Introduction A time series is a sequence of observations registered at consecutive time instants. The visualization of time series is intended to reveal changes of one or more quantitative variables through time, and to display the relationships between the variables and their evolution through time. The standard time series graph displays the time along the horizontal axis. On the other hand, time can be conceived as a grouping or conditioning variable.
Network analysis offers a perspective of the data that broadens and enriches any investigation. Many times we deal with data in which the elements are related, but we have them in a tabulated format that is difficult to import into network analysis tools.
Relationship data require a definition of nodes and connections. Both parts have different structures and it is not possible to structure them in a single table, at least two would be needed.
As the title reads, in this heterogeneous session we will see three topics of different interest. The first is a collection of three simple and useful one-function R packages that I use regularly in my coding workflow. The second collects some approaches to handling and performing linear regression with big data. The third brings in the freaky component: it presents tools to display graphical information in plain ASCII, from bivariate contours to messages from Yoda!