Visualization 1 (ggplot)

This visualization shows overall energy production in the United States from 1980-2019. The data was sourced from the US Energy Information Administration (EIA), which has the best energy related government open data portal I have found so far. Quad BTU is a unit of energy equal to 1 quadrillion. It is the standard unit used by the US Department of Energy when recording energy data for the US energy budget. The visualization shows trends in a sharp decrease in coal and a steady increase in natural gas. I choose a line graph because I felt like it best depicted these trends, without being an interactive time series visualization.

Visualization 2 (ggplot)

Nuclear power plants are a controversial approach to renewable energy. They provide a low emission energy source, but are hard to maintain in countries with low infrastructure, economic development or even better more suitable renewable alternatives. This bar chart depicts the number of nuclear power plants within OECD countries. The United States is the clear winner in sheer number, with France coming in a surprising second place. Next in line are Japan and Korea, considering their post industrial economic sizes this isn’t surprising. Hopefully the OECD will update this data soon so we can see the trend change over time.

Visualization 3 (ggplot)

Countries containing global financial centers have always been interesting to me. Their policy leaders seem to understand the purpose of diversification in order to mitigate risk. Above is the change in the percentage of renewable energy out of total energy production for four countries with the largest global financial centers (Shanghai, London, Tokyo & New York). These countries are not representative of what “good” energy policy looks like, but they are representative of diversified energy policy. As we can see from the graph from 2000-2019 all four countries move towards around between 6 to 12%. This clustering toward the end of the visualization seems like a significant relationship between the big four. I plan on researching this trend further throughout the course of the portfolio project, seeing how countries keep their renewable percentage around a certain threshold in order to mitigate risk of an energy crisis.

Visualization 4 (ggplot)

Coal power plants are a very controversial source of power. Most people associate coal with mammoth trains from the ninth century, networking cities across America building our national economy. Now coal is used more simply like any other natural resource, to generate electricity. This bar chart pulls data from the US Energy Information Administration. The chart shows us that Pennsylvania holds the majority of Coal power plants, with Texas following a close second. Surprisingly Illinois is tied with Indiana in third, with Illinois being a democratic majority state it is surprising to me to see it with such high number of nuclear power plants.

Visualization 5 (ggplot)

Coal had a huge part in building the American economy, especially in states that are isolated in comparison to the vast western plains of the continent. As we can see from this bubble map the majority of coal power plants exist along the Appalachians and the Midwest. Furthermore as we can see from the scale in mega watts, the most efficient of these power plants are on the Appalachian, moving further down past the mountains into the gulf coast of Texas. One interesting pattern to note is that these coal powered plants reside within Republican controlled states. This is in contrast with the Northwestern portion of the country that has only one coal power plant.

Visualization 6 (Tableau)

This was the first graph with my own data I created for the course. The data is of G20 crude oil production sources from the OECD. The simplicity of a tableau bar graph makes it both effective and versitile. It is quiet obvious to me now after using Tableau why they are the market leader in dashboarding technology for most businesses. ______________________________________________________________________________________________________________________________

Visualization 7 (Plotly)

This choropleth map is of total energy consumption by state. Dark green represents more energy expenditure, while lighter means less. BTU stands for British Thermal Unit, it is a unit of heat or energy. The data was sourced by the US energy information administration, which has an excellent open data portal and spatial data resources. The only significant change I made to the data was adding abbreviations through the merging of a Kaggle data set. Plolty only reads abbreviations, not state full names when creating the location specification for the map.

Visualization 8 (Plotly)

This is a plotly line graph of renewable energy consumption by type, data provided by the energy information administration. I had diffculty combinig 6 different graphs because I could not figure out how to add title and legend to all six. As you can see the y axis title is actually the title of the third graph, alotugh it applies to all of them. Formating errors aside this was an interesting ivsualization to make. ______________________________________________________________________________________________________________________________

Visualization 9(Plotly)

This is a chloropleth map of CO2 emissions of the United States scoping from 199 to 2019 same as the Gif below. I really liked making this one because I feel like it has a lot of other applications to social science related data such as unemployment over time by state or even electoral college election results. The formatting for this was also faily easy with a clear title for both the map and the legend, not to mention the built in title for year. ______________________________________________________________________________________________________________________________

Visualization 9(Plotly Gif)

Gif Bar Chart showing moving C02 Emissions from 1999 to 2019. The Bars are divded into states as you can see with the legend and x axis. This was a very simple GIF to make, buidlign off the format of the bar charts that I had previously built but adding the change over time feature.