  Data Analysis with R

4 - Operators

Postdoctoral Researcher

Operators Overview of operators Relational operators • find out relation between two operands
• six relational operations are supported in R
• output is logical (TRUE or FALSE) for all of these operators
• work element-wise
Operator Usage Description
< a < b a is LESS than b
> a > b a is GREATER than b
== a == b a is EQUAL to b
<= a <= b a is LESS than or EQUAL to b
>= a > = b a is GREATER than or EQUAL to b
!= a!=b a is NOT EQUAL to b

Example of relational operators # Example for numbers
a <- 10
b <- 5
print(a < b) # less
print(a >= b) # greater or equal
print(a != b) # not equal

Example of relational operators # Example for numbers
a <- 10
b <- 5
print(a < b) # less
##  FALSE
print(a >= b) # greater or equal
##  TRUE
print(a != b) # not equal
##  TRUE

Example of relational operators # Example for numbers
a <- 10
b <- 5
print(a < b) # less
##  FALSE
print(a >= b) # greater or equal
##  TRUE
print(a != b) # not equal
##  TRUE
# Example for vectors
a <- c(7.5, 3, 5)
b <- c(2, 7, 5)
print ( a <= b ) # less or equal
print ( a != b ) # not equal

Example of relational operators # Example for numbers
a <- 10
b <- 5
print(a < b) # less
##  FALSE
print(a >= b) # greater or equal
##  TRUE
print(a != b) # not equal
##  TRUE
# Example for vectors
a <- c(7.5, 3, 5)
b <- c(2, 7, 5)
print ( a <= b ) # less or equal
##  FALSE  TRUE  TRUE
print ( a != b ) # not equal
##   TRUE  TRUE FALSE

Logical (boolean) operators • work only for the basic data types (e.g. logical, numeric) and atomic vectors in R.  Example of combined boolean operators x <- 1:5
x[ x < 4 & x >= 2]
##  2 3

Example of combined boolean operators x <- 1:5
x[ x < 4 & x >= 2]
##  2 3
Step Usage 1 2 3 4 5
1 x < 4 TRUE TRUE TRUE FALSE FALSE
2
3

Example of combined boolean operators x <- 1:5
x[ x < 4 & x >= 2]
##  2 3
Step Usage 1 2 3 4 5
1 x < 4 TRUE TRUE TRUE FALSE FALSE
2 x >= 2 FALSE TRUE TRUE TRUE TRUE
3

Example of combined boolean operators x <- 1:5
x[ x < 4 & x >= 2]
##  2 3
Step Usage 1 2 3 4 5
1 x < 4 TRUE TRUE TRUE FALSE FALSE
2 x >= 2 FALSE TRUE TRUE TRUE TRUE
3 x < 4 & x >= 2 FALSE TRUE TRUE FALSE FALSE

Element- vs. operand-wise operation a <- c(TRUE, TRUE, FALSE, FALSE)
b <- c(TRUE, FALSE, TRUE, FALSE)

print(a | b)
##   TRUE  TRUE  TRUE FALSE
print(a || b)
##  TRUE

Other miscellaneous operators • are similarly important for manipulating data.
Operator Usage Description
: a:b Creates series of numbers from left operand to right operand
%in% a %in% b Identifies if an element(a) belongs to a vector(b)
%*% A %*% t(A) Performs multiplication of a vector with its transpose

Other miscellaneous operators • are similarly important for manipulating data.
Operator Usage Description
: a:b Creates series of numbers from left operand to right operand
%in% a %in% b Identifies if an element(a) belongs to a vector(b)
%*% A %*% t(A) Performs multiplication of a vector with its transpose

Example for %in% a <- c(25, 27, 76)
b <- 27
print(b %in% a)
##  TRUE
print(a %in% b)
##  FALSE  TRUE FALSE

Quiz 1: Relational operators

What does the following operation return (try to find the answer without using R):

a <- c(6, 80, 107, 164, 208, 53, 216, 268, 65, 283)
a < 60
1. NA
2. a numerical vector containing 6 and 53
3. TRUE
4. FALSE
5. a logical vector with TRUEs and FALSEs

R checks for each element in a whether its value is less than 60 and returns a TRUE or otherwise a FALSE. As we have 10 elements in a the returned logical vector has also 10 elements.

Quiz 2: Relational operators

How many TRUEs would you get from the following operation (try to find the answer without using R):

a <- c(6, 80, 107, 164, 208, 53, 216, 268, 65, 283)
a <= 80
1. 1
2. 6
3. 4
4. 3

R applies the operation element-wise: 6 <= 80? 80 <= 80? 107 <= 80?...

Four elements have values that are TRUEly less than or equal to 80, i.e. 6,53,65, and 80.

