As a true blue rookie coder, I am also good at making spaghetti… codes!

The sheer fun of coding sometimes makes me lose myself in time, as well as in the codes.

Until it reached a tipping point, which I think is today.

Lately I have several types of experimental data files (i.e. postural balance data, heart rate data, etc..) to process, and processing them all in a single R file is pretty daunting. It is easy to lost myself in the loops and if functions. Plus its not exactly easy to count the opening and closure of brackets.

I have been toying with the idea of running multiple codes from a main code, so that my main code looks more manageable. I am sure it is the kind of things the professional does, but it just didn’t occur to me soon enough.

Lo and behold, I found something on stackoverflow that does exactly what I wanted to do.

I am surprised it’s pretty easy.

The keeidea is:

1. Create a main R file. You can name it main.R or whatever that sounds kingly and important. This is the file that you will personally execute for it to call out the individual R files to run the sub-processes.
2. Within the main R file, include the function source to point it to the files that run the sub-processes:  source (“subProcess1.R”)
3. Create the various part R files that run your sub-processes.
4. In the main R file, you can organise your loops and if functions.

Example:

#Remember to put all the files in the same directory, for simplicity sake.
setwd("~/myDirectory/")

#Supposed there are 20 data files to parse through, a <em>for</em> loop is used.
for (i in 1:20){

#This is the sub-process that all 20 files will go through. The i index can be used in this sub-process  
source("subProcess1.R")

#Suppose there is a need to run sub-process 2a if n is greater than 1, and run sub-process 2b if otherwise. Use the if loop.
if (n > 1) {
  source("subProcess2a.R")
 } 
else 
{
  source("subProcess2b.R")
}

}

Pay careful attention to the allocation of variable names when running multiple processes. It can get messy!