Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
analyzing plastic waste across countries
May 27, 2019
The sourse of the data is library(tidyverse), library(Janitor), library(scales), library(wdi) He also used coast_vs_waste.csv. The rows represent a ton of different things over 100 of them.The data rows are all countrie. Rows vary from packages to labeling columns, to the name and date also the various filters used to specify the data. They tell you the topic at hand as in Coast vs Waste. The rows specify the topic. Hundreds of observations can be made all surounding the amount of waste in each country. variables include the entity, the code used, the year, the per capita mismanaged waste,the GDP per capita and the total population. 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?
Dave imported specific packages then filtered multiple things to get the specifics. Once all completed there were only a handful of metrics recorded. He used functions to get the clean data sets to get direct waste amounts. He got specific from 2010 on. China which is one of the largest countries had a far far greater mismanagemnet of waste then country around its size including the United states. Dave kept geting not applicable or no specifics but he kept flitering his functions and it would either work or it wouldn’t, getting him a more specific answer each time. In general the very rich countries hd very low plastic waste mismanagment. Hint: For example, importing data, understanding the data, data exploration, etc.
In the video daves uses a GG plot to see if there is any correlation in the data which there is not. It is all rather random and I remember in class you showing us if the dots were in a pattern it was concidered to be correlated but in here it is not. The dots are scattered all over the place. ## Q7 What is a major finding from the analysis. Larger countries does not equivilate to having more mismanaged waste. Chinas is unbelievable high however, India that has a lower per person income is very small concidering its size. You would think that India would be right there with them but they arent. So clearly China isdoing something wrong with it’s plastic waste management. For the most part wealthy countries have less wasted plastic. ## Q8 What is the most interesting thing you really liked about the analysis. It wasn’t the most rich or the most poor countries that were high in the waste mismanagment. It was the middle sized country with medium income that really rocked the charts with the most waste mismangment which really surprised me. I assumed it would be poor countries with large populations that would be the highest with waste mismanagment but it wasn’t. The high income countries for the most prt had fairly low waste mismanagment. Even the countries with the highest coastal population were not at the top. ## Q9 Display the title and your name correctly at the top of the webpage.