Learning Objectives
You can get output from R simply by typing math in the console:
3 + 5## [1] 812 / 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 best
practice to always use <- for assignments.
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, mean,
data, 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”. This may seem overwhelming at first. My suggestion is to focus on what makes sense to you, and that you are using naming conventions that will help you to remember what is stored in each object. Try and be consistent with spacing and capitalization (or not) — your future self will thank you.
What are known as objects in R are known as
variables in many other programming languages. 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) # will print the value of `weight_kg`## [1] 55weight_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 <- 60
2.2 * weight_kg## [1] 132This 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? 132 or 220?
What are the values stored in each object in the following (do this in your head)?
mass <- 50 # mass?
age <- 122 # age?
mass <- mass * 2.0 # mass?
age <- age - 22 # 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.
You can think of a function call generally as
do_this(to_that)
do_that(to_this, to_that, with_those)
do_that(to_this, to_that, with_those, in_this_way)Executing a function (‘running it’) is called calling the function. An couple of examples of function calls are:
print("hello world")## [1] "hello world"a <- 9
b <- sqrt(a)
b## [1] 3Here, 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.
We can also write our own functions. Although beyond the scope of this workshop, there is a carpentries lesson https://swcarpentry.github.io/r-novice-inflammation/02-func-R/
Briefly (very, very briefly),
fahrenheit_to_celsius <- function(temp_F) {
temp_C <- (temp_F - 32) * 5 / 9
return(temp_C)
}Try it out! Extra-credit (not really) homework tonight - write a function to go from celsius to fahrenheit, or inches to centimeters. Note that functions can be much more complicated than two lines.
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, 65, 82)
weight_g## [1] 50 60 65 82A 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] 4length(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 [1:4] 50 60 65 82str(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 50 60 65 82 90In 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). The set of
packages we are going to use a lot in this workshop, the
tidyverse uses tibbles(tibble) which as sort
of liked souped up data frames. Data frames are analogous to rectangular
spreadsheets: they are representations of datasets in R where the rows
correspond observations and the columns correspond to variables that
describe the observations.
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):
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?
How many values in combined_logical are “TRUE” (as a
character) in the following example:
num_logical <- c(1, 2, 3, TRUE)
char_logical <- c("a", "b", "c", TRUE)
combined_logical <- c(num_logical, char_logical)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(3, 2)]## [1] "dog" "rat"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:
weight_g <- c(21, 34, 39, 54, 55)
weight_g[c(TRUE, FALSE, TRUE, TRUE, FALSE)]## [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 55weight_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"animals %in% c("rat", "cat", "dog", "duck", "goat")## [1] FALSE TRUE TRUE TRUEanimals[animals %in% c("rat", "cat", "dog", "duck", "goat")]## [1] "rat" "dog" "cat"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)
mean(heights)## [1] NAmax(heights)## [1] NAmean(heights, na.rm = TRUE)## [1] 4max(heights, na.rm = TRUE)## [1] 6If your data include missing values (and that’s probably all of us!),
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.
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 the heights vector.
Use R to figure out how many people in the set are taller than 67 inches.
This section is from “Modern Dive, section 2.3”
R packages extend the functionality of R by providing additional functions, data, and documentation. They are written by a world-wide community of R users and can be downloaded for free from the internet. A good analogy for R packages is they are like apps you can download onto a mobile phone
So R is like a new mobile phone: while it has a certain amount of features when you use it for the first time, it doesn’t have everything. R packages are like the apps you can download onto your phone from Apple’s App Store or Android’s Google Play.
Let’s continue this analogy by considering Instagram. Say you have
purchased a new phone and you would like to share a recent photo you
have taken to Insta. You need to: 1. Install the app: Since your phone
is new and does not include Instagram, you need to download the app from
either the App Store or Google Play. You do this once and you’re set.
You might do this again in the future any time there is an update to the
app.
2. Open the app: After you’ve installed Instagram, you need to open the
app.
Once Instagram is open on your phone, you can then proceed to share your photo with your friends and family. The process is very similar for using an R package. You need to:
Let’s now show you how to perform these two steps for the
tidyverse metapackage (package of packages).
There are two ways to install an R package. For example, to install
the tidyverse package:
tidyverseinstall.packages("tidyverse") in the
Console pane of RStudio and hitting enter. Note you must include the
quotation marks.Much like an app on your phone, you only have to install a package once. However, if you want to update an already installed package to a newer version, you need to re-install it by repeating the above steps.
Practice what you have just done and install the R package
here.
Recall that after you’ve installed a package, you need to “load” it,
in other words open it. We do this by using the library()
command. For example, to load the tidyverse package, run
the following code in the Console pane. What do we mean by “run the
following code”? Either type or copy & paste the following code into
the Console pane and then hit the enter key.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(here)If after running the above code, a blinking cursor returns next to
the > “prompt” sign, it means you were successful and
the tidyverse package is now loaded and ready to use. If
however, you get a red “error message” that reads…
Error in library(tidyverse) : there is no package called ‘tidyverse’it means that you didn’t successfully install it. In that case, go back to the previous subsection and trying install it again.
One extremely common mistake new R users make when wanting to use
particular packages is they forget to “load” them first by using the
library() command we just saw. Remember: you have to load
each package you want to use every time you start RStudio. If you don’t
first “load” a package, but attempt to use one of its features, you’ll
see an error message similar to:
Error: could not find functionR is telling you that you are trying to use a function in a package that has not yet been “loaded.” Almost all new users forget do this when starting out, and it is a little annoying to get used. However, you’ll remember with practice.
This lesson was created by Aleeza Gerstein at the University of Manitoba. It is sourced primarily from material from The Carpentries introductory lesson. Made available under the Creative Commons Attribution license. License.
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