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Switch case in R

In many programming languages, the switch statement is a control structure used to execute one code block among several choices. R's version of the switch statement works a little differently compared to languages like C, Java, or JavaScript.

In R, switch evaluates one expression and returns one of several possible values depending on the value of the evaluated expression.

This tutorial will help you understand and implement the switch function in R.

1. Basic Syntax:

The basic syntax of the switch function in R is:

switch(EXPRESSION, CASE1, CASE2, CASE3, ...)

2. Using `switch` with Numbers:

When the EXPRESSION is a number, switch will return the n-th element of the list:

x <- 3

result <- switch(x,
                 "first",
                 "second",
                 "third",
                 "fourth")

print(result)  # This will print "third"

3. Using `switch` with Characters:

When the EXPRESSION is a character string, switch matches the names of the following arguments:

y <- "apple"

fruit_color <- switch(y,
                      apple = "red",
                      banana = "yellow",
                      cherry = "red")

print(fruit_color)  # This will print "red"

4. Default Case:

You can provide a default case by adding an unnamed argument to the end:

z <- "grape"

fruit_color <- switch(z,
                      apple = "red",
                      banana = "yellow",
                      cherry = "red",
                      "unknown")

print(fruit_color)  # This will print "unknown"

5. Things to Keep in Mind:

No Break Needed: Unlike some languages, in R, you don't need to use a break statement to exit the switch after a case is matched.
No Multiple Matches: Only the first matching case gets executed. This is different from some languages where all cases after the matching one would get executed until a break is encountered.
Not Just for Scalars: The value returned by switch can be a vector or even a list, not just scalar values.

6. When to Use `switch`:

Use switch when you have a series of conditions that depend on the value of a single expression.
If you have complex conditions, or conditions that depend on multiple expressions, consider using a combination of if, else if, and else instead.

Conclusion:

The switch function in R offers a concise way to choose between multiple options based on the value of an expression. It's a valuable tool to streamline code, making it more readable and maintainable.

Conditional Statements in R:
- Conditional statements control the flow of a program based on specified conditions.
```
# Example: Basic if statement
if (condition) {
  # Code to execute if condition is true
}
```

Using if-else in R:

Use the if-else construct for two possible outcomes.

# Example: if-else statement
if (condition) {
  # Code to execute if condition is true
} else {
  # Code to execute if condition is false
}

Multiple Conditions in R:

Evaluate multiple conditions using logical operators.

# Example: Multiple conditions
if (condition1 & condition2) {
  # Code to execute if both conditions are true
}

Handling Cases in R without Switch:

Use if-else statements for case-like behavior.

# Example: Handling cases without switch
if (case == "A") {
  # Code for case A
} else if (case == "B") {
  # Code for case B
}

R Switch Case Alternative:

Implement a switch-case alternative using if-else.

# Example: Switch-case alternative
if (case == "A") {
  # Code for case A
} else if (case == "B") {
  # Code for case B
}

Conditional Programming in R:

Use conditionals to make decisions in your code.

# Example: Conditional programming
result <- if (condition) {
  # Code for true condition
  value_if_true
} else {
  # Code for false condition
  value_if_false
}

Vectorized Conditions in R:

Apply conditions to entire vectors for efficient programming.

# Example: Vectorized conditions
vector_result <- ifelse(vector_condition, vector_if_true, vector_if_false)

Handling Multiple Cases with Named Vectors in R:
- Use named vectors for concise and readable code.
```
# Example: Handling multiple cases with named vectors
case_result <- case_vector[case]
```

R dplyr case_when Function:

Use case_when from the dplyr package for efficient conditional mapping.

# Example: dplyr case_when
library(dplyr)
df <- df %>% mutate(result = case_when(
  condition1 ~ value1,
  condition2 ~ value2,
  TRUE ~ default_value
))

Functional Programming in R for Conditional Logic:
- Apply functional programming concepts for cleaner and modular code.
```
# Example: Functional programming with purrr
library(purrr)
result <- if_else(condition, ~func1(x), ~func2(x))
```

Alternative to Switch Case in R:

Use if-else or match functions as an alternative to switch.

# Example: Alternative to switch case
result <- if (condition1) value1 else if (condition2) value2 else default_value

R purrr Package for Conditional Mapping:

Leverage purrr functions for efficient mapping based on conditions.

# Example: purrr map for conditional mapping
library(purrr)
result <- map(condition_vector, ~ifelse(.x, value_if_true, value_if_false))

Ternary Operators in R:
- Use the ternary operator for concise conditional statements.
```
# Example: Ternary operator
result <- condition ? value_if_true : value_if_false
```

R Tidyverse Approach to Conditional Statements:

Embrace the tidyverse approach for data manipulation with dplyr functions.

# Example: Tidyverse approach to conditional statements
library(dplyr)
df <- df %>%
  mutate(result = case_when(
    condition1 ~ value1,
    condition2 ~ value2,
    TRUE ~ default_value
  ))