Getting Help with R

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Getting help with R is important to learning the language and getting expertise in R.

Question: How one can get help with different commands in the R Language?
Answer: There are many ways to get help with the different commands (functions). There is a built-in help facility which is similar to the man facility in Unix. For beginners to get help, the help() function or the symbol ‘?‘ (question mark only) can be used to get help with different commands.

Help Function in R

Questions: Provide some examples of getting help with different functions used in R.
Answer: To get more information on any specific command (function), for example for getting help with solve( ), lm( ), plot( ), etc., write the following commands at the prompt:

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

Question: Can one get help for special symbols, and 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 be browsed with hyperlinks. It can be a better way to get help with different functions.

help.search Function

Question: There is help.search( ) command. For what purpose it is?
Answer: The help.search() command allows searching for help in various ways. To get what help.search( ) functions do, write this command at the 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")
Getting Help with R Language help.search("linear")

? Operator

Question: How ‘?‘ can be used to get help with different functions and objects in R language?
Answer: The ‘?‘ mark can be used to get help with the Windows version of the R Language. For example;

?print
?help
?"[["
?methods
?lm
Getting Help in R: Frequently Asked Questions About R

MCQs in Statistics

MCQs General Knowledge

Save and Load RData Workspace

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In this post, we will learn about Save and Load RData Workspace

How to Save Work in R Language

Question: Can I save my work in R Language?
Answer: R language facilitates saving one’s R work.

Question: How do you save work done in R?
Answer: All of the objects and functions that are created (your 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")

Workspace in R Language

Question: Is there an alternative to save workspace in R?
Answer: Yes! You can also save the workspace using the file menu. For this, click the 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.

Save and Load .RData

Question: How one can access the saved work, while work is saved using “save.image()” function?
Answer: The “load()” function can be used to load a .RData file.

load ("d:/filename.RData")

Question: Is there any other alternative to load the workspace in R?
Answer: The .RData file can be accessed through the file menu. To access the 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.

Save and Load RData Workspace

Saving Rhistory

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 the 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 a different path.

history("d:/filename.Rhistory")

Question: Can commands in the R session be saved through the File menu?
Answer: Yes, the command in the R session be saved through the 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.

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Handling Missing Values in R: A Quick Guide

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The article is about Handling Missing Values in R Language.

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

Answer: Missing values (NA) cannot be used in comparisons, as already discussed in the previous post on missing values in R. In other statistical packages (software) a “missing value” is assigned to 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 package systems, missing values are designated from other values.

Handling Missing Values in R

Question: What are NA options in R?
Answer: In the 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 built 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 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)
Handling Missing Values in R Language

Note that it is wise to investigate the missing values in your 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 your analysis.

FAQs about Missing Values in R

  1. What is meant by a missing value?
  2. How one can handle missing values in R?
  3. What is NA in R?
  4. How one can identify missing values in R?
  5. What is is.na() function?
  6. What is the use of na.omit() function in R?
  7. Why it is importance of investigate missing values before performing any data analysis?
Handling Missing values in R

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