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

# Problem Statement

Using data from the file `data.csv` that contains a grocery list. This list contains the details of each item:

You have to fetch this data in your program and output just the **list of items** in a file `out/outData.csv`

## Input
A file named `data.csv` that contains the grocery list.

## Output
A file name `output/outData.csv` containing a list of the **items** only.

## Sample Input
`data.csv`
```
Item, Price, Quantity
Apple, $10, 20
Banana, $5, 12
Mango, $17, 18
```

## Sample Output
`output/outData.csv`
```
Apple
Banana
Mango
```
## Test Yourself
Write your code in the given area. If you get stuck, you can look at the solution.

Let's test your ability to handle .csv files.

Getting Started

Variables

Exploring R Objects

R Operators and Notation

Conditional Statements

Loops

Functions

Input/Output

Exception Handling

Classes

Fun Challenges

Conclusion

Exercise 17: Handling CSV files

Problem Statement