2024-02-04

Explaination of A Thing

  • This was made for a college project, well not The Project but instead a project called HW3.
  • I’m supposed to find a data set I can work with (the hardest part) and show that I know how Ioslides, ggplot2, plotly, and Latex work through that data set.
  • You would not believe the loops of confusion caused by me messing up ‘sep=’.
  • I settled on a data set called Shop Customer Data from Kaggle.com which includes some generic imagined information about 2000 imagined customers.
  • Now onto the The Things of A Thing…

Starting with just a barplot with a plain background

(With the terms at being short for healthcare, Engineer, Lawyer, Entertainment, Artist, Executive, Doctor, Homemaker, and Marketing respectively.)

Now for a more dynamic plotly version of the same Basic idea

And now for the ggplot2 version

Hopefully Some of this makes sense

  • I suppose that what I was trying to with those three slides is to show the differences between the basic R plots, the plotly, and ggplot2 even while they’re presenting the same basic data.
  • With the code at points being as straightforward as:
DistributionPP <- plot_ly(x = UniqueP, y = AmountP, type = "bar", 
                          name = "Fancier Distribution of Professions Among Customers")
DistributionPP

Now I suppose I should try something more Spicy, so here’s the data but divided between Males and Females

Still not spicy enough? How about some math!

\[ 1 + 1 = 2 \] \[ \sqrt(-1) = i \] \[ pi = 3.14159265358979323846264338327950288419716939937510... \] Shocking stuff right here.

Now for some math and data, comparing age to family size.

\[ age = b_0 + b_1 * Family.Size + ε ; b_0 = 46.88 , b_1 = 0.552 \]

And here’s the same thing, but with ggplot2

The End, really?

  • Yeah, if it wasn’t already clear, this project was rather aimless.
  • I had no big point to make unfortunately, this is just some visualizations of some generic data.