt) objects already use the name “time”, so the obvious name “times” would be confusing. I’ve chosen “chrontimes” as a name, to indicate that it is the times object from the chron package.

Putting it all together

times_trans <- function() {
    fmt <- function(x) {
        format(x, simplify = !any(diff(x) < 1/(24*60)))
    }
    trans_new("chrontimes",
              transform = as.numeric,
              inverse = times,
              breaks = pretty_breaks(),
              format = fmt,
              domain=c(0,1))
}

Using the transformation in ggplot

Using the Time values previously created, and some other random data, make a data frame to plot.

dat <- data.frame(time = Time,
                  value = c(7L, 6L, 9L, 11L, 10L, 1L,
                            4L, 2L, 3L, 5L, 8L))

A default plot of this gives

ggplot(dat, aes(time, value)) + geom_point()

ggplot(dat, aes(time, value)) + geom_point() +
  scale_x_continuous(trans=times_trans())

Integrating as a ggplot scale

If you want to go the next step and create scale_*_times functions for using directly in ggplot, you can. In doing so, you may realize that, when done along a y-axis, you would expect time to run from top to bottom, not bottom to top as the y axis typically runs. Using the ideas of the reversed scale described in the previous post, a reversed times transformation can also be made. Then making the scale_x_times and scale_y_times is just a matter of passing the right transformation to scale_x_continuous and scale_y_continuous.

timesreverse_trans <- function() {
    trans <- function(x) {-as.numeric(x)}
    inv <- function(x) {times(-x)}
    fmt <- function(x) {format(x, simplify = !any(diff(x) < 1/(24*60)))}
    trans_new("chrontimes-reverse",
              transform = trans,
              inverse = inv,
              breaks = pretty_breaks(),
              format = fmt,
              domain=c(0,1))
}


scale_x_times <- function(..., trans=NULL) {
    scale_x_continuous(trans=times_trans(), ...)
}

scale_y_times <- function(..., trans=NULL) {
    scale_y_continuous(trans=timesreverse_trans(), ...)
}

Examples of plots with times on each axis in full ggplot syntax

ggplot(dat, aes(time, value)) + geom_point() +
  scale_x_times()

ggplot(dat, aes(value, time)) + geom_point() +
  scale_y_times()

Mapping Quantitative Values to Color

hollybeale — Sun, 05 Aug 2012 19:03:00 -0400

Nils Gehlenborg & Bang Wong discuss “Mapping Quantitative Values to Color” in this month’s issue of Nature Methods. The article is paywalled, but I was able to access the figure without a subscription. They map out a systematic approach to color choice, starting with considering the salient regions of your data range and any values with special meaning (i.e. zero, 32 degrees Fahrenheit or sea level). They make explicit two options for mapping gradients to values:

…we can translate the ends of the color gradient to (i) zero and the theoretical maximum value or (ii) the observed minimum and maximum. The former approach allows us to interpret the data in the context of the theoretical data range (Fig. 1a). However, if higher contrast is needed from the graphical representation and zero is irrelevant as a reference point, then it is reasonable to map the lowest observed value to the lightest color and the highest observed value to the darkest color (Fig. 1b).

Check out Baptiste’s posts about choosing color palettes for more ideas and implementation: Introduction and Educated Choices.

Computing your heart rate with a webcam, ruby, R and ggplot2

hadley — Fri, 03 Aug 2012 14:31:34 -0400

Computing your heart rate with a webcam, ruby, R and ggplot2

Using ggplot2 to examine the digital divide between those with and without internet access

joranelias — Thu, 02 Aug 2012 09:25:48 -0400

Using ggplot2 to examine the digital divide between those with and without internet access

Physics animation using R, packages planar, dielectric, ggplot2,...

baptiste-auguie — Thu, 26 Jul 2012 19:59:00 -0400

Physics animation using R, packages planar, dielectric, ggplot2, animation, gridExtra.

Violence In America Visualized via ggplot2

joranelias — Sat, 21 Jul 2012 09:26:26 -0400

Violence In America Visualized via ggplot2

ggplot syntax reminders

hollybeale — Thu, 19 Jul 2012 19:55:24 -0400

There are ggplot features that I use often enough to know they exist but not often enough to remember in detail. Lately I’ve started moving examples of these features to the menu bar. I use the Mac utility called ClipMenu, which I first started using as a clipboard manager, but now I’m using the snippets feature for this.

