Developing Data Products Course Project (a simple approach to jitter())

Understanding your raw data is as important as analyzing it this is something that a visualization approach can help you with. When you plot integer - or factor - variables much of the advantages of visualization slide away, UNLESS you understand a great function such as jitter()! This simple shinny app will let you plat with the jitter() function in both x and y axis for a plot with factor variables. It might not change the results of your predictive models, but it can sure help you understand your data

Jitter is a base function that add noise to a numeric vector. The function consist on 3 inputs:

• x numeric vector to which jitter should be added.
• factor numeric.
• amount this is out of this shiny app scope

During the next slides we are going to use the airquality data from the datasets library

library(datasets)
plot(Temp~Month,data=airquality)

Here you can see how all data points are oversocked on each value of the x axis

As you can see, by playing with the factor values included in the jitter function you can have a better undestanding on how it works and more importantly how can it help you understand your data in just one function.

Thank you and good luck to you too!

arturocm