Seeking Help

Last updated on 2025-04-29 | Edit this page

Overview

Questions

  • How can I get help in R?

Objectives

  • To be able to read R help files for functions and special operators.
  • To be able to use CRAN task views to identify packages to solve a problem.
  • To be able to seek help from your peers.

Reading Help Files

R, and every package, provide help files for functions. The general syntax to search for help on any function, “function_name”, from a specific function that is in a package loaded into your namespace (your interactive R session) is:

R 

?function_name
help(function_name)

For example take a look at the help file for write.table(), we will be using a similar function in an upcoming episode.

R 

?write.table()

This will load up a help page in RStudio (or as plain text in R itself).

Each help page is broken down into sections:

  • Description: An extended description of what the function does.
  • Usage: The arguments of the function and their default values (which can be changed).
  • Arguments: An explanation of the data each argument is expecting.
  • Details: Any important details to be aware of.
  • Value: The data the function returns.
  • See Also: Any related functions you might find useful.
  • Examples: Some examples for how to use the function.

Different functions might have different sections, but these are the main ones you should be aware of.

Notice how related functions might call for the same help file:

R 

?write.table()
?write.csv()

This is because these functions have very similar applicability and often share the same arguments as inputs to the function, so package authors often choose to document them together in a single help file.

Tip: Running Examples

From within the function help page, you can highlight code in the Examples and hit Ctrl+Return to run it in RStudio console. This gives you a quick way to get a feel for how a function works.

Tip: Reading Help Files

One of the most daunting aspects of R is the large number of functions available. It would be prohibitive, if not impossible to remember the correct usage for every function you use. Luckily, using the help files means you don’t have to remember that!

Special Operators

To seek help on special operators, use quotes or backticks:

R 

?"<-"
?`<-`

Getting Help with Packages

Many packages come with “vignettes”: tutorials and extended example documentation. Without any arguments, vignette() will list all vignettes for all installed packages; vignette(package="package-name") will list all available vignettes for package-name, and vignette("vignette-name") will open the specified vignette.

If a package doesn’t have any vignettes, you can usually find help by typing help("package-name").

RStudio also has a set of excellent cheatsheets for many packages.

When You Remember Part of the Function Name

If you’re not sure what package a function is in or how it’s specifically spelled, you can do a fuzzy search:

R 

??function_name

A fuzzy search is when you search for an approximate string match. For example, you may remember that the function to set your working directory includes “set” in its name. You can do a fuzzy search to help you identify the function:

R 

??set

When You Have No Idea Where to Begin

If you don’t know what function or package you need to use CRAN Task Views is a specially maintained list of packages grouped into fields. This can be a good starting point.

Other ways to get help

There are several other ways that people often get help when they are stuck with their R code.

  • Search the internet: paste the last line of your error message or “R” and a short description of what you want to do into your favourite search engine and you will usually find several examples where other people have encountered the same problem and came looking for help.
    • StackOverflow can be particularly helpful for this: answers to questions are presented as a ranked thread ordered according to how useful other users found them to be. You can search using the [r] tag.
    • Take care: copying and pasting code written by somebody else is risky unless you understand exactly what it is doing!
  • Ask somebody “in the real world”. If you have a colleague or friend with more expertise in R than you have, show them the problem you are having and ask them for help.
  • Sometimes, the act of articulating your question can help you to identify what is going wrong. This is known as “rubber duck debugging” among programmers.

Formulating your question

The StackOverflow page on how to ask a good question has tips on what information to include when posting a question on StackOverflow. Some of these are specific to StackOverflow but others are also relevant if you’re asking a colleague or friend for help, or even if just articulating your question to yourself.

R has a few useful functions to help you formulate your question:

R 

?dput

Will dump the data you’re working with into a format that can be copied and pasted by others into their own R session.

R 

sessionInfo()

OUTPUT 

R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

loaded via a namespace (and not attached):
[1] compiler_4.5.0 tools_4.5.0    yaml_2.3.10    knitr_1.50     xfun_0.51
[6] renv_1.1.4     evaluate_1.0.3

Will print out your current version of R, as well as any packages you have loaded. This can be useful for others to help reproduce and debug your issue.

Generative AI

It is increasingly common for people to use generative AI chatbots such as ChatGPT to get help while coding. You will probably receive some useful guidance by presenting your error message to the chatbot and asking it what went wrong. However, the way this help is provided by the chatbot is different. Answers on StackOverflow have (probably) been given by a human as a direct response to the question asked. But generative AI chatbots, which are based on an advanced statistical model, respond by generating the most likely sequence of text that would follow the prompt they are given.

