Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
Analyzing Car Fuel Efficiency in R
October 25, 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 source of the data can be found on fueleconomy.gov. Each row represents individual cars, and the variables describe an aspect of each car.There are so many variables that it hits every category of variables, categorical, numerical, and logical. Some of the variables include gas or electric car, city fuel efficency , highway fuel efficency, the model of the car, the make of the car, how many cilenders the car has, the engine type, and how the car drives.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave imported the data that he found from fueleconomy.gov. When he realized that he wanted to look at gas milage for cars, he found the data that would best suit his overall question. To understand the data Dave used a scatterplot to look for coorelation. Since there was a strong positive coorelation he could say with confidence that both electric cars are better on gas milage, and that highways are better for gas milage then cities. Dave also looked at the significance of each variable and used the most significant ones in his data analysis. Dave compared and contrasted each one until he found the chart that could answer his question of which cars are most fuel efficant with the most confidence.
In the video dave did a scatterplot. During class we have done many scatterplots with the code library(ggplot2) data(Salaries, package=“carData”)
ggplot(Salaries, aes(x = yrs.since.phd, y = salary)) + geom_point() but instead of CarData he used big_epa_cars
He then analzed the scatterplot seeing if there was a coorelation between data sets. We go over coorelation in class, where we have to understand if there is a positive, negative, or no coorelation between the data points.
In the analysis Dave found out that electric cars will always have better fuel efficiency for both highways and cities. This makes sense because they are electric compared to gas cars. With this being said if someone is looking for a car that has the best milage it would make complete sense to buy an electric car. Also, when Dave seperated electric cars from gas cars he found out that highways one will get a better milage efficency then cities.
The most interesting thing I liked about the analysis is how smooth Dave was with the coding. Dave has all the coding memorized so when he makes a video and uses the correct code quick, therefore it is easy for him to explain what the code means, why he is using the code, and is able to teach instead of spending time looking up the code. With that being said I found that it is interesting how much more efficient the highway is then the city on gas milage. If I were living in a city I would really consider an electric car.