At the end of this activity, you will be able to:
- Understand the structure of and be able to create a vector object in
What you need
RStudio to complete this tutorial. Also we recommend that you have an
earth-analytics directory setup on your computer with a
/data directory within it.
Vectors and data types
A vector is the most common data structure in
R. A vector is defined as a group of values, which most often are either numbers or characters. You can assign this list of values to an object or variable, just like you can for a single value. For example we can create a vector of animal weights:
weight_g <- c(50, 60, 65, 82) weight_g ##  50 60 65 82
A vector can also contain characters:
animals <- c("mouse", "rat", "dog") animals ##  "mouse" "rat" "dog"
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) ##  4 length(animals) ##  3
Vector data types
An important feature of a vector is that all of the elements are the same data type. The function
class() shows us the class (the data type) of an object:
class(weight_g) ##  "numeric" class(animals) ##  "character"
str() shows us the structure of the object and the elements it contains.
str() is a really useful function when working with large and complex objects:
str(weight_g) ## num [1:4] 50 60 65 82 str(animals) ## chr [1:3] "mouse" "rat" "dog"
You can add elements to your vector by using the
# add the number 90 to the end of the vector weight_g <- c(weight_g, 90) # add the number 30 to the beginning of the vector weight_g <- c(30, weight_g) weight_g ##  30 50 60 65 82 90
In the examples above, we saw 2 of the 6 atomic vector types that
These are the basic data tpes that all
R objects are built from. The other 4 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"that we won’t discuss further
Data type vs. data structure
Vectors are one of the many data structures that
R uses. Other important ones include: lists (
list), matrices (
matrix), data frames (
data.frame) and factors (
factor). We will look at
data.frames when we open our
boulder_precip data in the next lesson!
Optional challenge activity
Question: What happens when we create a vector that contains both numbers and character values? Give it a try and write down the answer.
Question: 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, '2.45') char_logical <- c('a', 'b', 'c', frog) tricky <- c(1, 2, 3, '4')
Question: Why do you think it happens?
Question: Can you draw a diagram that represents the hierarchy of the data types?
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 ##  "rat" animals[c(3, 2)] ##  "dog" "rat"
R indexes start at 1. Programming languages like
R start counting at 1, because that’s what human beings typically do. Languages in the
C family (including
Python) count from 0 because that’s simpler for computers to do.
We can subset vectors too. 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 ##  FALSE FALSE TRUE TRUE TRUE TRUE ## so we can use this to select only the values above 50 weight_g[weight_g > 50] ##  60 65 82 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] ##  60 65 82 90 weight_g[weight_g >= 30 & weight_g == 21] ## numeric(0)
When working with vectors of characters, if you are trying to combine many conditions it can become tedious to type. The function
%in% allows you to test if a value is found in a vector:
animals <- c("mouse", "rat", "dog", "cat") animals[animals == "cat" | animals == "rat"] # returns both rat and cat ##  "rat" "cat" animals %in% c("rat", "cat", "dog", "duck") ##  FALSE TRUE TRUE TRUE animals[animals %in% c("rat", "cat", "dog", "duck")] ##  "rat" "dog" "cat"
- Can you figure out why
"four" > "five"returns