The first principle of tidy data is to structure your raw data in a way that can facilitate efficient analysis of that data. That way, your analysis can move along faster and you do not have to spend a large amount of time just examining your data, you can actually work with it and input it into your overall work. Tidy data is supposed to be easily readable for other scholars to read as well. This leads to the second principle of tidy data, which is that tidy data is supposed to create a standardized way of mapping the meaning of a dataset through it’s structure. This requires you to take a step back and critically think about which data points would fit best with each category and organize the dataset in a way that best conveys the information as thoroughly as possible. The final principle of tidy data is actually the structure itself. In a tidy dataset, there should be only one observation per row, one variable per column, and only one item per cell. This structure ensures that those who download or access the dataset will be able to open and read it across multiple software currently available.
The best method for organizing your research would be the third principle of tidy data, which is that you should only have one observation for each row, only one variable for every column, and only one item per cell of data. The data may not look pretty to the eyes, however, it is very appealing to the computer and software. It is nice to have pretty-looking data for a publication or just for personal use, however, if the intended use of the dataset is for critical analysis or anything that is not only just for your own eyes, the data should be as tidy as can be.
In my scholarly practice, I plan to organize my data in a way that is tidy, so that if need be, I would have the ability to share my dataset and transfer it to other computer software for analysis. This standardized version of my data creates access to those who may not otherwise have any if my data were messy, be it the wrong software or the inability to comprehend my observations. I would like to create datasets that are as accessible as possible so that my research is not only reserved for those who understand the raw data but anyone and everyone who would like to analyze my findings and observations.