In this case study, we aim to provide bioinformaticians with an effective framework for the professional production of statistical metrics and reports based on the display of multiple descriptive statistics and plots in a single frame in the R environment.
First, we qualitatively survey a clinical trial dataset published by Harvard Dataverse (see: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EA1SAP) using functions such as "describe", "describeBy", and "pairs.panels" available in the package "psych" (see the Required-packages file for the multiple installation method in R). For a better understanding and familiarity with the functions provided by these libraries, you can refer to the address "https://cran.r-project.org/web/packages/available_packages_by_date.html" and the information there. Using these functions, it is then very easy to generate an R report with basic summary descriptive statistics (see the Descriptive-statistics file).
The "bp" library also provides us with a unique set of metrics to analyse our clinical trials (see the Metrics-summary file) and for more information see https://rdocumentation.org/packages/bp/versions/2.1.0). It is sufficient to simply extract all these standards using the each() function from the "plyr" library, after pre-processing the assumed dataset with the "process_data" function from the "bp" library in a command line.
Another feature of the "bp" library is the ability to create a comprehensive visual report from a series of statistical plots obtained from our analysis. This is easily done using an "bp_report" function. See the "Visualization-report" file developed in the R environment to learn more about using this and other visualisation techniques.