If you use it for nothing else, you should consider using R as your graphics engine. In up-to-date installations of R, the "graphics" library loads by default when R is started, so you should be ready to go as soon as R opens. It's possible to set up R in such a way that this is not the case, however. So if you are working with someone else's R installation, and graphics is not working, try this...
> library(graphics) # In any event, this does no harm.
Another command you may find handy is...
> par(ask=T) # Do this now!This command will cause R to pause between printing out graphs and ask you (in the Console window) to hit the Enter key. Usually, the message will be "Waiting to confirm page change". When you see this message, R is telling you it is waiting to print a graphic, and that you should press Enter to allow it to happen. If "ask=" is set to FALSE, and R has multiple graphics to print, they will fly by on your monitor so fast you will not be able to see them! (It's kind of annoying otherwise, so you may want to set "ask=" to FALSE after you run the following demos.) Now, for just a taste of what R is capable of graphics-wise, try running these commands in the R Console (output not shown here)...
> example(plot) # Remember to press Enter when prompted.
> example(barplot) # Some are better than others!
> example(boxplot)
> example(dotchart)
> example(coplot)
> example(hist)
> example(fourfoldplot)
> example(stars)
> example(image)
> example(contour)
> example(filled.contour)
> example(persp) # Saving the best for last!
This will fill your workspace with all kinds of stuff, so clean up before
going on.
Some Graphics Basics
For the real basics, see the Describing Data Graphically tutorial, which you probably should read first if you are not familiar with high-level vs. low-level graphics functions, etc.
In R, when you issue a high level plotting command, R will open a graphics window (which in R-speak is called a "device") if one is not already open. This window will have certain defaults. It will be 7 in. by 7 in. (672x672 pixels) in size. It will use a 12-point sans serif font for text. And so on. For example...
> ### Close any open graphics windows before doing this.
> plot(pressure)
To take personal control of the graphics window, close the graphics window now
open on your monitor, and try this...
In Windows
> windows(width=5, height=4, pointsize=18)
> plot(pressure)
You will notice windows( ) removes focus from the R Console, so you will
have to click in the Console window to continue working. Also, the function is
NOT window( ), which does something different. You are asking for the
Windows graphics device.
On the Mac
> quartz("Quartz", width=5, height=4, pointsize=18)
> plot(pressure)
You will notice quartz( ) removes focus from the R Console, so you will
have to click in the Console window to continue working. The first argument in
this function is the name you would like the graphics window to have. This is
annoying, if you ask me, because what do I care what the name of the window is?
I always use "Quartz". The name MUST be present nevertheless. Remember this
command, by the way, if you have a MacBook, because the default 7x7 in.
graphics device is too big to display on the MacBook screen.
In Linux
> x11(display=" ", width, height, pointsize)
I have yet to get this to work in Linux, so I have just given the syntax of the
command. According to the help page, the default for the "display=" option is
the value in the user's enivornment variable DISPLAY, but this is NOT the case.
There is NO DEFAULT, and a value must be specified inside the function. No
matter what I enter there, however, it just doesn't work!
All UsersSee the help file for the OS-specific command above for more options that can be set. You can open multiple graphics devices at once, if you have some need for that. All graphics devices can be closed like this...
> graphics.off()
The current graphics device can be closed with dev.off( ). You can also
see a list of currently open graphics devices with dev.list( ). To
illustrate...
> graphics.off() # Turn off all graphics devices to begin. > windows(5, 5, 18) # On the Mac: quartz("Quartz", 5, 5, 18) > plot(eruptions~waiting, data=faithful) > title(main="Old Faithful Data") > windows(7, 3, 10) # On the Mac: quartz("Quartz", 7, 3, 10) > plot(eruptions~waiting, data=faithful) > title(main="Old Faithful Data in Another Format") > windows(6, 5, 14) # On the Mac: quartz("Quartz", 6, 5, 14) > boxplot(decrease~treatment, data=OrchardSprays) > title(main="Orchard Sprays Data") > dev.list() # See a list of open devices. windows windows windows ### This is in Windows. Output will 2 3 4 ### be different in other OSes. > dev.off() # Close the current device. windows 2 > graphics.off() # Close 'em all. The function plot.new( ) will also open a new graphics window and will paint in the default background color. There are no options for setting window size with this command, however. If you have your graphics window set up for multiple plots (see below), plot.new( ) can be used to skip a panel.
