# Writing statistical programs for procedures and methods

There is a general consensus among the statistics community that R is a statistical programming language. It’s an alternative to other statistical programming languages such as SPSS and SAS. However, R is the most commonly used statistical programming language and can be used to perform tasks such as plotting graphs, regression, and analysis of variance. These are the tasks that you would do from time to time. But to show your prowess in the software, you have to learn how to write statistical programs or functions. As a novice programmer, using inbuilt functions can be the trick. It can also convince you that you have mastered the programming language. But in real sense, there are those instances where there are no inbuilt functions.

Starting to write R statistical program

If you are looking for resources to help you start writing R programs, you will not fall short. There are so many resources that you can use online. This article will provide you with some of the basic knowledge when writing R programs.

Programming style with R

Generally speaking, programming is a free-form of writing. You can write your code in any structure, but just because you have that freedom does not mean you should write it in paragraph form. There are few generally agreed conventions commonly found in all programming languages. Here are some of them that should get you started.

1. Indentation convention. Indentation refers to the placement of text away from the left or right margin. In programming, it can be used when creating functions that will help you or anyone reading the code to identify that the indented line of code is part of the function. Indentation is also applied in looping constructs. Again, here it identifies the line of code as part of the looping construct. In R, if you create a function, R will automatically indent the line of code until the closing curly braces. In some cases or if need be you can manually indent a line of code. In such a case, simply press in the tab key on your keyboard. Equivalently you could hit the space bar four times.
2. Naming conventions. You will always have to name your functions or variables that you will be using. R has its own naming conventions, which you should always adhere to. Apart from these naming conventions, it is advisable to name the variables and functions using the names that are easy to remember and related to the task that you are doing.
3. Assigning a variable. R presents itself with two ways of assigning a variable which is = and <. The equal sign can be used in many programming languages, but when it comes to R, you should avoid it.

There is probably a lot that you need to know before you can become an expert at R. But what are the advantages of following the programming style advised here?

You will develop the lines of code that bare easy to read and comprehend. Anyone reading the code at a later date will understand what is going on. Who knows, it might be you trying to go through the code eight months later. Frankly speaking, by that time, there is a high chance that you would have forgotten a few things about the code.

Secondly, if your code is not well structured, you will have a hard time while debugging. A code that is easy to read is much simpler to debug than a code that is not readable.

Where to go from here?

Programming is something that is learned actively. This implies that you have to write the code while reading. Reading alone will not help you in any way. What you need is to enjoy the process. Programming with R isn’t very hard. There are lots of resources that you can use. Ensure that you explore a lot of these resources, and we can guarantee that you are on the right path to becoming an R statistical programming software expert. Be patient with your approach.

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