Essay Sample on Data Tidying: Matching Physical Layout & Intended Meaning

Introduction

Tidy data is an output of messy ones. They are the kind of statistical representations that use rows and columns to represent information. The purpose of this kind of database is to provide individuals or organizations with standardized means of linking database structures with their semantics. Generally, data tiding is the process of matching physical layout with the intended meaning (Wickham, 2014). The data structure is usually triangular tables composed of rows and columns. They are typically labeled to illustrate the references of each side of data representation. When putting this information in tables for tiding, the layout could change depending on owners' preferences (Wickham, 2014). Tables can look different but still represent the same data. Hence, the tiding of data is the best means of making it clear and accurate.

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

The Tidying Up of Data

Messy data has to get processed until all variables are in one column. To achieve data cleaning, observations are supposed to get represented in rows. Then, each unit type of representation ends up forming a table. Most databases are usually messy. They do not follow these three provisions of tidy data. Individuals keep pilling information unsystematically until its too ambiguous to sort. It is hard to get a database that is easy to analyze. Most of them require tidying up first. The most common mistakes made in data storage are described below. Making column headers variable names is not right. Individuals should know that these are just values. It is inappropriate to store many variables in the same column. They should get saved each in its place. The storage of variables in rows and columns is also wrong (Wickham, 2014). The tidying up process indicates that they should be on different sides. It is also provided that diverse observations should get stored on different tables to avoid content confusion. Similarly, single views should not get represented on many tables but a common one. Through melting, splitting, string, and casting, tidying up messy data is made a straightforward procedure.

Data Manipulation Process

The procedure transformation of the messy database to that which is tidy. The process uses four stages of passage. The first one is the filtering of information. The step focuses on doing away with that which was observed from some conditions. Thus, only relevant data remains. Secondly, there is a transformation where variables are added to or modified. The modifications conducted are usually either on single or multiple variables. Aggregation then takes place by turning multiple information representations into unique values (Wickham, 2014). Lastly, sortation takes place, and the order of observation gets changed. The desired layout is solely dependent on an individual or organization.

Visualizations

These are tools used to make sure that the tidy sets of the database are similar to their visual output. However, some tools are in existence to display messy data. In these kinds of devices, information is usually in multiple columns. That is so because individuals are very busy to realize that there is any problem. However, they should arrange every file installed appropriately. Everyone desires success, and that's usually the aim of visualization tools. They show the extent to which tiding up gets done.

Modeling

The inspiration behind tidying up is modeling tools. Many of them perform better when the data used is tidy. The model is usually a statistical connection of variables which connects predictions to responses. It gets displayed as the duplicate or an example of what one would like their data to resemble. The models could be imaginary images of previous observation (Wickham, 2014). Either way, they produce guidance for the tidying of data.

References

Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23. Retrieved from http://vita.had.co.nz/papers/tidy-data.pdf