“Everyone knows that debugging is twice as hard as writing a program in the first place. So, if you’re as clever as you can be when you write it, how will you ever debug it?”, said a well-known computer scientist, Brian W. Kernighan.
This is what every programmer experiences when they have to write the code and the journey is still not reached. There is always a point in a programmer’s life where either writing a code becomes difficult or re-writing it after debugs brings them saturation. What about programming for statistical computation? What do you think it sounds like?
Well, R Programming is one such programming language that is used by the statisticians and data miners for developing statistical software. It allows specialised statistical techniques, graphical devices, import/export capabilities, and, reporting tools through its user-created packages. R-programming is done using Integrated Development Environment (IDE) provided by R-Studio, which needs further knowledge about the tool so that it could be implemented appropriately. Most of the student population find working with R an upstream task. It is because it works for statistical computing and graphics which isn’t pure program-based. R-programming assignment helps in many ways yet toughens the task.
How to Manage R-Programs Efficiently: A Guide By R-Experts.
Any programmer, inevitably, writes tons of codes in his daily work. However, not all programmers inculcate the habit of writing clean codes which can be easily be understood by others. One of the reasons can be the lack of awareness among programmers of the best practices followed in writing a program. This is especially the case for novice programmers. In this post, we list some of the R programming best practices which will lead to improved code readability, consistency, and, repeatability. Here is a list of some practices that can make your lives easy with R:
- Describe your code – When you start coding describe what the R code does in the very first line. For subsequent blocks of codes follow the same method of describing the blocks. This makes it easy for other people to understand and use the code.
- Load packages – Next to describing your code, you have got to load to the relevant packages for your code execution by using the library functions.
- Always use updated packages – Use package version function to check if you are using an updated R package.
- Organise all your source files in the same directory — Use a relative path to access all the necessary files that will be used or sourced in the code that you stored in a single directory.
- Use a consistent “data structures type” style — R programming provides you with a number of data structures like vectors, factors, data frames, matrices, and lists. Ensure that you name them similar while using the codes, makes the search easy while spotting problems.
- Indentation of your code is important – If your code is full of a nested statement, make sure that you are indenting your code.
- Remove all temporary objects—If you want no more memory issues and efficiency issues, remove the temporary objects once used in the code (eps., the larger codes)
- Vectorise the code: Vectorisation is really useful, especially when your code is big. It helps in faster execution of the code.
- Review your code rigorously: Before delivering your code, ensure you rigorously test the code, on different input parameters. It is best to the code reviewed by someone who knows R.
If you are confused with any of the points above and have no idea about what should be done further, you can easily approach to availing an expert’s help.
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