Tips to Start Your R Programming Assignment

For some reason, it is generally believed that learning to program in the R language is difficult. If there are people here who already know how to do this, do you agree with this position? Personally, I don’t. Doing R programming assignments can be as interesting and easy as possible if you follow some tips. How to make everything go so easily? Where to begin? How not to get stuck? I will answer these and other questions below!

1. Use the “calculator” Principle

You don’t need to know how to program in order to do assignments in R. The syntax of the language (at least of the main libraries) is readable, understandable, and elegant – it will not be difficult for you to understand someone else’s code if you know English at a more or less adequate level. R works like a calculator: write a line, and it’s done; write another line – done again. You can write all the code – the main thing is that you feel comfortable and convenient. This is the best feature of scripting languages, which is significant for R. If we talk about more complex and rare libraries, of course, there can be very different surprises (they are still written by ordinary people, like you and me). In this way, you can get R homework help on special services. Thus, the constructions of these libraries will become familiar and understandable to you.

2. Read Corresponding Literature

If you are completely new to programming, I advise you to start by reading how a computer works in general and what “programming” is. Find out how the programs you write are related, such as the machine code and the hardware. It will be useful to everyone – and in general, it does not matter in what language you are doing assignments.

3. Get Acquainted With The Infrastructure

Get acquainted with the infrastructure that has developed around R. Learn about the R language, how and why it was created, where are the main libraries, who writes them, how to connect them, what to write code in, how to work with RStudio, and so on. These are basic things – even before starting your assignment, it would be good to find answers to these questions so that later you can understand the whole picture.

4. Get Acquainted with Data Types, Basic Functions, and Libraries

The next step is already directly related to the language – get acquainted with data types, basic functions, and libraries. Find out more about tidyverse, what is included, how and why you should work with it, and so on. Formulate for yourself – what do you personally need to do your assignment? Do the core libraries cover your needs, or do you need to include third-party libraries? If you need additional modules, you can work with them. This will not be difficult; all libraries are built according to the same principle. There are a lot of libraries. They will certainly cover almost all your tasks, so there is absolutely no point in learning everything at once. Learn something new as the need arises. You get a new assignment, study a new library or a new function, and immediately apply it and remember it forever. This is how the process of doing assignments in languages such as R and Python is built.

5. Go Through The Plan

1. Basic data types, data structures
2. Loading data from main sources
3. Data cleaning and processing
4. Functions and loops
5. Basic graphics
6. Advanced graphics
7. Further work based on your specific tasks

This order can help you with assignments in R. I think this is a classic method – knowing how to work with these basic things, you can easily master any other library. It is just a matter of practice and your desire.

6. Use Built-in Datasets

The most effective assignment development is through real-life examples. However, it is not always wise to learn from such data; in life, everything is often very difficult, and plunging into R programming fully immediately is not always the right way. So, what should you do? There is a solution. RStudio has a large number of built-in interesting datasets on which you can hone almost any of your skills: building visualizations, cleaning data, formatting tables, creating analytical reports, machine learning – in a word, whatever your heart desires. At the same time, if you also learn the history of these datasets, the process will become even more interesting – boring assignments will take on the side of a historical quiz. Again, you don’t have to do something simple and tedious while doing your assignment. For example, with the help of a small Anscombe dataset, you can demonstrate the famous Anscombe paradox, show off your erudition, and hone your programming skills. If there are not enough built-in datasets, go to Kaggle, look for a suitable dataset, and work on it. The great thing is that on one dataset you can rehearse many skills at once – this will make the process of doing assignments much faster.

Wrapping up

R is not a difficult language at all. On the contrary, it is very simple and interesting. The main thing is to correctly build the process for doing assignments. Do not listen to what others are saying: if you personally like this language, or if it just helps you in solving your problems, work with it. Naturally, do tasks taking into account the points that we discussed above.