Playing with Gradient Descent in R

Gradient Descent is a workhorse in the machine learning world. As proof of its importance, it is one of the first algorithms that Andrew Ng discusses in his canonical Coursera Machine Learning course. There are many flavors and adaptations, but starting simple is usually a good thing. In this example, it is used to minimize the cost function (the sum of squared errors or SSE) for obtaining parameter estimates for a linear model. I.e.:

\text{minimize} J(\theta_0, \theta_1) = \dfrac {1}{2m} \displaystyle \sum _{i=1}^m \left (h_\theta (x^{(i)}) - y^{(i)} \right)^2

Which, when applied to a linear model becomes:

\theta_0 := \theta_0 - \alpha \frac{1}{m} \sum\limits_{i=1}^{m}(h_\theta(x^{(i)}) - y^{(i)})

\theta_1 := \theta_1 - \alpha \frac{1}{m} \sum\limits_{i=1}^{m}\left((h_\theta(x^{(i)}) - y^{(i)}) x^{(i)}\right)

Where \theta_0 is our intercept and \theta_1 is the parameter estimate of our only predictor variable.

Ng’s course is Octave-based, but manually calculating the algorithm in an R script is a fun, simple exercise and if you’re primarily an R-user it might help you understand the algorithm better than the Octave examples. The code full code is in this repository, but here is the walkthrough:

  • Create some linearly related data with known relationships
  • Write a function that takes the data and starting (or current) estimates as inputs
  • Calculate the cost based on the current estimates
  • Adjust the estimates in the direction and magnitude indicated by the scaling factor \alpha.
  • Recursively run the function, providing the new parameter estimates each time
  • Stop when the estimate converges (i.e., meets the stopping criteria based on the change in the estimates)

This code is for a simple single variable model. Adding additional variables means calculating the partial derivatives with respect to each item. In other words, adding a version of the \theta_1 cost component for each feature in the model. I.e.,

\theta_j := \theta_j - \alpha \frac{1}{m} \sum\limits_{i=1}^{m}\left((h_\theta(x^{(i)}) - y^{(i)}) x_j^{(i)}\right)

I sometimes use Gradient Descent as a ‘Hello World’ program when I’m playing with statistical packages. It helps you get a feel for the language and its capabilities.

Global Indicator Analyses with R

I was recently asked by a client to create a large number of “proof of concept” visualizations that illustrated the power of R for compiling and analyzing disparate datasets. The client was specifically interested in automated analyses of global data. A little research led me to the WDI package.

The WDI package is a tool to “search, extract and format data from the World Bank’s World Development Indicators” (WDI help). In essence, it is an R-based wrapper for the World Bank Economic Indicators Data API. When used in combination with the information on the World Bank data portal it provides easy access to thousands of global datapoints.

Here is an example use case that illustrates how simple and easy it is to use, especially with a little help from the countrycode and ggplot2 packages:


# Use the WDIsearch function to get a list of fertility rate indicators
indicatorMetaData <- WDIsearch("Fertility rate", field="name", short=FALSE)

# Define a list of countries for which to pull data
countries <- c("United States", "Britain", "Sweden", "Germany")

# Convert the country names to iso2c format used in the World Bank data
iso2cNames <- countrycode(countries, "", "iso2c")

# Pull data for each countries for the first two fertility rate indicators, for the years 2001 to 2011
wdiData <- WDI(iso2cNames, indicatorMetaData[1:2,1], start=2001, end=2011)

# Pull out indicator names
indicatorNames <- indicatorMetaData[1:2, 1]

# Create trend charts for the first two indicators
for (indicatorName in indicatorNames) { 
  pl <- ggplot(wdiData, aes(x=year, y=wdiData[,indicatorName], group=country, color=country))+
    scale_x_continuous(name="Year", breaks=c(unique(wdiData[,"year"])))+
    ggtitle(paste(indicatorMetaData[indicatorMetaData[,1]==indicatorName, "name"], "\n"))
  ggsave(paste(indicatorName, ".jpg", sep="&"), pl)

WDI package visualization 1WDI package visualization 2

This code can be adapted to quickly pull and visualize many pieces of data. Even if you don’t have an analytic need for the WDI data, the ease of access and depth of information available via the WDI package make them perfect for creating toy examples for classes, presentations or blogs, or conveying the power and depth of available R packages.

