Chapter 2 Getting Started with R

Learning Objectives

  • Create R objects and and assign values to them.
  • Use comments to inform script.
  • Do simple arithmetic operations in R using values and objects.
  • Call functions with arguments and change their default options.
  • Inspect the content of vectors and manipulate their content.
  • Subset and extract values from vectors.
  • Correctly define and handle missing values in vectors.
  • Use the built-in RStudio help interface
  • Interpret the R help documentation
  • Provide sufficient information for troubleshooting with the R user community.
  • Download, install, and load R packages.

2.1 Creating objects in R

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

weight_kg <- 55

<- is the assignment operator. It assigns values on the right to objects on the left. So, after executing weight_kg <- 55, the value of weight_kg is 55. The arrow can be read as 55 goes into weight_kg. You can also use = for assignments, but not in every context. Because of the slight differences in syntax, it is good practice to always use <- for assignments.

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

Here are a few rules as of how to name objects in R.

  • 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., weight_kg is different from Weight_kg).
  • There are some names that cannot be used because they are the names of fundamental functions in R (e.g., if, else, for, see here for a complete list). In general, even if it is allowed, it’s best to not use other function names (e.g., c, T, mean, data, df, weights). If 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 is 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.). Using a consistent coding style makes your code clearer to read for your future self and your collaborators.
    In R, three popular style guides are Google’s, Jean Fan’s and the tidyverse’s. The tidyverse’s is very comprehensive and may seem overwhelming at first. You can install the lintr to automatically check for issues in the styling of your code.

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

weight_kg <- 55    # doesn't print anything
(weight_kg <- 55)  # but putting parenthesis around the call prints the value of `weight_kg`
weight_kg          # and so does typing the name of the object

Now that R has weight_kg in memory, we can do arithmetic with it. For instance, we may want to convert this weight into 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 <- 100
2.2 * weight_kg

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

weight_lb <- 2.2 * weight_kg

and then change weight_kg to 50.

weight_kg <- 50

Challenge

What do you think is the current content of the object weight_lb? 110 or 220?

2.1.1 Comments

The comment character in R is #, anything to the right of a # in a script will be ignored by R. It is useful to leave notes, and explanations in your scripts. RStudio makes it easy to comment or uncomment a paragraph: after selecting the lines you want to comment, press at the same time on your keyboard Ctrl + Shift + C. If you only want to comment out one line, you can put the cursor at any location of that line (i.e. no need to select the whole line), then press Ctrl + Shift + C.

Challenge

What are the values after each statement in the following?

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

2.1.2 Functions and their arguments

Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc.

They all have in common that they are executed by typing their name followed by round brackets, in which we provide one or more parameters (or arguments) for the function to do something, separated by commas.

Many functions are predefined, or can be made available by importing R packages (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:

a <- 64
b <- sqrt(a)

Here, we assign the value 64 to he variable a, which is then given to the sqrt() function. The sqrt() function calculates the square root, and returns the value which is then assigned to variable b. 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 dataset. We’ll see that when we read data files into 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). Some functions take arguments which may either be specified by the user, or, if left out, take on a default value: these are called options. Options are typically used to alter the way the function operates, such as whether it ignores ‘bad values’, or what symbol to use in a plot. However, if you want something specific, you can specify a value of your choice which will be used instead of the default.

Let’s try a function that can take multiple arguments: round().

round(3.14159)
#> [1] 3

Here, we’ve called round() with just one argument, 3.14159, and it has returned the value 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

And if you do name the arguments, you can switch their order:

round(digits = 2, x = 3.14159)
#> [1] 3.14

Note:

  • R evaluates function arguments in three steps: first, by exact matching on argument name, then by partial matching on argument name, and finally by position.

  • you do not have to specify all of the arguments. If you don’t, R will use default values if they are specified by the function. If no default value is specified, you will receive an error.

It’s good practice to put the non-optional arguments (like the number you’re rounding) first in your function call, and to specify the names of all optional arguments. If you don’t, someone reading your code might have to look up the definition of a function with unfamiliar arguments to understand what you’re doing.