Writing Graphics Output Directly to a File
When you are getting a complex graphic ready for publication, the best way to go about it is to write your commands in a script. You can open the script editor from the menu in the R Console GUI (unless you are in a terminal in Linux or on the Mac) and write your commands there. These commands can then be executed over and over from the editor menu and edited until you get them just the way you want. Then what?
At this point, you may want to write your graphics output directly to a file. The following simple example will illustrate how this is done...
> setwd("../Desktop") # optional; use setwd("Desktop") on the Mac/Linux
# use setwd("../../Desktop") if you are in Rspace
# in Windows or setwd("../Desktop") on the Mac
> png(filename="myplot.png", width=500, height=500, pointsize=10)
> plot(pressure)
> title(main="Vapor pressure of mercury")
> dev.off() ### This is crucial!
null device
1
The first line of this example changes your working directory to the Desktop,
as long as you are still in the default working directory. The graphics will be
written to your working directory, and I like to put them on the Desktop where
I can find them easily. The second line opens a file for output of a graphic in
png format. This is a good format for graphics with few colors in them, and in
Windows these files are written very economically. On the Mac, the file will be
about five times as large as it is in Windows. However, it's still a handy
thing to be able to do, since from the pulldown menus on the Mac graphics
device, the only format in which files can be saved is PDF. For graphics with
subtle shades of color in them, use jpeg( ) instead of png( ). Other
formats available are tiff( ) and bmp( ). The first option gives the
filename. Then the width and height of the plot are given in pixels (default is
480 by 480, which is not the same as the default graphics device). Finally, the
pointsize of the font is given (default is 12).
After this command is issued, all further graphics output is sent to this
file. Finally, the dev.off( ) function writes and closes the file. DON'T
FORGET TO DO THIS! See the help page for more options that can be set. You can
also write out graphics files using pdf( ) and postscript( ).
However, the function syntax is different, and you should consult the help
pages before using these functions. PDF files can be very nice since they are
scalable, but postscript files are not as useful on Windows or Mac systems.
Both of these OSes will convert them to PDF before displaying them.Interacting With a Graph
There are add-on packages that allow you to do amazing things with R plots, including rotate them in three dimensions. I'm not familiar with them, so I will stick to the two basic R functions that allow you to interact with plots: locator( ) and identify( ).
First, let's get a plot to work with...
> boxplot(decrease ~ treatment, data=OrchardSprays) # output not shown
More plants being sprayed with insecticides...what are ya gonna do? If you
execute this command in R, you will see a series of boxplots labeled A through
H on the horizontal axis. There are two outliers, one for treatment C and one
for treatment G. Suppose we want to identify the data values that correspond
to those points. There are a number of ways to do it, but I'll do it by
interacting with the plot using the locator( ) function. A warning in
advance: this function returns the coordinates of a mouse click made on a
graph. It does NOT return the coordinates of a data point. So by clicking on
these points, we will get the APPROXIMATE data values. Second warning:
locator( ) will remain active until it locates as many mouse clicks as you
asked for, or until you tell it to stop. How you tell it to stop can be
difficult (for me) to remember, and is operating system dependent. So BE SURE
you give it a number as an argument when you use this function. It works like
this...
> locator(2) $x [1] 2.996876 6.996876 $y [1] 84.25115 24.25114After you issue the command in the R Console, the current graphics device window will come into focus, and your mouse pointer will turn into a little crosshair. Center the crosshair over the point you want to identify and click the left mouse button. Nothing will happen, except maybe an audio cue. Click on the other point (you've asked to identify two points) and R will return information about those points. (Default is 512 points! In Windows, there is a "Stop" menu in the graphic device window when you initiate this function, so you can stop at any time by using that menu.) On the first line of the print out is the x-coords of the points you clicked on (not interesting in this case), and on the second line are the y-coords of those points. Once again, these are NOT the exact data values of the points, but if you were careful, they should be pretty close. This would have been the more sensible ways to get the actual data values...
