Data Frame in R Language

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

Naming/ Renaming Columns in a Data Frame

Question: How do you 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 the second and third columns of the data frame

names(irisi)[c(2,4)] <- c("A", "D")

Note that names(iris) command can be 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)
Data Frame in R Language

Exporting a Data Frame in R

Question: How a data frame can be exported in R so that it can be used in other statistical software?
Answer: Use the 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 the first column only, then at the 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 a first and third row, then

iris[c(1,2), ]

Dealing with Missing Values in a Data Frame

Question: How do you 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 named “file.csv” that contains missing values represented by a “.” (period), then on the 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)

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FAQs about R Language

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The article is FAQs about R Language, that is R Frequently Asked Questions (R FAQs).

FAQS about R Language

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

R Foundation

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 and provides a reference point for individuals, institutions, or commercial enterprises who want to support or interact with the R development community. R foundation also holds and administers 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 software, etc. It is based on GForge and offers easy access to the best in SVN, daily built and checked R packages, mailing lists, bug tracking, message board or forum, website 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.

Mailing Lists of R

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

R Language Documentation

Question: What documentation exists for R language?
Answer: For most of the R functions 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 the 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 hard copy via LaTeX. The up-to-date HTML version of R documentation is always available for web browsers at http://stat.ethz.ch/R-manual. A lot of R books and manuals are also available as R documentation.

R documentation refers to the various resources available to help you learn and use the R programming language.

  • Official R Language Documentation: The R Project itself maintains a collection of manuals and guides covering different aspects of the R language. This includes (i) an introduction to R, (ii) Reference manuals for specific functions and packages, and (iii) information on using the R interface.
  • Package Documentation: Many R packages also have their documentation, which one can access within the R environment using the help function. These help files (package documentation) explain how to use the specific functions and features provided by the package.
  • Online Resources: Many online resources provide tutorials, explanations, and examples for working with R Language. These can be a great way to learn the language and find solutions to specific problems https://rdocumentation.org/.
  • Books and Other Publications: There are also several books and other publications available that cover R in more depth. These books and publications can help get a more comprehensive understanding of the language and its capabilities.
RFAQs: Frequently Asked Questions About R

How to get help in R follow the link Getting Help in R Language.

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Important R Basics Questions and Answers

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Here are R FAQS about R Basics, Questions and answers will be updated frequently and on the demand of visitors.

R Basics Questions and Answers

Question: On what Operating Systems does R Language Run?
Answer: R Language can run on Unix, Linux, and Windows Operating System.

Question: On what machine R can Run?
Answer: R is developed for Unix-like, Windows, and Mac families of operating systems. The current version of R Language is configured to run on machines such as CPU-Linux-gnu for i386, amd64, alpha, arm/armel, hppa, ia64, m68k, mips/mipsel, PowerPC, s390 and Sparc CPUs, i386-hurd-gnu, cpu-kfreebsd-gnu for i386 and amd64, PowerPC-apple-darwin, mips-sgi-irix, i386-freebsd, rs6000-ibm-aix and sparc-sun-Solaris.

Question: What is the current version of R?
Answer: The current released version (at the time of this post) of R is 3.2.2. The current version is 3.6.1 (Action of the Toes) released on 2019-07-05.

Question: How and where from R can be obtained?
Answer: Sources, binaries, and documentation of R language can be obtained via the Comprehensive R Archive Network (CRAN).

R Basics Questions and Answers

Question: How to install R on the Windows Operating System?
Answer: The “bin/windows” directory of the CRAN site contains binaries for a base distribution and add-on packages from CRAN to run on Windows XP, Vista, Windows 7, etc (32-bit or 64bit versions of Windows) on ix86 and x86_64 chips.

Comprehensive R Archive Network

Question: What is CRAN?
Answer: The CRAN is a “Comprehensive R Archive Network”. CRAN is a collection of sites that carry identical material, consisting of R distribution(s), the contributed extensions, documentation for R, and binaries.

The CRAN master site is https://CRAN.R-project.org at WU (Wirtschaftsuniversität Wien) in Austria.

Following are some mirrors from CRAN:
https://cran.wu.ac.at/    (Wirtschaftsuniversität Wien, Austria)
https://cran.ms.unimelb.edu.au/    (University of Melbourne, Australia)
https://cran-r.c3sl.ufpr.br/    (Universidade Federal do Paraná, Brazil)
https://stat.ethz.ch/CRAN/    (ETH Zürich, Switzerland)
https://mirrors.dotsrc.org/cran/    (dotsrc.org, Aalborg, Denmark)
https://cran.rediris.es/    (Spanish National Research Network, Madrid, Spain)
http://cran.dcc.fc.up.pt/    (RadicalDevelop, Lda, Portugal)
https://www.stats.bris.ac.uk/R/    (University of Bristol, United Kingdom)

For further R Basics Questions and Answers follow the R FAQ link: Hornik, K., (2015). R FAQs, https://CRAN.R-project.org/doc/FAQ/R-FAQ.html.

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