## Introduction to R plot() function

Question: Can we draw graphics in R language?
Answer: Yes. R language produces high quality statistical graphs. There are many useful and sophisticated kinds of graphs available in R.

Question: Where graphics are displayed in R?
Answer: In R, all graphs are produced in a windows named Graphic Windows which can be resized.

Question: What is the use of plot function in R?
Answer: In R, plot() is a generic function that can be used to make a variety of point and line graphs. plot() function can also be used to define a coordinate space.

Question: What are the arguments of plot() function?
Answer: There are many arguments used in plot() function. Some of these arguments are x, y, type, xlab, ylab, etc. To see the full list of arguments of plot(), write the command in R console;

args(plot.default)

Question: Does all arguments are necessary to be used in R?
Answer: No. The first two arguments x and y provide the horizontal and vertical coordinates of points or lines to be plotted and also define a data-coordinate system for the graph. At least argument x is required.

Question: What is the use of the argument type in plot() function?
Answer: The argument type determines the type of the graph to be drawn. There are several types of graph that can be drawn. The default type of graph type=’p’, plots points at the coordinates specified by x and y argument. Specifying type=’l’ produces a line graph, and type=’n’ sets up the plotting region to accommodate the data set but plots nothing.

Question: Is there other types of graph are?
Answer: Yes. Setting type=’b’, draw graphs having both points and lines. Setting type=’h’ draws histogram like vertical lines and setting type=’s’ and type=’S’ draws stair-step-like lines starting horizontally and vertically respectively.

Question: What is the use of xlim and ylim in plot() function?
Answer: The argument xlim and ylim may be used to define the limits of the horizontal and vertical axes. Usually these arguments are unnecessary, because R langauge reasonably pick limits from x and y.

Question: What is the purpose of xlab and xlab argument in plot() function?
Answer: xlab and ylab argument tack character-string arguments to label the horizontal and vertical axes.

Question: Provide few examples of plot() function?
Answer: Suppose of have data set on variable x and y, such as

x <- rnorm(100, m=10, sd=10)
> y <- rnorm(100)
> plot(x, y)
> plot(x, y, xlab=’X  (Mean=10, SD=10)’,   ylab=’Y (Mean=1, SD=1)’ , type=’l’)
> plot(x, y, xlab=’X  (Mean=10, SD=10)’,   ylab=’Y (Mean=1, SD=1)’ , type=’o’)
> plot(x, y, xlab=’X  (Mean=10, SD=10)’,   ylab=’Y (Mean=1, SD=1)’ , pch=10)

## R Basics

Question 1: How can I retrieve (load) the work that is saved using history function in R?

This function will load file named “file_name.Rhistory” from D: drive.

The other way may be to access .Rhistory file through the file menu. For this click File and then Load history. From the dialog box appeared  browse to the folder where you saved the .Rhistory file and click open to start working.

Question 2: How do I use a script of commands and functions saved in a text file?
Answer: The script of commands and functions saved in a text file (also called script file) can be used by writing the following command.

> source(“d:/file_name.txt”)

The “file_name.txt” will load from D: drive.

Question 3: How do I get R to echo back the R commands and functions in a script file that I am sourcing into R? That is, the functions that I have written, I want to see these functions are being executed.
Answer: use echo=TRUE argument by using source() function

source(“d:/file_name.txt”, echo=T)

Question 4: How do I close the help file when working on a Macintosh operating system?
Answer: Typing just q will close the help file and bring you back to the R console.

Question 5: How can I see a list of currently available objects in R?
Answer: Use the objects() or ls() functions to see the list of objects currently available

objects()
> ls()

Question 6: How do I remove/delete unwanted objects and functions?
Answer: The rm() function can be used to delete or remove the objects that are not required. Commands  below will delete objects named object_name1 & object_name2 and functions named function_name1 & function_name2.

> rm(object_name1, object_names2)
> rm(function_name1, function_name2)

## R FAQs: Getting Help in R

Question: How one can get help about different command in R Language?
Answer: There are many ways to get help about different command (functions). R has built-in help facility which is similar to man facility in Unix. For beginners of R language, help() function or ? can be used to get help about different commands of R language.

