# Key R Operators

**r – Jumping Rivers**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

# 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) head(colleague_countries) ## [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 ## <chr> <dbl> ## 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

<code>%add%</code> = function(a, b) a + b 2 %add% 3 ## [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!

The post Key R Operators appeared first on Jumping Rivers.

**leave a comment**for the author, please follow the link and comment on their blog:

**r – Jumping Rivers**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.