R Introduction - R Vectors and the Environment

R Programming – Introduction and
Vectors
Ivo Bernardo – Udemy Course

The R Language
“R is a language and environment for statistical computing and graphics
[…] R provides a wide variety of statistical (linear and nonlinear modelling,
classical statistical tests, time-series analysis, classification, clustering, …)
and graphical techniques, and is highly extensible.”

R Usage
- Use as a calculator.
- Compute several statistics about data.
- Plot data.
- Develop machine learning algorithms.
Complexity

R as a Calculator
R performs most of the mathematical calculations you can think of:
- 1 + 2 on the console yields 3
- 100-50,6 yields 49.4
- ...
R has special functions to compute well known mathematical operations:
- sqrt() function computes a square-root.
- exp() computes an exponential.
- log() computes the logarithm.

R as a Calculator
You can also perform complex calculations that obey to mathematical
rules:
- (10/10) / (5/5) = 1
- exp(0) * 20 = 20
- exp(sqrt(9)) = 20.08554

R objects
Most of the stuff we use in R are objects.
These objects have their:
- Own set of characteristics (size, dimensions, data types, etc.)
- Own set of functions.
Example of R objects:
- Vector;
- Matrix;
- List;
- DataFrame;

R Vectors
One of the most simple, yet powerful, object is the vector.
- Vectors are characterized by their size and type of the elements.
- All elements have to be of the same type in a vector.
- Vectors are index-based and you can access the elements by their
position (index).
- You create a vector with the command c()
- To create a vector melons with 4 melons and their weight, in kilograms:
- melons <- c(3.4, 3.1, 3, 4.5)
- To access the weight of melon 1 we would use melons[1]

The Environment
Assigning objects into variables using = or <- is the same!
melons = c(3.4, 3.1, 3, 4.5)
melons <- c(3.4, 3.1, 3, 4.5)

Vector Operations
It would be good if R would let us make calculations on our vector..
melons <- c(3.4, 3.1, 3, 4.5)
“There was a problem when weighting, the melons have only half the
weight”
melons/2
Each item in the vector is divided by 2!
“There was a problem when weighting, the melons have two times
the weight”
bananas*2
Each item in the vector is multiplied by 2!

Vector Operations
melons <- c(3.4, 3.1, 3, 4.5)
“There was a problem on the measurements, you have to add these
values to their weight: 0.4, 0.2, 0.4, 0.3”
Melons + c(0.4, 0.2, 0.4, 0.3)
Each melon will have it’s weight summed with the corresponding
element of the new vector so the resulting vector will be:
(3.4+0.4, 3,1+0.2, 3+0.4, 4+0.3)
The cool thing is that we can make this more meaningful by calling
the c(0.4, 0.2, 0.4, 0.3) vector something related such as
adjust_weight and then our calculation could be:
new_melons = melons+adjust_weight

Vector Operations
melons <- c(3.4, 3.1, 3, 4.5)
“The value of the melons weight is the square root of the value we
gave you”
sqrt(melons)
“Sum all the melons’ weight please”
sum(melons) would yield 14
“What’s the mean of the melons’ length?”
mean(melons) would yield 3.5

Vector Operations
melons <- c(3.4, 3.1, 3, 4.5)
“The value of the melons weight is the square root of the value we
gave you”
sqrt(melons)
“Sum all the melons’ weight please”
sum(melons) would yield 14
“What’s the mean of the melons’ weight?”
mean(melons) would yield 3.5

Vector Operations
melons <- c(3.4, 3.1, 3, 4.5)
R also let you do comparison calculations:
“Which Melons weight more than 4 kilograms?”
melons>4 This would yield:
[FALSE, FALSE, FALSE, TRUE]
“Which Melons weight exactly 3 kilograms?”
melons==3 This would yield:
[FALSE, FALSE, TRUE, FALSE]

Vector Operations
melons <- c(3.4, 3.1, 3, 4.5)
AND and OR are also possible:
“Which Melons weight exacly 4 kilograms and 3 kilograms?”
melons == 4 & melons== 3 This would yield:
[FALSE, FALSE, FALSE, FALSE]
“Which Melons weight exacly 4 kilograms or 3 kilograms?”
melons == 4 | melons== 3 This would yield:
[FALSE, FALSE, TRUE, FALSE]

Watch out!
When you divide something by 0 you get the symbol meaning
infinite:
Inf
If you use a vector that has an Infinite element in it, our calculations
do not stand:
sum(c(3.4, 3.1, 3, 4.5/0)) would yield Inf
The same happens for NaN (short for Not a Number):
sum(c(3.4, 3.1, 3, sqrt(-1)) would yield NaN
The same happens for NA (short for Not Available):
sum(c(3.4, 3.1, 3, NA)) would yield NA

Watch out!
- NA’s are pretty common in data analysis so watch out for them.
Fortunately, most functions can dodge this by using an extra
argument na.rm = TRUE
sum(c(3.4, 3.1, 3, NA), na.rm=TRUE) would yield 9.5
mean(c(3.4, 3.1, 3, NA), na.rm=TRUE) would yield
3.166667

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