If you're new to the R programming language and eager to get hands-on experience, this introductory session is designed just for you. Before delving into the world of statistical analysis and data visualization, it's crucial to familiarize yourself with the basics of the R environment.
Starting R
Begin by launching R on your computer. The specific steps for starting R depend on your platform. Once R is up and running, you'll be greeted by a banner that marks the beginning of your R journey.
Navigating Online Help
To make your exploration smoother, kickstart with the online help feature by typing `help.start()`. This command launches the HTML interface to R's online help, accessible through your web browser. Take a moment to explore the features with your mouse, as this will be a valuable resource throughout your R endeavors.
Generating Random Data
Let's dive into some practical examples. Copy and paste the following commands into your R console:
R
x <- rnorm(50)
y <- rnorm(x)
plot(x, y)
ls()
rm(x, y)These commands:
- Generated two pseudo-random normal vectors of x- and y-coordinates.
- Plotted the points in the plane, creating a graphics window.
- Checked which R objects are currently in the workspace.
- Removed unnecessary objects to clean up.
Data Manipulation and Analysis
Continue with the following commands:
R
x <- 1:20
w <- 1 + sqrt(x)/2
dummy <- data.frame(x=x, y= x + rnorm(x)*w)
fm <- lm(y ~ x, data=dummy)
summary(fm)
fm1 <- lm(y ~ x, data=dummy, weight=1/w^2)
summary(fm1)These commands:
- Created a 'weight' vector of standard deviations.
- Formed a data frame of two columns, x and y.
- Conducted a simple linear regression and analyzed the results.
- Performed a weighted regression using known standard deviations.
Exploring Visualizations
Now, let's explore visualizations:
R
attach(dummy)
lrf <- lowess(x, y)
plot(x, y)
lines(x, lrf$y)
abline(0, 1, lty=3)
abline(coef(fm))
abline(coef(fm1), col = "red")
detach()This series of commands:
- Made columns in the data frame visible as variables.
- Created a nonparametric local regression function.
- Plotted a standard point plot and added local regression lines.
- Checked for heteroscedasticity using a regression diagnostic plot.
Further Exploration
The journey doesn't end here. The next section delves into classical experiments with the Michelson dataset. Execute the following commands:
R
filepath <- system.file("data", "morley.tab" , package="datasets")
file.show(filepath)
mm <- read.table(filepath)
mm$Expt <- factor(mm$Expt)
mm$Run <- factor(mm$Run)
attach(mm)
plot(Expt, Speed, main="Speed of Light Data", xlab="Experiment No.")
fm <- aov(Speed ~ Run + Expt, data=mm)
summary(fm)
detach()These commands:
- Loaded the Michelson dataset.
- Converted Expt and Run into factors.
- Plotted a comparison of five experiments with boxplots.
- Conducted an analysis of variance on the Speed variable.
Advanced Graphical Features
To conclude the introductory session, explore some graphical features:
R
x <- seq(-pi, pi, len=50)
y <- x
f <- outer(x, y, function(x, y) cos(y)/(1 + x^2))
contour(x, y, f)
contour(x, y, f, nlevels=15, add=TRUE)These commands:
- Created a contour map of a mathematical function.
- Explored contour plots with varying levels of detail.
Feel free to experiment with these commands, modify them, and observe the changes in the outputs. As you progress, you'll find R to be a powerful tool for statistical analysis and data visualization. When you're done, type `q()` to exit the R program, and you'll be prompted to save your workspace—ideal for more extensive projects but unnecessary for this exploratory session. Enjoy your R journey!
