Introduction + Visualization Inspiration
Unemployment is always a concern in the United States, even more so during the 2008 recession and now post lockdown era of Covid-19. As a graduate student myself who would start to look for employment in my field in the next year, this is a concern of mine that weighs on me. The job market is rough, especially in a time where layoffs are happening in the thousands in some companies. When I found this dataset on US unemployment, I thought it would be interesting to see the trends that emerge.
I was also inspired by the visualizations done by the US Department of Labor, which shows an interactive map of unemployment rate visualizations for veterans, and I was hoping to do the same, albeit not as fancy, with the skills and tools that I have.
Here are the research questions I had in mind while doing this project:
1. How has unemployment changed over the years? Which year had the worst unemployment?
2. Which states had the lowest and highest rates of unemployment in the most recent year?
3. What has unemployment been like for states with major cities?
Datasets/Tools
I collected my dataset through Kaggle titled “Unemployment in America, Per US State” and decided to use that as it had thousands of rows split up by state, month, and year. I then opened the dataset in OpenRefine to check if it needed to be cleaned up, which I realized I had to do as some column’s cells were not numeric, some needed to be retitled for better understanding, and some rows needed to be removed. Once I cleaned it up, I used R, which is a programming language, and RStudio, which is a software tool that involves datasets and R coding to create visualizations.
Methods + Process
Cleanup
1. Remove “Los Angeles County” “District of Columbia” and “New York city” as they are outliers from actual states by using a text facet and clustering to delete
2. Rename rows in a way that would work in RStudio (no spaces, no symbols, shortened)
3. Make sure columns are shown as numerical when necessary by using a Transform edit
Creating Visualization + Peer Review
I put in my dataset through RStudio and input multiple packages and codes to get my results. I input packages like “treemap” so that I could get the desired visualization. When consulting with my partner Sandy for peer-review, I asked what kind of charts and graphs she imagines seeing for this kind of data. She gave some insight that bar and line graphs would make the most sense, especially with such a large set of data. She also mentioned how using these kinds of graphs would be really useful for comparison when analyzing them.
Results + Findings
1. How has unemployment changed over the years? Which year had the worst unemployment?
Unemployment has been relatively unsteady based on the bar graph and line graph. There have been many ups and downs in just the last 4 decades alone. 2020 stands out as the year unemployment was at its highest, most likely due to the pandemic and people losing their jobs. 2000 seemed to have low unemployment compared to later years, which is notable as that was an election year as well (Bush & Gore).
2. Which states had the highest rate of unemployment in the most recent year?
Although the chart is very chaotic looking due to there being 50 states being shown, the clear high rate of unemployment would be in California, followed by Texas, in the most recent year 2022.
3. What has unemployment been like for states with major cities?
For this, I chose states that have major cities that I was familiar with. Similar to the previous chart, the tree map shows that California had the highest number of unemployed people who would be eligible for employment.
Reflections
Honestly, this lab was a bit difficult for me as a beginner to visualizing datasets. Especially when the last project involved a relatively simple software tool like Tableau, RStudio definitely was a challenge. I even had to add the text for the tree map because I couldn’t figure out why it wasn’t showing up in the visualization, despite using the same code format provided in the R Graph Gallery. I feel like it would take a lot of practice for me to get the hang of it, as for me it seems better fit for advanced users who know what they’re doing. I also probably should have been a bit more organized about my coding, as some of the ones I did I kept replacing with new inputs instead of copy/pasting, so one or two codes did get lost since I messed around with it too much trying to figure it out.
Sources
Download the RStudio Ide. RStudio. (n.d.). Retrieved March 20, 2023, from https://support–rstudio-com.netlify.app/products/rstudio/download/
6 types of unemployment and what makes them different – TheStreet. Retrieved March 20, 2023, from https://www.thestreet.com/personal-finance/types-of-unemployment-14721084 – Image Source
Oh, J. (2023, March 2). Unemployment in America, per US state. Kaggle. Retrieved March 21, 2023, from https://www.kaggle.com/datasets/justin2028/unemployment-in-america-per-us-state
Visualization gallery. DOL. (n.d.). Retrieved March 21, 2023, from https://www.dol.gov/agencies/odg/visualization-gallery/vets-unemployment-rate
Holtz, Y. (n.d.). Help and inspiration for R charts. The R Graph Gallery. Retrieved March 21, 2023, from https://r-graph-gallery.com/