- Define the following terms as they relate to R: object, assign, call, function, arguments, options.
- Create objects and assign values to them in R.
- Learn how to name objects.
- Save a script file for later use.
- Use comments to inform script.
- Solve simple arithmetic operations in R.
- Call functions and use arguments to change their default options.
- Inspect the content of vectors and manipulate their content.
- Subset and extract values from vectors.
- Analyze vectors with missing data.
You can get output from R simply by typing math in the console:
However, 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:
<- is the assignment operator. It assigns values on the right to objects on the left. So, after executing
x <- 3, the value of
3. The arrow also looks like a mouth (with tongue), which makes it easy to pronounce as
x eats 3. 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 almost any name such as
subject_id. Here are some further guidelines on naming objects:
2xis not valid, but
weight_kgis different from
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.,
weights). If in doubt, check the help to see if the name is already in use.
.) within names. Many function names in R itself have them and dots also have a special meaning (methods) in R and other programming languages. To avoid confusion, don’t include dots in names.
lintrpackage to automatically check for issues in the styling of your code.
Objects vs. variables
What are known as
Rare known as
variablesin many other programming languages. Depending on the context,
variablecan 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:
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):
We can also change an object’s value by assigning it a new one:
This 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,
and then change
weight_kg to 100.
What do you think is the current content of the object
weight_lb? 126.5 or 220?
Up to now, your code has been in the console. This is useful for quick queries but not so helpful if you want to revisit your work for any reason. A script can be opened by pressing Ctrl + Shift + N. It is wise to save your script file immediately. To do this press Ctrl + S. This will open a dialogue box where you can decide where to save your script file, and what to name it. The
.R file extension is added automatically and ensures your file will open with RStudio.
Don’t forget to save your work periodically by pressing Ctrl + S.
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:
Here, the value of 10 is given to the
sqrt() function, the
sqrt() function calculates the square root, and returns the value which is then assigned to the object
weight_kg. 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:
#>  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) to find what arguments it takes, or look at the help for this function using
#> function (x, digits = 0) #> NULL
We see that if we want a different number of digits, we can type
digits = 2 or however many we want.
#>  3.14
If you provide the arguments in the exact same order as they are defined you don’t have to name them:
#>  3.14
And if you do name the arguments, you can switch their order:
#>  3.14
It’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
A vector can also contain characters:
The quotes around “mouse”, “rat”, etc. are essential here. Without the quotes R will assume objects have been created called
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:
An important feature of a vector, is that all of the elements are the same type of data. The function
class() indicates what kind of object you are working with:
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:
You can use the
c() function to add other elements to your vector:
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
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:
"double"). These are the basic building blocks that all R objects are built from. The other 4 atomic vector types are:
FALSE(the boolean data type)
"integer"for integer numbers (e.g.,
Lindicates 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 further
You 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 (
- 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?
R implicitly converts them to all be the same type
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?
Vectors can be of only one data type. R tries to convert (coerce) the content of this vector to find a “common denominator” that doesn’t lose any information.
How many values in
"TRUE"(as a character) in the following example (reusing the 2
..._logicals from above):
Only one. There is no memory of past data types, and the coercion happens the first time the vector is evaluated. Therefore, the
num_logicalgets converted into a
1before it gets converted into
- 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?
logical → numeric → character ← logical
If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:
#>  "rat"
#>  "dog" "rat"
We can also repeat the indices to create an object with more elements than the original one:
#>  "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:
#>  21 54 55
Typically, 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:
#>  FALSE FALSE FALSE TRUE TRUE
#>  54 55
You can combine multiple tests using
& (both conditions are true, AND) or
| (at least one of the conditions is true, OR):
#>  34 39
#>  21 55
> for “greater than”,
< stands for “less than”,
<= for “less 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:
#>  "rat" "cat" "cat"
#>  FALSE TRUE TRUE TRUE TRUE
#>  "rat" "dog" "cat" "cat"
- Can you figure out why
"four" > "five"returns
When using “>” or “<” on strings, R compares their alphabetical order. Here “four” comes after “five”, and therefore is “greater than” it.
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
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 as if the missing values were removed (
rm stands for ReMoved) first.
If your data include missing values, you may want to become familiar with the functions
complete.cases(). See below for examples.
## Extract those elements which are not missing values. heights[!is.na(heights)] ## Returns the object with incomplete cases removed. #The returned object is an atomic vector of type `"numeric"` (or #`"double"`). na.omit(heights) ## Extract those elements which are complete cases. #The returned object is an atomic vector of type `"numeric"` (or #`"double"`). heights[complete.cases(heights)]
Recall that you can use the
typeof() function to find the type of your atomic vector.
Using this vector of heights in inches, create a new vector,
heights_no_na, with the NAs removed.
Use the function
median()to calculate the median of the
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) # 3. heights_above_67 <- heights_no_na[heights_no_na > 67] length(heights_above_67)
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.
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