Writing Statistical Programs for Procedures and Methods in R
Many applications used in industry, government, academia, etc. depend on the statistical computing language R. This language started as an open-source language. R strives to make different analytical and statistical tools available to researchers and the public. Over time, it has grown to be one of the major software languages. Analysts and statisticians can now use it to develop up-to-date, sound, and flexible analytical tools. Also, they can include these tools within a framework which is well-integrated with other essential tools.
The R studio provides a user-friendly and functional interface for R. It is an efficient IDE (Integrated Development Environment) in which a variety of programming languages, web applications, and other key tools are readily available to the user.
Getting started with writing statistical programs in R
R is free and open-source software. You can download it for free online. We recommend using an IDE called R studio. There are a variety of R studio versions that suits different users. Some of them include the R studio desktop, Commercial, Server, etc. There are numerous reasons why statisticians prefer R to other tools. The two most compelling reasons are:
- R is free. You do not need to pay for any part of the R software or the contributed packages like add-on modules. Also, there are few restrictions to use for those who want to contribute to the packages.
- A code or program written in the R environment can be replicated. This ensures accessibility and transparency for the general user. This also means that users can find a diversity of codes, features, and functions that they need.
The R studio IDE can be customized. The user can easily have access to data, plots, help, files, object, and other features that they need to work efficiently. The R studio is a self-contained and well-organized environment that provides everything you may need. It has an embedded functionality to utilize collaborative version-control software. This includes GitHub and Subversion and other sets of powerful tools.
Tools like Rmarkdown can be used to integrate written narrative with embedded R code and other content. Shiny Web Apps provide an interactive user-friendly interface that allows the user to actively engage with a wide variety of tools built-in R.
Conventions of programming in R and best practices
R code is usually typeset using a monospace font which is syntax highlighted. The R output lines which usually appear in the console also begin with ##. However, they are not syntax highlighted. Two essential R packages can help you apply the R coding style best practices.
- Styler – This package allows users to interactively restyle selected text, files, or even the entire project. It has an R studio add-in which is the easiest way to restyle existing code. Styler provides non-invasive pretty-printing of R source code. It adheres to the tidyverse formatting rules. You can install it using this code:
- Lintr – The lintr package performs automated checks to confirm if your code is conforming to the style guide. You can install this package using the following R code:
File naming conventions in R
All file names should be meaningful and end with .R. The file names should not have special characters. You should stick to numbers, letters, -, and_. You should prefix your files with numbers if you want them to run in a particular order
File content structure
Make sure you load all the required packages at the very beginning of the file. You can also use commented lines of – and = if you want to break up your file into easily readable chunks.
Object naming conventions in R
- Use numbers and lowercase letters only
- The underscore which is also called the snake case can be used to separate words within a name
- The names used should be concise and meaningful
- Variable names should generally be nouns while function names should be verbs. Do no re-use names of common functions and variables. This can confuse the readers of your code.
- Consider using a list or data frame instead, if you find yourself attempting to cram data into variable names.
Space should be put after a comma not before, just like in regular English.
Spaces should not appear outside or inside a parenthesis for regular function calls.
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