This screen shot shows two ggplot snippets. The first one contains the code “+ opts(axis.text.x=theme_text(angle=90, hjust=0)).” To insert it in an R document, I click the clipmenu icon on the menu bar, highlight “ggplot” and click on “rotate axis text.”

I’m sure there are numerous other ways this type of strategy could be implemented; Quicksilver’s Shelf feature comes to mind. A web search suggests TextExpander might suffice in Windows; this page lists some more options.

Exploring Oracle's revenue with ggplot2

hadley — Thu, 19 Jul 2012 09:27:02 -0400

Exploring Oracle's revenue with ggplot2

Exploring the Tour de France with R and ggplot2

hadley — Thu, 12 Jul 2012 16:12:01 -0400

Exploring the Tour de France with R and ggplot2

Soda vs. Pop with Twitter

baptiste-auguie — Mon, 09 Jul 2012 15:44:32 -0400

Soda vs. Pop with Twitter

Defining a new transformation for ggplot2/scales

brian-diggs — Tue, 26 Jun 2012 14:01:35 -0400

Inspired by writing an answer to this question on StackOverflow, I decided to write up a more detailed description of creating a new transformation using the scales package (and also to make sure that I understood all the details about how to really do it).

Background

To start with, it helps to understand the philosophy behind the scales package. From the description of the scales package:

Scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.

Within the realm of scales, a transformation allows for a maniuplation of the data space prior to its mapping to an aesthetic. In particular, it is responsible for

The mapping, in both directions, between the data space and an intermediate representation space
Providing a mechanism for determining “nice” breaks in the data space
Providing a mechanism for formatting the labels in the data space

There are two main use cases for a transformation:

Taking an existing continuous scale and performing a functional transformation of it prior to mapping. For example, taking the logarithm, exponential, square root, recriprocal, inverse, etc. of a variable.
Providing a way of handling a variable of a type which represents a continuous quantity, but has specific structure and/or formatting conventions, typically represented with a class. Prototypical examples of this are dates and datetimes.

These variable transformations take place before any stats are performed on the data. In fact, they are equivalent, in terms of effects on data, as putting a transform in as the variable itself (though the axes breaks and labels are different). Quoting from ggplot2: Elegent Graphics for Data Analysis (page 100):

Of course, you can also perform the transformation yourself. For example, instead of using scale_x_log(), you could plot log10(x). That produces an identical result inside the plotting region, but the axis and tick labels won’t be the same. If you use a transformed scale, the axes will be labelled in the original data space. In both cases, the transformaiton occurs before the statistical summary.

I reproduce Figure 6.4 using the current version of the code because it is different than what was published.

qplot(log10(carat), log10(price), data=diamonds)
qplot(carat, price, data=diamonds) + 
  scale_x_log10() + scale_y_log10()

Building blocks

The pieces that are needed to create a transformation are described on the help page for trans_new, but I’ll go through them in more detail.

`transform` and `inverse`

These are the workhorses of the transformation and define the functions that map from the original data space to the intermediate data space (transform) and back again (inverse). These can be specified as a function (an anonymous function or a function object) or as a character string which will cause a function of that name to be used (as determined by match.fun).

Each of these functions should take a vector of values and return a vector of values of the same length. Callilng inverse on the results of transform should result in the original vector (to within any error introduced by floating point arithmetic). That is all.equal(inverse(tranform(x)), x) should be TRUE for any x (for which transform is defined; see domain below).

Both of these functions are required.

`breaks`

breaks is a function which takes a vector of length 2 which represents the range of the data, expressed in the original data space, that is to be represented. This will include any requested expansion, in addition to the actual data values. breaks should return a vector of whatever length it deems appropriate such that each break is represented by one element of the vector. Optionally, the vector can be a named vector. If it is, the default formatter will use the names as the displayed version of the values.

In general, this is a hard problem, primarily because breaks should look “nice” which is difficult for an algorithm to determine. Luckily, others have spent time working on the problem and often much of what they have learned and implemented can be used without having to do much yourself. In partciular, there are existing break determination algorithms in scales such as pretty_breaks (which is based on base::pretty) which find breaks for a simple numeric scale, extended_breaks which is based on extensions of work by Wilkinson which covers the same terrirory, log_breaks which give integer breaks on a log-transformed scale, and date_breaks which works with date data.

