R Language Basics

In this article, we will discuss 9 ways to get help in R Language. R Language has a very useful and advanced help system that helps the R user to understand the R language and lets him know how programming should be done in the R language.

Get Help in R Language

To get help in R language you need to click the Help button on the toolbar of RGui (R Graphical User Interface) windows. If you have internet access on your PC you can type CRAN in Google and search for the help you need at CRAN.

Use of “?” for Help

On the other hand, if you know the name of the function, you need to type the question mark (?) followed by the name of the required function on the R command line prompt. For example to get help about “lm” function type ?lm and then press the ENTER key from the keyboard.
help(lm) or ?lm have the same search results in the R language.

help.start()

Getting General help in R write the following command at the R command prompt

help.start()

## Output
help.start()
starting httpd help server ... done
If nothing happens, you should open
‘http://127.0.0.1:13825/doc/html/index.html’ yourself

Use of help.search()

Sometimes it is difficult to remember the precise name of the function, but you know the subject on which you need help for example data input. Use the help.search function (without question mark) with your query in double quotes like this:

help.search("data input")

Press the ENTER key, you will see the names of the R functions associated with the query.  After that, you can easily use ?lm to get help in R.

Use of find(” “)

Getting help in R, find, and apropos are also useful functions. The find function tells you what package something is in: for example

find("cor") gives output that the cor in the stats package.

Use of apropos()

The apropos function returns a character vector giving the names of all objects in the search list that match your inquiry (potentially partial) i.e., this command lists all functions containing your string. For example

apropos("lm")

will give the list of all functions containing the string lm

Use of example()

example(lm) will provide an example of your required function that is in this case, an example of the function lm()

Online Help

There is a huge amount of information about R on the web. On CRAN you will find a variety of help/ manuals. There are also answers to FAQs (Frequently Asked Questions) and R News (contains interesting articles, book reviews, and news of forthcoming releases. The search facility of the site allows you to investigate the contents of the R documents, functions, and searchable mail archives.

Help Manuals and Archived Mailing lists {RSiteSearch()}

You can search your required function or string in help manuals and archived mailing lists by using

RSiteSearch("read.csv")

Get Vignettes

vignette is an R jargon for documentation and is written in the spirit of sharing knowledge, and
assisting new users in learning the purpose and use of a package. To get some help in R try ?vignette. Vignettes are optional supplemental documentation, that’s why not all packages come with vignettes.

vignette()          # will show available vignettes
vignette("foo")     # will show specific vignette

Now you have learned about getting help in R, now you can continue with the other R tutorials. It is possible that you do not understand something discussed in the coming R tutorials. If this happens then you should use the built-in help system before going to the internet. In most cases, the help system of R Language will give you enough information about the required function that you have searched for.

Some Sources of R Help/ Manual/ Documentations

https://cran.r-project.org/manuals.html

https://cran.r-project.org/other-docs.html

https://www.r-project.org/help.html

https://cran.r-project.org/bin/windows/base/rw-FAQ.html

The post is about an introduction to workspace, R objects, and .Rdata file in R language. When working in R, everything you create – variables, data frames, functions – exists in your workspace (also called the global environment). Understanding how to manage these objects and save your work properly is crucial for:

✔ Reproducible analysis
✔ Efficient memory management
✔ Sharing work with collaborators
✔ Recovering from crashes

Here, we will Learn how to efficiently manage R objects, control your workspace, and master .RData files for better workflow organization and reproducibility in R programming.

R Language as Functional Language

The R program’s structure is similar to the programs written in other computer languages such as C or its successors C++ and Java. However, important differences between these languages and R are (i) R has no header files, (ii) most of the declarations are implicit, (iii) there are no pointers in R, and (iv) text and strings as vectors can be defined and manipulated directly.

R is a functional language. Most of the computation in R is handled using functions. The R language environment is designed to facilitate the development of new scientific computation tools.

R Objects

All data in R exists as objects with specific types and classes. Everything (such as functions and data structure) in R is an object. To see the names of all R objects in the workspace, on the R command prompt, just type,

ls()

objects() is an alternative to ls() function. Similarly, typing the name of any object on the R prompt displays (prints) the content of that object. As an example type q, mean, and lm, etc. on the R prompt.

# Numeric
x <- 3.14

# Character
name <- "Data Analysis"

# Logical
is_valid <- TRUE

# Vector
ages <- c(23, 45, 32)

# Data Frame
df <- data.frame(id=1:3, name=c("A","B","C"))

# Function
my_func <- function(x) x^2

One can check the class and structure of R Objects using class() and str() functions.

class(x)    # Check object class
str(df)     # View structure of df object
ls()        # List all objects

R Workspace

It is possible to save individual objects or collections of objects into a named image file. The named image file has an extension of .RData. Some possibilities to save an object from R workspace are:

To save the content of R workspace into a file .RData, type

save.image()

To save objects in the file archive.RData, type

save.image(file = "archive.RData")

To save some required objects in data.RData, type

save(x, y, file = "data.RData")

These image files can be attached to make objects available in the next R session. For example.

attached ("arvhive.RData")

Note that when quitting, R offers the option of saving the workspace image. By default, the workspace is saved in an image file (.RData) in the working directory. The image file can be used in the next R session. Saving the workspace image will save everything from the current workspace. Therefore, use the rm() function to remove objects that are not required in the next R session.

Managing the R Workspace

The R workspace contains all active objects. The essential workspace Commands are:

ls()          # List all objects
rm(x)         # Remove object x
rm(list=ls()) # Clear entire workspace

# Remove multiple objects
rm(list=c("df", "ages"))

# Check memory usage
object.size(df)

Best Practices about R Objects

✔ Regularly clean unused objects
✔ Use meaningful object names
✔ Group related objects in lists

Summary of R Objects, Workspace, and .RData Files

Mastering workspace management in R leads to:

• More reproducible analyses
• Better memory efficiency
• Easier collaboration
• Reduced risk of data loss

Pro Tip: Combine with version control (Git) for maximum reproducibility.

For further details about saving and loading R workspace, visit: Save and Load R Workspace

Learn Statistics and Data Analysis