Extra Fundamentals of R

ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.

Graphical Parameters

You can customize many features of your graphs (fonts, colors, axes, titles) through graphic options.

One way is to specify these options in through the par( ) function. If you set parameter values here, the changes will be in effect for the rest of the session or until you change them again. The format ispar(optionname=value, optionname=value, ...)

# Set a graphical parameter using par()<br> <br> par() # view current settings<br> opar <- par() # make a copy of current settings<br> par(col.lab="red") # red x and y labels <br> hist(mtcars$mpg) # create a plot with these new settings <br> par(opar) # restore original settings

A second way to specify graphical parameters is by providing the optionname=value pairs directly to a high level plotting function. In this case, the options are only in effect for that specific graph.

# Set a graphical parameter within the plotting function <br> hist(mtcars$mpg, col.lab="red")

See the help for a specific high level plotting function (e.g. plot, hist, boxplot) to determine which graphical parameters can be set this way.

Boxplots

Boxplots can be created for individual variables or for variables by group. The format is boxplot(x, data=), where x is a formula and data= denotes the data frame providing the data. An example of aformula is y~group where a separate boxplot for numeric variable y is generated for each value of group. Add varwidth=TRUE to make boxplot widths proportional to the square root of the samples sizes. Addhorizontal=TRUE to reverse the axis orientation.

# Boxplot of MPG by Car Cylinders

boxplot(mpg~cyl,data=mtcars, main="Car Milage Data", xlab="Number of Cylinders", ylab="Miles Per Gallon")

Lattice Graphs

The lattice package, written by Deepayan Sarkar, attempts to improve on base R graphics by providing better defaults and the ability to easily display multivariate relationships. In particular, the package supports the creation of trellis graphs - graphs that display a variable or the relationship between variables, conditioned on one or more other variables.

The typical format is

<em>graph_type</em>(<em>formula</em>, data=)

where graph_type is selected from the listed below. formula specifies the variable(s) to display and any conditioning variables . For example ~x|A means display numeric variable x for each level of factor A.y~x | A*B means display the relationship between numeric variables y and x separately for every combination of factor A and B levels. ~x means display numeric variable x alone.

Simulation uses methods based on random numbers to simulate a process of interest on the computer. The goal is to learn important statistical and/or practical information about the process. In statistics, simulations can be used to create simulated data sets in order to study the accuracy of mathematical approximations and the effect of assumptions being violated. We will study properties of some quantities that can be calculated from a set of data which are a random draw from a population. Random numbers form a basic tool for any simulation study. Simulations require the ability to generate random numbers. On a computer, it is only possible to generate 'pseudo-random' numbers which for practical purposes behave as if they were drawn randomly.

Handling and processing text strings in R? Wait a second . . . you exclaim, R is not a scripting language like Perl, Python, or Ruby. Why would you want to use R for handling and processing text? Well, because sooner or later (I would say sooner than later) you will have to deal with some kind of string manipulation for your data analysis. So it's better to be prepared for such tasks and know how to perform them inside the R environment.

Another very useful function is cat() which allows us to concatenate objects and print them either on screen or to a file. Its usage has the following structure:

cat(..., file = "", sep = " ", fill = FALSE, labels = NULL, append = FALSE)

A regular expression (a.k.a. regex) is a special text string for describing a certain amount of text. This “certain amount of text” receives the formal name of pattern. Hence we say that a regular expression is a pattern that describes a set of strings.

There are two main aspects that we need to consider about regular expressions in R. One has to do with the functions designed for regex pattern matching. The other aspect has to do with the way regex patterns are expressed in R. In this section we are going to talk about the latter issue: the way R works with regular expressions. Some find more convenient to first cover the specificities of R around regex operations, before discussing the functions and how to interact with regex patterns.

To find exactly where the pattern is found in a given string, we can use the regexpr() function. This function returns more detailed information than grep() providing us: a) which elements of the text vector actually contain the regex pattern, and b) identifies the position of the substring that is matched by the regular expression pattern.

# some text

text = c("one word", "a sentence", "you and me", "three two one")

# default usage

regexpr("one", text)

The function gregexpr() does practically the same thing as regexpr(): identify where a pattern is within a string vector, by searching each element separately. The only difference is that gregexpr() has an output in the form of a list. In other words, gregexpr() returns a list of the same length as text, each element of which is of the same form as the return value for regexpr(), except that the starting positions of every (disjoint) match are given.

# some text

text = c("one word", "a sentence", "you and me", "three two one")

# pattern

pat = "one"

# default usage

gregexpr(pat, text)

grep() is perhaps the most basic functions that allows us to match a pattern in a string vector. The first argument in grep() is a regular expression that specifies the pattern to match. The second argument is a character vector with the text strings on which to search. The output is the indices of the elements of the text vector for which there is a match. If no matches are found, the output is an empty integer vector.

# some text

text = c("one word", "a sentence", "you and me", "three two one")

# pattern

pat = "one"

# default usage

grep(pat, text)