This interactive R course covers everything from the basics, variables, and data types to advanced topics, such as recursion, file handling, and S3/S4 classes in R.

In today’s data-driven world, the ability to analyze large datasets is becoming a vital skill across industries. R, one of the most powerful languages for data analysis.
This interactive R course is designed for beginners, with no prior knowledge of R programming required. You will begin with fundamental concepts, such as R variables, data types in R, and basic functions like R print and R cat. As you progress, you will dive into more complex topics, including R vectors, lists, arrays, matrices, and data frames in R programming. You’ll also learn how to perform operations using arithmetic operators in R, relational operators in R, and logical operators in R. Further, the course covers advanced features like if statements in R, switch statements in R, loops in R, and recursion in R. 
Finally, you’ll gain hands-on experience with file handling in R, exception handling with try and except in R, and object-oriented programming using S3 and S4 classes in R.

Learn R

Most of the programs written in R language involve some kind of manipulation of **vectors**. Vectors are basic R Objects.

## Use of `ifelse()` Function

Now, imagine a case where we have to write `if...else` statement for each element present in a vector. This task can be easily and efficiently coded using `ifelse()`. It is the vector equivalent form of the `if…else` statement in R.

### Syntax
```r
ifelse(expression, condition1, condition2)

# Here "expression" is an object which can be coerced to logical mode.
# Condition1 is returned for elements that satisfy the expression (return TRUE)
# Condition2 is returned for elements that do not satisfy the expression (return FALSE)
```

> The output of `ifelse()` function is always a vector! Each element of the output vector tells whether the test (expression) passed for that element of the input vector.

Let's revisit our original positive and negative numbers problem using vectors this time.

x <- c(5, -5)
ifelse(x > 0, "positive", "negative") # if the expression is satisfied return condition1 else return condition2

Notice that the size of the output vector is the same as the input vector. Also, remember that the `expression` parameter must return a logical vector. In our code, $x > 0$ returns a logical vector `[TRUE, FALSE]`. The `TRUE` value corresponds to the first condition and `FALSE` to the second condition. 


Most of the programs written in R language involve some kind of manipulation of **vectors**. Vectors are basic R Objects.

# Use of `ifelse()` Function

Now, imagine a case where we have to write `if...else` statement for each element present in a vector. This task can be easily and efficiently coded using `ifelse()`. It is the vector equivalent form of the `if…else` statement in R.

## Syntax
```r
ifelse(expression, condition1, condition2)

# Here "expression" is an object which can be coerced to logical mode.
# Condition1 is returned for elements that satisfy the expression (return TRUE)
# Condition2 is returned for elements that do not satisfy the expression (return FALSE)
```

> The output of `ifelse()` function is always a vector! Each element of the output vector tells whether the test (expression) passed for that element of the input vector.

Let's revisit our original positive and negative numbers problem using vectors this time.

Here, we are going to have a look at the method "ifelse()" provided by R language.

Introduction to R

R variables

Data Structures in R

Operator in R

Conditional Statements in R

Loops in R

Function in R

Input/Output in R

Exception Handling in R

Classes in R

R Programming Challenges

Conclusion

R ifelse()

Use of `ifelse()` Function

Syntax