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

Course <- c("Maths", "English", "Science")
LecturesTaken <- c("12", "49", "52")
Grade <- c('F', 'B', 'A')

studentDataframe <- data.frame(Course, LecturesTaken, Grade)
print(studentDataframe)

### Explanation
The solution to this exercise is simple. We first create vectors of `Course`, `LecturesTaken`, and `Grade`, according to the given data. Later we pass the three vectors to `data.frame()` and simply print it.

> Note: We cannot use `cat()` here because `cat()` takes objects that contain data of only one type. But a data frame for example in this exercise contains data of multiple types: **character**, **integer**, etc. This is why we use only `print()` here.

## Explanation
The solution to this exercise is simple. We first create vectors of `Course`, `LecturesTaken`, and `Grade`, according to the given data. Later we pass the three vectors to `data.frame()` and simply print it.

> Note: We cannot use `cat()` here because `cat()` takes objects that contain data of only one type. But a data frame for example in this exercise contains data of multiple types: **character**, **integer**, etc. This is why we use only `print()` here.

In this review, we give 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: Data Frames

Solution: Using `data.frame()`

Explanation