While responses from generative AI tools can often be helpful, they are not always reliable. These tools sometimes generate plausible but incorrect or misleading information, so (just as with an answer found on the internet) it is essential to verify their accuracy. You need the knowledge and skills to be able to understand these responses, to judge whether or not they are accurate, and to fix any errors in the code it offers you.

In addition to asking for help, programmers can use generative AI tools to generate code from scratch; extend, improve and reorganise existing code; translate code between programming languages; figure out what terms to use in a search of the internet; and more. However, there are drawbacks that you should be aware of.

The models used by these tools have been “trained” on very large volumes of data, much of it taken from the internet, and the responses they produce reflect that training data, and may recapitulate its inaccuracies or biases. The environmental costs (energy and water use) of LLMs are a lot higher than other technologies, both during development (known as training) and when an individual user uses one (also called inference). For more information see the AI Environmental Impact Primer developed by researchers at HuggingFace, an AI hosting platform. Concerns also exist about the way the data for this training was obtained, with questions raised about whether the people developing the LLMs had permission to use it. Other ethical concerns have also been raised, such as reports that workers were exploited during the training process.

We recommend that you avoid getting help from generative AI during the workshop for several reasons:

  1. For most problems you will encounter at this stage, help and answers can be found among the first results returned by searching the internet.
  2. The foundational knowledge and skills you will learn in this lesson by writing and fixing your own programs are essential to be able to evaluate the correctness and safety of any code you receive from online help or a generative AI chatbot. If you choose to use these tools in the future, the expertise you gain from learning and practising these fundamentals on your own will help you use them more effectively.
  3. As you start out with programming, the mistakes you make will be the kinds that have also been made – and overcome! – by everybody else who learned to program before you. Since these mistakes and the questions you are likely to have at this stage are common, they are also better represented than other, more specialised problems and tasks in the data that was used to train generative AI tools. This means that a generative AI chatbot is more likely to produce accurate responses to questions that novices ask, which could give you a false impression of how reliable they will be when you are ready to do things that are more advanced.

Challenge 1

Look at the help page for the c function. What kind of vector do you expect will be created if you evaluate the following:

R 

c(1, 2, 3)
c('d', 'e', 'f')
c(1, 2, 'f')

The c() function creates a vector, in which all elements are of the same type. In the first case, the elements are numeric, in the second, they are characters, and in the third they are also characters: the numeric values are “coerced” to be characters.

Challenge 2

Look at the help for the paste function. You will need to use it later. What’s the difference between the sep and collapse arguments?

To look at the help for the paste() function, use:

R 

help("paste")
?paste

The difference between sep and collapse is a little tricky. The paste function accepts any number of arguments, each of which can be a vector of any length. The sep argument specifies the string used between concatenated terms — by default, a space. The result is a vector as long as the longest argument supplied to paste. In contrast, collapse specifies that after concatenation the elements are collapsed together using the given separator, the result being a single string.

It is important to call the arguments explicitly by typing out the argument name e.g sep = "," so the function understands to use the “,” as a separator and not a term to concatenate. e.g.

R 

paste(c("a","b"), "c")

OUTPUT 

[1] "a c" "b c"

R 

paste(c("a","b"), "c", ",")

OUTPUT 

[1] "a c ," "b c ,"

R 

paste(c("a","b"), "c", sep = ",")

OUTPUT 

[1] "a,c" "b,c"

R 

paste(c("a","b"), "c", collapse = "|")

OUTPUT 

[1] "a c|b c"

R 

paste(c("a","b"), "c", sep = ",", collapse = "|")

OUTPUT 

[1] "a,c|b,c"

(For more information, scroll to the bottom of the ?paste help page and look at the examples, or try example('paste').)

Challenge 3

Use help to find a function (and its associated parameters) that you could use to load data from a tabular file in which columns are delimited with “\t” (tab) and the decimal point is a “.” (period). This check for decimal separator is important, especially if you are working with international colleagues, because different countries have different conventions for the decimal point (i.e. comma vs period). Hint: use ??"read table" to look up functions related to reading in tabular data.

The standard R function for reading tab-delimited files with a period decimal separator is read.delim(). You can also do this with read.table(file, sep="\t") (the period is the default decimal separator for read.table()), although you may have to change the comment.char argument as well if your data file contains hash (#) characters.

Resources

Key Points

  • Use help() to get online help in R.