> with(OrchardSprays, tapply(decrease, treatment, range)) ## output not shown...but I wanted to illustrate the function. The locator( ) function can also be used to plot points on a graph and do draw lines between plotted points or mouse clicks. You can read the help page to see how that works. In my opinion, the most useful aspect of this function is for putting text or other elements on the graph exactly where you want them. Suppose we want to label those two points with their values (which are 84 and 24). We can use text( ) to add text to an existing graph, but text( ) has to be fed x- and y-coordinates, and this can involve some guesswork. Or we could do this...
> text(locator(1),"84") # Now click where you want the label.
> text(locator(1),"24")
The text inside the quotes will be added to the graph, CENTERED on your mouse
click (so click where you want the CENTER of the label). A bit later we'll use
this trick to add legends to graphs.
The identify( ) function is a bit tricker. And as is often the case in
R, the help page is of very little help until after someone actually explains
to you how the function works! And furthermore, R people, your example is
atrocious! Even Murrell (2006), the best source on R graphics I know of, shies
away from explaining this function, stating merely that the "function can be
used to add labels to data symbols on a plot. The data point closest to the
mouse click gets labelled" (p. 41). Well, often it doesn't, and often when it
does the label is not placed in a very sensible location. I haven't found the
function very useful, and I would suggest that using locator( ) is a much
better way of labeling points.All that having been said, the identify( ) function can be useful for identifying data points in scatterplots, especially when the data are from a data frame with rownames...
> state.data = as.data.frame(state.x77)
> plot(Murder ~ Illiteracy, data=state.data)
As you can see, there is a distinct positive relationship between illiteracy
and murder rate. Now do this...
> identify(x=state.data$Illiteracy, y=state.data$Murder, + labels=rownames(state.data), n=3)R is now waiting for you to click on any 3 data points on the scatterplot. When you do, the points will be labeled with the name of the state. (If we hadn't specified "labels=", the labels would be the index number of the case in the data frame.) However, as I said, the labels are not always placed in sensible locations. I clicked on three points, and for one the label was placed below the point, for another it was placed to the right of the point, and for the third it was place to the left of the point. This was particularly unfortunate in the third case since this point was near the left side of the plot, and only the last letter of the label actually appeared in the plot area. So sometimes useful for identifying points, but not a very intelligent way of labeling points. By the way, "x=" specifies the x-coords of the plotted points, "y=" specifies the y-coords, "labels=" is a character vector of labels to be used, and "n=" specifies the number of points to be labeled (default is number of values in x). (Note: try setting "atpen=T" for a better result, and click slightly to the right and below the data point. This will work unless the point is too near the right side of the plot. You could rescale the x-axis to leave room for these labels.)
Adding Legends
Some high level plotting functions in R have an option for adding a legend. For example...
> data.table = margin.table(Titanic, c(2,1)) > barplot(data.table, beside=T, legend=T) # output not shown Convenient, but wouldn't it be nice if we could put that legend in the upper left corner of the graph rather than in the upper right corner where it overlaps one of the bars? We can.
There is a function called legend( ) that allows you to customize the legend to your heart's content. Let's start the graph over again...
> graphics.off() # or just click it away
> windows(4, 4, 10) # on a Mac: quartz("Quartz", 4, 4, 10)
> barplot(data.table, # you can put all this on one line
+ beside=T, # plot the bars side by side
+ axis.lty=1, # get an x-axis with ticks
+ ylim=c(0,1000), # set scale of y-axis
+ col=c("blue","pink")) # it is important to know the colors used
> title(xlab="Class of Passengers",
+ ylab="Number of Passengers") # label the axes
> title(main="Passengers by Class: Titanic") # add a main title
And now for the legend...
