R Operators: Arithmetic, Rational, Logical | Beginning R Programming Part 4

R operators are not only useful for doing calculations on data but can also be used to compare values or set up conditions for values. Learn the 3 main types of operators: arithmetic, rational, and logical.

If you want to do a quick calculation on
some numeric values such as calculating
the difference between values
or compare values to see if they match or meet a
certain condition
then you’ll need to know the different operators
you can work with
we’ll focus on three main types of operators:
arithmetic, rational, and logical
let’s first look at arithmetic
you have your typical
addition, subtraction, multiplication, division
remainder, and exponent
what’s also important to know with these
arithmetic operators
is their order of operations
so when calculating some values is first
calculates anything inside the
parentheses followed by anything that
has an exponent, then a multiplied number
then division, addition, and subtraction
This is important because when you’re
calculating the mean of some numbers so
for example
you’re going to some these numbers here
then divide it by the numbers of numbers
you’ll see that this results
in a different number to if we
had to use parentheses so
if we had of
summed of these numbers first
then divided
As the default order is division comes
before addition we want to tell the
program to first calculate additions
and then move on to division
this gives us the correctly calculated mean
So rational and logical operators allow us
to compare data values to see if they
match, don’t match, are above, below, or
equal to numeric thresholds
or extract data that
meet a number of these conditions
your rational operators include
checking if a numeric value is
greater than or less than a threshold
is greater than or equal to
or is less than or equal to
A numeric value or a character string
that is equal to or
matches another value
or is not equal to a value
your logical operators include
“and”, “or”, and “not”
you use “and” when you want
to extract data that meets both one
condition and the other condition
or more
for example it has to be both
greater than this number and equal to
this category
and “or” means that data
will be extracted if it only meets one
of these conditions or options that apply
for example it can either be greater
than this value or belong to this category
If it is greater than this value
then it will extract the data and
will have no need to check any other
condition as it’s already satisfied at least one
if it doesn’t meet the first
condition it will search the data based
on the next condition and the next
condition after that and so on and so forth
until it meets at least one of the given
conditions
The not logical operator
basically extracts out everything that
is not one or more of the conditions
So, for example, I want to get everything
that does not belong to this category
I’m interested in everything except for
those things that are in this category
So I’ll give you an example we have some
data here, which I ran into R, and we’ll
cover reading data into R in another
video dedicated to this but we’re just
using this to demonstrate operators
So this data set looks at the average
income across main U.S. cities
across different job roles
So then I as a product manager
I want to know if San Francisco
pays higher on average for my
job role than where I’m currently living
in New York City so I’ll show you how to
use some operators to extract these data
So we’ll first extract New York City
average income for product managers and
store this in a variable called
“nyc.product.managers”
and we’re going to use our income
data set here and inside this
we want to look for our city variable
and have this equal to or match “New York City”
We’re going to use an end condition as well
because we also want that
to match product managers
so people who
live in New York City and are product
managers
and we’re interested in the job
title variable we would like this to
equal to product managers
or product manager
Okay I’ll print this here
Awesome so what we’re interested in here
is the value under the average income here
this variable
now we need to also get the
same for San Francisco so we can compare them
so we’ll just call this
“sf.product.managers”
and using our income data set
So for our income data set
we’re interested in our “city” variable
and we would like it to equal to
“San Francisco”
and we would also like it to equal to
“job title”
“product manager”
Okay cool,
so we have the average income for product
managers in New York City and San Francisco
so first I want to know if
it’s true that product managers living
in San Francisco have an higher
income on average than people in
New York City
so what I’m going to do is
is San Francisco product managers average
income greater than NYC product
managers average income?
Okay the results say this is true
So basically
San Francisco product managers are paid
higher on average so then I might
consider relocating to this to city
but I’ll go bits
I’ll go step further than that I want to
know how much more San Francisco folks
are paid on average in terms of a dollar figure
so I’m looking at the difference
between San Francisco product managers
average income
I’m gonna minus New York City’s
product managers average income
okay so the difference is 7,000
that might or might not be a big enough a
difference for me to make the relocation
worth it but it’s not bad either
and now you know how to use operators
to extract useful data
next we’ll cover how to read data into R

Prerequisites:
Data Set
How to Install R

Part 5: 
Read and Write Data

Part 3:
Creating Variables in R

Full Series:
Beginning R Programming

More Data Science Material:
[Video] Power BI for R Visualizations
[Blog] Unleash the potential of Recommender Systems
[Blog] Math for Aspiring Data Scientists
[Blog] Top Math Resources for Data Scientists

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Rebecca Merrett
About The Author
- Rebecca holds a bachelor’s degree of information and media from the University of Technology Sydney and a post graduate diploma in mathematics and statistics from the University of Southern Queensland. She has a background in technical writing for games dev and has written for tech publications.

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