Module # 10 assignment

Issaiah. Jennings 

Module # 10 assignment

For this project, I used the economics dataset in R to analyze unemployment trends from 2000 to 2008 using ggplot2. First, I added a year column to make it easier to categorize the data by time. I then created a line plot to visualize the unemployment rate over time, providing a clear look at how the number of unemployed individuals changed each month. Next, I generated another line plot to examine the median duration of unemployment, highlighting periods when people stayed unemployed longer. Finally, I created a scatter plot with paths to compare the unemployment rate and median duration, using color to distinguish different years.



Visualizing time series data with ggplot2 helped uncover trends and patterns that might not be as obvious in raw numbers. The line plots made it easy to track changes over time, while the scatter plot provided insights into the relationship between unemployment and duration. Customizing the colors and layout improved clarity and made the information easier to interpret. This exercise reinforced how effective visualizations can simplify complex data and make trends more apparent in time series analysis.

> library(ggplot2)

> library(dplyr)

> 

> data("economics")  # Load the economics dataset

> 

> # Adding a year column

> economics$year <- as.POSIXlt(economics$date)$year + 1900

> 

> economics_filtered <- economics %>%

+   filter(date >= as.Date("2000-01-01") & date <= as.Date("2008-12-31"))

> 

> # 1. Line plot of Unemployment Rate

> ggplot(economics_filtered, aes(x = date, y = unemploy / pop)) +

+   geom_line(color = "darkblue") +

+   labs(title = "Unemployment Rate Over Time (2000-2008)",

+        x = "Year", y = "Unemployment Rate") +

+   theme_minimal()

> 

> # 2. Line plot of Median Unemployment Duration

> ggplot(economics_filtered, aes(x = date, y = uempmed)) +

+   geom_line(color = "darkgreen") +

+   labs(title = "Median Unemployment Duration (2000-2008)",

+        x = "Year", y = "Median Unemployment Duration (weeks)") +

+   theme_minimal()

> 

> # 3. Scatter plot of Unemployment vs. Duration with Year Coloring

> ggplot(economics_filtered, aes(x = unemploy / pop, y = uempmed, color = factor(year))) +

+   geom_point() +

+   geom_path() +

+   labs(title = "Unemployment vs. Duration (2000-2008)",

+        x = "Unemployment Rate", y = "Median Unemployment Duration (weeks)", color = "Year") +

+   theme_minimal()








> 



Comments

Post a Comment

Popular posts from this blog

Final project in this class Spring 2025

Image
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...
Read more

Module # 5 assignment

Image
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...
Read more

Assignment # 4: Time Series Visualization Using Tableau Public

Image
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...
Read more