> legend(locator(1), # we will place it with a mouse click
+ legend=c("males","females"), # the text to be used in the legend
+ fill=c("blue","pink"), # the colors to fill legend boxes
+ bty="n") # turn off putting a box around legend
R is now waiting for you to click somewhere on the graph. Click where you want
the upper left corner of the legend leaving a bit of allowance for the fact
that normally the legend will have a box around it. Some notes: You can also
specify "x=" and "y=" if you know the xy-coordinates where you want the legend
placed. The default is to draw a box around the legend, but just to be cute, I
turned it off in this example. The legend( ) function has a very large
number of options, and I can't explain them all here. See the help page and
experiment! The useful options will vary depending upon what kind of graph you
have. But at least this example will get you started.
This will give you some idea of what's possible...
> graphics.off()
> example(legend)
> rm(list=ls())
I turned off your graphics device to begin with to start with fresh plots.
There are a good many examples, so don't forget to press the Enter key when you
get the prompt to do so. Finally, this makes a mess in your workspace, so I
cleaned it out at the end, but you may want to be more circumspect if there is
stuff there you want to keep (hopefully not too late to remind you of this).
The only caution I have about legends is to know what colors and symbols are
being used in your plots, because you will have to duplicate them in the
legend.
This is where R's color palettes may come in handy. R will keep track of the
colors for you. Let's redraw that last graph...
> barplot(data.table, # you can put all this on one line
+ beside=T, # plot the bars side by side
+ axis.lty=1, # get an x-axis with ticks
+ ylim=c(0,1000), # set scale of y-axis
+ col=rainbow(2)) # use a color palette
> title(xlab="Class of Passengers",
+ ylab="Number of Passengers")
> title(main="Passengers by Class: Titanic")
This time instead of asking for specific colors, we let R choose them by
specifying a color palette called "rainbow" and telling it how many colors we
wanted (2). To add the appropriate legend, we do the same thing...
> legend(x=2, y=890, # use xy-coords (found with locator())
+ legend=c("males","females"), # as before
+ fill=rainbow(2)) # as in the barplot() command
This time I included the default box around the legend, and I used xy-coords to
place the legend, not because I had to, but to illustrate those options. Color
palettes available include rainbow( ), heat.colors( ),
terrain.colors( ), topo.colors( ), and cm.colors( ). Although
not technically a color palette, you can also use gray( ) or
grey( ). The syntax is a bit different, so see the help page.
Customizing Graphics With par( )
Although the default graphs produced by high level plotting functions look pretty good, in my opinion, there is hardly anything you can't change about them if you're of a mind to. To get an idea of the graphics options ("parameters") you can fool with, do this...
> par()
$xlog # x-axis set to logarithmic (doesn't work)
[1] FALSE
$ylog # y-axis set to logarithmic (doesn't work)
[1] FALSE
$adj # justification of text (0=left, 1=right)
[1] 0.5
$ann # whether or not to plot labels and titles
[1] TRUE
$ask # ask before displaying next plot
[1] FALSE
$bg # background color (not what you think)
[1] "transparent"
$bty # box type drawn by box( ) or by default
[1] "o"
$cex # text and symbol size multiplier
[1] 1
$cex.axis # size of axis ticks
[1] 1
$cex.lab # size of axis labels
[1] 1
$cex.main # size of main title (relative to "cex")
[1] 1.2
$cex.sub # size of sub-title (which appears under xlab)
[1] 1
$cin # character size (inches) -- read only
[1] 0.15 0.20
$col # colors used for drawing lines, bars, ...
[1] "black"
$col.axis # color of axis tick labels
[1] "black"
$col.lab # color of axis labels
[1] "black"
$col.main # color of main title
[1] "black"
$col.sub # color of sub-title
[1] "black"
$cra # size of character (pixels) -- read only
[1] 14.4 19.2
$crt # character rotation
[1] 0
$csi # same as "cin" but different units
[1] 0.2
$cxy # size of character -- read only
[1] 0.02604167 0.03875970
$din # size of graphics device -- read only
[1] 6.999999 6.999999
$err # apparently doesn't do anything
[1] 0
$family # font family for text (serif, sans, mono)
[1] ""
$fg # color of axes, boxes, etc.