Functions usually return someting back to you as output. Whatever they return (a table, some informational text, a logical value, …) is by default written to the console, so you can see it right away.

Oftentimes, however, we want re-use the output of such a function. That is when you assign the output to an R object to be accessed later on.

2.1.3 Objects vs. variables

What are known as objects in R are known as variables in many other programming languages. Depending on the context, object and variable can have drastically different meanings. However, in this lesson, the two words are used synonymously. For more information see: https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects

2.2 Vectors and data types

A vector is the most common and basic data type in R, and is pretty much the workhorse of R. A vector is composed by a series of values, which can be either numbers or characters. We can assign a series of values to a vector using the c() function. For example we can create a vector of weights and assign it to a new object weight_g:

weight_g <- c(21, 34, 39, 54, 55)
weight_g
#> [1] 21 34 39 54 55

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(weight_g)
#> [1] 5

An important feature of a vector, is that all of the elements are the same type of data. The function class() indicates the class (the type of element) of an object:

class(weight_g)
#> [1] "numeric"

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

str(weight_g)
#>  num [1:5] 21 34 39 54 55

You can use the c() function to add other elements to your vector:

weight_g <- c(weight_g, 90) # add to the end of the vector
weight_g <- c(30, weight_g) # add to the beginning of the vector
weight_g
#> [1] 30 21 34 39 54 55 90

In the first line, we take the original vector weight_g, add the value 90 to the end of it, and save the result back into weight_g. Then we add the value 30 to the beginning, again saving the result back into weight_g.

We can do this over and over again to grow a vector, or assemble a dataset. As we program, this may be useful to add results that we are collecting or calculating.

A vector can also contain characters:

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

The quotes around “mouse”, “rat”, etc. are essential here. Without the quotes R will assume there are objects called mouse, rat and dog. As these objects don’t exist in R’s memory, there will be an error message.

Lastly, we will introduce a vector with logical values (the boolean data type).

has_tail <- c(TRUE, TRUE, TRUE, FALSE)
has_tail 

We just saw 3 of the 6 main atomic vector types (or data types) that R uses: "character", "numeric" and "logical". These are the basic building blocks that all R objects are built from. The other 3 are:

  • "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

Challenge

  • We’ve seen that atomic vectors can be of type character, numeric, integer, and logical. But what happens if we try to mix these types in a single vector?

  • What will happen in each of these examples? (hint: use class() to check the data type of your objects):

    num_char <- c(1, 2, 3, 'a')
    num_logical <- c(1, 2, 3, TRUE)
    char_logical <- c('a', 'b', 'c', TRUE)
    tricky <- c(1, 2, 3, '4')
  • Why do you think it happens?

  • You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

2.3 Subsetting vectors

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

animals[2]
#> [1] "rat"
animals[c(3, 2)]
#> [1] "dog" "rat"

: is a special function that creates numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance. We can use this to select a sequence, like this:

animals[2:4]
#> [1] "rat"  "dog"  "bear"

You can exclude elements of a vector using the “-” sign:

animals[-2]
#> [1] "mouse" "dog"   "bear"
animals[-c(1:3)]
#> [1] "bear"

We can also repeat the indices to create an object with more elements than the original one:

more_animals <- animals[c(1, 2, 3, 2, 1, 4)]
more_animals
#> [1] "mouse" "rat"   "dog"   "rat"   "mouse" "bear"

R indices start at 1. Programming languages like Fortran, MATLAB, Julia, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

2.3.1 Conditional subsetting

Another common way of subsetting is by using a logical vector. TRUE will select the element with the same index, while FALSE will not.

has_tail
#> [1]  TRUE  TRUE  TRUE FALSE
animals[has_tail]
#> [1] "mouse" "rat"   "dog"

Typically, these logical vectors are not typed out by hand, but are the output of other functions or logical tests.

A typical example is to search for certain strings in a vector. One could use the “or” operator | to test for equality to multiple values, but this can quickly become tedious. The function %in% allows you to test if any of the elements of a search vector are found:

animals[animals == "bear" | animals == "rat"] # returns both rat and bear
#> [1] "rat"  "bear"
animals %in% c("rat", "cat", "dog", "duck", "goat")
#> [1] FALSE  TRUE  TRUE FALSE
animals[animals %in% c("rat", "cat", "dog", "duck", "goat")]
#> [1] "rat" "dog"

Equivalently, if you wanted to select only the weights above 50:

weight_g > 50    # will return logicals with TRUE for the indices that meet the condition
#> [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
## so we can use this to select only the values above 50
weight_g[weight_g > 50]
#> [1] 54 55 90

You can combine multiple tests using & (both conditions are true, AND) or | (at least one of the conditions is true, OR):

weight_g[weight_g < 30 | weight_g > 50]
#> [1] 21 54 55 90
weight_g[weight_g >= 30 & weight_g <= 90]
#> [1] 30 34 39 54 55 90

Here, < stands for “less than”, > for “greater than”, >= for “greater than or equal to”, and == for “equal to”. The double equal sign == is a test for numerical equality between the left and right hand sides, and should not be confused with the single = sign, which performs variable assignment (similar to <-).

Challenge

  • Can you figure out why "four" > "five" returns TRUE?

2.4 Missing data

As R was designed to analyze datasets, it includes the concept of missing data (which is uncommon in other programming languages). Missing data are represented in vectors as NA.

When doing operations on numbers, most functions will return NA if the data you are working with include missing values. This feature makes it harder to overlook the cases where you are dealing with missing data. You can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.

heights <- c(2, 4, 4, NA, 6)
max(heights)
#> [1] NA
sum(heights)
#> [1] NA
max(heights, na.rm = TRUE)
#> [1] 6
sum(heights, na.rm = TRUE)
#> [1] 16

If your data include missing values, you may want to become familiar with the functions is.na(), na.omit(), and complete.cases(). See below for examples.

# Extract elements which are not missing values.
heights[!is.na(heights)]
#> [1] 2 4 4 6
# Returns the object with incomplete cases removed. The returned object is atomic.
na.omit(heights)
#> [1] 2 4 4 6
#> attr(,"na.action")
#> [1] 4
#> attr(,"class")
#> [1] "omit"
# Extract elements which are complete cases.
heights[complete.cases(heights)]
#> [1] 2 4 4 6

Challenge

  1. Using this vector of length measurements, create a new vector with the NAs removed.

    lengths <- c(10,24,NA,18,NA,20)
  2. Use the function median() to calculate the median of the lengths vector.

2.5 Common R Data Structures

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

2.5.1 Matrix

If we arrange data elements of a vector in a two-dimensional rectangular layout we have a matrix. To construct a matrix, we use a function conveniently called matrix().

y <- matrix(1:20, nrow=5,ncol=4) # generates 5 x 4 numeric matrix

Subset a matrix with [row , column]:

y[,4]       # 4th column of matrix
y[3,]       # 3rd row of matrix
y[2:4,1:3]  # rows 2,3,4 of columns 1,2,3

2.5.2 List

Lists can have elements of any type. Here is how we construct lists. You may have guessed that to construct a list, we use the list() function:

myl <- list(id="ID_1", a_vector=animals, a_matrix=y, age=5.3) # example of a list with 4 components
myl[[2]] # 2nd component of the list
myl[["id"]] # component named id in list

2.5.3 Data frame

Data frames in R are a special case of lists, as they can have elements of any type, but they have to all be of the same length.

A data frame is the most common way of storing tabular data in R and something you will likely deal with a lot. As a first approximation, which holds true, probably in the most cases, you can really think of it as a table or a spreadsheet.

Here is how you could construct a data frame.

mydf <- data.frame(ID=c(1:4),
                   Color=c("red", "white", "red", NA),
                   Passed=c(TRUE,TRUE,TRUE,FALSE),
                   Weight=c(99, 54, 85, 70),
                   Height=c(1.78, 1.67, 1.82, 1.59))

mydf

We will go into more detail about data frames. For now, try the following:

Challenge

  1. Create a data frame that holds the following information for yourself, your right and your left neighbor:
  • first name
  • last name
  • lucky number
  1. There are a few mistakes in this hand-crafted data.frame, can you spot and fix them? Don’t hesitate to experiment!

    animal_data <- data.frame(animal=c("dog", "cat", "sea cucumber", "sea urchin),
                              feel=c("furry", "squishy", "spiny"),
                              weight=c(45, 8 1.1, 0.8))

2.6 Extending R base functionality

R comes with a base system and some contributed core packages. This is what you just downloaded. The functionality of R can be significantly extended by using additional contributed packages. Those packages typically contain commands (functions) for more specialized tasks. They can also contain example datasets. We will make use of external packages later.