RStudio Development Environment

Compared to many other languages of equal popularity, there are realtively few development environments for R. In fact, the total number of production ready R IDEs could probably be counted on one hand. That deficiency is a small price to pay to use R and if you’re not already accustomed to using IDEs for other languages, you probably haven’t missed it too much. But RStudio goes a long way toward providing a full-featured R development platform, that, once you’ve used it, quickly becomes hard to give up again.

RStudio has some nice graphical features and the layout is clean and logical for the most part. Functionally, some of the best features are:

  • Plot caching (allows you to flip back through previous graphs without rerunning them, making it much easier to review your graphical output)
  • Function, object and parameter completion that works even with user-defined functions (see below)
  • Shortcuts for quickly drilling down into functions

RStudio paramater completion

RStudio also provides version control integration (Git, SVN) which could prove to be very helpful, but I haven’t yet tested it. I can’t speak to how well it works, just that it is available.

In addition to these positives, RStudio has an active support system with developer participation via the RStudio support site.

Overall, I’ve been very impressed with RStudio over the past few weeks. If you haven’t yet tested it, I suggest you give it a try. Given the growth of R over recent years, I think it’s time we expected development tools to mature to the level that they have for other programming languages with similar levels of adoption. The only way that will produce sustainable, mature products is if there is a constant demand in the market.

Already using something else? Feel free to mention your favorite R IDE in the comments.

Installing quantstrat from R-forge and source

R is used extensively in the financial industry; many of my recent clients have been working in or developing products for the financial sector. Some common applications are to use R to analyze market data and evaluate quantitative trading strategies. Custom solutions are almost always the best way to do this, but the quantstrat package can make it easy to quickly get a high-level understanding of a strategy’s potential. However, quantstrat is still under development, and this, combined with a lack of documentation and the complex nature of the tasks involved, make it difficult to work with. This article addresses one of the most basic issues with quantstrat – getting it installed. quantstrat and it’s required packages currently aren’t available on CRAN – you have to get them from R-forge. As a result, the installation is slightly less straightforward than other packages and provides an opportunity to discuss how to install packages from R-forge and locally from source. Although this article focuses on installing quantstrat, these instructions will help with any R-package that you need to build from source.

If you’re installing from R-forge, the process is only moderately different than installing from CRAN; simply change the install.packages command to point to the R-forge repository:

install.packages("FinancialInstrument", repos="")

install.packages("blotter", repos="")

install.packages("quantstrat", repos="")

Since the FinancialInstrument and blotter packages are dependencies for quantstrat, you can download and install all three at once with just the last line.

In some cases, you may need to build the packages yourself. You’ll need to set your system up to compile R source code if it isn’t already. To do so, follow steps 1-3 below. If your system is already set up to compile R source code, you can skip to step 4.

1) Install Rtools package (must be done manually from

2) Install LaTex from

3) Install InnoSetup

4) Download the three package source files available from R-forge

5) Install the packages using the commands below (substituting the appropriate version numbers):

install.packages("C:/yourpath/FinancialInstrument_0.9.18.tar.gz", repos = NULL, type="source")

install.packages("C:/yourpath/blotter_0.8.4.tar.gz", repos = NULL, type="source")

install.packages("C:/yourpath/quantstrat_0.6.1.tar.gz", repos = NULL, type="source")

Note that these directions are relevant until the packages are available on CRAN, after which, you’ll be able to download and install them like any other package (I’ll make a note on this post once that happens). Also note that since these packages are under heavy development, you’ll want to update them often.

