The last post I wrote was a general compilation of the year of 2019 for Brazil’s environment. It was mostly a qualitative and analytical approach to a number of events — but of course far from a rigorous scientific process, as this isn’t my approach for the blog.
But a lot happened throughout the year that made me want to try out a couple things for the blog!
First, I discovered that I am really, really into statistics. (…I know, right?)
This happened during a Quantitative Methods semester subject I had for my Master’s. I got introduced to statistical methods, SPSS and R, the latter being a crazy newfound passion project. This all overwhelmed me a lot — I had never had stats before and the first topic on that subject was multiple regression. *this is just to illustrate that at the time I felt like a 2 month-old being given a quantum physics equation to solve
Countless YouTube videos, book chapters, meetings with my stats professor, stats exercises and projects later, I found myself totally sucked into the data world. Cue to Coursera courses (by the way, shout out to everyone making learning accessible!), Udemy courses and hours upon hours spent on trying to figure out R. Whew what a steep learning curve it’s been!
If anyone is just starting on their R journey, especially from a non-programming background, I know the feel — but you can do it! One little step (or function) at a time.
Since a brief introduction to R (since we used SPSS in class, which is more user-friendly), I had set a goal for myself of learning it, little by little. Every stats blog or video would mention R as one of the best stats programming language to learn, on account of its open-source qualities and flexibility. If I’m honest, I also thought it’d be cool to just learn how to program.
I digress, but the point is: I want to use the blog as a vehicle to keep myself motivated to learn and practice stats analyses and R more and more. I decided to give it a fun twist in addition to the environmental analyses I intend to carry out. I created a “Fun statistics” page within the website for posting the for-practice fun datasets I am using to learn data analysis and R programming, and also as a hub of links for some of the resources I am using to learn and practice in case any other stats/R beginners want to take a look.
I am definitely not a pro in R (…yet, hopefully), but I do enjoy very much learning and using it. So of course I don’t claim there aren’t any mistakes in my codes and conclusions, and I earnestly ask you to help me correct my mistakes if you see them! Please do feel free to reach out or comment if you’d like to point something out, or just to share your adventures in this exciting data world!
So all of this to say that I’ll be aiming to give my environmental analysis in the blog a quantitative turn, as well as just taking environment datasets to explore and derive insights from.