In this discussion you are asked to watch a presentation by Andrew Flowers on the topic of data visualization and data journalism (part of the RStudio Conference 2017) https://resources. rstudio. com/rstudio-conf-2017/finding-and-t elling-stories-with-r-andrew-flower s (Links to an external site.)

Several different examples throughout are presented in this talk, discussing the types of data stories, and the "dangers" and "tactics" for each of the type.

Mention the type of data story that caught your attention the most and the reasons behind your choice.
Find and share a data story, and classify it in any of the categories described in the presentation. Include a link to the source, and a relevant data visualization by the creator(s) of the story you share.

Types of data stories (as presented by Andrew Flowers)

Novelty - A story that presents new data, or data that a reader/viewer hasn't understood before in a specific context.
Outlier - This is the most common and the most effective data story, Flowers said. We're naturally drawn to stories about "best", "worst", etc.
Archetype - Telling story about something that's not necessarily different or new, but interesting.
Trend - What's changed? What's new? Can be effective around a breaking-news event.
Debunking - Attacking an alleged misperception.
Forecast - These are typically done with probability models, simulations and scenarios.