R syntax and Datatypes

Assignment operator

<- is the assignment operator; it is like an arrow that points from the value to the object assigning values on the right to objects on the left. Mostly similar to = but not always. Learn to use <- as it is good programming practice. Using = in place of <- can lead to issues down the line.

Shortcut, typing Alt + - (push Alt, the key next to your space bar at the same time as the - key) will write <- in a single keystroke.

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

3 + 5
12/7

However, 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:

weight_kg <- 55

Naming variables

• 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) nor can they contain hyphens.
• R is case sensitive (e.g., weight_kg is different from Weight_kg).
• There are some names that cannot be used because they are reserved the names of fundamental functions in R (e.g., if, else, for, see here for a complete list).
• Even if it's allowed, it's best to not use other function names (e.g., c, T, mean, data, df, weights). 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

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

(weight_kg <- 55)
weight_kg

Now that R has weight_kg in memory, we can do arithmetic with it. For instance, we may want to convert this weight in pounds (weight in pounds is 2.2 times the weight in kg):

2.2 * weight_kg

We can also change a variable's value by assigning it a new one:

weight_kg <- 57.5
2.2 * weight_kg

## [1] 126.5

This means that assigning a value to one variable does not change the values of other variables. For example, let's store the animal's weight in pounds in a variable.

weight_lb <- 2.2 * weight_kg

and then change weight_kg to 100.

weight_kg <- 100

What do you think is the current content of the object weight_lb? 121 or 220?

Note: When typing out a variable that is stored try pressing the Tab key after typing only weight. You will find that RStudio will tab-complete the variable name.

mass <- 47.5           # mass?
age  <- 122            # age?
mass <- mass * 2.0     # mass?
age  <- age - 20       # age?
massIndex <- mass/age  # massIndex?

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 a group of values, mainly either numbers or characters (or can be both). 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 animal weights:

weights <- c(50, 60, 65, 82)

A vector can also contain characters:

animals <- c("mouse", "rat", "dog")

Alternatively a vector can contain both:

scores <- c("A", "B", 80, 60, 20)
scores

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(weights)
length(animals)

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

class(weights)

## [1] "numeric"

class(animals)

## [1] "character"

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(weights)

##  num [1:4] 50 60 65 82

str(animals)

##  chr [1:3] "mouse" "rat" "dog"

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

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

## [1] 30 50 60 65 82 90

What happens here is that we take the original vector weights, 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; true and false values do not require quotations)
• "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).

Functions and their arguments

Functions are "self contained" modules of code that accomplish a specific task. Functions usually take in data, process it, and return a result. We have already used a few examples of basic functions above i.e length, class and str.

Let's look at a more advanced function call below read.csv. This function is used to read in data from a csv (comma separated values) file. There are numerous other functions to load in data depending on your filetype, we will discuss this in detail in the next section.

Note: When typing out read.csv try pressing the Tab key after typing only read. You will find that a drop-down menu will appear listing all read options for loading in files. The window to the right gives you more information on the function and its arguments as you scroll down and highlight each individually.

Why does this not work?

read.csv(file='mouse_exp_design.csv') # do not assign it to a variable yet

## Warning in file(file, "rt"): cannot open file 'mouse_exp_design.csv': No
## such file or directory

## Error in file(file, "rt"): cannot open the connection

We need to make sure that the path to our file is correct. First check what our working directory is, then set the path accordingly.

getwd()
read.csv(file="meta//mouse_exp_design.csv")

The file= part inside the parentheses is called an argument, and most functions use arguments. Arguments modify the behavior of the function. Typically, they take some input (e.g., some data, an object) and other options to change what the function will return, or how to treat the data provided. Simple functions like the function length don't need additional modifications. It will take input (i.e. vector), process it (i.e. count the number of elements) and return value.

Most functions can take several arguments, but most are specified by default so you don't have to enter them. To see these default values, you can either type args(read.csv) or look at the help for this function (e.g., ?read.csv).

args(read.csv)

## function (file, header = TRUE, sep = ",", quote = "\"", dec = ".", 
##     fill = TRUE, comment.char = "", ...) 
## NULL

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

read.csv(file="meta/mouse_exp_design.csv", header=TRUE) # is identical to:
read.csv("meta/mouse_exp_design.csv", TRUE)

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. If you include names do not use the assignment operator. A = should be used to specify the values of arguments in functions. (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:

read.csv(file="meta/mouse_exp_design.csv", header=TRUE) # is identical to:
read.csv(header=TRUE, file="meta/mouse_exp_design.csv")

Packages and Libraries

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:

sessionInfo()

R version 3.1.2 (2014-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

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. An example is given below as we install the gplots package required for some images we will create later on.

install.packages('gplots')

Alternatively, packages can also be installed from Bioconductor by using the biocLite.R installation script. You will first need to install Bioconductor and all the standard packages (this only needs to be done once ever):

source("http://bioconductor.org/biocLite.R")
biocLite()

Once you have the standard installed, you can add 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 will install another package useful for visualization ggplot2:

biocLite('ggplot2')

Once you have the package installed, you can load it into your R session for use. Note that quotations are not required here.

library(gplots)
library(ggplot2)