10 Class 17. Intro to R.

Intro to R: Adapted from: https://datacarpentry.org/R-genomics/01-intro-to-R.html

Start RStudio – Let’s start by learning about our tool.

• R-Studio contains 4 windows: the console, scripts, environments, and plots
• Code and workflow are more reproducible if we can document everything that we do.
• Our end goal is to do things in a way that anyone can easily and exactly replicate our workflow and results.

Get started by organizing your work:

• Under the File menu, click on New project, choose New directory, then New project
• Enter a name for this new folder, and choose a convenient location for it. This will be your working directory for the rest of the day (e.g., ~/Bioinform_R)
• Create a new R script (File > New File > R script) and save it in your working directory (e.g. Learn_R-script.R)

Interacting with R

There are two main ways of interacting with R: using the console or by using script files (plain text files that contain your code).

The console window (in RStudio, the bottom left panel) is the place where R is waiting for you to tell it what to do, and where it will show the results of a command. You can type commands directly into the console, but they will be forgotten when you close the session. It is better to enter the commands in the script editor, and save the script. This way, you have a complete record of what you did, you can easily show others how you did it and you can do it again later on if needed. You can copy-paste into the R console, but the Rstudio script editor allows you to ‘send’ the current line or the currently selected text to the R console using the PC keyboard shortcut Ctrl-Enter . On a Mac, hit Command-Return.

If R is ready to accept commands, the R console shows a > prompt. If it receives a command (by typing, copy-pasting or sent from the script editor using Ctrl-Enter), R will try to execute it, and when ready, show the results and come back with a new > prompt to wait for new commands.

If R is still waiting for you to enter more data because it isn’t complete yet, the console will show a + prompt. It means that you haven’t finished entering a complete command. This is because you have not ‘closed’ a parenthesis or quotation. If you’re in Rstudio and this happens, click inside the console window and press Esc; this should help you out of trouble.

Organizing your working directory

You should separate the original data (raw data) from intermediate datasets that you may create for the need of a particular analysis. For instance, you may want to create a data/ directory within your working directory that stores the raw data, and have a data_output/ directory for intermediate datasets and a figure_output/ directory for the plots you will generate.

Creating objects in R

You can get an output from R simply by typing in math in the console

We can also comment on what it is that we’re doing

What happens if we type that same command without the # sign in the front?

Now R is trying to run that sentence as a command, and it doesn’t work. Now we’re stuck over in the console. The + sign means that it’s still waiting for input, so we can’t type in a new command. To get out of this press the Esc key. This will work whenever you’re stuck with that + sign.

It’s great that R is a glorified calculator, but obviously we want to do more interesting things.

To do useful and interesting things, we need to assign values to objects. To create objects, we need to give it a name followed by the assignment operator <- and the value we want to give it.

For instance, instead of adding 3 + 5, we can assign those values to objects and then add them.

<- is the assignment operator. It assigns values on the right to objects on the left. So, after executing x <- 3, the value of x is 3. The arrow can be read as 3 goes into x. You can also use = or ->for assignments but not in all contexts so it is good practice to use <- for assignments. = should only be used to specify the values of arguments in functions, see below.

Exercise

• Change a to a value of 5 and then re-add a and b.
• We can also assign a + b to a new variable, c. How would you do this?

Notes on objects

Objects can be given any name such as x, current_temperature, or subject_id. You want your object names to be explicit and not too long. They cannot start with a number (2x is not valid but x2 is). R is case sensitive (e.g., Genome_length_mb is different from genome_length_mb). There are some names that cannot be used because they represent the names of fundamental functions in R (e.g., if, else, for, see here for a complete list). In general, even if it’s allowed, it’s best to not use other function names (e.g., c, T, mean, data, df, weights). When in doubt, check the help to see if the name is already in use. It’s also best to avoid dots (.) within a variable name as in my.dataset. There are many functions in R with dots in their names for historical reasons, but because dots have a special meaning in R (for methods) and other programming languages, it’s best to avoid them. It is also recommended to use nouns for variable names, and verbs for function names. It’s important to be consistent in the styling of your code (where you put spaces, how you name variables, etc.). In R, two popular style guides are Hadley Wickham’s and Google’s.

