Visual Analytics

Final Project: U.S. State Public-School Expenditures   Name: Issaiah Jennings   Course: LIS4317.001S25.15856 – Visual Analytics   Semester: Spring 2025   Software Used: RStudio   Step 1: Selecting the Dataset For this project, I decided to work with the Anscombe U.S. State Public-School Expenditures dataset, which comes built into R, so it was easy to access and start working with. I chose this dataset because it includes a solid mix of variables related to education spending and student outcomes across all 50 U.S. states. It’s the kind of data that can help answer real-world questions about how money might impact education. Some of the main variables I focused on are: Expenditure per student – This shows how much each state spends on average for every student enrolled in public school. Graduation rate – This tells us what percentage of students are finishing high school in each state. Average income of residents – This giv...

Issaiah. Jennings Module # 13 For this cool animation, I used the animation package in R to create a GIF that shows how a histogram changes as the mean of a dataset increases over time. It’s basically a moving picture of data that shifts a little more to the right with each frame. I really enjoyed this assignment give it a try!  library(animation) > > ani.options(interval = 0.05) # 100 / 0.05 = 20 fps > > > # Save the animated histogram > saveGIF({ + par(mar = c(4, 4, 2, 1)) # Set margins + for (i in 1:100) { + # Simulate normal data, shifting the mean over time + data <- rnorm(200, mean = i * 0.1, sd = 1) + + # Plot histogram + hist(data, + breaks = 20, + col = "skyblue", + border = "white", + main = paste("Iteration:", i), + xlim = c(0, 20), + ylim = c(0, 40), + xlab = "Value", + ylab = "Frequency") + + # Add ...

 Issaiah. Jennings Module # 12 For this project, I explored how to visualize a simple social network using RStudio. Instead of using NodeXL in Microsoft Excel, I chose to use RStudio because I’m more comfortable with it and wanted to try out the ggnet2 function from the GGally package, which works well with ggplot2 . install.packages("GGally") install.packages("network") install.packages("sna") install.packages("ggplot2") library(GGally) library(network) library(sna) library(ggplot2) # Create a random adjacency matrix for 10 nodes net_matrix = rgraph(10, mode = "graph", tprob = 0.5) # Convert the matrix into a network object net = network(net_matrix, directed = FALSE) # Assign names to each node (a to j) network.vertex.names(net) = letters[1:10] ggnet2(net) ggnet2(net,        node.size = 6,        node.color = "black",        edge.size = 1,        edge.color = "grey") ggnet2(net, size = 6, color = re...

Issaiah. Jennings Module # 11 assignment For this assignment, I recreated a classic Tufte-style dot-dash plot using ggplot2 in R. I started with a dataset that shows yearly values from 1967 to 1977. Using ggplot2, I made a clean line graph with dots showing the exact values for each year. I also added dashed horizontal lines to highlight important thresholds at 5 and 6, which matches the minimalist, data-focused style Tufte is known for. I customized the y-axis to show dollar values and used serif fonts and annotations to keep the design simple and clear. # Load required libraries library(ggplot2) # Data x <- 1967:1977 y <- c(0.5, 1.8, 4.6, 5.3, 5.3, 5.7, 5.4, 5, 5.5, 6, 5) df <- data.frame(Year = x, Value = y) # Plot ggplot(df, aes(x = Year, y = Value)) + geom_line(color = "black") + geom_point(size = 3) + geom_hline(yintercept = 5, linetype = "dashed") + geom_hline(yintercept = 6, linetype = "dashed") + annotate("text", x = m...