Correlation is a mutual relationship or connection between two or more things. A good way to show the correlation of data is by using a scatter plot.

For this weeks assignment we used ggplot2 which is a data visualization package for the statistical programming language R.

I used the mtcars data set to create the scatter plot above using ggplot2. It shows the correlation between the miles per gallon and horse power of the cars in the data set.

The data shows that cars with a higher mpg tend to have a lower horse power while cars with a high horse power have a lower mpg.

I added a white regression line to show the flow of the data.



When spotting differences it is essential to classify or group units into categories and then from there one can find the outliers or standouts. What we are trying to do is to look at each variable for differences while also seeing the differences across all variables.

This week we used RStudio to create a visual. I decided to play around with the mtcars dataset that comes with the r programming language.

x <- setNames(mtcars$mpg[1:6], row.names(mtcars)[1:6])

Above is the code I used and below is the result.

Top mpg cars from the mtcars dataset

I realized this visual didn’t do a good job of showing the value differences so I created a more descriptive one.



This week we used the program Plotly which is a technical computing company that develops online data analystics and visualization tools.

We focused on Part of a Whole when it comes to visual analytics and it doesn’t get any simpler than the titoe. We are focusing our visual analytics on a certain part of a whole of data provided by our professor.

As described by Few, “The most frequent and simplest types of analysis involves comparing parts of a whole and ranking them by value.”

As we can see from the visual display as time passes the amount of time passes the average position does to. The Graph above shows how there is an increase in average position as time passes and eventually evens out by the end.