Functions in R
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The other key feature of R is functions. Functions are “self contained” modules of code that accomplish a specific task. Functions usually take in some sort of data structure (value, vector, dataframe, etc.), process it, and return a result.
The input(s) are called arguments and can be anything, not only numbers or characters, but also other data structures. Exactly what each argument means differs per function, and must be looked up in the documentation (we will discuss help options at the end of Functions session). If an argument alters the way the function operates, such as whether to ignore ‘bad values’, it is sometimes called an option.
Most functions can take several arguments, but many have so-called defaults. If you don’t specify such an argument when calling the function, the function itself will fall back on using the default. This is a standard value that the author of the function specified as being “good enough in standard cases”. An example would be what symbol to use in a plot. However, if you want something specific, simply change the argument yourself with a value of your choice.
We have already used a few examples of basic functions in the previous lessons i.e c(), and factor(). These functions are available as part of R’s built in capabilities, and we will explore a few more of these base functions below. You can also get functions from libraries (which we’ll talk about in a bit), or even write your own.
Let’s revisit a function that we have used previously to combine data, c(). The arguments it takes is any number of numbers, characters or strings and performs the task of combining them into a single vector. You can also use it to add elements to an existing vector:
What happens here is that we take the original vector glengths, and we are adding another item first to the end of the other ones, and then another item at the beginning. We can do this over and over again to build a vector or a dataset.
Since R is used for statistical computing, many of the base functions involve mathematical operations. One example would be the function sqrt(). The input (argument) must be a number, and the output is the square root of that number. Let’s try finding the square root of 81:
Executing a function (or ‘running it’) is referred to as calling the function.
Now what would happen if we called the function on a vector of values instead of a single value?
In this case the task was performed on each individual value of the vector number and the respective results were displayed.
Let’s try a function that we can change some of the options, for example round:
We can see that we get 3. That’s because the default is to round to the nearest whole number. If we want a different number of digits, we can type digits=2 or however many we may want.
If you provide the arguments in the exact same order as they are defined (in the help manual) you don’t have to name them:
However, it’s usually not recommended practice because it’s a lot of remembering to do, and if you share your code with others that includes less known functions it makes your code difficult to read (It’s however OK to not include the names of the arguments for basic functions like mean, min, etc…). Another advantage of naming arguments, is that the order doesn’t matter. This is useful when there start to be more arguments
Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. The two terms are sometimes used synonomously and there has been discussion amongst the community to resolve this. It is somewhat counter-intuitive to load a package using the library() function and so you can see how confusion can arise.
There are a set of standard (or base) packages which are considered part of the R source code and automatically available as part of your R installation. Base packages contain the basic functions that allow R to work, and enable standard statistical and graphical functions on datasets; for example all of the functions that we have been using so far in our examples.
You can check what base packages are loaded by typing into the console:
In this course we will mostly be using functions from the standard base packages. However, the more you work with R you will come to realize that there is a cornucopia of R packages that offer a wide variety of functionality. To use additional packages will require installation.
Packages for R can be installed from the CRAN package repository using the install.packages function. An example is given below for the ggplot2 package that will be required for some images we will create later on. If you do not have access to internet, do not run this code. Instead we will install from source
Alternatively, packages can also be installed from Bioconductor, another repository of packages but mostly pertaining to genomic data analysis. There are many packages that are available in CRAN and Bioconductor, but there are also packages that are specific to one repository. Generally, you can find out this information with a Google search or by trial and error. To install from Bioconductor, you will first need to install Bioconductor and all the standard packages. This only needs to be done once ever for your R installation. For older versions of R (R < 3.5.0):
Once you have the standard packages installed, you can install additional packages using the biocLite.R script. If it’s a new R session you will also have to source the script again. Here we show that the same package ggplot2 is available through Bioconductor:
The current release of Bioconductor is version 3.16; it works with R version 4.2.2. To get the latest version of Bioconductor by entering the commands:
To install core packages, type the following in an R command window:
Install specific packages, e.g., DESeq2
Finally, R packages can also be installed from source. This is useful when you do not have an internet connection (and have the source files locally), since the other two methods are retrieving the source files from remote sites. For this class, we can install ggplot2 from source, because we have provided for you a compressed file containing all the required information to build and install the package into your environment. First locate the file ggplot2_3.1.1.tar.gz in your directory. To install it, we use the same install.packages function, but we have additional arguments that need to be changed from defaults:
Suppose we didn’t know how to use the round function and wanted more digits; the best way of finding out this information is to use the ? followed by the name of the function. Doing this will open up the help manual in the bottom right panel of RStudio:
If you know the function, but just need to remind yourself of the names of the arguments, you can use:
If you are looking for a function to do a particular task, you can use help.search() (but only looks through the installed packages):
Start by googling the error message. However, this doesn’t always work very well because often, package developers rely on the error catching provided by R. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”).
The key to get help from someone is for them to grasp your problem rapidly. You should make it as easy as possible to pinpoint where the issue might be.
Try to use the correct words to describe your problem. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. Be as precise as possible when describing your problem.
If possible, try to reduce what doesn’t work to a simple reproducible example. If you can reproduce the problem using a very small data.frame instead of your 50,000 rows and 10,000 columns one, provide the small one with the description of your problem. When appropriate, try to generalize what you are doing so even people who are not in your field can understand the question.
To share an object with someone else, if it’s relatively small, you can use the function dput(). It will output R code that can be used to recreate the exact same object as the one in memory:
If the object is larger, provide either the raw file (i.e., your CSV file) with your script up to the point of the error (and after removing everything that is not relevant to your issue). Alternatively, in particular if your question is not related to a data.frame, you can save any other R data structure that you have in your environment to a file:
The content of this file is however not human readable and cannot be posted directly on stackoverflow. It can however be sent to someone by email who can read it with this command:
Last, but certainly not least, always include the output of sessionInfo() as it provides critical information about your platform, the versions of R and the packages that you are using, and other information that can be very helpful to understand your problem.
Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
If your question is about a specific package, see if there is a mailing list for it. Usually it’s included in the DESCRIPTION file of the package that can be accessed using packageDescription("name-of-package"). You may also want to try to email the author of the package directly.
If you can’t find what you are looking for, you can use the that searches through the help files across all packages available.
However, you should check . Search using the R tag. Most questions have already been answered, but the challenge is to use the right words in the search to find the answers in stackoverflow.
The can also be dense for people with little programming experience but it is a good place to understand the underpinnings of the R language.
The is dense and technical but it is full of useful information.
: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 min.
: it is read by a lot of people (including most of the R core team), a lot of people post to it, but the tone can be pretty dry, and it is not always very welcoming to new users. If your question is valid, you are likely to get an answer very fast but don’t expect that it will come with smiley faces. Also, here more than everywhere else, be sure to use correct vocabulary (otherwise you might get an answer pointing to the misuse of your words rather than answering your question). You will also have more success if your question is about a base function rather than a specific package.
There are also some topic-specific mailing lists (GIS, phylogenetics, etc…), the complete list is .
The for the R mailing lists.
The "" site provides useful guidelines.
