The R environment is not intended to be a programming
language per se, but rather an interactive tool for exploring data sets,
including the generation of a wide range of graphic representations of
data properties. You can save both the generated graphics and the steps taken
during a session for later use, which is especially useful in picking up working environments,
per project, where you last left off. By
default, R commands are saved in a session history, but you can also save particularly helpful sequences of instructions in .R files that you can source() within a session.
The creators of R describe their goal in "An Introduction to R" (see Resources below for a link to the full text):
It is recommended that you should use separate working directories for analyses conducted with R. It is quite common for objects with names x and y to be created during an analysis. Names like this are often meaningful in the context of a single analysis, but it can be quite hard to decide what they might be when [...] several analyses have been conducted in the same directory.
Hidden files that contain binary serializations of working objects in a session are generated in each directory. This allows you to restart a session with all your prior active variables.
The (GPL'd) R programming language has two parents, the proprietary S/S-PLUS programming language, from which it gets most of its syntax, and the Scheme programming language, from which it gets many (more subtle) semantic aspects. S dates back to 1984 in its earliest incarnation, with later versions (including S-PLUS) adding many enhancements. Scheme (as Lisp), of course, dates back to days when the hills were young. R emerged as a Free Software superset of S/S-frPLUS in 1997 and has had a thriving user and developer community since then. You need not worry about its heritage to benefit from R. Cameron Laird provides some good background and resources related to R in his developerWorks article, R handy for crunching data.
R is available in compiled form for many computer platforms: Linux, Windows, Mac OS X, and Mac OS Classic. Naturally, source is also available for compiling to other platforms (for example, coauthor Brad built R to FreeBSD with no difficulties). R suffers from some glitches on various platforms: for example, plot output using Quartz on David's Mac OS X machine produces an unresponsive display window; and worse still, on Brad's FreeBSD/AMD Athlon box, exiting R can actually force a reboot (this probably has to do with incorrect SSE kernel options, but the behavior is still troublesome). Nonetheless, R is generally stable, fast, and comes with an absolutely amazing range of statistical and math functions. Optional packages add even further to the huge collection of standard packages and functions.
The basic data object in R is a vector. A number of variants on vectors add capabilities such as (multi-dimensional) arrays, data frames, (heterogeneous) lists, and matrices. Much like in NumPy/NumArray or Matlab, operations on vectors and their siblings operate elementwise on member data. A few quick examples of R in action give the feel for its syntax (shell prompts and responses are included in these initial examples):
Listing 1. Vectors and elementwise operators
> a <- c(3.1, 4.2, 2.7, 4.1) # Assign with "arrow" rather than "="
> c(3.3, 3.4, 3.8) -> b # Can also assign pointing right
> assign("c", c(a, 4.0, b)) # Or explicitly to a variable name
> c # Concatenation "flattens" arguments
[1] 3.1 4.2 2.7 4.1 4.0 3.3 3.4 3.8
> 1/c # Operate on each element of vector
[1] 0.3225806 0.2380952 0.3703704 0.2439024 0.2500000 0.3030303 0.2941176
[8] 0.2631579
> a * b # Cycle shorter vector "b" (but warn)
[1] 10.23 14.28 10.26 13.53
Warning message:
longer object length
is not a multiple of shorter object length in: a * b
> a+1 # "1" is treated as vector length 1
[1] 4.1 5.2 3.7 5.1
|
More examples of indexing, slicing, named, and optional
arguments, and other elements of R syntax are included below. The R shell
prompt -- especially if you have GNU readlines installed -- is a
wonderful
interface for exploration. Keep in mind the help(function) command
to learn more as you work (you can also use ?function). Users of the
Python shell will find the R shell immediately familiar -- and will
appreciate the utility of both.
Brad has been collecting temperature data from four thermometers in and around his house for almost a year, and automatically compiling sliding windows of readings into Web-accessible graphs using GnuPlot (see Resources for a link to more information on Gnuplot). While such a hackerish data collection may not really serve any broad scientific purpose, it has a number of excellent characteristics that resemble scientific data.