[1] "black"
$fig # location of figure region (don't ask!)
[1] 0 1 0 1
$fin # size of figure region (inches)
[1] 6.999999 6.999999
$font # font face (1=normal, 2=bold, 3=italic)
[1] 1
$font.axis # font face for axis tick labels
[1] 1
$font.lab # font face for axis labels
[1] 1
$font.main # font face for main title (2=bold)
[1] 2
$font.sub # font face for sub-title
[1] 1
$gamma # gamma correction for colors (tech stuff!)
[1] 1
$lab # approximate number of ticks on axes
[1] 5 5 7
$las # rotation of axis labels (0=parallel to axis)
[1] 0
$lend # line end style
[1] "round"
$lheight # line spacing multiplier
[1] 1
$ljoin # line join style
[1] "round"
$lmitre # line mitre limit
[1] 10
$lty # line type (e.g., solid, dashed, dotdash)
[1] "solid"
$lwd # line width
[1] 1
$mai # size of figure margins (inches)
[1] 1.02 0.82 0.82 0.42
$mar # size of figure margins (lines of text)
[1] 5.1 4.1 4.1 2.1
$mex # line spacing in margins
[1] 1
$mfcol # controls number of graphs per device
[1] 1 1
$mfg # I wouldn't care to hazard a guess!
[1] 1 1 1 1
$mfrow # same as mfcol
[1] 1 1
$mgp # placement of ticks and tick labels
[1] 3 1 0
$mkh # currently unimplemented in R
[1] 0.001
$new # has a new plot been started?
[1] FALSE
$oma # a pic of your grandmother (I don't know!)
[1] 0 0 0 0
$omd # more stuff like oma (setting up plot margin)
[1] 0 1 0 1
$omi # and still more!
[1] 0 0 0 0
$pch # point character (data symbols)
[1] 1
$pin # size of plot region (inches)
[1] 5.759999 5.159999
$plt # location of plot region
[1] 0.1171429 0.9400000 0.1457143 0.8828571
$ps # pointsize of text
[1] 12
$pty # aspect ratio of plot region
[1] "m"
$smo # unimplemented in R
[1] 1
$srt # rotation of text in plot region
[1] 0
$tck # length of axis ticks relative to plot size
[1] NA
$tcl # length of axis ticks relative to text size
[1] -0.5
$usr # range of scales on axes
[1] 0 1 0 1
$xaxp # number of ticks on x-axis
[1] 0 1 5
$xaxs # something to do with scale range of x-axis
[1] "r"
$xaxt # x-axis style (s=standard)
[1] "s"
$xpd # whether to confine plotting to plot region
[1] FALSE
$yaxp # number of ticks on y-axis
[1] 0 1 5
$yaxs # something to do with scale range of y-axis
[1] "r"
$yaxt # y-axis style (s=standard)
[1] "s"
This shows you how you currently have your graphics options set (or the default
settings if you haven't changed them). You can ask about any one of these
options just by naming it inside the function: par("mfrow"). This will also
open a graphics device as well, if one is not open, because asking about a
nonexistent graphics device doesn't make much sense.
Some of these appear not to do anything (noted above), some are read only
because they can't be modified for an open device, and some can only be set
via par. The par( ) help page is extensive and explains all of this in
more detail than I am able to. See also Murrell (2006).You can set these options using par( ), or in many cases they can be set from inside the plotting function. The following examples will illustrate...
> op = par( # store old settings in op
+ mfrow=c(2,2), # plot two rows and two cols of graphs
+ family="serif") # use a serif font (tacky!)
