Assignment and Special Operators
Explore >, %in%, and the pipe operator in R.
Assignment and Special Operators is a free R Academy lesson on CoddyKit. This is lesson 3 of 4. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 4 lessons in total.
Assignment in R
R has several ways to assign values to variables. The most idiomatic is <-, which is the preferred style. You will also encounter = for assignment, though it behaves slightly differently in some contexts.
# Both assign a value, <- is preferred
name <- 'Alice'
age = 30
cat('Name:', name, '
')
cat('Age:', age, '
')Left Arrow <- vs Equals =
<- always creates a variable in the current environment. = can also assign, but inside function calls it sets named arguments rather than creating a variable. This distinction matters.
# <- creates a variable outside function calls
x <- 42
# = inside function call sets argument, does NOT create x
mean(x = c(1, 2, 3)) # x is an arg here, not a variable
# But <- inside a function call DOES create a variable
mean(y <- c(1, 2, 3)) # y is now also in workspace
cat('y was created:', y, '
')Right Assignment Operator ->
R also supports right-to-left assignment with ->. This assigns the left side to the name on the right. While valid, it is rarely used in practice since it reduces readability.
# Right assignment operator
100 -> max_score
'R Programming' -> course_name
cat('Max score:', max_score, '
')
cat('Course:', course_name, '
')
# Same as:
# max_score <- 100
# course_name <- 'R Programming'Global Assignment with <<-
<<- assigns a value to a variable in the parent (enclosing) environment, not the local function environment. It is used inside functions to modify a variable that exists outside the function.
counter <- 0
increment <- function() {
counter <<- counter + 1 # modifies the global counter
}
increment()
increment()
increment()
cat('Counter after 3 increments:', counter, '
')The %in% Membership Operator
%in% tests whether each element on the left appears in the vector on the right. It returns a logical vector of the same length as the left operand. It is the cleanest way to test set membership in R.
fruits <- c('apple', 'banana', 'cherry', 'date', 'elderberry')
# Check membership
cat('Is mango in fruits?', 'mango' %in% fruits, '
')
cat('Is cherry in fruits?', 'cherry' %in% fruits, '
')
# Check multiple values at once
search <- c('banana', 'mango', 'date')
cat('Which are in fruits:', search %in% fruits, '
')Filtering with %in%
%in% is particularly powerful when used to filter vectors or data frame rows. It lets you select elements that belong to a predefined set of allowed values.
countries <- c('USA', 'UK', 'Canada', 'Germany', 'France', 'Japan')
populations <- c(331, 67, 38, 83, 67, 126) # millions
# Keep only G7 European members
g7_europe <- c('UK', 'Germany', 'France', 'Italy')
mask <- countries %in% g7_europe
cat('G7 Europe countries:', countries[mask], '
')
cat('Populations:', populations[mask], '
')Matrix Multiplication with %*%
%*% performs true matrix multiplication (dot product), as opposed to * which multiplies element-by-element. For two matrices A (m x n) and B (n x p), the result is an (m x p) matrix.
A <- matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)
B <- matrix(c(5, 6, 7, 8), nrow = 2, ncol = 2)
cat('Matrix A:
')
print(A)
cat('Matrix B:
')
print(B)
cat('A %*% B (matrix mult):
')
print(A %*% B)
cat('A * B (element-wise):
')
print(A * B)The Native Pipe |>
Introduced in R 4.1, the native pipe |> passes the result of the left-hand expression as the first argument of the right-hand function. It makes chains of operations much more readable.
# Without pipe
result1 <- round(sqrt(abs(-16)), 2)
cat('Without pipe:', result1, '
')
# With native pipe |>
result2 <- -16 |> abs() |> sqrt() |> round(2)
cat('With pipe:', result2, '
')
# Reading left to right: take -16, get abs, sqrt, then roundPipe with Vectors
The native pipe |> is especially useful with vectors and numeric operations. You can chain transformations without creating intermediate variables, making your code cleaner and easier to follow.
scores <- c(88, 45, 92, 61, 73, 55, 84, 79)
# Chain: keep passing scores, then compute mean
passing_mean <- scores |> \(x) x[x >= 60] |> mean()
cat('Mean of passing scores:', round(passing_mean, 2), '
')
# Another chain: sort, reverse, take top 3
top3 <- scores |> sort(decreasing = TRUE) |> head(3)
cat('Top 3 scores:', top3, '
')Custom Infix Operators
R allows you to define custom infix operators using the %name% syntax. This is how packages like dplyr define the %>% pipe. You can create your own for domain-specific operations.
# Define a custom infix operator for 'not in'
'%notin%' <- function(x, table) !(x %in% table)
cities <- c('Paris', 'London', 'Berlin', 'Tokyo', 'Sydney')
exclude <- c('Tokyo', 'Sydney')
cat('Cities not in exclude list:', cities[cities %notin% exclude], '
')Operator Quick Reference
Here is a summary of the special operators covered in this lesson:
<-— standard assignment (preferred)=— assignment (also used for function args)<<-— global/parent environment assignment%in%— membership test%*%— matrix multiplication|>— native pipe (R 4.1+)%name%— custom infix operator syntax
# Demonstrate %in% as a filter
all_nums <- 1:20
special <- c(3, 6, 9, 12, 15, 18)
cat('Multiples of 3 up to 20:', all_nums[all_nums %in% special], '
')
# Demonstrate pipe
1:10 |> mean() |> cat('
')Quick Check
What does the %in% operator return when used as c(2, 4, 6) %in% c(1, 2, 3)?
Recap: Special Operators
Well done! Here are the key takeaways from this lesson:
- Use
<-for assignment — it is the R convention =works for assignment but is ambiguous inside function calls<<-modifies a variable in the parent (global) environment from inside a function%in%is the cleanest way to test set membership in R%*%performs matrix multiplication (not element-wise)- The native pipe
|>(R 4.1+) chains function calls left-to-right for readable code - Custom operators follow the
%name%pattern
# Pipe + %in% combined
data <- c(5, 12, 3, 8, 15, 7, 20, 1)
valid_range <- 5:15
result <- data |> \(x) x[x %in% valid_range] |> sort()
cat('Values in range 5-15:', result, '
')
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Frequently Asked Questions
Is the “Assignment and Special Operators” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “Assignment and Special Operators”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 4 lessons in total.
What will I learn in “Assignment and Special Operators”?
Explore >, %in%, and the pipe operator in R. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 3 of 4.
How long does the “Assignment and Special Operators” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
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All lessons in this course
- Arithmetic and Comparison Operators
- Logical Operators and Boolean Logic
- Assignment and Special Operators
- Operator Precedence and Complex Expressions