The data is collected every three minutes, which makes for a lot of data points over a year (around 750,000 between the four measurement sites). Some of the data is missing, because of various failures in the thermometer, the transmission channel, or the recording computer. In a small number of cases, it is known that the single-wire transmission channel transposes simultaneous readings because of timing errors. In other words, Brad's temperature data looks a lot like real-world scientific data that is pretty good, but still subject to glitches and imperfections.
The temperature data is collected into four separate data files, named by collection site, each having a format like this:
Listing 2. Format of initial temperature data files
2003 07 25 16 04 27.500000 2003 07 25 16 07 27.300000 2003 07 25 16 10 27.300000 |
A first pass at reading the data might look something like:
Listing 3. A first pass at reading temperature data
> lab <- read.fwf('therm/lab', width=c(17,9)) # Fixed width format
> basement <- read.fwf('therm/basement', width=c(17,9))
> livingroom <- read.fwf('therm/livingroom', width=c(17,9))
> outside <- read.fwf('therm/outside', width=c(17,9))
> l_range <- range(lab[,2]) # Vector of data frame: entire second column
> b_range <- range(basement[,2]) # range() gives min/max
> v_range <- range(livingroom[,2])
> o_range <- range(outside[,2])
> global <- range(b_range, l_range, v_range, o_range)
> global # Temperature range across all sites
[1] -19.8 32.2
|
The naive initial data format has some problems. For one thing, missing data is not explicitly indicated, but is simply marked by an absent line and time stamp. Moreover, dates are stored in a non-standard format (rather than ISO8601/W3C), with internal spaces. As a smaller matter, repeating timestamps in four files is space inefficient. Certainly we could clean up the data in R itself, but instead we took the recommendation of the R authors in the document "R Data Import/Export" (see Resources for a link). Text processing is generally best done in a language specialized to that task: in our case we wrote a Python script to generate a unified data file that is straightforwardly readable using R. For example, the first few lines of the new data file, glarp.temps, read:
Listing 4. Unified temperature data format
timestamp basement lab livingroom outside 2003-07-25T16:04 24.000000 NA 29.800000 27.500000 2003-07-25T16:07 24.000000 NA 29.800000 27.300000 2003-07-25T16:10 24.000000 NA 29.800000 27.300000 |
Let's work with the improved dataset:
Listing 5. Working with unified temperature data
> glarp <- read.table('glarp.temps', header=TRUE, as.is=TRUE)
> timestamps <- strptime(glarp[,1], format="%Y-%m-%dT%H:%M")
> names(glarp) # What column names were detected?
[1] "timestamp" "basement" "lab" "livingroom" "outside"
> class(glarp[,'basement']) # Kind of data is in basement column?
[1] "numeric"
> basement <- glarp[,2] # index by position
> lab <- glarp[,'lab'] # index by name
> outside <- glarp$outside # equiv to prior indexing
> livingroom <- glarp$living # name with unique initial name
> summary(glarp) # Handy built-in to describe most R objects
timestamp basement lab livingroom
Length:171349 Min. : 6.40 Min. : -6.40 Min. : 7.20
Class :character 1st Qu.: 17.00 1st Qu.: 16.60 1st Qu.: 18.10
Mode :character Median : 19.10 Median : 17.90 Median : 20.30
Mean : 18.88 Mean : 18.12 Mean : 20.17
3rd Qu.: 20.50 3rd Qu.: 19.50 3rd Qu.: 22.00
Max. : 27.50 Max. : 25.50 Max. : 31.30
NA's :1854.00 NA's :2406.00 NA's :1855.00
outside
Min. : -19.800
1st Qu.: 2.100
Median : 9.800
Mean : 9.585
3rd Qu.: 17.000
Max. : 32.200
NA's :1858.000
|
We have seen the range() function: min()
and max() find the
individual extremes of a data's range. The summary() obviously also
displays this information, but not in a way directly usable in other
computations. Let's start out by finding a few more very basic
statistical properties of this data:
Listing 6. Basic statistical calculations on temperature data
> mean(basement) # Mean fails if we include unavailable data
[1] NA
> mean(basement, na.rm=TRUE)
[1] 18.87542
> sd(basement, na.rm=TRUE) # Standard deviation must also exclude NA
[1] 2.472855
> cor(basement, livingroom, use="all.obs") # All observations: no go
Error in cor(basement, livingroom, use = "all.obs") :
missing observations in cov/cor
> cor(basement, livingroom, use="complete.obs")
[1] 0.9513366
> cor(outside, livingroom, use="complete.obs")
[1] 0.6446673
|
As you would intuitively expect, the two indoor temperatures are more correlated than either is with the outdoors. Still, it is easy to check.