> plot(pressure) # begin with a default plot
> title(main="Vapor pressure of Hg") # add a main title to it
> plot(pressure,
+ bty="n", # no box this time
+ family="sans", # use a sans serif font
+ pch=16, # and a different data point character
+ col="red") # and make them red while we're at it
> title(main="Vapor pressure of Hg") # still serif--this hasn't changed
> plot(pressure,
+ bty="o", # the box is back
+ family="mono", # use a mono spaced font
+ pch=20, # change the data symbols again
+ col="blue", # make them blue this time
+ font=2, # make the axis values bold
+ log="y") # and use a logarithmic y-axis
> title(main="Vapor pressure of Hg", family="sans")
> plot(pressure,
+ family="sans", # sans serif font
+ pch=2, cex=1.5, # new and bigger data symbols
+ type="b", # plot both points and lines
+ xlab="temperature (deg C)", # customize the x-label
+ lwd=2) # make the plotted lines thicker
> title(main="Vapor pressure of Hg", family="sans")
> par(op) # reset the old graphics options
Phew! It's a lot to keep straight, isn't it? What can I say? With power comes
complexity. I'm VERY far from being an expert in R graphics, so don't for a
moment think the above example is all there is. We've barely scratched the
surface.
Special Topic: Getting Big Data Labels to Fit
Try this...
> data.table = margin.table(Titanic, c(2,1)) # if you've already erased it
> dimnames(data.table) = list(
+ Sex=c("Male","Female"),
+ Class=c("First Class","Second Class","Third Class","Crew"))
> windows(4,4,12) # or quartz("Quartz",4,4,12)
> barplot(data.table, beside=T, axis.lty=1)
That's annoying, isn't it? The data labels on the x-axis are too long to all
fit, so R has printed only those it can squeeze in (left hand graph below). A
program like Excel would print those data labels vertically so they would all
fit. R will as well, but it won't unless you tell it to, which in my opinion is
a nice touch. It gives me control over what the graph looks like instead of
having to accept whatever the software has decided. More work, but more
flexibility as well.
You may have noticed in the last section the graphics option "las", which
controls the orientation of axis labels. The default value is 0=always parallel
to the axis, but other possibilities are 1=always horizontal, 2=always
perpendicular to the axis, and 3=always vertical. Try this...
> barplot(data.table, beside=T, axis.lty=1, las=2) # output not shown
Doesn't quite do it, does it? But don't fail to notice the value labels on the
y-axis. These have now also turned perpendicular to the axis, which may be
required by some editors (or instructors). This is how you would achieve it.
(Note to my students: Check your APA Manual! You're going to want to know how
to do this!)
We could make the font smaller, but I'm stubborn. I don't wanna! The data
labels are printed in the margins of the plotting area, and we can see what
those are by querying the "mar" settings...
> par("mar")
[1] 5.1 4.1 4.1 2.1
I don't know the units (and the help page does not give up this
info--apparently, it is top secret), but these are respectively the bottom,
left, top, and right margin sizes. What we need to do is shift the graph up in
the plotting window to make room for the data labels on the x-axis...
> par(mar=c(6.4,4.1,2.7,2.1)) > barplot(data.table, beside=T, axis.lty=1, las=2) > title(main="Passengers by Class:Titanic") > title(ylab="Number of Passengers")
Special Topic: Error Bars
One of the more serious shortcomings of R, in my opinion, is that it provides no convenient way to show error bars on barplots or line graphs. Error bars are such a common part of scientific graphics that I have to stand amazed at this obvious oversight. However, there is no need to dispair. Adding error bars is not very difficult.
There are add-on packages that incorporate functions to do error bars. Also, various people have written scripts. For example, see Crawley (2007), page 462. However, once you understand a few simple things about R graphs, drawing in your own error bars just takes a few simple steps, and you can customize them to your heart's content.
Error Bars on Barplots
Barplots are produced from a table of means (or whatever summary statistic you care to use)...
> data(InsectSprays)
> names(InsectSprays)
[1] "count" "spray"
> means = with(InsectSprays, tapply(count, spray, mean))
> means
A B C D E F
14.500000 15.333333 2.083333 4.916667 3.500000 16.666667
> windows(4,3,8) # open a graphics window
> barplot(means) -> bp.out # default plot
> barplot(means, ylim=c(0,20), axis.lty=1) # make room for error bars
> title(xlab="Spray")
> title(ylab="Insect Count")
> title(main="Effectiveness of Insect Sprays")
Now here's the trick to adding error bars. We need to know the x-coordinates of
the centers of those bars in order to draw the error bars in the right place.