Bayesian Computation with R – Albert (2009)

Title: Bayesian Computation with R
Author(s): Jim Albert
Publisher/Date: Springer/2009
Statistics level: High
Programming level: Low
Overall recommendation: Recommended

Bayesian Computation with R focuses primarily on providing the reader with a basic understanding of Bayesian thinking and the relevant analytic tools included in R. It does not explore either of those areas in detail, though it does hit the key points for both.

As with many R books, the first chapter is devoted to an introduction of data manipulation and basic analyses in R. This introductory chapter focuses more heavily on analyses that many of the other similarly focused chapters in other texts. The new R user who hasn’t yet built up a library of these chapters will find it useful, but for experienced R users or those with multiple R texts, there is little new information.

Albert’s introduction to the foundational Bayesian concepts (e.g., Bayes’ theorem) is concise and will be clear to those with a statistical background, but others may need to refresh their statistical knowledge before they can fully grasp the content in the second chapter. Those from programming backgrounds without extensive statistical knowledge may be better off beginning with a text that deals specifically with Bayesian analysis.

Many of the topics discussed in this text have limited application, but possibly the most broadly applicable chapter deals with Bayesian regression. Those interested in learning how to run and diagnose Bayesian regression in R will find almost everything they need to know here.

As with many R texts, Bayesian Computation with R has an accompanying package of functions available on CRAN (“LearnBayes”). The functions in this package are focused mainly on teaching Bayesian analysis, but also include some useful basic implementations.

This book straddles the line between introductory theory and intermediate-level statistical programming. Because of the omissions of information on each side of that line, the reader will get the most mileage from the text if he or she has access to resources (i.e., other texts, colleagues, or previous knowledge) that can fill in those omissions. For that reason, it would work well as a text for an upper-level course on Bayesian statistics and their application, but it is not well suited as a reference text, or as a guide for real-world analysis.

Overall, I recommend this book, with the caveat that interested readers should review the sample pages available on the Springer website here and the functions in the “LearnBayes” package prior to purchasing. The text is currently available for approximately $50 in paperback and $40 for the Kindle version.

Building Scoring and Ranking Systems in R

This guest article was written by author and consultant Tristan Yates (see his bio below). It emphasizes R’s data object manipulation and scoring capabilities via a detailed financial analysis example.

Scoring and ranking systems are extremely valuable management tools. They can be used to predict the future, make decisions, and improve behavior – sometimes all of the above. Think about how the simple grade point average is used to motivate students and make admissions decisions.

R is a great tool for building scoring and ranking systems. It’s a programming language designed for analytical applications with statistical capabilities. The capability to store and manipulate data in list and table form is built right into the core language.

But there’s also some validity to the criticism that R provides too many choices and not enough guidance. The best solution is to share your work with others, so in this article we show a basic design workflow for one such scoring and ranking system that can be applied to many different types of projects.

The Approach
For a recent article in Active Trader, we analyzed the risk of different market sectors over time with the objective of building less volatile investment portfolios. Every month, we scored each sector by its risk, using its individual ranking within the overall population, and used these rankings to predict future risk.

Here’s the workflow we used, and it can be applied to any scoring and ranking system that must perform over time (which most do):

  1. Load in the historical data for every month and ticker symbol.
  2. Load in the performance data for every month and ticker symbol.
  3. Generate scores and rankings for every month and ticker symbol based upon their relative position in the population on various indicators.
  4. Review the summary and look for trends.

In these steps, we used four data frames, as shown below:

Name Contents
my.history historical data
my.scores scoring components, total scores, rankings
my.perf performance data
my.summary   summary or aggregate data

One of my habits is to prefix my variables – it helps prevent collisions in the R namespace.

Some people put all of their data in the same data.frame, but keeping it separate reinforces good work habits. First, the historical data and performance data should never be manipulated, so it makes sense to keep it away from the more volatile scoring data.

