Building Logic:
Operators, Conditions, and Functions

IOC-R Week 4

Recap Week 3

Brief Recap Week 3

  • Data frames: row-column structure
df <- data.frame(x = 1:3, y = c("a", "b", "c"))
df
  x y
1 1 a
2 2 b
3 3 c
  • Lists: store anything
my_list <- list(x = 1:3, y = letters[5:1])
my_list
$x
[1] 1 2 3

$y
[1] "e" "d" "c" "b" "a"

What are the outputs for following codes?

df$x
df[[2]]
df[3, ]
df[, "y", drop = FALSE]
df[1, 2]


my_list$x
my_list[1]
my_list["y"]
my_list[[2]][3]

Operators

Assignment and Arithmetic Operators

  • Assignment operators: <-, =
x <- 1
x
[1] 1
y = "abc" # not recommanded
y
[1] "abc"
# assign value to function’s parameter
mean(x = 1:3)
[1] 2
  • Arithmetic operators: +, -, *, /, ^ (exponentiation), etc.
10^3
[1] 1000
x <- c(2, 8, 3)
x - 1
[1] 1 7 2
x - 1:3
[1] 1 6 0
y <- c(6, 4, 1)
x + y
[1]  8 12  4
x * y
[1] 12 32  3
x / y
[1] 0.3333333 2.0000000 3.0000000

Comparison Operators

Element-wise comparison: >, <, >=, <=, ==, !=, returns logical results.

10 > 3
[1] TRUE
10 >= 3
[1] TRUE
10 < 3
[1] FALSE
10 <= 3
[1] FALSE
10 == 3 # equal
[1] FALSE
10 != 3 # not equal
[1] TRUE
x <- c(2, 8, 3)
y <- c(6, 8, 1)
x == y
[1] FALSE  TRUE FALSE
x != y
[1]  TRUE FALSE  TRUE
x > y
[1] FALSE FALSE  TRUE
x <= y
[1]  TRUE  TRUE FALSE

What are the expected results?

  • x == 8
  • y > 1
  • a is a vector of numbers 1 to 5, a[a > 3] returns?

Logical Operators

Element-wise comparison: NOT (!), AND (&), OR (|), etc., returns logical values.

!TRUE
[1] FALSE
!FALSE
[1] TRUE

To combine two conditions A and B:

A B A & B A | B
TRUE TRUE TRUE TRUE
TRUE FALSE FALSE TRUE
FALSE TRUE FALSE TRUE
FALSE FALSE FALSE FALSE

What are the expected results?

  • (3 > 1) & (7 < 5)
  • (3 > 1) | (7 < 5)
(3 > 1) & (7 < 5)
[1] FALSE
(3 > 1) | (7 < 5)
[1] TRUE

The %in% Operator

We use %in% to check if left-side values are present in right-side, it returns logical values.

## check membership
1 %in% 1:3
[1] TRUE
1:3 %in% 1
[1]  TRUE FALSE FALSE
"a" %in% c("abc", "a")
[1] TRUE

Data frame df has two columns named x and y, what is the result of following code?

"col3" %in% colnames(df)

"col3" %in% colnames(df)
[1] FALSE

Bonus: Operator Precedence

Simplified precedence order1 (highest to lowest):

  1. Arithmetic (^ > *, / > +, -)
  2. Relational (>, <, >=, <=, ==, !=)
  3. Membership (%in%)
  4. Logical (! > & > |)

Order can be change using ():

1 + 2 * 3
[1] 7
(1 + 2) * 3
[1] 9
TRUE | TRUE & FALSE
[1] TRUE
(TRUE | TRUE) & FALSE
[1] FALSE

any(), all() and which()

Given a set of logical vectors:

-3:3
[1] -3 -2 -1  0  1  2  3
-3:3 > 0
[1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
  • any(): is at least one of the values TRUE?
any(-3:3 > 0)
[1] TRUE
  • all(): are all of the values TRUE?
all(-3:3 > 0)
[1] FALSE
  • which(): return indices of TRUE values.
which(-3:3 > 0)
[1] 5 6 7

We have a vector log2FoldChange <- c(1.2, -0.5, 0.9, 0.7, -1.1), what are the expected results for the following codes?

  • which(log2FoldChange > 0.8)
  • any(log2FoldChange > 0.8)
  • all(log2FoldChange > 0)

Conditions

Conditional Statements

Conditional statements allow us to make decisions based on logical conditions, guiding how the code behaves in different scenarios.

flowchart LR
  AA[temperature] --> A((&gt; 37))
  A --> B(TRUE)
  A --> C(FALSE)
  B --> D[Fever]
  C --> E[Normal]


flowchart LR
  AA[log2FC] --> A((&gt; 0))
  A --> B(TRUE)
  A --> C(FALSE)
  B --> D[Up-regulated]
  C --> E[Not up-regulated]

if and if else

Syntax:

## if statement
if (condition) {
  # code to run if `condition` is TRUE
}

## if else statement
if (condition) {
  # code to run if `condition` is TRUE
} else {
  # code to run if `condition` is FALSE
}
  • You can use if without else but never in the opposite way.
  • The condition must evaluate to exactly one logical value (TRUE or FALSE) and cannot be NA.

flowchart LR
  AA[log2FC] --> A((&gt; 0))
  A --> B(TRUE)
  A --> C(FALSE)
  B --> D[Up-regulated]
  C --> E[Not up-regulated]


log2FC <- 2.5
if (log2FC > 0) {
  # code to run if condition is TRUE
  print("Up-regulated")
} else {
  # code to run if condition is FALSE
  print("Not up-regulated")
}
[1] "Up-regulated"

What will you get when log2FC is 0?

[1] "Not up-regulated"

The ifelse() Function

Syntax:

ifelse(test, yes, no)

Example:

log2FC <- -1
ifelse(test = log2FC > 0, yes = "Up-regulated", no = "Not up-regulated")
[1] "Not up-regulated"
# nested condition
ifelse(
  test = log2FC > 0,
  yes = "Up-regulated",
  no = ifelse(log2FC < 0, yes = "Down-regulated", no = "No change")
)
[1] "Down-regulated"

The “test” parameter can be a logical vector.

vec_lfc <- c(-1, 0, 1)
ifelse(vec_lfc > 0, yes = "Up-regulated", no = "Not up-regulated")
[1] "Not up-regulated" "Not up-regulated" "Up-regulated"

Functions

What is a Function?

Functions = Reusable blocks of code.

flowchart LR
  A[Inputs] --> B(Code block for specific task)
  B --> C[Output]

To use a function, write the function’s name followed by (), place any required input (arguments/parameters) inside the ().

  • Built-in functions
mean(x = 1:3)
[1] 2

Use ? or help() to view the function’s documentation, e.g.: ?mean, help(mean)

Using Functions

Functions = Reusable blocks of code.

  • Built-in functions
  • Functions from additional packages
# install.packages("readr")         # one time only if not yet installed
library(readr)                      # load the package (every new R session)
read_csv(file = "path/to/data.csv")

We’ll talk about packages next week!

  • Custom functions
add_one <- function(x) {x + 1}
add_one(x = 1:3)
[1] 2 3 4

We’ll talk about customised functions in session 8!

Let’s Practice !

Today’s Goals

  • Understand and use operators to filter data with precision.
  • Apply functions to perform basic calculations and data manipulation.