# Arithmetic, Rational, Logical Operators – Introduction to R Programming – Part 4

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

This series is a good overview of the R basics. Thank you for posting! One minor point of clarification: the rational operators are actually referred to as

relationaloperators.I literally cannot stomach this speaking tone. It just grates me.