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# Operators you should make more use of in R

Only recently have I discovered the true power of some the operators in
R. Here are some tips on some underused operators in R:

### The %in% operator

This funny looking operator is very
handy. It’s short for testing if several values appear in an object. For
instance

x = c(2, 6, 4, 4, 6, 8, 10, 14, 2)

To grab all the values where x is 2, 4 or 14 we could do

x[x == 2 | x == 4 | x == 14]

## [1]  2  4  4 14  2

or we could use %in%

x[x %in% c(2, 4, 14)]

## [1]  2  4  4 14  2

This is something I use all the time for filtering data. Imagine you’ve
got a tibble of data relating to the world (step up spData)

library("dplyr")
library("sf")
library("sp")
data(world, package = "spData")

# drop the geometry column because we don't need it
world = world %>%
st_drop_geometry()

Your colleague sends you a list of 50 countries (I’m going to randomly
sample the names from the data) and says that they want the average life
expectency for each continent group within these 50 countries.

colleague_countries = world %>%
sample_n(50) %>%
pull(name_long)

## [1] "Yemen"         "New Zealand"   "Kyrgyzstan"    "New Caledonia"
## [5] "Morocco"       "Ecuador"

We could then ask R to return every row where the column name_long
matches any value in colleague_countries using the %in% operator

world %>%
filter(name_long %in% colleague_countries) %>%
group_by(continent) %>%
summarise(av_life_exp = mean(lifeExp, na.rm = TRUE))

## # A tibble: 6 x 2
##   continent     av_life_exp
##
## 1 Africa               63.6
## 2 Asia                 72.3
## 3 Europe               79.0
## 4 North America        74.6
## 5 Oceania              80.3
## 6 South America        74.3

#### Did you know?

You can make your own %% operators! For instance

%add% = function(a, b) a + b

## [1] 5

### The && and || operators

If you look on the help page for the logical operators & and |,
you’ll find && and ||. What do they do and hope they actually differ
from their single counterparts? Let’s look at an example. Take a vector
x

x = c(2, 4, 6, 8)

To test for the values in x that are greater than 3 and less than 7 we
would write

x > 3 & x < 7

## [1] FALSE  TRUE  TRUE FALSE

Then to return these values we would subset using square brackets

x[x > 3 & x < 7]

## [1] 4 6

What happens if we repeat these steps with &&?

x > 3 && x < 7

## [1] FALSE

x[x > 3 && x < 7]

## numeric(0)

What is happening here is that the double & only evaluates the first
element of a vector. So evaluation proceeds only until a result is
determined. This has another nice consequence. For example, take the
object a

a = 5

In the following test

a == 4 & a == 5 & a == 8

## [1] FALSE

All 3 tests are evaluated, even though we know that after the first
test, a == 4, this test is FALSE. Where as in using the double &&

a == 4 && a == 5 && a == 8

## [1] FALSE

Here we only evaluate the first test as that is all we need to determine
the result. This is more efficient as it won’t evaluate any test it
doesn’t need to. To demonstrate this, we’ll use two toy functions

a = function(){
print("Hi I am f")
return(FALSE)
}
b = function(){
print("Hi I am g")
return(TRUE)
}

a() & b()

## [1] "Hi I am f"
## [1] "Hi I am g"

## [1] FALSE

When using the single &, R has to evaluate both functions even thought
the output of the left hand side is FALSE

a() && b()

## [1] "Hi I am f"

## [1] FALSE

But using &&, R only has to evaluate the first function until the
result is determined! It’s the same rule for ||

b() | a()

## [1] "Hi I am g"
## [1] "Hi I am f"

## [1] TRUE

b() || a()

## [1] "Hi I am g"

## [1] TRUE

### The xor() function

This last one isn’t so much an operator as a function. The xor()
function is an exclusive version of the |. Take two vector, x and y

x = c(1,1,2)
y = c(1,2,2)

To get all the elements where either x is 1 or y is 2 we would write

x == 1 | y == 2

## [1] TRUE TRUE TRUE

However, this will also return the elements where x = 1 AND y = 2. If we
only want elements where only one statement is TRUE, we would use
xor()

xor(x == 1 , y == 2)

## [1]  TRUE FALSE  TRUE

That’s all for this time. Thanks for reading!

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