The Javascript version of Rweb runs in 4 different browser windows and assumes
your browser understands JavaScript.
To start the Javascript version of **Rweb** click on the "Open Code Window"
buttom below.
This will open the window where you type
**R ** (or Splus) code.
After you have typed in the code you want to execute, just click on the submit button.

At the bottom of the code page is a text area where you can enter the
URL for a Web accessible dataset and a browse button for selecting a
dataset on your computer.
Either way, the dataset will be read in using
**read.table**
with **header=T**
and stored in a dataframe called **X**.
The dataframe, **X**, will then be attached so you can use the
variable names.

After your code has be executed three more browser windows will open to display the results.

- An Image window for graphical output
- An Analysis window for text output (such as an ANOVA table)
- A Debug window ... the text showing how R executed your code. Look in this window if you are not getting any output.

Once all of the window are open you can keep typing code into the code window, edit what's there, or erase everything and start over. You can cut and paste between an editor window and the code window. You can also cut text or images out of the Rweb windows and paste them into documents (if the document editor supports pasting images).

Here is some **R** code you can use to test things out.

# A little Regression x <- rnorm(100) # 100 random numbers from a normal(0,1) distribution y <- exp(x) + rnorm(100) # an exponential function with error result <- lsfit(x,y) # regress x on y and store the results ls.print(result) # print the regression results plot(x,y) # pretty obvious what this does abline(result) # add the regression line to the plot lines(lowess(x,y), col=2) # add a nonparametric regression line (a smoother) hist(result$residuals) # histogram of the residuals from the regression

## Boxplots n <- 10 g <- gl(n, 100, n * 100) x <- rnorm(n * 100) + sqrt(codes(g)) boxplot(split(x, g), col = "lavender", notch = TRUE)

# Scatter plot matrix data("iris") pairs(iris[1:4], main = "Edgar Anderson's Iris Data", font.main = 4, pch = 19) pairs(iris[1:4], main = "Edgar Anderson's Iris Data", pch = 21, bg = c("red", "green3", "blue")[codes(iris$Species)])

#Coplots data(quakes) coplot(long ~ lat | depth, data = quakes, pch = 21, bg = "green3")

#Image and contour plots (These are Owww-Ahhh plots) opar <- par(ask = interactive() && .Device == "X11") data(volcano) x <- 10 * (1:nrow(volcano)) x.at <- seq(100, 800, by = 100) y <- 10 * (1:ncol(volcano)) y.at <- seq(100, 600, by = 100) image(x, y, volcano, col = terrain.colors(100), axes = FALSE) rx <- range(x <- 10*1:nrow(volcano)) ry <- range(y <- 10*1:ncol(volcano)) ry <- ry + c(-1,1) * (diff(rx) - diff(ry))/2 tcol <- terrain.colors(12) par(opar); par(mfrow=c(1,1)); opar <- par(pty = "s", bg = "lightcyan") plot(x = 0, y = 0,type = "n", xlim = rx, ylim = ry, xlab = "", ylab = "") u <- par("usr") rect(u[1], u[3], u[2], u[4], col = tcol[8], border = "red") contour(x, y, volcano, col = tcol[2], lty = "solid", add = TRUE) title("A Topographic Map of Maunga Whau", font = 4) abline(h = 200*0:4, v = 200*0:4, col = "lightgray", lty = 2, lwd = 0.1) par(opar)