Second, it helps draw a clear distinction between what we know at one point in time – which is historical data – and what we will know later – which is the performance data. That’s absolutely necessary for the integrity of the scoring system.

My.history, my.scores, and my.perf are organized like this:

 yrmo   ticker    var1     var2     etc…  
200401   XLF      
200401   XLB      

yrmo is the year and month and ticker is the item to be scored. We maintain our own list of dates (in yrmo format) and items in my.dates and my.items. Both these lists are called drivers, as they can help iterate through the data.frame, and we also have a useful data.frame called my.driver which has only the yrmo and ticker.

One trick – we keep the order the same for all of these data.frames. That way we can use indexes on one to query another. For example, this works just fine:

  Vol.spy <- my.history$vol.1[my.score$rank==1]

Loading Data
First, we get our driver lists and my.driver data.frame set up. We select our date range and items from our population, and then build a data.frame using the rbind command.

  #this is based on previous analysis
  my.dates <- m2$yrmo[13:(length(m2$yrmo)-3)]
  my.items <- ticker.list[2:10]

  #now the driver
  my.driver <- data.frame()
  for ( in my.dates) {
    my.driver <- rbind(my.driver,data.frame(ticker=my.items,

Next, let’s get our historical and performance data. We can make a function that can be called once for each row in my.driver that then loads any data needed.

  my.seq <- 1:length(my.driver[,1])
  my.history <- data.frame(ticker=my.driver$ticker,yrmo=my.driver$yrmo,

Each variable can be loaded by a function called with the sapply command. The function first looks up the ticker and yrmo from my.driver using the index provided, and then returns the data. You would have one function for each indicator that you want to load. My.perf, which holds the performance data, is built in the exact same way.

The rbind command is slow unfortunately, but loading the historical and performance data only needs to be done once.

Scoring The Data
This is where R really shines. Let’s look at the highest level first.

  my.scores <- data.frame()
  for (z.yrmo in my.dates) {
    my.scores <- rbind(my.scores,calc.scores.fn(z.yrmo))
  my.scores$p.tot <- (my.scores$p.vol.1)

Every indicator gets its own score, and then that can be combined in any conceivable way to create total score. In this very simple case, we’re only scoring one indicator, so we just use that score as the total score.

For more complex applications, the ideal strategy is to use multiple indicators from multiple data sources to tell the same story. Ignore those who advocate reducing variables and cross-correlations. Instead, think like a doctor that wants to run just one more test and get that independent confirmation.

Now the calc functions:

  scaled.score.fn <- function(z.raw)
  scaled.rank.fn <- function(z.raw) {rank(z.raw)}

  calc.scores.fn <- function(z.yrmo) {
    z.df <- my.history[my.history$yrmo==z.yrmo,]
    z.scores <- data.frame(ticker=z.df$ticker,yrmo=z.df$yrmo,

The calc.scores.fn function queries the data.frame to pull the population data for just a single point in time. Then, each indicator is passed to the scaled.score.fn and scaled.rank.fn function, returning a list of scores and ranks.

Here, we use the pnorm function to calculate a statistical Z-score, which is a good practice for ensuring that a scoring system isn’t dominated by a single indicator.

Checking the Scores
At this point, we create a new data.frame for summary analysis. We use the always useful and always confusing aggregate function and combine by rank. Notice how we easily we can combine data from my.history, my.scores and my.perf.


