Posts for Tag: data

500 Most Popular Guitar Songs To Learn To Play

Posted In: Music
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Here are the most popular songs on ultimate-guitar.com, a site dedicated to helping guitar players learn to play guitar songs.

Since sheltering-in-place for the last few months, I’ve been playing guitar more often than I used to. ultimate-guitar.com is a website I visit frequently in order to learn the chords and lyrics to new songs and also keep a “notebook” of songs that I like to play. I thought I’d check to see what songs are most popular on the website.

These songs are mostly user submitted and voted on. Many popular songs may have many different user submitted versions. I downloaded the most popular songs and aggregated across the different versions of the same song to get this list of the 500 most viewed songs on the website.

Sources and Tools:

Data was downloaded from the ultimate-guitar.com website. And the visualization was created using javascript and the open source plotly graphic library.

US city names

Renewable Electricity Generation in US is Now Greater Than Coal

Posted In: Energy | Environment
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A remarkable thing is happening in the United States and in other places around the world. Partly due to the coronavirus pandemic and partly due to changes in natural gas and renewable energy prices, renewable electricity is now a larger fraction of the US electricity grid than coal. As of early May 2020, the fraction of coal generation of US electricity is about 18% while renewables (hydroelectric, wind, solar and geothermal) account for nearly 20%.

For the entire year of 2019, coal accounted for about 24.2% of US electricity generation, while renewables accounted for 17%.4. And in 2018, coal was 28.4% and renewables were 16.8%. When you include nuclear (not technically a renewable resource, but zero emissions of greenhouse gases), about 42% of US electricity generation in 2020 comes from zero carbon sources, while fossil fuels make up the remaining 68%.

This is good news because renewables produce little to no pollution that contributes to urban air quality, health issues and climate change. Coal is by far the worst electricity generation source when it comes to air pollution that impacts human health and climate change. So this shift away from coal and towards renewables is very good news.

Here’s the same graph but showing instead the fraction of electricity from each source (you can hover over the graph to get daily values).

Source and Tools

Data is downloaded from the US Department of Energy’s Energy Information Agency (EIA).. The graph is made using the open source, javascript Plot.ly graphing library.

coronavirus deaths

US Coronavirus Deaths

Posted In: Health
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This visualization is meant to demonstrate the exponential growth of coronavirus deaths in the United States starting in early March when the first confirmed deaths took place. Some reports indicate that the first deaths may have been as early as early February, though that is not shown in this data.

In the animation, each day is about 1 second long so on days with fewer deaths, the figures show up more slowly, while on days with greater deaths, the figures come very, very quickly.

Deaths stop growing exponentially in early April and start to level off and plateau. However, they haven’t yet started to decline significantly so we are still seeing thousands of deaths each day (as of late April).

The data and visualization will be updated daily with data from Covidtracking.com.

For more information about the virus and the disease and data collection, you can find good information on the CDC website.

Sources and Tools:

Coronavirus cases are obtained from covidtracking.com. And the visualization was created using javascript and the font with people figures is called Wee People and was created by Albert Cairo and Propublica.

coronavirus deaths

Visualizing the scale of unemployment due to COVID-19 pandemic

Posted In: Unemployment
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The number of Americans who have recently filed for unemployment due to the coronavirus pandemic is equal to the entire labor force of several states put together.

click on the button below to see a new set of states.

A record 16 million Americans just filed for unemployment due to the coronavirus pandemic at the end of March and early April 2020. This is an amazingly large number of people and I wanted to visualize how many people this actually is. For context, the US Department of Labor statistics states that in February 2020 (before the pandemic hit the United State) there were 164.2 million workers in the Civilian Labor Force.

The Bureau of Labor Statistics (BLS) site defines “Civilian Labor Force” as such:

    “The labor force includes all people age 16 and older who are classified as either employed and unemployed, as defined below. Conceptually, the labor force level is the number of people who are either working or actively looking for work.”

This basically means that approximately 10% of the entire workforce of people (both employed and unemployed in Feb 2020) are now out of a job. While 10% is a large, unprecedented number in our lifetimes, comparing these number to the size of the workforce in several states helps to provide more context. The visualization shows a random collection of states whose total labor force is equal to the latest unemployment numbers. If you click the button you can see a different set of states that have the same total labor force.

Predictions are that the number of unemployed will grow as the shutdowns and social distancing measures to contain the virus continue through April and into May. I will update this graph to reflect new numbers as they come out.

And we can only hope that people will be able to manage these tough economic times until we contain the virus and the economy rebounds.

Stay safe out there: stay away from people and wash your hands!

Sources and Tools:

Data on unemployment was obtained from the US Department of Labor website and labor force numbers by state are downloaded from the Bureau of Labor statistics. And the visualization was created using javascript and the open source leaflet javascript mapping library.

US city names

Tracking US Coronavirus Cases by State

Posted In: Maps

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The coronavirus (SARS-CoV-2) is literally affecting the entire globe right now and changing the way we live our lives here in the US and all over the world.

