To get started writing code to work with data, you'll need to take care a of a few preliminaries before you can dive in:

• identyify a platform you'll use to do your work

• obtain software for developing code to process and analyze data

• know and apply best practices 

identify a platform

obtain software

best practices

Regardless of which platform or computing system you use, constructing and running code in an organized manner permits both your collaborators and your future self to understand the processing steps, how various files relate to one another, and the provenance of data sets and results which are the product of your code. Code management and data management are often closely related, and there is usually no one "right" code structure for any given situation. A non-exhaustive list of good coding principles that apply in most situations include:

• Write a little bit of code at a time and test it to make sure that each part works and that you get the expected result.

  • Most IDEs will feature a log or other mechanism that shows whether code execution generated errors, warnings, and other messages.

  • Check the number of observations before and after when merging, dropping, or adding rows.

  • Check distributions (and note missing data) when creating, combining, or recoding variables.

• Within a file, write code in the order it has to be run so that the whole program could run on its own once it's done.

• Name variables, data sets, and other objects with fairly short but descriptive names (avoid names like x, temp, var1,ugh98, etc.).

• Add comments in the code to document what each section does.

• Use your software's dedicated commenting character(s) to block out sections that you may not want to run but don't want to delete.

• Keep code files (scripts) fairly short so that each has one major focus; name the files accordingly.

• Consider adding a "read.me" text document to the folder where you store code that describes what each program does, the order they should be run, and how they relate to each other and data, results, and other files in the project.

• Consider using version control to keep track of changes and versions of code files; it's especially important when writing code with collaborators but is very helpful for individuals too.