Module 8 - Correlation Analysis and ggplot2

 For this assignment, the class was tasked with creating a correlation analysis diagram. I chose to create a heatmap of the mtcars data set. The diagram demonstrates the correlation between each variable in the dataset. Items in light blue are highly correlated, whereas the darker colors are less closely correlated. 


Below is the code I used to create my heatmap:

qplot(x=Var1, y=Var2, data=melt(cor(mtcars)), fill=value, geom="tile")

I went through several iterations of the qplot function before realizing that the code had been provided. At first, I was confused as to how qplot worked and tried to use only two variables that I named, before consulting with ChatGPT to develop a working solution. I discovered that heatmaps represent a correlation analysis between groups of variables that are identified as var1 and var2, x and y respectively. 

This assigment helped me to understand how heatmaps can represent correlation analysis of variables within larger datasets.

Comments

Post a Comment

Popular posts from this blog

Module 2 - Geographic Visualizations in Tableau

Image
The following visualization (which can be interacted with here: https://tinyurl.com/publichousinginflorida) was developed based on Housing and Urban Development data for public housing developments in the United States, updated in December of 2023. The visualization represents the average household income (size of the marks) and average rent (shade of the marks) for each development in the state of Florida.  The dataset used for this visualization contains many variables, including number of occupied dwellings and vacancies, percentage of demographic groups in each development (Native American, Hispanic, female head of household with and without children), among many others.  Initially, I found it difficult to organize the visualization, because my impulse was to create pie chart markings based on occupancy. However, this proved difficult. This visualization is the result of a compromise I made with myself to represent some aspect of the data without thinking too hard about ho...
Read more

Final Project

Image
 For the final project, I created a correlated graph a selection of data from the Orlando Police Department. View here. Between 2009 and 2024, suspect fatalities were fewer than suspects shot. Suspect fatalities were alarmingly high.
Read more

Module 7 - Distribution Analysis

Image
 For this module, we were assigned to create a visualization based on distribution analysis. I created a bar graph based on the mpg table of the mtcars dataset in R. Below is my graph: This graph shows the miles per gallon of each vehicle in the mtcars dataset. A drawback of this graph is that the vehicles are not identified on the x axis, therefore, the graph does not demonstrate the best information on distribution for this dataset.  Here is the code:  data("mtcars") head(mtcars) counts <- table(mtcars$mgp) counts2 <- table(mtcars$wt) counts View(mtcars) barplot(mtcars$mpg, main = "Miles per Gallon", xlab = "MPG", ylab = "count", col = "orange")
Read more