R FAQ missing values

Question: Can missing values be handled on R?
Answer: Yes, in R language one can handle missing values. The way of dealing with missing values is different as compared to other statistical softwares such as SPSS, SAS, STATA, EVIEWS etc.

Question: What is the representation of missing values in R Language?
Answer: In R missing values or data appears as NA. Note that NA is not a string nor a numeric value.

Question: Can R user introduce missing value(s) in matrix/ vector?
Answer: Yes user of R can create (introduce) missing values in vector/ Matrix. For example,

    x <- c(1,2,3,4,NA,6,7,8,9,10)
    y <- c(“a”, “b”, “c”, NA, “NA”)

Note that on y vector the fifth value of strong “NA” not a missing value.

Question: How one can check that there are missing value in a vector/ Matrix?
Answer: To check which values in a matrix/vector recognized as missing value by R language, use the is.na function. This function will return a vector of TRUE or FALSE. TRUE indicate that the value at that index is missing while FALSE indicate that the value is not a missing value. For example

> is.na(x)    # fifth element will appear as TRUE while all other will be FALSE
> is.na(y)    # fourth element will be true while all others as FALSE

Note that “NA” in second vector is not a missing value, therefore is.na will return FALSE for this value.

Question: In R language, can missing values be used comparisons?
Answer: No missing values in R cannot be used in comparisons. NA (missing values) is used for all kinds of missing data. Vector x is numeric and vector y is a character object. So Non-NA values cannot be interpreted as missing values. Write the command, to understand it

x < 0
y == NA
is.na(x) <- which(x–7); x1

Question: Provide an example for introducing NA in matrix?
Answer: Following command will create a matrix with all of the elements as NA.

matrix(NA, nrow=3, ncol=3)
matrix(c(NA,1,2,3,4,5,6,NA, NA), nrow=3, ncol=3)

R FAQS: R Packages

R FAQS: R Packages

Question: What is an R Package?
Answer: R package is a collection of objects that R Language can use. A package contain functions, data set, and documentation (which helps how to use the package) or other objects such as dynamically loaded libraries of already compiled code.

Question: How do I see which packages I have available?
Answer: To see which packages you have use the command at R prompt

> library()

Question: Which packages do I already have?
Answer: To see what packages are installed one can use the installed.packages() command a R prompt. Output will show the packages installed.

> installed.packages()
> installed.packages()[1:5,]

Question: How one can load a Package in R language?
Answer: Basic packages are already loaded. If you want to load downloaded version of packages use the command

> library(“package name”)
> library(“car”)

where package name is the name of the package you want to load. Here in example we used the “car”, it means “car” package will be loaded.

Question: How one can see the documentation of a particular package?
Answer: To see the documentation of particular package use the command

> library(help=”package name”)
> help(package=”package name”)
> help(package=”car”)
> library(help=”car”)

for more information about getting help follow the link: Getting Help in R Language

Question: How do I see the help for a specific function?
Answer: To get help about a function in R use command

> help(“function name”)
> ? function name
> ?Manova
> help(“Manova”)

Question: What functions and datasets are available in a package?
Answer: To check what functions and datasets are in a package using the help command at R prompt. This will provide package information giving list of functions and datasets.

> help(package=”MASS”)

Note that once a package is loaded, the help command can also be used with all available functions and datasets.

Question: How can one add or delete a package?
Answer: A package can be installed using command

> install.packages(“package name”)

and package can be removed or deleted using command

> remove.packages(“package name”)

R FAQs about Data Frame

Please load the require data set before running the commands given below in R FAQs related to data frame. As an example for R FAQs about data frame we are assuming iris data set that is available already in R. At R prompt write data(iris)

Question: How to name or rename a column in a data frame?
Answer: Suppose you want to change/ rename the 3rd column of the data frame, then on R prompt write

>names (iris)[,3] <- “new_name”

Suppose you want to change second and third column of the data frame

>names(irisi)[c(2,4)] <- c(“A”, “D”)

Note that names(iris) command is used to find the names of each column in a data frame.

Question: How you can determine the column information of a data frame such as the “names, type, missing values” etc.?
Answer: There are two built-in functions in R to find the information about columns of a data frame.

> str(iris)
>summary(iris)

Question: How a data frame can be exported in R, so that it can be used in other statistical software?
Answer: Use write.csv command to export the data in comma separated format (CSV).