Quiz 3: Relational operators

How many TRUEs would you get from the following operation (try to to find the answer without using R):

a <- c(16, 47, 207)
b <- c(0, 49, 410)
a <= b
1. 1
2. 2
3. 5

R applies the operation element-wise in both vectors: 16 <= 0? 47 <= 49? 207 <= 410?

Two values in a are TRUEly less than or equal to the corresponding values in b, i.e. 16 and 47.

Quiz 4: Relational operators

What do the following operations on these vectors return:

a <- c(4, 5, 1, 8, 8, 10)
b <- c(0, 0, 3, 6, 7, 9); c <- 3
1. a[a < b]
2. b[b == c]
3. sum(c >= b)

If a vector is shorter than the other it gets recycled for the element-wise comparison

1. Recall, a < b returns a logical vector of length a and b (=c(FALSE,FALSE,TRUE,FALSE,FALSE,FALSE)), which you then use to subset the vector a. Only the 3rd element in a (=1) is TRUEly less than the corresponding element in b (=3) and its number is then returned: 1

2. c gets first recycled (meaning that the value 3 gets repeated 6 times) for the element-wise comparison, in which only one element has the same value as c and the value of that element is then returned: 3.

3. c >= b returns a vector with 3 TRUEs and 3 FALSEs. Why? When you calculate the sum, R coerces the elements to integers (recall, TRUE turns into 1 and FALSE into 0), so the correct answer is 3.

Logical operators

For 6 days it was measured whether it was sunny (sunny = TRUE) and whether it was hot (hot = TRUE). Now we want to check for several conditions (try to to find the answer without using R):

sunny <- c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE)
hot <- c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)

Quiz 5: Logical operators

What does the following return?

sunny <- c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE)
hot <- c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)
sunny & hot
1. a vector of length 12 (with 6 TRUEs and 6 FALSEs)
2. a vector of length 6 (with 1 TRUE and 5 FALSEs)
3. a vector of length 6 (with 3 TRUEs and 3 FALSEs)

& is an element-wise AND operator: a TRUE is only returned if it is sunny and hot (both TRUE)

Both vectors have a length of 6 (6 days), hence,the returned vector has also 6 element. It contains only 1 TRUE (in position 2) as only at day 2 the weather was sunny AND hot.

Quiz 6: Logical operators

What does the following return?

sunny <- c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE)
hot <- c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)
sunny | hot
1. a vector with 6 TRUEs
2. a vector with 5 TRUEs and 1 FALSE
3. a vector with 1 TRUE and 5 FALSEs

| is an element-wise OR operator: a TRUE is returned if it is sunny or hot (at least one of both is TRUE).

Every day it was sunny or hot, except for day 5 (hence, here a FALSE in the returned vector).

Quiz 7: Logical operators

What does the following return?

sunny <- c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE)
hot <- c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)
sunny || hot
1. FALSE
2. TRUE

The question to this operation would be: Was is for any of the 6 days at least sunny or hot?

|| carries out a logical OR operation consolidated for all elements: first the OR operation is carried out element-wise and then it is checked whether at least one of the returned element is a TRUE.

Quiz 8: Combining operators

Which values do you get from the following vector:

a <- c(6, 80, 107, 164, 208, 53, 216, 268, 65, 283)
1. a[a > 50 & a < 60]
2. a[a > a & a < a]
3. sum(a > 250 | a < 100)
4. sum(a[a %in% 1:60])
1. 53.
2. 216.
3. 6.
4. 59.

Quiz 9 - Challenge: Using operators for subsetting

df <- data.frame(
sample = letters[1:10],
group = c(rep(1, 5), rep(2, 5)),
value = c(6, 80, 107, 164, 208, 53, 216, 268, 65, 283)
)

Subset this data frame using the operators you just learned:

1. Extract all observations from group 2
2. Extract all observations where values are greater than 150.
3. Extract all observations from group 1 where values are less than 50 or greater than 250.
4. Extract all observations that have the letters "a", "c", "g", or "j"

(for a hint press p and for a solution code see last slide)

p

Well Done!!!! You mastered the most important R basics that are fundamental for all that follows.

You are now able to ... How do you feel now.....?

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Then go grab a coffee, lean back and enjoy the rest of the day...!  Thank You borrowed and mentioned with proper source: statements.
Image on title and end slide: Section of an infrared satallite image showing the Larsen C ice shelf on the Antarctic
Peninsula - USGS/NASA Landsat: A Crack of Light in the Polar Dark, Landsat 8 - TIRS, June 17, 2017
sel_group <- df$group == 2 # returns a logical vector df[sel_group, ] # column index is empty as we want all columns 2.Extract all observations where values are greater than 150. sel_value <- df$value > 150
sel_group <- df$group == 1 sel_value <- df$value < 50 | df$value > 150 df[sel_group & sel_value, ] 4.Extract all observations that have the letters "a", "c", "g", or "j". sel_sample <- df$sample %in% c("a", "c", "g", "j")