Here’s the result. We could check plots or correlations, but the trend – higher relative volatility in the past (vol.p1, p.tot) is more likely to mean higher relative volatility in the future (vol.1, sd.1) - is crystal clear.

rank  p.tot  ret.1   sd.1    vol.1   vol.p1  
1 12.1 0.131 4.03 16.5 13.8
2 19.4 0.0872 4.82 16.6 16.1
3 27.1 0.2474 4.96 20.1 18
4 35.6 0.4247 5.31 20.9 19.9
5 44.9 0.6865 5.98 22.1 21.7
6 53 0.3235 5.84 21.5 23.2
7 65.1 1.019 5.86 24.6 25.4
8 78 0.7276 6.04 26.9 28.4
9 96.4 0.0837 9.34 35.2 38.3

In the case of our analysis, the scores aren’t really necessary – we’re only ranking nine items every month. If we did have a larger population, we could use code like this to create subgroups (six groups shown here), and then use the above aggregate function with the new my.scores$group variable.

  my.scores$group <- cut(my.scores$p.tot,

We ultimately only ended up scoring one variable, but it’s pretty easy to see how this framework could be expanded to dozens or more. Even so, it’s an easy system to describe – we grade each item by its ranking within the population. People don’t trust scoring systems that can’t be easily explained, and with good reason.

There’s not a lot of code here, and that’s a testimony to R’s capabilities. A lot of housekeeping work is done for you, and the list operations eliminate confusing nested loops. It can be a real luxury to program in R after dealing with some other “higher level” language.

We hope you find this useful and encourage you to share your own solutions as well.

Tristan Yates is the Executive Director of Yates Management, a management and analytical consulting firm serving financial and military clients. He is also the author of Enhanced Indexing Strategies and his writing and research have appeared in publications including the Wall Street Journal and Forbes/Investopedia.

Helpful statistical references

In a previous article I provided a list of R programming resources. As a complement to that post, I’ve compiled a list of statistically oriented websites that colleagues and I have found useful below. For the most part, these sites focus on statistics and quantitative research methods rather than programming.

This first grouping lists sites that are mostly one-stop-shops for research design and analytical information. The first two, (and especially the UCLA website) are Tier I statistics/research methods sites. They are indispensable. The three remaining sites in this section cover less advanced topics and focus more on basics, but may be helpful for the R user who is more programmer than statistician.

The second group of sites is comprised of technical references such as statistical dictionaries and notation guides. The final section list two sites that have detailed information and examples focused on running statistical analyses in R. Note that the UCLA site also includes many examples using R.

Comprehensive coverage

Statistical computing at UCLA

Statnotes: Topics in Multivariate Analysis, by G. David Garson

Introductory Statistics: Concepts, models, and applications

Social Research Methods Knowledge Base

Wolfram MathWorld

Technical References

StatSoft statistical glossary

Glossary of technical notation

Dictionary of Algorithms and Data Structures

R specific sites

Journal of Statistical Software


If you know of another site for either R programming or statistics that I’ve missed, mention it in the comments below and I’ll add it to the proper list.

Positioning charts with fig and fin

R offers several ways to spatially orient multiple graphs in a single graphing space. The layout() function and mfrow/mfcol parameter settings are adequate solutions for many tasks and allow the graphing space to be broken up into tabular or matrix-based arrangements. For more fine grained manipulation, the fig and fin parameter settings are available. This article illustrates the capabilities and use of fig and fin.

First we’ll create some simulation data to work with:

# create data <- cbind(replicate(5,runif(8,min=0, max=100)))

The code above results in a matrix object with eight rows and three columns.

The fig and fin parameters affect the same graphing elements via different units. The fig parameter takes normalized device coordinates (NDC) and fin takes dimensions in inches of the device region. Because the fig units are generally more user friendly, I will use it in the examples below; however, selecting equivalent dimensions using the fin would have an identical effect. Similar to other functions that use NDC to define graphing space, fig takes a four item vector wherein positions one and three define, in percentages of the device region, the starting points of the x and y axes, respectively, while positions two and four define the end points. The default fig setting is (0, 1, 0, 1) and uses the entire device space. The default fig setting is (0, 1, 0, 1) and uses the entire device space. The graph below illustrates the default settings of fig.