> write.csv(iris, “iris.csv”, row.names=FALSE)

Question: How one can select a particular row or column of a data frame?
Answer: The easiest way is to use the indexing notation []

Suppose you want to select first column only, then at R prompt, write

>iris[,1]

Suppose we want to select the first column and also want to put the content in a new vector, then

>new <- iris[,1]

Suppose you want to select different columns, for example columns 1, 3, and 5, then

>newdata <- iris[, c(1, 3, 5)]

Suppose you want to select first and third row, then

>iris[c(1,2), ]

Question: How to deal with missing values in a data frame?
Answer: In R language it is easy to deal with missing values. Suppose you want to import a file names “file.csv” that contains missing values represented by a “.” (period), then on R prompt write

>data<-read.csv(“file.csv”, na.string= “.”)

If missing values are represented as “NA” values then write

>dataset<-read.csv(“file.csv”, na.string=”NA”)

For the case of built in data such (here iris), use

>data<-na.omit(iris)

 

FAQs about R

Question: Why R language is named as R?
Answer: The name of R language is based on the first letters of its authors (Robert Gentleman and Ross Ihaka).

Question: What is the R Foundation?
Answer: The R foundation is a non-profit organization working in the public interest, founded by the members of the R Core Team. This foundation provides support for the R project and other innovations in statistical computing, provides reference point for individual, institutions or commercial enterprises whom want to support or interact with the R development community. R foundation also holds and administer the copyright of R language software and its documentation. For more information about R foundation follow the link https://www.R-project.org/foundation

Question:What is R-Forge?
Answer: R-Forge provides a central platform for the development of R packages, R-related softwares etc. It is based on GForge that offers easy access to the best in SVN, daily built and checked R packages, mailing lists, bug tracking, message board or forum, web-site hosting, permanent file archival, full backups and total web-based administration. For more information see

  • The R-Forge web page
  • Stefan Theußl and Achim Zeileis (2009), “Collaborative software development using R-Forge”, The R Journal, 1(1), 9-14.

Question: What mailing lists exist for R language?
Answer: There are four mailing lists devoted to R language

  • R-announce: A moderated mailing list for major announcements about the R development and the availability of new R code.
  • R-packages: A moderated mailing list for announcement on the availability of new or further enhanced contributed packages.
  • R-help: The main R mailing list for discussion and problems and solution using R, announcements about the development of R and the availability of new R code. R-help is intended to people who want to use R to solve problems.
  • R-devel: A mailing list for questions and discussions about code development in R language.

Question: What documentation exists for R language?
Answer: For most of the R function and variables in R online documentation exists and this documentation can be printed on screen by typing help(name) or ?name at the R prompt, where name is the name of the topic for which help is required. The R documentation can also be made available in PDF and HTML formats and as a hardcopy via LaTeX. Up-to-date HTML version of R documentation is always available for web browsers at http://stat.ethz.ch/R-manual.Lot of R books and manuals are also available as R documentation.
How to get help in R follow the link Getting Help in R Language.

 

R Basic FAQs

Question: How to start (Run) R Language in Windows Operating System?
Answer: In Microsoft Windows, during installation the R installer will have created a Start menu item and an icon for R on your system’s desktop. Double click R icon from desktop or from start menu list to Run R program. For windows 7, 8 or 10, you can use search term like “R x64 3.2.1” (64 bit version) or “R i386 3.2.1” (32 bit version). R GUI will launch.

Question: How R can be used as calculator.
Answer: Starting R will open the console where user can type commands. To use R as calculator one have to enter the arithmetical expression after > prompt. For example

> 5 + 4
> sqrt(37)
> 2*4^2+17*4-3

Question: How to Quit R session?
Answer: In R console on R command prompt just type

> q( )

Question: What is q()?
Answer: The q() is a function that is used to tell R to quit. When q() is entered in R console and press Enter key, you will be asked whether to save an image of the current workspace or not or to cancel. Note that only typing q tells R to show the content of this function. The action of this function is to quit R.

Question: What is workspace in R?
Answer: The workspace in R is an image that contains a record of the computations one have done and it may contain some saved results.

Question: How to record work in R?
Answer: Rather than saving the workspace, one can record all the commands that one have entered in R console. Recording work in R, the R workspace can be reproduced. The easiest way is to enter the commands in R’s script editor available in the File menu of R GUI.

Question: What is R Script Editor?
Answer: R script editor is a place where one can enter commands. Commands can be executed by highlighting them and hitting CTRL+R (mean RUN). At the end of a R session one can save the final script for a permanent record of one’s work. A text editor such as Notepad can also be used for this purpose.
Note that in R console only one command can be entered at a time because after pressing Enter key the R command executed immediately.