Beginning R - Summary

Well done for getting this far! We’ve covered a lot in this course:

• RStudio (introduction)
• The print() function
• Basic data types: strings and numerics (floats and integers)
• Indexing, slicing and appending to lists
• Branching logic using if statements
• Looping over different data structures such as lists, sequences, dictionaries and files
• Creating, modifying and accessing dictionaries
• Reading files

You’re not expected to have comitted all of this to memory already, learning any programming language takes more than three hours. It is only by continuing to use R and practise that you will become confident. These notes will remain online so feel free to refer back to them at any time.

EXERCISE

Let’s end this session with a more difficult challenge. We’re going to make a calculator which can read numerical operations from a file and print the answers to the screen.

Start by making a file called calc.txt with the following contents:

4 * 6

5 + 6

457 - 75

54 / 3

4 + 6

Make sure that you have the spaces between each number and the operator.

Write a program which reads in that file and performs the mathematical operations listed. It should output something like:

4 * 6 is 24

5 + 6 is 11

457 - 75 is 382

54 / 3 is 18.0

4 + 6 is 10

There are some extra functions that you may need;

strsplit(line, " ")[[1]] will split a line of text into words and will return a list of those words.

cat(line, "is", value, "\n") will print values to the screen, while adding a newline onto the end of each line.

Answer

This workshop has given you a beginning introduction on R as a programming language. This, together with our Intermediate R should provide you with a firm foundation as you continue to learn R and apply it for statistical programming and data science applications.

If you’d like to learn more R take a look at this excellent book by Hadley Wickham:

• Advanced R - this book is a great way to learn more about R as a programming language.
• R for Data Science - this book is a brilliant introduction on how to use R for data science.

Once you have completed this workshop and Intermediate R, you will be ready to take are future workshops, Introduction to Data Analysis in R and “Applied Data Analysis in R”. These will be linked here once they are ready.

Credits

This “Beginning R” workshop closely mirrors our Beginning Python workshop, which was developed by Matt Williams, based on an earlier Beginning Python workshop written by Christopher Woods.

All text is published under a Creative Commons Attribution 4.0 International License with all code snippets licensed as MIT.

The source for the material can be found on GitHub where fixes are welcome.

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