Key operators and functions

Nicky Wakim

Note

Let’s expand on some basic operators

We went over some operators in our lesson on Basics

Basic math operators:

Operation Written In R
Addition \(1+1\) 1+1
Subtraction \(2-1\) 2-1
Multiplication \(3\times 2\) or \(3\cdot 2\) 3*2
Division \(3 / 2\) or \(\dfrac{3}{2}\) 3/2
Exponential \(4^2\) 4^2
Square root \(\sqrt{4}\) or \(4^{1/2}\) 4^(1/2) or sqrt(4)

Basic relational operators:

Operation In R
Less than 1<1
Greater than 1>1
Less than or equal to 1<=1
Greater than or equal to 1>=1
Equal to 1==1
Not equal to 1!=1

We can further expand the relational operators

  • If we want combine two logical vectors, we need to use boolean algebra
Operation In R Example in R Example in plain language
Group criteria ( ) (1 == 1) We are grouping this statement to make it easier to read
And & (1 == 1) & (3 > 2) Is 1 equal to 1 AND 3 greater than 2? (If BOTH are true, returns true)
Or | (1 == 1) | (1 > 2) Is 1 equal to 1 OR is 1 greater than 2? (If one of them are true, returns TRUE)
Not ! !(1 > 2) Is 1 NOT greater than 2?

Adapted from this R for Data Science

Is something in something else?

  • The %in% operator is helpful when you want to check if one thing is in a list of other things

  • Let’s say I have a vector

my_vector <- c("a", "b", "c", "d")
1
We create a vector with letters a-d
  • I want to ask: “Is”a” in my vector?”
"a" %in% my_vector
2
I want to see if "a" is in the vector 
[1] TRUE
"m" %in% my_vector
3
Then I want to see if "n" is in the vector 
[1] FALSE
"m" %in% my_vector | "a" %in% my_vector
4
We can combine operators as well! I’m asking: “Is”m” in my vector or “a” in my vector?” 
[1] TRUE
"A" %in% my_vector
5
It is also case sensitive 
[1] FALSE

Some good functions to know

Purpose Function Example
rounding round(x, digits = n)

round(3.5, digits = 0)

round(4.5, digits = 0)
rounding janitor::round_half_up(x, digits = n) janitor::round_half_up(3.5, digits = 0)
ceiling (round up) ceiling(x) ceiling(3.1234)
floor (round down) floor(x) floor(3.1234)
absolute value abs(x) abs(3.1234)
square root sqrt(x) sqrt(3.1234)
exponent exponent(x) exponent(3.1234)
natural logarithm log(x) log(3.1234)
log base 10 log10(x) log10(3.1234)
log base 2 log2(x) log2(3.1234)

Useful statistical functions

  • If x is a vector of numeric values, then we can perform several statistical functions on it
Objective Function
mean (average) mean(x, na.rm=T)
median median(x, na.rm=T)
standard deviation sd(x, na.rm=T)
quantiles* quantile(x, probs)
sum sum(x, na.rm=T)
minimum value min(x, na.rm=T)
maximum value max(x, na.rm=T)
range of numeric values range(x, na.rm=T)
summary** summary(x)

CAUTION: The functions above will by default include missing values in calculations. Missing values will result in an output of NA, unless the argument na.rm = TRUE is specified. This can be written shorthand as na.rm = T.

Other useful functions

Objective Function Example
create a sequence seq(from, to, by) seq(1, 10, 2)
repeat x, n times rep(x, ntimes) rep(1:3, 2) or rep(c("a", "b", "c"), 3)
take a random sample sample(x, size) sample(1:15, size = 5, replace = TRUE)

Extra note: we will “set a seed” for sample() because the process is random, and the output will change everytime. set.seed() will anchor us in a specific instance of the random number generator.

Wrap-up

  • We learned some boolean operators to expand our true/false logical work
  • We learned many new functions
    • We’ll get to practice them in our practice assignment!

 

  • Main goal was to gain recognition of functions
  • Hopefully, we can use what we learned in the lesson on getting help in R to investigate the functions more!

Resources