Statistical Analysis with R

The digital world we live in has given companies and people access to staggering amounts of data, and anyone who can make use of that to drive valuable insights stands to benefit. The ability to work with large amounts of data is becoming a standard requirement for countless jobs across multiple industries.

R, probably the most well-known language for data analysis, has been used for years due to its robust statistical functionality, outstanding graphing ability, and extensibility through packages.

This interactive module will get us up to speed with R and assumes no prior knowledge of it. We'll start with the very basics and work our way up to advanced concepts like exception handling. By the time we're done, we'll be able to write detailed, useful code in R.

Learn R from Scratch

testVariable <- c (2, 10, 7, 400)
cat(ifelse(testVariable %% 2 == 0, "even", "odd"))

cat(ifelse(testVariable %% 2 == 0, "even", "odd"))

### Explanation

The `testVariable` in this case is a vector of numbers. The method `ifelse()` takes a boolean expression and the two statements: **"even"** and **"odd"**. The first statement is returned if the expression returns true for that element and if the expression evaluates to false for the given element it returns the second statement.

> Remember, the output of this method is a vector where each element is the _result of the expression_ for the corresponding input element.

## Explanation

The `testVariable` in this case is a vector of numbers. The method `ifelse()` takes a boolean expression and the two statements: **"even"** and **"odd"**. The first statement is returned if the expression returns true for that element and if the expression evaluates to false for the given element it returns the second statement.

> Remember, the output of this method is a vector where each element is the _result of the expression_ for the corresponding input element.

In this review, we provide a detailed analysis of the solution to this problem.

Getting Started

Variables

Exploring R Objects

R Operators and Notation

Conditional Statements

Loops

Functions

Input/Output

Exception Handling

Classes

Fun Challenges

Conclusion

Solution Review: ifelse()

Solution: Using `ifelse()`

Explanation