# graph cases by first column using default fig
# settings of 0 1 0 1 (the full device width and height)
par(mar=c(2, 2, 1, 1), new = FALSE, cex.axis = .6, mgp = c(0, 0, 0))

#open plot
plot(c(0,100), c(-1,1), type = "n", ylab = "", yaxt = "n", xlab = "")
points([,1], replicate(8, 0), pch = 19, col = 1:8, cex = 1.5)
# add center reference line
legend("bottomright", fill = c(1:8), legend = c(1:8), ncol = 4)
fig default

To make the horizontal dimensions of the graph smaller or to move the graph left or right, adjust the starting and ending x coordinates, given by the first and second positions of the fig value vector. To make the vertical dimensions of the graph smaller or to move the graph up or down, adjust the staring and ending y coordinates given in the third and fourth positions as below.

# decrease horizontal span
par(fig=c(0, 1, .2, .8))

#open plot
plot(c(0,100), c(-1,1), type = "n", ylab = "", yaxt = "n", xlab = "")
points([,1], replicate(8, 0), pch = 19, col = 1:8, cex = 1.5)
# add center reference line
legend("bottomright", fill = c(1:8), legend = c(1:8), ncol = 4)
fig thin

It is possible to resize and move a single graph to any spatial orientation on the graphing device using the approach above. Additionally, you can also use this method to add multiple graphs of various sizes to a single device:

# place graph one in the bottom left
par(fig=c(0, .25, 0, .25), mar=c(2,.5,1,.5), mgp=c(0, 1, 0))

#open plot
plot(c(0,100), c(-1,1), type = "n", ylab = "", yaxt = "n", xlab = "")
points([,1], replicate(8, 0), pch = 19, col = 1:8)
# add center reference line

# place graph two in the top right
# set graphing parameters for next plot and set new parameter to TRUE
par(fig=c(.75, 1, .75, 1), new = TRUE)

#open plot
plot(c(0,100), c(-1,1), type = "n", ylab = "", yaxt = "n", xlab = "")
points([,2], replicate(8, 0), pch = 19, col = 1:8)
# add center reference line

# place main graph in the center
# set graphing parameters for next plot and set new parameter to TRUE
par(fig=c(.25, .75, .25, .75), new = TRUE)

#open plot
plot(c(0,100), c(-1,1), type = "n", ylab = "", yaxt = "n", xlab = "")
points([,3], replicate(8, 0), pch = 19, col = 1:8, cex = 1.5)
# add center reference line
legend("bottomright", fill = c(1:8), legend = c(1:8), ncol = 4)
fig multiple

For simplicity I have mostly avoided labels and titles in these graphs; however they can be added and manipulated as they would be without the use of fig or fin.

A Handbook of Statistical Analyses Using R – Everitt and Hothorn (2006)

Title: A Handbook of Statistical Analyses Using R
Author(s): Brian S. Torvitt; Torsten Hothorn
Publisher/Date: Chapman & Hall/2006
Statistics level: Intermediate to advanced
Programming level: Intermediate
Overall recommendation: Highly recommended

A Handbook of Statistical Analyses Using R addresses a list of several common statistical analyses in great detail. Over a course of 15 chapters, the handbook takes the reader from an introduction to R through a discussion of statistical inference, to linear and logistic regression, tree analysis, survival analysis, longitudinal analysis, meta-analysis, factoring, scaling, and clustering. The handbook has a peer-reviewed journal style that will be familiar to academic researchers and each chapter stands on its own. This approach makes the text exceptionally useful in the academic setting as a professor can distribute and assign the first chapter of the book to her Research Methods 101 course; the final chapters on scaling and dimensionality to her Psychometrics Methods course; the last chapter on clustering to her Marketing Research course; and require the entire book for her graduate methods course. For custom research shops making the transition to R or who frequently hire new entry level R users, this book will work well as a reference and training manual.

