Wrangling Data

1. One takeaway I got from The Journalism Handbook: I guess I’ve never really thought about it, but working with data often comes with a larger set of challenges that I’d never considered. Learning to compile and make “clean data”/ “cleaning messy data” that involves organization and rearrangement of the formatting and information in a database to make things clear and concise, because large sets of multivariabled data gets messy very quickly and it’s extremely important to keep it clean to garner actually useful results.
2. My Graph — Title in case it’s hard to read: “Probability Of Dying Among Youth Ages 20–24 years (per 1,000) in Portugal (According to The World Bank)

Now why exactly did I pick such a morbid data set? Well, a while back I was reading about what Portugal did, where back in 2001 they Decriminalized all drugs and it had a huge positive impact on the country’s serious drug problem, drastically reducing number of drug users through major policy change. It’s a fascinating subject, I’d recommend reading more on it. Anyways I decided I wanted to find data that could possibly relate to this. When I went to The World Bank website and downloaded Portugal’s data onto Excel, I struggled to find any metrics on drug use, drug deaths, or anything remotely related, so I decided upon probability of death among young adults, where drug usage is among the highest, and graphed that. Now it wasn’t surprising to me that the obvious trend visible in this data set is that the probability of death has been declining, and I think this is due to a vast number of reasons that I won’t begin to try to brainstorm on, but maybe decreased deaths in drug overdose and substance abuse related incidents have at least had a small overall impact on this larger trend.