If you will be performing actions more than once it is better to create modular code that does the task. The alternative, copy-pasting code and modifying it for the new target, is an error-generating process. It also contains a large inefficiency: when you want to change something about what the code does, you must change it as many times as you have repeated the statement.
In contrast, a modular piece of code need only be changed once, and
in one place, then re-run against the targets.
In R, one option for modular code is to create a custom function.
Functions take arguments, and return results, but other than that
have nearly unlimited flexibility.
R has some quirks. One is that despite being written
primarily as a statistical software language, it does not come
with a built-in function to calculate the standard error of the mean of a sample.
This is a very useful property to calculate in many descriptive statistical
summaries.
Create a new script and save it with a useful name that indicates it contains a function for calculating the SEM.
Using comments, document what the function is meant to do, what inputs it uses, and what it is expected to return.
sd()sqrt()length()Write a function which takes as its arguments a vector of numbers x
and returns the standard error of the mean of those numbers. Name the
function se_m.
Open a separate script. Make se_m()
available in the environment by using the source() function and
the name of the function definition script you created.
Optional: modify your function to operate even when a vector of numbers includes an NA value.
[click here for output]