The handbook does show typical first edition flaws. There are sporadic mistakes in grammar such as misspellings and incorrect words. The overall organization of the book is strong, but the chapter level organization is less effective. Each chapter begins with a discussion of all of the datasets used in that chapter and is followed by examples and applications based on those datasets. In chapters where there are several examples, the discussion of the data is too detached from its corresponding example. When the reader reaches the example based on the first dataset they have likely forgotten the relevant details about that data’s structure. Grouping the data discussions with the examples they accompanied would have made the example based approach more effective.

The introductory section on R is one of the best introductory sections I have read. It strikes an almost perfect balance between the programming and statistical features of R. I frequently recommend this initial chapter to colleagues who have research experience but are new to R. There are numerous graphs included in the examples in the text and although there is virtually no general discussion of producing graphs in R, each graph presented in this text includes the code required to reproduce it. This omission is a welcome one, as it allows the authors to focus more on statistical details. Readers looking for a more general discussion of how to produce graphs in R should consider Data Analysis and Graphing Using R.

Controlling margins and axes with oma and mgp

When creating graphs, we’re usually most concerned with what happens near the center of our displays, as this is where most of the important information is generally held. But sometimes, either for aesthetics or clarity, we want to adjust what’s outside of the box – in the margins, labels or tick marks. The par() function offers several ways to do this and I’ll discuss two that deal primarily with spatial orientation – rather than content – below.

The oma, omd, and omi options

To control the width of the outer margins of your graph (the empty sections outside of the axes and labels) use either the oma, omd, or omi option of the par() function. All three of these options have the same effect and differ only in the units used to define the parameter. oma defines the space in lines, omd as a fraction of the device region, and omi specifies the size in inches. oma and omi take a four item vector where position one sets the bottom margin, position two the left margin, position three the top margin and position four the right margin. omd uses a four item vector where positions one and three define, in percentages of the device region, the starting points of the x and y axes, respectively, while positions two and four define the end points. Because these options all effect the same graph space, changing one also changes the remaining two. A few examples of code and the charts they produce are shown below. To help illustrate the different margin sizes, the blue area indicates the dimensions of the device display:

# generate some data

# oma, omd, and omi defaults

[1] 0 0 0 0

[1] 0 1 0 1

[1] 0 0 0 0

# plot using default margin settings
plot(x,pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
oma default
# add four lines to bottom and top margins
par(oma = c(4, 0, 4, 0))
plot(x, pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
title("oma = c(4, 0, 4, 0)")
oma 2
# change via omd
par(omd = c(.15, .85, .15, .85))
plot(x, pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
title("omd = c(.15, .85, .15, .85)")
oma 3
# because oma, omd, and omi all affect the same graph space
# this doesn't make sense
par(omi = c(0, 0, 0, 0), omd = c(.10, .90, .10, .90))

# reset oma, omd, and omi to default by changing omi
par(omi = c(0, 0, 0, 0))

The mgp option

In addition to changing the margin size of your charts, you may also want to change the way axes and labels are spatially arranged. One method of doing so is the mgp parameter option. The mgp setting is defined by a three item vector wherein the first value represents the distance of the axis labels or titles from the axes, the second value is the distance of the tick mark labels from the axes, and the third is the distance of the tick mark symbols from the axes. As with the oma option discussed above, the distances are given in line widths. The defaults for the mgp setting are c(3, 1, 0). The examples below illustrate the effects of changing the various mgp values. Note: the mgp.axis() function in the Hmisc package can be used to change these settings for each axis individually.

# mgp default settings
plot(x, pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
mgp default
# move labels close to axes
par(mgp = c(0, 1, 0))
plot(x, pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
mgp move labels
# move tick labels out
par(mgp = c(0, 3, 0))
plot(x, pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
mgp move tick labels
# move tick lines out
par(mgp = c(0, 3, 2))
plot(x, pch=1, col = "red", ylab = "Y Label", xlab = "X Label")
mgp move tick lines

The oma, omd, omi, and mgp parameter settings can be useful in defining and adjusting the outer regions of your charts. To arrage and size multiple graphing areas you may also find other par() settings such as fig, fin, or layout helpful.