You have seen the mean and standard deviation, and intuitively you might expect temperatures to be distributed normally. Let's check:
Listing 7. Generate a histogram in one short line
> hist(outside) |
Many R commands will pop up a second window with a plot,
chart, or diagram of a data set. Details of how this is done vary with
platform and personal configuration. You may also redirect these
graphics to external files for later use. The above hist() command
produces:
Figure 1.
Not bad for a first try. A few parameters can narrow the rounding threshold:
Listing 8. Change the histogram rounding density
hist(outside, breaks=1000, border="blue") |
Figure 2.
Notice the odd roughness of the dense histogram in the region around 7-12 degrees, with both very high frequency of some measurements and unexpectedly low frequencies of others. We believe these strong discontinuities indicate a sample bias, perhaps as a result of instrument characteristics. On the other hand, the large but narrow spike around 24 degrees -- right around the thermostat-regulated indoor temperature -- is more likely to result from the measurement transpositions we mentioned above concerning the instrument transmission channel. In any case, the graphic reveals something interesting to explore and analyze.
A couple more quick variations show indoor temperature distributions:
Listing 9. Living room temperature histograms
> hist(livingroom, breaks=40, col="blue", border="red") > hist(livingroom, breaks=400, border="red") |
Figure 3.
Figure 4.
The living room temperature distributions seem more reasonable. Some discontinuities appear in the higher resolution that seem to result from small-scale instrument bias. But the general pattern follows the trimodal distribution we would expect based on Brad's timer-controlled thermostat (large peak around 21, smaller ones around 16 and 24 degrees).
Each measurement site is a linear vector of temperature values. But intuitively, we would expect two primary cycles in the data: daily and yearly (nights and winters are cold).
The first problem we have is turning a 1-D data vector into a 2-D matrix of data points. Then we would like to visualize this 2-D data set:
Listing 10. Reshape vector and plot temperature
> oarray <- outside[1:170880] # Need to truncate a few last-day readings > dim(oarray) <- c(480,356) # Re-dimension the vector to a 2-D array > plot(oarray[1,], col="blue", type="l", main="4 p.m. outside temp", + xlab="Day of year (starting July 25, 2003)", + ylab="Temperature (Celsius)") |
Figure 5.
Once we convert the vector into a "Time X Day" matrix (a 2-D array), it is natural to pull off a temperature for each day and graph the yearly pattern. You could do it otherwise -- by extracting every 480th point from the vector; R's way is much more elegant.
What about representing the whole year of temperature measurements? One approach is a color-coded thermal graph:
Listing 11. Creating a thermal graph
> x <- 1:480 # Create X axis indices > y <- 1:356 # Create Y axis indices > z <- oarray[x,y] # Define z-axis (really same as oarray) > mycolors <- c(heat.colors(33),topo.colors(21))[54:1] > image(x,y,z, col=mycolors, + main="Outside temperatures near Glarp", # Brad's name for his house + xlab="Minutes past 4 p.m", + ylab="Days past July 25, 2003" ) > dev2bitmap(file="outside-topo.pdf", type="pdfwrite") |
Figure 6.
A few comments on what we have just done. Defining axes and
points is fairly obvious once you recognize the Python-like slice
notation to create a list of numbers. Indexing by x
and y in the
creation of z creates an array of the width
and height of the
indices. In this case, z is trivial -- the same as oarray; but it
would be easy enough to systematically change the values or the
offsets used to reach them. The color map mycolor came from some
trial-and-error: we felt that using reds and yellows was good for "hot"
temperatures (in other words, above 0 degrees Celsius), but it seemed wrong for
cold temperatures. So, we concatenate some blues/greens to the color
vector. It turned out that we also wanted the colors in the reverse order
to that generated by the standard colormap functions -- easy enough with
indexing.
You might notice that the thermal map is drawn a bit more sharply than
were prior graphics. An adequate but less impressive image is drawn
on screen by the image() command. Exporting the "current image" to
an external file can often produce better results, as it does here.