Questions: Provide some examples about getting help?
Answer: To get more information on any specific R command (function), for example for getting help about solve(), lm(), plot() etc, write the following commands at R prompt:

> help(solve)
> help(lm)
> help(plot)

Question: Can one get help for special symbols, characters in R Language?
Answer: Yes one can get help for special characters. For example;

> help(“[[“)
> help(“[“)
> help(“^”)
> help(“\$”)
> help(“%%”)

Question: What help.start() does?
Answer: The help.start() will launch a web browser that allows the help pages to browsed with hyperlinks. It can be a better way to get help about different functions.

Question: There is help.search() command. What for purpose it is?
Answer: The help.search() command allows searching for help in various ways. To get what help.search() functions does, write this command at R prompt;

> help(help.search)

Question: Provide some details about help.search() function and also illustrate it by providing some examples?
Answer: The help.search() allows for searching the help system for documentation matching a given character string in the (file) name, alias, title, concept or keyword entries (or any combination thereof), using either fuzzy matching or regular expression matching. Names and titles of the matched help entries are displayed nicely formatted. The examples are:

> help.search(“linear”)
> help.search(“linear models”)
> help.search(“print”)
> help.search(“cat”)

Question: How ? can be used to get help in R language?
Answer: The ? mark can be used to get help in Windows version of R Language. For example;

> ?print
> ?help
> ?”[[“
> ?methods
> ?lm

Question: Can I save my work in R Language?
Answer: R language facilitates to save ones R work.

Question: How to save work done in R?
Answer: All of the objects and functions that are created (you R workspace) can be saved in a file .RData by using the save() function or the save.image() function. It is important that when saving R work in a file, remember to include the .RData extension.

> save(file=”d:/filename.RData”)
> save.image(“d:/filename.RData”)

Question: Is there alternative to save workspace in R?
Answer: Yes! You can also save work space using file menu. For this, click File menu and then click save workspace. You will see the dialog box, browse to the folder where you want to save the file and provide the file name of your own choice.

Question: How one can access the saved work, while work is saved using save.image() function?

Question: Is there any other alternative to load workspace in R?
Answer: The .RData files can be accessed through the file menu. To access file click File and then load workspace. A dialog box will appear, browse to the folder where you saved the .RData file and click open.

Question: How do one can save all the commands that are used in an R session?
Answer: Saving R commands used in an R session means you want to save history of your R session in an .Rhistory file by using the history() function. It is important to include the .Rhistory extension when saving the file at different path.

> history(“d:/filename.Rhistory”)

Question: Can commands in R session be saved through File menu?
Answer: Yes command in R session be saved through file menu. For this click File and then save history. A dialog box will appear, browse to the folder where you want to save the file (that will contain R commands in a session) and provide the file name of your own choice.

## R FAQs: Handling Missing values in R

Question: What are the differences of missing values in R and other Statistical Packages?

Answer: Missing values (NA) cannot be used in comparisons, as already discussed in previous post on missing values in R. In other statistical packages (softwares) a “missing value” is assigned some code either very high or very low in magnitude such as 99 or -99 etc. These coded values are considered as missing and can be used to compare to other values and other values can be compared to missing values. In R language NA values are used for all kinds of missing data, while in other packages, missing strings and missing numbers are represented differently, for example, empty quotations for strings, and periods, large or small numbers. Similarly non-NA values cannot be interpreted as missing while in other packages system missing values are designate from other values.

Question: What are NA options in R?

Answer: In previous post on missing values, I introduced is.na() function as a tool for both finding and creating missing values. The is.na() is one of several functions build around NA. Most of the other functions for missing values (NA) are options for na.action(). The possible na.action() settings within R are:

• na.omit() and na.exclude(): These functions return the object with observations removed if they contain any missing (NA) values. The difference between these two functions na.omit() and na.exclude() can be seen in in some prediction and residual functions.
• na.pass(): This function returns the object unchanged.
• na.fail(): This function returns the object only if it contains no missing values.

To understand these NA options use the following lines of code.

getOption(“na.action”)
(m<-as.data.frame(matrix(c(1:5, NA), ncol=2)))
na.omit(m)
na.exclude(m)
na.fail(m)
na.pass(m)

Note that it is wise to both investigate the missing values in you data set and also make use of the help files for all functions you are willing to use for handling missing values. You should be either aware of and comfortable with the default treatments (handling) of missing values or specifying the treatment of missing values you want for you analysis.