All these functions are generators, meaning that they are functions which return functions which do the actual work of finding the breaks. These function can take parameters which define the properties of the breaking algorithm, such as the number of breaks, the base of the logarithm, or the spacing in time between dates.

This argument is optional, and if not supplied a default algorithm is used which will evenly space the ticks in the original data space.

`format`

format is a function which takes a vector of values in the original data space (those returned by the breaks function) and returns either a character vector of the same length or a list of expressions of the same length. The latter is useful for making expressions that can be handled by plotmath.

scales includes many formatting functions including comma_format which puts commas between thousands, millions, billions, etc.; dollar_format which rounds to either cents or dollars (threshold definable in generator) and adds a “$” in front and commas; percent_format which multiples by 100 and add a percent sign (“%”); and parse_format and math_format which aid in making plotmath expressions for lables.

As with breaks, all the functions are generators which means that they are functions which return functions. The returned function is the one that takes a single vector, and is what is assigned to the format argument.

This argument is optional and if not supplied, the default algorithm will use any names returned with the breaks. If there are no names, then format is called on the passed values.

`domain`

The domain is the values over which the transform function is defined. For example, square root and logarithm are only defined for positive values (ggplot does not deal with complex values); and arcsine transformation would only be defined between -1 and 1. This is represented by a length 2 vector of the endpoints (inclusive) of the defined range.

This argument is optional, and if missing it is assumed that the transformation if valid over all numeric values.

`name`

A character string to identify the transformation. It is used in summary output, but has no computational value.

Example: reverse logarithm

There are built in transformations for logarithms and for reversing a scale, but there is not one to do both at once (largest to smallest, left to right). The code for each of these, taken from the scales package, is

log_trans <- function(base = exp(1)) {
  trans <- function(x) log(x, base)
  inv <- function(x) base ^ x
  trans_new(str_c("log-", format(base)), trans, inv,
    log_breaks(base = base), domain = c(1e-100, Inf))
}

reverse_trans <- function() {
  trans_new("reverse", function(x) -x, function(x) -x)
}

To make a reversed log scale, the breaks are the same as for a regular log scale, so that part does not need to be recreated. In fact, much of log_trans can be reused with changes being made to just the transformation and inverse functions.

reverselog_trans <- function(base = exp(1)) {
    trans <- function(x) -log(x, base)
    inv <- function(x) base^(-x)
    trans_new(paste0("reverselog-", format(base)), trans, inv,
              log_breaks(base = base), domain = c(1e-100, Inf))
}

I have opted here to follow the general pattern of the other trans functions making this a generator of the transformation, parameterizable by the base of the logarithm. Since I’ve used the _trans naming convention, I can also just call it (with the default parameters) as a string in the trans argument of scale_x_continuous. Some examples of it at work:

dat <- data.frame(x=1:20, y=1:20)

ggplot(dat, aes(x,y)) + geom_point() +
    scale_x_continuous(trans="reverselog")

ggplot(dat, aes(x,y)) + geom_point() +
    scale_x_continuous(trans=reverselog_trans(base=2))

Reproducibility details

R-2.15.1, ggplot2-0.9.1, scales-0.2.1

code available at https://github.com/BrianDiggs/trans

Dot density measurements

hollybeale — Tue, 26 Jun 2012 10:01:55 -0400

Jim Perkins is a scientific illustrator who recently contributed a guest post about the various meanings of DPI (dots-per-inch) at Symbiartic, a Scientific American blog about “The art of science and the science of art.” He also has a couple previous posts about calibrating monitors.

Annotating histograms to indicate the location of specific observations

joranelias — Fri, 22 Jun 2012 09:23:00 -0400

Annotating histograms to indicate the location of specific observations

Simulating conditions of legislative violence

hollybeale — Tue, 19 Jun 2012 09:57:00 -0400

Christopher Gandrud uses ggplot to illustrate his analysis of violence in national legislative chambers (e.g. Turkey, above). After gathering a data set of incidents of legislative violence, he applied logistic regression for rare events to identify the most important variables and the extent of their importance. He then predicted the probability of violence in a range of conditions with a round of simulations, depicted below.

Christopher discusses his approach to this plot in detail here.

The take home on legislative violence: new democracies with poor concordance between votes from the electorate, seats in the legislature, and proportion of governmental power are more likely to see legislative violence.