While we personally like the prior flat thermal map, many viewers of graphics might find information better conveyed by pseudo-perspective into three dimensional data. It requires little extra work in R to produce quite stunning perspectival topographic maps of 3-D data. For example:
Listing 12. Creating a topographic surface graph
> persp(x,y,z, theta=10, phi=60, ltheta=40, lphi=30, shade=.1, border=NA, + col=mycolors[round(z+20)], d=.5, + main="Outside temperatures near Glarp", + xlab="Minutes past 4 p.m", + ylab="Days past July 25, 2003", + zlab="Temperature", ) |
Figure 7.
The few basic statistical analyses we performed and the basic plots we generated in this article represent only a miniscule subset of the statistical capabilities of R. For virtually every field of science, and for virtually every well-known (or even obscurely known) statistical technique, there are either standard functions or extension packages to support the relevant mathematical techniques.
With this article, we hope we have given you a feel for what it is like to work with R. But R offers many other riches than we've shown here. In the second of this three-part series on R, we'll delve into intermediate and advanced techniques in R -- starting with linear and nonlinear regressions.
- In the next installment in this series, Brad and David dive into the functionality of R, using the language's capabilities to find and analyze anomalous data.
- View the home page for the R
Project for Statistical Computing. This R Web site contains extensive documentation, everything from tutorials to complete API descriptions. Two documents of
particular interest to those readers first encountering R are:
- An Introduction to R (PDF format)
- R Data Import/Export (PDF format)
- Several readers of David's Charming Python column, being Python
users, have expressed a particular fondness for the Python binding to R.
Actually, there are two of them: RPy and the older RSPython, which is also good
(but David's impression is that the RPy
binding is better). Either one of these bindings lets you call the full
range of R functions transparently from Python code, using Python objects
as arguments to the functions.
- David wrote a Charming Python installment on Numerical
Python, which has a similar feel and many of the same capabilities of
R (R is considerably more extensive, though). You may also want to visit
the Numerical Python home
page.
- For what it is worth, on most systems you can launch a browser with
generated HTML pages for R documentation by entering
help.start()at the R command line. - A summary
of the history of S and S-Plus is available online.
- Cameron Laird's Server
clinic: R handy for crunching data (developerWorks, 2003)
gives a good overview of R (as well as S), and has a wealth of resources.
- Gnuplot can also help you to plot
and analyze data. In his article, Visualize your data with gnuplot (developerWorks,
2004), Nishanth Sastry
introduces Gnuplot, and also discusses the problem of missing data points.
- Standards for data do exist, but aren't so well known. For instance,
Brad might have been wiser to use the National Space Science Data Center's
Common Data Format (CDF) from
the beginning. But this is a common error: if you are ever in a similar
situation, and need to convert raw data into a form that's usable by tools
like R, NumPy, and Gnuplot, then tools like Perl, Python, or the GNU text
utilities can help you. David introduces the last of these in the
developerWorks tutorial, Using
the GNU text utilities (developerWorks, 2004).
- You can practice converting or analyzing the temperature data David
and Brad used in this article (their Python script for converting the
initial log format to a nicer tabular format is also here):
- Find more resources for Linux developers in the developerWorks Linux zone.
- Download no-charge trial versions of IBM middleware products that run on Linux, including WebSphere® Studio Application Developer, WebSphere Application
Server, DB2® Universal Database, Tivoli® Access Manager, and Tivoli Directory Server,
and explore how-to articles and tech support, in the Speed-start your Linux app section of developerWorks.
- Get involved in the developerWorks community by participating in
developerWorks blogs.
- Browse for books on these and other technical topics.
To David Mertz, all the world is a stage; and his career is devoted to providing marginal staging instructions. Suggestions and recommendations on past or future articles are welcome. You can reach David at mertz@gnosis.cx; you can investigate all aspects of his life at his personal Web page. Check out his book, Text Processing in Python.
Brad has been doing UNIX systems administration and network engineering for about 14 years at what used to be three different companies. He is currently working on a Ph.D. in Applied Mathematics at the University of Colorado in Boulder, and pays the bills by doing UNIX support for the Computer Science department.
