Introduction to R

The R syntax

Start by showing an example of a script

• Point to the different parts:
• a function
• the assignment operator <-
• the = for arguments
• the comments # and how they are used to document function and its content
• the $ operator
• Point to indentation and consistency in spacing to improve clarity

Creating objects

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

3 + 5
12/7

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

# I'm adding 3 and 5. R is fun!
3+5

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

I'm adding 3 and 5. R is fun!
3+5

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.

# assign 3 to a
a <- 3
# assign 5 to b
b <- 5

# what is the value of a
a
# what is the value of b
b

#Add a and b
a + b

<- 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.

In RStudio, typing Alt + - (push Alt at the -same time as the - key) will write <- in a single keystroke in a PC, while typing Option + - (push Option at the same time as the - key) does the same in a Mac.

Challenge

• What happens if you change the value assigned to a and then re-add a and b?
• We can assign the result of 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:

# Assigns a value to a variable
genome_size_mb <- 35

# Assigns a value to a variable and prints it out on the console
(genome_size_mb <- 35)

# Prints out the value of a variable on the console
genome_size_mb

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 a function that can take multiple arguments round.

round(3.14159)

## [1] 3

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.

args(round)

## function (x, digits = 0) 
## NULL

?round

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

round(3.14159, digits=2)

## [1] 3.14

If you provide the arguments in the exact same order as they are defined you don’t have to name them:

round(3.14159, 2)

## [1] 3.14

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.

Challenge

We’re going to work with genome lengths. - Create a variable genome_length_mb and assign it the value 4.6

genome_length_mb <- 4.6
genome_length_mb

genome_length_mb / 978.0

genome_length_mb <- 3000.0
genome_length_mb / 978.0

genome_weight_pg <- genome_length_mb / 978.0

genome_length_mb <- 100

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:

glengths <- c(4.6, 3000, 50000)
glengths

A vector can also contain characters:

species <- c("ecoli", "human", "corn")
species

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:

length(glengths)
length(species)

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

5 * glengths

or we can add the data in the two vectors together

new_lengths <- glengths + glengths
new_lengths

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.

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

class(glengths)
class(species)

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:

str(glengths)
str(species)

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

lengths <- c(glengths, 90) # adding at the end
lengths <- c(30, glengths) # adding at the beginning
lengths

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

?barplot

args(lm)

??geom_point

help.search("kruskal")

dput(head(iris)) # iris is an example data.frame that comes with R

## structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4), 
##     Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4, 
##     1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2, 
##     0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L, 
##     1L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
## "Sepal.Width", "Petal.Length", "Petal.Width", "Species"), row.names = c(NA, 
## 6L), class = "data.frame")

saveRDS(iris, file="/tmp/iris.rds")

some_data <- readRDS(file="~/Downloads/iris.rds")

sessionInfo()

## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  base     
## 
## loaded via a namespace (and not attached):
##  [1] compiler_3.4.3  backports_1.1.2 magrittr_1.5    rprojroot_1.3-1
##  [5] tools_3.4.3     htmltools_0.3.6 yaml_2.1.16     Rcpp_0.12.14   
##  [9] stringi_1.1.6   rmarkdown_1.8   knitr_1.17      methods_3.4.3  
## [13] stringr_1.2.0   digest_0.6.13   evaluate_0.10.1