Assessing the Price of Solid State Harddrives

baptiste-auguie — Mon, 18 Jun 2012 06:53:00 -0400

Over a staff meeting at work, the topic of price of solid state hard drives came up (what are they, is it non linear with size, etc.). I decided to sample 120 solid state hard drives from newegg.com and recorded their size (in GB) and price (in USD) as well as their class (SATA II or SATA III). Note that the sampling was semi-random, in that I had no particular agenda, but did not go to great lengths to sample randomly. To look at this, I used ggplot2.

 ssd <- read.csv("http://joshuawiley.com/files/ssd.csv")
 ssd$class <- factor(ssd$class)

 require(ggplot2)
 ## first pass
 p <- ggplot(ssd, aes(x = price, y = size, colour = class)) +
     geom_point()
 print(p)

Not too bad, but the data is sparser at higher sizes and prices, so we can use a log-log scale to make it a little easier to see, and add locally weighted regression (loess) lines to assess linearity (or lack there of).

 ## add smooths and log to make clearer
 p <- p +
  stat_smooth(se=FALSE) +
  scale_x_log10(breaks = seq(0, 1000, 100)) +
  scale_y_log10(breaks = seq(0, 600, 100))

Okay, that is nice. Lastly, let’s add better labels, make the x-axis text not overlap, and include the intercept and slope parameters for the linear lines of best fit for each class of hard drive.

 ## fit separate intercept and slope model
 m <- lm(size ~ 0 + class*price, data = ssd)
 est <- round(coef(m), 2)

 size2 <- paste0("II Size = ", est[1], " + ", est[3], "price")
 size3 <- paste0("III Size = ", est[2], " + ", est[4], "price")

 ## finalize
 p <- p +
  annotate("text", x = 100, y = 600, label = size2) +
  annotate("text", x = 100, y = 500, label = size3) +
  labs(x = "Price in USD", y = "Size in GB") +
  opts(title = "Log-Log Plot of SSD Size and Price",
       axis.text.x = theme_text(angle = 45, hjust = 1, vjust = 1))

(guest post by Joshua Wiley)

Data Visualization in the Neurosciences: Overcoming the Curse of Dimensionality

baptiste-auguie — Sun, 17 Jun 2012 09:26:56 -0400

Data Visualization in the Neurosciences: Overcoming the Curse of Dimensionality:

Summary:

In publications, presentations, and popular media, scientific results are predominantly communicated through graphs. But are these figures clear and honest or misleading? We examine current practices in data visualization and discuss improvements, advocating design choices which reveal data rather than hide it.

A 14 page cheatsheet for ggplot2

hadley — Sat, 16 Jun 2012 09:23:02 -0400

A 14 page cheatsheet for ggplot2:

Written by Ramon Saccilotto, shared by @m4xl1n

Name popularity

hollybeale — Fri, 15 Jun 2012 22:54:00 -0400

This dynamic representation of the popularity of names over the years is a favorite. It’s not new, but I still find new things to appreciate, like names that used to apply to both sexes and now only one (Ellie), or vice versa (Harley). It seems like there are more very popular male names than very popular female names; I can hardly guess what underlies that.

ggplot2

How to use your favorite fonts in R charts

ggbio

Defining a new transformation for ggplot2/scales - Part II

Time

Existing solutions

transform and inverse

breaks

format

domain

name

Putting it all together

Using the transformation in ggplot

Integrating as a ggplot scale

Mapping Quantitative Values to Color

Computing your heart rate with a webcam, ruby, R and ggplot2

Using ggplot2 to examine the digital divide between those with and without internet access

Physics animation using R, packages planar, dielectric, ggplot2,...

Violence In America Visualized via ggplot2

ggplot syntax reminders

Exploring Oracle's revenue with ggplot2

Exploring the Tour de France with R and ggplot2

Soda vs. Pop with Twitter

Defining a new transformation for ggplot2/scales

Background

Building blocks

transform and inverse

breaks

format

domain

name

Example: reverse logarithm

Reproducibility details

Dot density measurements

Annotating histograms to indicate the location of specific observations

Simulating conditions of legislative violence

Assessing the Price of Solid State Harddrives

Data Visualization in the Neurosciences: Overcoming the Curse of Dimensionality

A 14 page cheatsheet for ggplot2

Name popularity

`transform` and `inverse`

`transform` and `inverse`

`breaks`

`format`

`domain`

`name`