And those coordinates are not 1, 2, 3, 4, 5, 6, as you might expect. Lucky for
us, we have saved those coordinates in the data object "bp.out". You were
wondering what that was about, weren't you?
> bp.out
[,1]
[1,] 0.7
[2,] 1.9
[3,] 3.1
[4,] 4.3
[5,] 5.5
[6,] 6.7
Now we need a vector of errors that will determine the length of the error bars.
These can be anything you want: standard errors, confidence intervals, whatever
floats your boat...
> sem = function(x) sqrt(var(x)/length(x))
> errors = with(InsectSprays, tapply(count, spray, sem))
> errors
A B C D E F
1.3623732 1.2329647 0.5701984 0.7225621 0.5000000 1.7936476
We could have calculated the standard errors a little more laboriously, but
I've used a trick. I created a function to do it (see Writing Your Own Functions and Scripts for more about
this). Now we are ready to VERY CAREFULLY add the error bars. (Once again, if
you are doing all this inside a script, less care is needed as you can
always make changes and redo the entire graph, but I'm living on the edge here!)
We could add the error bars one at a time, but I'm going to do it with a loop...
> for (i in 1:length(bp.out)) { # repeats following for all six bars...
+ arrows(bp.out[i],means[i],bp.out[i],means[i]+errors[i],angle=90,length=.1)
+ arrows(bp.out[i],means[i],bp.out[i],means[i]-errors[i],angle=90,length=.1)
+ } # curly brace ends loop
arrows(x0, y0, x1, y1, angle= length= )
The first two values, x0 and y0, are the starting coordinates of the arrow. We
started them at the center of a bar and the mean. The next two values are the
ending coordinates of the pointy end of the arrow. We ended them at the center
of a bar, and the mean ± error. The "angle=" option gives the
angle of the arrowhead to the shaft--90° in this case, or at right angles.
The "length=" option gives the length of each side of the arrowhead. Frankly,
I'm a bit surprised they came out so long in this example, but once again, if
you are scripting this, you can change them until they agree with your own
artistic sense. One more thing you should not fail to notice--the first and
second line of that loop differ by only one character. So don't retype the
entire line just to change one little thing. Use the up arrow key to recall the
previous line and edit it.
Error Bars on Line GraphsError bars on line graphs are even easier. Nevertheless, I am going to do the sensible thing and script this one in the script editing window (see Writing Your Own Functions and Scripts) so I can get it just the way I want it...
> ls()
[1] "bp.out" "errors" "i" "InsectSprays" "means"
[6] "sem"
> rm(bp.out,errors,i,InsectSprays,means) # save that sem function
> data(Orange)
> names(Orange)
[1] "Tree" "age" "circumference"
> means = with(Orange, tapply(circumference, age, mean))
> errors = with(Orange, tapply(circumference, age, sem))
The data record information on the growth of five orange trees, specifically
trunk circumference in millimeters at seven ages in days ranging from 118 to
1582. Since the explanatory variable is numeric, a line graph would be more
appropriate than a bar graph...
> age.days = Orange$age[Orange$Tree==1] # get one copy of x-coords
> rm(Orange) # don't need this anymore
> plot(age.days,means,type="b") # let's see what we've got
> errors
118 484 664 1004 1231 1372 1582
0.6324555 3.6523965 7.7097341 11.5991379 13.0713427 14.6853669 14.8842198
Okay, close that graphics window, and then open a new one...
> windows(7,4,10)Now I'll shift over to a script...
### Begin copying here.
plot(age.days, means,
type="b", pch=16, lty=3, ylim=c(0,200),
ylab="Trunk Circumference (mm) with sem",
xlab="Tree Age (days)",
main="Growth of Orange Trees (n = 5)")
len = .07
for (i in 1:7) {
arrows(age.days[i], means[i],
age.days[i], means[i]+errors[i],
angle=90, length=len)
arrows(age.days[i], means[i],
age.days[i], means[i]-errors[i],
angle=90, length=len)
}
### End copying here.
You can copy and paste this script into the R editor and play with it all you
want.