When assigning a value to an object, R does not print anything. You can force to print the value by using parentheses or by typing the name:

Functions

The other key feature of R are functions. These are R’s built in capabilities. Some examples of these are mathematical functions, like sqrt and round. You can also get functions from libraries (which we’ll talk about in a bit), or even write your own.

Functions are “canned scripts” that automate something complicated or convenient or both. Many functions are predefined, or become available when using the function library() (more on that later). A function usually gets one or more inputs called arguments. Functions often (but not always) return a value. A typical example would be the function sqrt(). The input (the argument) must be a number, and the return value (in fact, the output) is the square root of that number. Executing a function (‘running it’) is called calling the function. An example of a function call is:

sqrt(a)

Here, the value of a is given to the sqrt() function, the sqrt() function calculates the square root. This function is very simple, because it takes just one argument.

The return ‘value’ of a function need not be numerical (like that of sqrt()), and it also does not need to be a single item: it can be a set of things, or even a data set. We’ll see that when we read data files in to R.

Arguments can be anything, not only numbers or filenames, but also other objects. Exactly what each argument means differs per function, and must be looked up in the documentation (see below). If an argument alters the way the function operates, such as whether to ignore ‘bad values’, such an argument 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.

Let’s try the round function that can take multiple arguments.

We can see that we get 3. That’s because the default is to round to the nearest whole number. If we want more digits we can see how to do that by getting information about the round function. We can use args(round) or look at the help for this function using ?round.

We see that if we want a different number of digits, we can type digits=2 or however many we want.

If you provide the arguments in the exact same order as they are defined 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.

Vectors and data types

A vector is the most common and basic data structure in R, and is pretty much the workhorse of R. It’s basically just a list of values, mainly either numbers or characters. They’re special lists that you can do math with. You can assign this list of values to a variable, just like you would for one item. For example we can create a vector of genome lengths of three different species:

A vector can also contain characters:

There are many functions that allow you to inspect the content of a vector. length() tells you how many elements are in a particular vector:

We can find the average of a vector of numeric values by using the mean function. You can put the object name into this function, or specify the numeric values directly.

You can also do math with whole vectors. For instance, if we wanted to multiply the genome lengths of all the genomes in the list, we can do

or we can add the data in the two vectors together

This is very useful if we have data in different vectors that we want to combine or work with.

There are few ways to figure out what’s going on in a vector. If you view the “ENVIRONMENT” window in the top right corner of R-studio, you will see a list of the objects and information about what they contain. You can also access this information by using several functions:

class() indicates the class (the type of element) of an object:

The function str() provides an overview of the object and the elements it contains. It is a really useful function when working with large and complex objects:

You can add elements to your vector simply by using the c() function:

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. As we program, this may be useful to autoupdate results that we are collecting or calculating.

We just saw 2 of the 6 data types that R uses: “character” and “numeric”. The other 4 are:

• “logical” for TRUE and FALSE (the boolean data type)
• “integer” for integer numbers (e.g., 2L, the L indicates to R that it’s an integer)
• “complex” to represent complex numbers with real and imaginary parts (e.g., 1+4i) and that’s all we’re going to say about them
• “raw” that we won’t discuss further

Vectors are one of the many data structures that R uses. Other important ones are lists (list), matrices (matrix), data frames (data.frame) and factors (factor).

Seeking help

I know the name of the function I want to use, but I’m not sure how to use it

If you need help with a specific function, let’s say barplot(), you can type:

If you just need to remind yourself of the names of the arguments, you can use:

If the function is part of a package that is installed on your computer but don’t remember which one, you can type:

I want to use a function that does X, there must be a function for it but I don’t know which one…

If you are looking for a function to do a particular task, you can use help.search() (but only looks through the installed packages):

If you can’t find what you are looking for, you can use the rdocumention.org website that search through the help files across all packages available.

I am stuck… I get an error message that I don’t understand

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”).

However, you should check stackoverflow.com. 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: http://stackoverflow.com/questions/tagged/r

The Introduction to R 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 R FAQ is dense and technical but it is full of useful information.

Asking for help

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.

Where to ask for help?

• Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
• Stackoverflow: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 min.
• The R-help: 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.
• 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.
• There are also some topic-specific mailing lists (GIS, phylogenetics, etc…), the complete list is here.