R Vectors
A vector is a list of values of the same type, and it is the most important building block in R.
Creating a Vector
Use c(), which stands for combine:
Example
marks <- c(78, 85, 62, 90, 74)
marks
length(marks)
cities <- c("Karachi", "Lahore", "Quetta")
cities[1] 78 85 62 90 74 [1] 5 [1] "Karachi" "Lahore" "Quetta"
length() tells you how many values a vector holds.
In R, even a single value like 5 is a vector of length one. That is why every result starts with [1].
All Values Have One Type
A vector holds only one type. If you mix types, R changes them all to the most flexible type, usually text:
Example
c(1, "two", TRUE)[1] "1" "two" "TRUE"
If a number column suddenly shows quotes, check for one text value hidden among the numbers. It turns the whole vector into text.
Quick Ways to Make Vectors
| Code | Result | Meaning |
|---|---|---|
1:5 | 1 2 3 4 5 | A sequence of whole numbers |
seq(0, 100, by = 25) | 0 25 50 75 100 | Steps of a chosen size |
rep("Pass", 3) | "Pass" "Pass" "Pass" | Repeat a value |
Example
1:10
seq(0, 100, by = 25)[1] 1 2 3 4 5 6 7 8 9 10 [1] 0 25 50 75 100
Picking Items by Position
Use square brackets. In R, positions start at 1, not 0:
Example
fruits <- c("mango", "banana", "apple", "guava")
fruits[1]
fruits[c(2, 4)]
fruits[-1]
fruits[length(fruits)][1] "mango" [1] "banana" "guava" [1] "banana" "apple" "guava" [1] "guava"
A minus sign removes that position. fruits[-1] means "everything except the first item".
Changing and Adding Items
Example
stock <- c(5, 12, 0)
stock[3] <- 20
stock <- c(stock, 7)
stock[1] 5 12 20 7
Maths on a Whole Vector
This is where R shines. An operation works on every value at once, with no loop:
Example
prices <- c(120, 250, 80)
prices * 2
prices + c(10, 20, 30)
sum(prices)[1] 240 500 160 [1] 130 270 110 [1] 450
Useful functions for numeric vectors:
| Function | What it gives |
|---|---|
sum() | The total |
mean() | The average |
median() | The middle value |
max(), min() | The largest and smallest |
sort() | The values in order |
round() | Rounded values |
Example
marks <- c(78, 85, 62, 90, 74)
mean(marks)
sort(marks, decreasing = TRUE)[1] 77.8 [1] 90 85 78 74 62
Picking Items by Condition
A comparison on a vector gives a vector of TRUE and FALSE. Put it inside brackets to keep only the TRUE items:
Example
marks <- c(78, 85, 62, 90, 74)
marks > 75
marks[marks > 75]
sum(marks > 75)
which(marks == 90)[1] TRUE TRUE FALSE TRUE FALSE [1] 78 85 90 [1] 3 [1] 4
sum() on TRUE/FALSE values counts the TRUEs, so sum(marks > 75) tells you how many students scored above 75. which() gives the positions.
Named Vectors
You can give each value a name:
Example
sales <- c(Mon = 4500, Tue = 3800, Wed = 5200)
sales
sales["Tue"]Mon Tue Wed 4500 3800 5200 Tue 3800
Missing Values
Real data often has gaps. R marks them with NA. Most maths functions return NA if any value is missing, unless you add na.rm = TRUE:
Example
x <- c(10, NA, 30)
mean(x)
mean(x, na.rm = TRUE)
is.na(x)[1] NA [1] 20 [1] FALSE TRUE FALSE
Exercise
Exercise
What does fruits[-1] return if fruits <- c("mango", "banana", "apple")?