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.
The title of this screencast is Tidy Tuesday screencast: Analyzing Squirrels in NYC. David Robinson analyzed a dataset of squirrels in NYC as an example of exploratory data analysis and machine learning in R, performed without looking at the data in advance
It was published on November 1st, 2019.
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?
He imported the data from a link, the raw data was from the NY Data Portal.For the locations of the squirrels, there was longitutde and latitutde.There was also a zip code.The variables are hectare squirrel numbers, age and fur color. Primary fur color is if the squirrel is grey, cinnamon or black. The value of age is either adult or juvenile.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave approached the analysis by zip codes, and then used ggplot for x/y longitutde and latitude. Dave then chose to agregate by the hectare and summarize the avg long and lat in each hectare. He then categorized it by_hectare, to see the nuber of squirrels in each hecatre. Dave wanted to understand if there are patterns of the squirrels colors in their territory.He makes a value of pct_grey and picks primary fur color to be grey. This then shows us that in certsin hectares, only 25 percent of squirrels are grey, or in another, 75 percent are grey. After this, the graph showed that downtown squirrels were less likely to be grey.
When making a graph, he used ggplot and used longitutde and latitude, similar to what we have done in class with x and y graphs. He also used library tidyverse to begin the data, which is one we used frequently in class. Another thing he used was geom_point, which we use to pin point a more specific location. one thing I noticed that he could maybe improve on is that when he was typing quickly, alot of times he would mistype and have to go back to correct himself. In class, we copy and paste so we dont mispell something.
A major finding from this analysis for me is that the higher north you go, the more likely they are to be eating. The highest number of activites reported were 1435 foraging sightings.Another major finding is that squirrels are more likely to run away in the northwest corner of central park.
Overall, I really enjoyed the uniqueness of this assignment. Anlayzing squirrels in NYC is not something you think about everyday and its cool to see the data played out in front of you. I never wouldve thought that squirrels are more likely to run away in a different location of a park than another. In my experience, they run away regardless. But its cool to see all of the findings.