Data Analysis Using R Programming

Abstract

Beginning R

R is an open-source, freely available, integrated software environment for data manipulation, computation, analysis, and graphical display. The R environment consists of

• *a data handling and storage facility,

• *operators for computations on arrays and matrices,

• *a collection of tools for data analysis

• *graphical capabilities for analysis and display, and

• *an efficient, and continuing developing programming algebra-like programming language which consists of loops, conditionals, user-defined functions, and input and output capabilities.

Many R programs are available for biostatistical analysis in Genetic Epidemiology. Typical examples are shown.

Appendix 1

2.1.1 Documentation for the plot function

plot {graphics}

R Documentation

Generic X-Y Plotting

2.1.1.1 Description

Generic function for plotting of R objects. For more details about the graphical parameter arguments, see par.

For simple scatter plots, plot.default will be used. However, there are plot methods for many R objects, including functions, data.frames, density objects, etc. Use methods(plot) and the documentation for these.

2.1.1.2 Usage

plot(x, y, …)

2.1.1.3 Arguments

X	the coordinates of points in the plot. Alternatively, a single plotting
	structure, function or any R object with a plot method can be provided.
Y	the y coordinates of points in the plot, optional if x is an appropriate
	structure.

2.1.1.4 Details

The two step types differ in their x-y preference: Going from (x1,y1) to (x2,y2) with x1 < x2, type = “s” moves first horizontal, then vertical, whereas type = “S” moves the other way around.

2.1.1.5 See Also

plot.default, plot.formula and other methods; points, lines, par.

For X-Y-Z plotting see contour, persp and image.

2.1.1.6 Examples

require(stats) plot(cars) lines(lowess(cars)) plot(sin, -pi, 2*pi) # see ?plot.function ## Discrete Distribution Plot: plot(table(rpois(100,5)), type = “h”, col = “red”,lwd=10, main=”rpois(100,lambda=5)”) ## Simple quantiles/ECDF, see ecdf() {library(stats)} for a better one: plot(x <- sort(rnorm(47)), type = “s”, main = “plot(x, type = \”s\”)”) points(x, cex = .5, col = “dark red”)