In the paper “overviewR - Easily Explore Your Data in R” (published in JOSS), my co-author and I compare the key features of other available EDA packages in R with our package overviewR. While overviewR was developed with a specific focus on time series data, its functionality can be applied to a broader range of use cases. I’ll use this comparison as a basis here to show the key features of each package: (View Highlight)
The first extension on the list is {DataExplorer}. What I like most about it is how much you can get out of it (and your data) with just a single line of code. (View Highlight)
As you can see, DataExplorer covers not only standard descriptive statistics such as distributions of correlation plots, but also pays attention to the memory usage of your data. While this may not be relevant for standard use cases, it can certainly be an important asset when you’re dealing with efficient structuring of your data and thinking about data storage. (View Highlight)
{overviewR} was originally developed to provide the user with a detailed view of cross-sectional time series data. In addition to providing insights for initial exploratory data analysis, it can be helpful when merging your data (and identifying time series gaps in your data). (View Highlight)
