Visual Analytics

Issaiah Jennings  Module # 7 assignment In this post, I tested visual distribution analysis using the mtcars dataset in R, following the recommendation from our textbook to use a grid layout for comparing variables. The mtcars dataset is built into R, so you can easily access it by typing data(mtcars) . I created scatter plots to compare variables like miles per gallon ( mpg ), weight ( wt ), and horsepower ( hp ). By organizing these plots into a grid, I could quickly see the relationships between the variables. Here's the R code I used to generate the plots: # Load the necessary libraries install.packages("gridExtra") library(ggplot2) library(gridExtra) # Load the mtcars dataset data(mtcars) # Create scatter plots plot1 <- ggplot(mtcars, aes(x=mpg, y=wt)) + geom_point() + ggtitle("MPG vs Weight") plot2 <- ggplot(mtcars, aes(x=mpg, y=hp)) + geom_point() + ggtitle("MPG vs Horsepower") plot3 <- ggplot(mtcars, aes(x=wt, y=hp)) + geom_point() +...

Issaiah Jennings  Module # 5 assignment In this visualization, I explored two design frameworks: Part to Whole and Ranking, using the dataset containing Average Position and Time. Part to Whole: Pie Chart The pie chart shows how Time is distributed across different Average Position values. Each slice represents a proportion of Time for a given position, helping to visualize how much each position contributes to the total Time. This is a typical "Part to Whole" design, which makes it easy to see the relationship between the categories and their contributions. Ranking: Scatter Plot The scatter plot shows how Average Position affects Time. Each point on the graph represents a pair of Average Position and Time values. By looking at this, you can spot trends—like how Time increases as Average Position rises. This type of plot is great for understanding relationships and rankings between two variables. import pandas as pd  # Import pandas for data manipulation import plotly.express...

Time Series Visualization  The goal of this assignment is for you to  create a time-series visualization  using  Tableau Public  based on a curated dataset. You will work with  Monthly Modal Time Series data  sourced from  Data.gov , which contains  12 different variables I created a tree map to show the ridership of a select few cities in the dataset. A tree map is a great way to visually compare the relative size of each city’s ridership. The bigger the block, the higher the ridership for that city. From the tree map, it’s clear that Philadelphia has a much larger ridership compared to the other cities. This makes sense because it’s one of the largest cities in the U.S. with a dense transit system. This chart helped me quickly identify which cities are leading in ridership and which ones have smaller numbers. For the Area Chart, I focused on vehicle revenue miles, vehicle revenue hours, and ridership from 2014 to 2019. An area chart...