There are quite a number of different coronavirus-related dataviz out there, but as we shelter-in-place I wanted to add a map that looked at a number of different metrics that tell us about the coronavirus pandemic by US states and look at those metrics on a population basis.

There are a number of data sources that I’ve found that publish data about the coronavirus and the resulting disease (Covid-19) in the United States:

This map is based on the data compiled from covidtracking.com, partly because it has a good API and also lists testing, cases and deaths. The data I’ve included on the map is:

  • Numbers of coronavirus cases – i.e. tested positive for virus
  • Numbers of coronavirus tests administered
  • Numbers of deaths due to coronavirus

Each of these is also calculated per 100,000 population in the state:

  • Numbers of coronavirus cases per 100k people- i.e. tested positive for virus
  • Numbers of coronavirus tests administered per 100k people
  • Numbers of deaths due to coronavirus per 100k people

These latter metrics are important because numbers of cases or deaths can be obscured by small or large populations but per capita data (or per 100k capita data) can point out interesting outliers.

It is important to note that the data is far from perfect. There is probably significant underreporting of tests, cases and deaths. The data is a collection for the various local and state agencies that are working hard to deal with the medical, social and political ramifications of the pandemic, while also collecting data. We don’t know how many Americans have coronavirus because of lack of testing.

Also important is that the number of positive cases is a function of how much testing is taking place so cases does not necessarily represent the exact prevalence of the virus, though there will probably be good correlation between cases and actual coronavirus infections. Luckily it sounds like tests are becoming more widely available so hopefully those numbers will go up sharply.

For more information about the virus and the disease and data collection, you can find good information on the CDC website.

Sources and Tools:

Coronavirus cases are obtained from covidtracking.com. And the visualization was created using javascript and the open source leaflet javascript mapping library.



coronavirus by state

US County Electoral Map – Land Area vs Population

Posted In: Maps | Uncategorized | Voting
Election Results by County

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County-level Election Results from 2016

This interactive map shows the election results by county and you can display the size of counties based on their land area or population size.

A little while ago, I made a map (cartogram) that showed the state by state electoral results from the 2016 Presidential Election by scaling the size of the states based on their electoral votes. The idea for that map was that by portraying a state as Red or Blue, your eye naturally attempts to determine which color has a greater share of the total. On a normal election map of the 2016 Election, Red states dominate, especially because a number of larger, less populated states happen to vote Republican. That cartogram changed the size of the states so that large states with low population, and thus low electoral votes tended to shrink in size, while smaller states with moderate to larger populations tended to grow in size. Thus, when your eyes attempt to discern which color prevails, the comparison is more accurate and attempts to replicate the relative ratio of electoral votes for each side.

This map looks at the election results, county by county. An interesting thing to note is that this view is even more heavily dominated by the color red, for the same reasons. Less densely populated counties tend to vote republican, while higher density, typically smaller counties tend to vote for democrats. As a result, the blue counties tend to be the smaller ones so blue is visually less represented than it should be based on vote totals. More than 75% of the land area is red, when looking at the map based on land areas, while shifting to the population view only about 46% of the map is red. Neither of these percentages is exactly correct because each county is colored fully red or blue and don’t take into account that some counties are won by a large percentage and some are essentially tied. However, the population number is certainly closer to reality as Trump won about 48.8% of the votes that went to either Trump or Clinton.

Instructions

This tool should be relatively straightforward to use. Just click around and play with it.
The map has a few different options for display:

  • Hide Circles – just shows the county map
  • Show land circles – where the area of the circle matches the area of the county itself, though obviously shaped like a circle. The counties are colored red or blue depending on whether Trump or Clinton won more votes in that county.
  • Show population circles – where the area of the circle matches the relative population of the county itself. More populated counties will grow larger while less populated counties will shrink. The counties are colored red or blue depending on whether Trump or Clinton won more votes in that county.
  • Selecting the No County Overlap checkbox will spread out all of the circles so you can see them all. The total displayed area of the county circles is the same in either land and population view, though if the circles are overlapping, you may see less total colors.

Visualization notes

This was my second attempt at using d3 to generate visualizations. I typically use leaflet to do web-based mapping but I wanted the power of d3 which has functions for the circles to prevent overlapping. This map was inspired by Karim Douieb’s cool visualization of 2016 election results. I modified it in a number of different ways to try to make it more interactive and useful.

This visualization does not actually simulate the collisions between the circles on your browser. It is a bit computationally taxing and causes my computer fan to turn on after awhile. So instead I ran the simulation on my computer and recorded the coordinates for where each circle ended up for each category. Then your browser is simply using d3 transitions to shift positions and sizes of the circles between each of the maps, which is simpler, though with 3142 counties, it can still tax the computer occasionally.

Data and Tools
County level election data is from MIT Election Lab. Population data used is for 2018. The visualization was created using d3 javascript visualization library.