Last updated on 2023-09-10 | Edit this page
- How can I get help in R?
- 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.
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:
For example take a look at the help file for
write.table(), we will be using a similar function in an
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:
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.
To seek help on special operators, use quotes or backticks:
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
vignette("vignette-name") will open the specified
If a package doesn’t have any vignettes, you can usually find help by
RStudio also has a set of excellent cheatsheets for many packages.
If you’re not sure what package a function is in or how it’s specifically spelled, you can do a fuzzy search:
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:
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.
If you’re having trouble using a function, 9 times out of 10, the
answers you seek have already been answered on Stack Overflow. You can search
[r] tag. Please make sure to see their page on how to ask a good
If you can’t find the answer, there are a few useful functions to help you ask your peers:
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 version 4.3.1 (2023-06-16) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.3 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 locale:  LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8  LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8  LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C  LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C time zone: UTC tzcode source: system (glibc) attached base packages:  stats graphics grDevices utils datasets methods base loaded via a namespace (and not attached):  compiler_4.3.1 tools_4.3.1 rstudioapi_0.15.0 yaml_2.3.7  knitr_1.43 xfun_0.40 renv_1.0.2 evaluate_0.21
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.
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.
To look at the help for the
paste() function, use:
The difference between
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.
 "a c" "b c"
paste(c("a","b"), "c", ",")
 "a c ," "b c ,"
paste(c("a","b"), "c", sep = ",")
 "a,c" "b,c"
paste(c("a","b"), "c", collapse = "|")
 "a c|b c"
paste(c("a","b"), "c", sep = ",", collapse = "|")
(For more information, scroll to the bottom of the
?paste help page and look at the examples, or try
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).
??"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
although you may have to change the
as well if your data file contains hash (#) characters.