Getting Help With Functions In R Programming
In our previous articles we described how to install and start using R/RStudio. We also provide the essentials of R programming.
Getting help on a specific function
To read more about a given function, for example mean, the R function help() can be used as follow:
Or use this:
The output look like this:
If you want to see some examples of how to use the function, type this: example(function_name).
Note that, typical R help files contain the following sections:
- Description: a short description of what the function does.
- Usage: the syntax of the function.
- Arguments: the description of the arguments taken by the function.
- Value: the value returned by the function
- Examples: provide examples on how to use the function
If you want to read the general documentation about R, use the function help.start():
The output is a web page, on most R installations, which can be browsed by clicking the hyperlinks.
- apropos(): returns a list of object, containing the pattern you searched, by partial matching. This is useful when you don’t remember exactly the name of the function:
# Returns the list of object containing "med" apropos("med")
 ".__C__namedList" "elNamed" "elNamed<-" "median" "median.default"  "medpolish" "runmed"
- healp.search() (alternatively ??): Search for documentation matching a given character in different ways. It returns a list of function containing your searched term with a short description of the function.
help.search("mean") # Or use this ??mean
This analysis has been performed using R software (ver. 3.2.3).
Show me some love with the like buttons below... Thank you and please don't forget to share and comment below!!
Montrez-moi un peu d'amour avec les like ci-dessous ... Merci et n'oubliez pas, s'il vous plaît, de partager et de commenter ci-dessous!
Recommended for You!
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Coursera - Online Courses and Specialization
- Course: Machine Learning: Master the Fundamentals by Standford
- Specialization: Data Science by Johns Hopkins University
- Specialization: Python for Everybody by University of Michigan
- Courses: Build Skills for a Top Job in any Industry by Coursera
- Specialization: Master Machine Learning Fundamentals by University of Washington
- Specialization: Statistics with R by Duke University
- Specialization: Software Development in R by Johns Hopkins University
- Specialization: Genomic Data Science by Johns Hopkins University
Popular Courses Launched in 2020
- Google IT Automation with Python by Google
- AI for Medicine by deeplearning.ai
- Epidemiology in Public Health Practice by Johns Hopkins University
- AWS Fundamentals by Amazon Web Services
- The Science of Well-Being by Yale University
- Google IT Support Professional by Google
- Python for Everybody by University of Michigan
- IBM Data Science Professional Certificate by IBM
- Business Foundations by University of Pennsylvania
- Introduction to Psychology by Yale University
- Excel Skills for Business by Macquarie University
- Psychological First Aid by Johns Hopkins University
- Graphic Design by Cal Arts
Books - Data Science
- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet
Want to Learn More on R Programming and Data Science?
Follow us by Email On Social Networks: