01_Getting_Started_with_R.RmdWe’re now all going to log in to the class server. A server is a remote computer - this enables us to do computing tasks without having to use up our own compute resources. It also enables me, as the professor, to control the user environment, and ensure everyone gets the same user experience.
Navigate to:
http://class.selucompbio2019.org:8787/
You will be prompted for a username and password. You username will be your first name, and your password will be selucomp.
What you see is the four-panel RStudio default. Let’s talk about each of these panels.
Under Tools, choose Shell
In the window that pops up, type:
passwdYou will be prompted to enter your default password and a new one. As you type nothing will appear. Words are still being entered, but the display hides them.
It is good practice to keep a set of related data, analyses, and text self-contained in a single folder, called the working directory. All of the scripts within this folder can then use relative paths to files that indicate where inside the project a file is located (as opposed to absolute paths, which point to where a file is on a specific computer). Working this way makes it a lot easier to move your project around on your computer and share it with others without worrying about whether or not the underlying scripts will still work.
RStudio provides a helpful set of tools to do this through its “Projects” interface, which not only creates a working directory for you, but also remembers its location (allowing you to quickly navigate to it) and optionally preserves custom settings and open files to make it easier to resume work after a break. Go through the steps for creating an “R Project” for this tutorial below.
https://github.com/Paleantology/BiologicalDataAnalysis2019
Click on Create Project.
(Optional) Set Preferences to ‘Never’ save workspace in RStudio.
Using a consistent folder structure across your projects will help keep things organized, and will also make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you may create directories (folders) for scripts, data, and documents.
You may want additional directories or subdirectories depending on your project needs, but these should form the backbone of your working directory.
In the lower left window, you should now see a file explorer. Open
the Vignettes folder, and double click
01_Getting_Started_with_R.Rmd
The basis of programming is that we write down instructions for the computer to follow, and then we tell the computer to follow those instructions. We write, or code, instructions in R because it is a common language that both the computer and we can understand. We call the instructions commands and we tell the computer to follow the instructions by executing (also called running) those commands.
There are two main ways of interacting with R: by using the console or by using script files (plain text files that contain your code). The console pane (in RStudio, the bottom left panel) is the place where commands written in the R language can be typed and executed immediately by the computer. It is also where the results will be shown for commands that have been executed. You can type commands directly into the console and press Enter to execute those commands, but they will be forgotten when you close the session.
Because we want our code and workflow to be reproducible, it is better to type the commands we want in the script editor, and save the script. This way, there is a complete record of what we did, and anyone (including our future selves!) can easily replicate the results on their computer.
RStudio allows you to execute commands directly from the script editor by using the Ctrl + Enter shortcut (on Macs, Cmd + Return will work, too). The command on the current line in the script (indicated by the cursor) or all of the commands in the currently selected text will be sent to the console and executed when you press Ctrl + Enter. You can find other keyboard shortcuts in this RStudio cheatsheet about the RStudio IDE.
At some point in your analysis you may want to check the content of a variable or the structure of an object, without necessarily keeping a record of it in your script. You can type these commands and execute them directly in the console. RStudio provides the Ctrl + 1 and Ctrl + 2 shortcuts allow you to jump between the script and the console panes.
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, will 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, i.e. you don’t have the same number of left-parentheses as right-parentheses, or the same number of opening and closing quotation marks. When this happens, and you thought you finished typing your command, click inside the console window and press Esc; this will cancel the incomplete command and return you to the > prompt.
Let’s just try a simple command to make sure we’re all up and running:
name <- "April"
name## [1] "April"You can get output from R simply by typing math in the console:
3 + 5## [1] 8
12 / 7## [1] 1.714286However, to do useful and interesting things, 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
x <- 3, the value of x is 3.
The arrow can be read as 3 goes into x.
For historical reasons, 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 in a PC, while typing Option +
- (push Option at the same time as the
- key) does the same in a Mac.
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’s 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 an
object 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 object
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 objects,
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
package to automatically check for issues in the styling of your
code.
Objects vs. variables
What are known as
objectsinRare known asvariablesin many other programming languages. Depending on the context,objectandvariablecan 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
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`## [1] 55
weight_kg # and so does typing the name of the object## [1] 55Now 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## [1] 121We can also change an object’s value by assigning it a new one:
weight_kg <- 57.5
2.2 * weight_kg## [1] 126.5This means that assigning a value to one object does not change the
values of other objects For example, let’s store the animal’s weight in
pounds in a new object, weight_lb:
weight_lb <- 2.2 * weight_kgand then change weight_kg to 100.
weight_kg <- 100What do you think is the current content of the object
weight_lb? 126.5 or 220?
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?
Functions are “canned scripts” that automate more complicated sets of
commands including operations assignments, etc. Many functions are
predefined, or can be made available by importing R packages
(more on that later). A function usually takes 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:
b <- sqrt(a)Here, the value of a is given to the sqrt()
function, the sqrt() function calculates the square root,
and returns the value which is then assigned to the object
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] 3Here, 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) to find what arguments it takes, or look at the
help for this function using ?round.
args(round)## function (x, digits = 0)
## NULL
?roundWe 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] 3.14If 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] 3.14And if you do name the arguments, you can switch their order:
round(digits = 2, x = 3.14159)## [1] 3.14It’s good practice to put the non-optional arguments (like the number you’re rounding) first in your function call, and to then 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.
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 animal weights and assign it to a new object
weight_g:
weight_g <- c(50, 60, 62.5, 65, 82, 90)
weight_g## [1] 50.0 60.0 62.5 65.0 82.0 90.0A vector can also contain characters:
animals <- c("mouse", "rat", "dog")
animals## [1] "mouse" "rat" "dog"The quotes around “mouse”, “rat”, etc. are essential here. Without
the quotes R will assume objects have been created called
mouse, rat and dog. As these
objects don’t exist in R’s memory, there will be an error message.
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] 6
length(animals)## [1] 3An 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"
class(animals)## [1] "character"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 [1:6] 50 60 62.5 65 82 90
str(animals)## chr [1:3] "mouse" "rat" "dog"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] 30.0 50.0 60.0 62.5 65.0 82.0 90.0 90.0In 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.
An atomic vector is the simplest R data
type and is a linear vector of a single type. Above, we saw 2
of the 6 main atomic vector types that R uses:
"character" and "numeric" (or
"double"). These are the basic building blocks that all R
objects are built from. The other 4 atomic vector types
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" for bitstreams that we won’t discuss furtherYou can check the type of your vector using the typeof()
function and inputting your vector as the argument.
Vectors are one of the many data structures that R
uses. Other important ones are lists (list), matrices
(matrix), data frames (data.frame), factors
(factor) and arrays (array).
Challenge
We’ve seen that atomic vectors can be of type character, numeric (or double), 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):Why do you think it happens?
How many values in
combined_logicalare"TRUE"(as a character) in the following example: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?
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 <- c("mouse", "rat", "dog", "cat")
animals[2]## [1] "rat"
animals[c(2, 3)]## [1] "rat" "dog"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" "cat"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.
Another common way of subsetting is by using a logical vector.
TRUE will select the element with the same index, while
FALSE will not:
## [1] 21 39 54Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests. For instance, if you wanted to select only the values above 50:
weight_g > 50 # will return logicals with TRUE for the indices that meet the condition## [1] FALSE FALSE FALSE TRUE TRUE
## so we can use this to select only the values above 50
weight_g[weight_g > 50]## [1] 54 55You 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
weight_g[weight_g >= 30 & weight_g == 21]## numeric(0)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 <-).
A common task 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 <- c("mouse", "rat", "dog", "cat")
animals[animals == "cat" | animals == "rat"] # returns both rat and cat## [1] "rat" "cat"## [1] FALSE TRUE TRUE TRUE## [1] "rat" "dog" "cat"Challenge (optional)
- Can you figure out why
"four" > "five"returnsTRUE?
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.
## [1] NA
max(heights)## [1] NA
mean(heights, na.rm = TRUE)## [1] 4
max(heights, na.rm = TRUE)## [1] 6If 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 those 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 an atomic vector of type `"numeric"` (or `"double"`).
na.omit(heights)## [1] 2 4 4 6
## attr(,"na.action")
## [1] 4
## attr(,"class")
## [1] "omit"
## Extract those elements which are complete cases. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
heights[complete.cases(heights)]## [1] 2 4 4 6Recall that you can use the typeof() function to find
the type of your atomic vector.
Challenge
Using this vector of heights in inches, create a new vector,
heights_no_na, with the NAs removed.heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)Use the function
median()to calculate the median of theheightsvector.Use R to figure out how many people in the set are taller than 67 inches.
heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65) # 1. heights_no_na <- heights[!is.na(heights)] # or heights_no_na <- na.omit(heights) # or heights_no_na <- heights[complete.cases(heights)] # 2. median(heights, na.rm = TRUE)## [1] 64# 3. heights_above_67 <- heights_no_na[heights_no_na > 67] length(heights_above_67)## [1] 6
Now that we have learned how to write scripts, and the basics of R’s data structures, we are ready to start working with the Portal dataset we have been using in the other lessons, and learn about data frames.