CSC121 - R Console
Friday January 26th
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> # Indexing vectors with values
> v <- c(1:10)
> v
 [1]  1  2  3  4  5  6  7  8  9 10
> v[2:7]
[1] 2 3 4 5 6 7
> # Index vectors with an expression
> # we have to be careful
> v[3:14 / 2]
 [1] 1 2 2 3 3 4 4 5 5 6 6 7
> # this is unexpected behaviour!
> # We wanted to index from 3 to 7
> # To properly index with an expression, surround the expression with round brackets
> v[3:(14 / 2)]
[1] 3 4 5 6 7
> 
> # Empty Vectors
> # We've been dealing with vectors that have values
> # In R however, we can have vectors that have no values in them.
> # These are called empty vectors
> # To create an empty vector, you have to specify the type of data that the vector should have (remember that vectors can only hold one type of data)
> # To make an empty numeric vector:
> numeric(0)
numeric(0)
> # 'numeric(0)' represents an empty vector in R
> emptyVector <- numeric(0)
> emptyVector
numeric(0)
> length(emptyVector)
[1] 0
> emptyVector[1]
[1] NA
> # NA means that the data is Not Available
> # there is no data there to find
> emptyVector[1] <- 3
> emptyVector
[1] 3
> # By assigning a value at index 1, emptyVector is no longer empty
> emptyVector[5] <- 3
> emptyVector
[1]  3 NA NA NA  3
> # Be careful when you assign values in a vector
> # If you assign a number to a vector at some index, you should ensure that the indexes before it are also assigned
> # Having NA in your vector is usually a sign that you did something *wrong*
> # If you want to indicate something like not having a value at some index,
> # Then you should fill those NAs with zeros
> newVector <- numeric(7)
> newVector
[1] 0 0 0 0 0 0 0
> # This can be useful when you want to start with a 'blank' vector, but not have any NA's in it
> # You can set values to every index without having to assign anything in between
> newVector[5] <- 87
> newVector
[1]  0  0  0  0 87  0  0
> # This will be useful later when we work with real data
>
> # Removing one element from a one-element vector gives back an empty vector
> v <- 3
> v[-1]
numeric(0)
> # this may be useful in E2. You may end up with an empty vector
> # As a reminder: 0 is an even number.
> 
> ## Logical Operators
> 
> # We've learned about comparison operators: >, >=, <, <=, ==, !=
> # There are also some logical operators that can combine logical values to create new logical values
> t1 <- TRUE
> t2 <- TRUE
> 
> # One of these operators is known as the 'AND' operator. It is represented by '&&'
> # Given two logical values, t1 and t2, 't1 && t2' returns TRUE if and only if t1 and t2 are both true
> t1 && t2
[1] TRUE
> t2 <- FALSE
> t1 && t2
[1] FALSE
> 
> # Another operator is known as the 'OR' operator. It is represented by '||'
> # Given two logical values, t1 and t2, 't1 || t2' returns TRUE if and only if *one* of t1 or t2 is true
> t1
[1] TRUE
> t2
[1] FALSE
> t1 || t2
[1] TRUE
> t2 <- FALSE
> t1 <- FALSE
> t1 || t2
[1] FALSE
> 
> # We also have a NOT operator, represented by '!'
> # Given one logical value t (unary operator), !t returns TRUE if t is FALSE, and return FALSE if t is TRUE
> t1
[1] FALSE
> !t1
[1] TRUE
> t2 <- TRUE
> !t2
[1] FALSE
> 
> # We can use these logical operators to create conditions for if-statements by combinding different logical values
> # if (t1 && t2) {
> #    ...
> # }
> 
> # Using logical values can makes your code cleaner and easier to read
> # But be careful! Do not use logical operators on logical values that don't have anything to do with each other
> # Your code might still work, but it will create messy code with a lot of extra if's andd else if's
> # For example our LaptopPrice function from last time.
> # It is cleaner to write two seperate if statements than to use && operator and then have to write more conditions for every other case anyway
>