I will use two examples of data in a spreadsheet format that I often encounter during my research. So, once the data is organised according to the tidy data principles, making the graphs is relatively straightforward.īelow, I will explain how data in the typical spreadsheet format (and I’m not naming names here) can be converted to tidy data using R. The tidy data standard defines a consistent way to structure datasets, facilitating data manipulation and visualization ( Wickham, 2014). Tidy data contains exactly the same information as non-tidy, spreadsheet data, but it is organised in a different way. In addition, I am used to read and present data in a tabular format. The reason that I was struggling is that as a researcher I am used to collect, process and summarise my data in spreadsheets. Before I could make those graphs, however, I was struggling with the tidy data structure, which is a requirement for using ggplot2. For instance, the graphs that I made for a previous blog advocating the importance of showing the actual data instead of summaries were made with ggplot2 (scripts available here). With ggplot2, a free, open source package for R, you can make high-quality graphs. When I started to use ggplot2 for data visualisation, I was impressed by its power and elegance. Here, I explain how to convert typical spreadsheet data to tidy data to facilitate data visualisation. But understanding the structure of tidy data does not come naturally (in my experience), since it is quite different from the structure of data in spreadsheets or tables. Structuring data according to the ‘ tidy data‘ standard simplifies data analysis and data visualisation.
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