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 my screencast i chose was “Tidy Tuesday screencast: analyzing plastic waste across countries.”
My screen cast was published May 27th, 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?
The data being described the the plastic waste is structred though many variabes like the year, type of material, total population and across different countries that can be shown through 3 letter codes. They measue the unit of plastic waste in terms of metric tons for different countires. Dave also cleans through his data to elimiate terms that may though off his data results and bring variables with not alot of avaliable resualts and leaves them out and determinds this through a countrys GDP as well.
Hint: For example, importing data, understanding the data, data exploration, etc.
When dave approches his data hi does not waste any time, he dives striaght into his material and goes though the material retaining its information building from one peice to the next. When he sorts through the analytical data he begins asking himeself questions outloud and at the same time writes codes to do those functions for his demands. Its very interesting to see how he reads and determinds the datas meaning so quick and picks up what he wants to do and shape his data to a more narrow result for awnsers he has. When looking for his data and direction to guide him he went to a webpage with codes that he copy and pasted to give him more information and more variables to his data that would enable him to gather greather in depth infromation.
Something that i had saw in the video and learned in class is the view function, by running the view code with ur data it creates a seperate browser next to you.rmd and allows you to display and visulize youre dat in a chart rather than running the webpage. Another thing we learned in class shown in the video is the ggplot code and displaying variable data in a scatter plot that is displayed through runnning the code, this allowed him to visulaize his date very easy and to see his data top and low consumers and contributers to plastic waste by country.
Some of the major findings through the analusis of plastic waste is when he ran a map_data code and displayed a larger visual of the map and color scale of the contributers of plastic waste antion wide by country and could detrmine the colors through different vairbles he chose like population, GDP and date. The map data was used multiple times later in the video and used to display alot of his results through his combined data of multiple packages. Alot of his major findings was found through doing relations in ggplots and displaying his data.
Something i really enjoyed discorving was the filter feature in which he used alot through out his data analysing, he would use this to narrow down his results quickly and display pinpoint things by variable names he labeled through out his reaserch, this is defiantly somthing i will be implying in my future application to data anaylysis. Another thing i found very interesting was the amount of times he copy and pasted multiple funtions and codes into seperate scenerios and ran his data so fast but fluently at the same time, making a hard task look this easy is impressive and something i was baffled by when he would cut so many lines out of it without second thinking.