title: “Tidytuesday Screencast” subtitle: “Australian Animals” author: “Jaycie Ingalls” output: html_document: toc: true —
Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
You must follow the instructions below to get credits for this assignment.
Australian Animal pets
It was published on July 21st, 2020
Hint: What’s the source of the data; what does the row represent; how many observations?; what are the variables; and what do they mean?
The source of the data is from the RSPCA, “its Australia’s most trusted welfare organization.” this is stated from Daves video. It shows the significant outcomes from cats and dogs that have been achieved. The data shows how many dogs/cats have been reclaimed, rehomed and or euthanized. Adoption and reclaiming have been increasing overtime. The row represents the status of the animals each year. When he was comparing outcomes by region in 2018, “x” was # of animals. He had the verticle axis as “euthanized”.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave’s first approach was to start with “animal outcomes”, he ggploted the outcomes per year which gave him a chart that had plots from currently in care, euthanized, in stock, reclaimed, rehomed and released. He then looked into it by region to get a trend between all the factors. He goes over the data so its easy to follow along, He goes off of each topic and re knits it so the graph saves but updates so you can compare. He did web scaping on Wikipedia to find out more accurate numbers and periods overtime were cats were more prone to this horrible thing. He graphed new codes in each R code, which gave the final product to the graph at the end.
When the video first started, he did create a new markdown file, we had done the same thing in class. He also used the terms “equal to” and had put variables after to recreate the codes in graphs. He uses code chunk R to create the codes to create not just the sheet but the actual graph. He did a lot of “outcomes overtime” we had worked on this in class whether it be with years for cars, prices, etc. We hadn’t really worked on web scaping, but it reminded me of when we had to go back to the textbook and read up the information about the codes and problems we were given in each scenario for the quizzes.
I found out from this analysis that he gave based off of region and cats and dogs, that cats were more likely to be euthanized within the regions given. Cats were actually at a much higher rate than dogs and that was based off findings from, dogs, horses, livestock, wildlife and other animals. He states that cats were more likely to euthanized in not just Australia but other regions as well.
The thing that I found the most interesting was the graph at the end. Dave managed to create Australia spilt up into regions. He color coordinated the points starting from orange being a really high percentage and dark blue being a low percentage. He gathered the information from years 2000-2018. It was a live graph which means it was playing every second and the map was lighting up different colors. I found out that closer towards 2018 the graph was steadily on the blue side meaning that the “euthanized” rate was decreasing overtime. A lot of places starting improving around the year 2010. I think it was cool that he was able to make a chart/graph to show the rate overtime of cats being euthanized.