2.6.1 Installing additional packages

To install additional packages there are two main options:

  1. You can use the RStudio interface like this:
How to install an R package with the RStudio interfaceHow to install an R package with the RStudio interface

Figure 2.1: How to install an R package with the RStudio interface

  1. You can install from the R console like this:
# to install a package called "lubridate", for example: (more on this later)
install.packages("lubridate", dependencies = TRUE)

2.6.2 Make use of the installed packages

In order to actually use commands from the installed packages you also will need to load the installed packages. This can be automated (whenever you launch R it will also load the libraries for you - see for example here) or otherwise you need to sumbit a command:

library(lubridate)

or

require(lubridate)

The difference between the two is that library will result in an error, if the library does not exist, whereas require will result in a warning.

Challenge

  1. Google for an R package that might be of interest for your research.
  2. Install and load it into R.

2.7 Seeking help

2.7.1 Use the built-in RStudio help interface

The RStudio help interface

Figure 2.2: The RStudio help interface

One of the most immediate ways to get help, is to use the RStudio help interface (Figure 2.2. In the default conficuration this panel by default can be found at the lower right hand panel of RStudio. As seen in the screenshot, by typing the word “Mean”, RStudio tries to also give a number of suggestions that you might be interested in. The description is then shown in the display window.

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

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

?barplot

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

args(lm)

2.7.3 There must be a function to do X but I don’t know which one…

If you are looking for a function to do a particular task, you can use the help.search() function, which is called by the double question mark ??. However, this only looks through the installed packages for help pages with a match to your search request

??kruskal

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

Finally, a generic Google or internet search “R <task>” will often either send you to the appropriate package documentation or a helpful forum where someone else has already asked your question.

2.7.4 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”). If the message is very generic, you might also include the name of the function or package you’re using in your query.

However, you should check Stack Overflow. 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.

2.7.5 How to ask for help

The key to receiving help from someone is for them to rapidly grasp your problem. 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. For instance instead of using a subset of your real dataset, create a small (3 columns, 5 rows) generic one. For more information on how to write a reproducible example see this article by Hadley Wickham.

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:

dput(head(iris)) # iris is an example data frame that comes with R and head() is a function that returns the first part of the data frame
#> 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")), row.names = c(NA, 
#> 6L), class = "data.frame")

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 R object to a file:

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

The content of this file is however not human readable and cannot be posted directly on Stack Overflow. Instead, it can be sent to someone by email who can read it with the readRDS() command (here it is assumed that the downloaded file is in a Downloads folder in the user’s home directory):

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

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.

sessionInfo()
#> R version 3.5.2 (2018-12-20)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Mojave 10.14.2
#> 
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] compiler_3.5.2   magrittr_1.5     bookdown_0.9     tools_3.5.2     
#>  [5] htmltools_0.3.6  rstudioapi_0.9.0 yaml_2.2.0       Rcpp_1.0.0      
#>  [9] stringi_1.2.4    rmarkdown_1.11   highr_0.7        knitr_1.21      
#> [13] stringr_1.3.1    xfun_0.4         digest_0.6.18    evaluate_0.12

2.7.6 Where to ask for help?

  • The person sitting next to you during the workshop. Don’t hesitate to talk to your neighbor during the workshop, compare your answers, and ask for help. You might also be interested in organizing regular meetings following the workshop to keep learning from each other.
  • Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
  • Stack Overflow: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 min. Remember to follow their guidelines on how to ask a good question.
  • The R-help mailing list: 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 anywhere 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, or open an issue on the code repository (e.g., GitHub).
  • There are also some topic-specific mailing lists (GIS, phylogenetics, etc…), the complete list is here.

2.7.7 Resources on getting help