Background

One of the most frustrating aspects of the Trump administration is the lack of honesty around simple things, from President Trump’s White House visitor log to his golf outings. However, thanks to the work of Factbase, there does exist a fairly comprehensive database of the President’s public schedule, which outlines his activities in the White House and his domestic and international travel.

To see what data and trends we might be able to glean from this information, I ingested the entirety of President Trump’s public calendar and press pool schedule, and built a series of visualizations highlighting what I felt were the notable trends. Note that exact while exact amounts may be imprecise due to how Factbase’s data is collected, many of the trends are clear and meaningful. Please see my key takeaways below!

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Now that we’re a few weeks into the NBA season, it’s fun to see who is having note-worthy starts to the year. The chart above shows the top twenty scores in the league by game and their gain/decline relative to last season. It’s fun to see players like Kemba Walker and Zach LaVine elevating their games amongst the league elite thus far this year, and will be intersting to see if they can continue to keep pace with the likes of Curry, Durant, and Lillard.

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Background

If you’re a fan of the National Football League, so far this season you’ve heard an uninterrupted string of commentary surrounding penalties, rule changes, and the role of violence in the game. This is a fascinating topic because a number of greater trends have coincided to cause this moment - particularly the NFL’s desire to protect quarterbacks, rule changes that have reduced the amount of practice time teams have during the offseason and between games, and game-calling decisions designed to encourage passing and free up downfield receivers. Put together, these factors have led to a slow inflation in the number of penalties called per NFL game, which is now reaching a tipping point.

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A little over two weeks ago, my dad and I set out on a week-long adventure across the United States. Starting in DC and ending in Santa Monica, we spaced the trip out over seven days so that we would have a few free hours each day to stop and explore along the way! Check out the driving schedule we decided on below…

Trip Statistics

To distract us from our boredom along the way (looking at you, Indiana and Kansas), we tracked all of our gas fill-ups throughout the trip. Since we were pulling a U-Haul trailer, and knew there would be some significant elevation changes when going through the Appalachian and Rocky Mountains, we were curious to both have visibility into our gas usage, and some rudimentary miles-per-gallon data along the way.

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Recently, I discovered that I had set up my IFTTT account to log all of my Uber rides to a Google Sheets workbook. News to me! After some investigation, I found about six months of Uber ride data, nicely categorized with time, location, and driver data for each trip. If you’re looking to do any sort of analysis, or are interested in tracking your Uber rides, I highly recommend this IFTTT applet!

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Background

Unless you’ve been living under a rock, you’ve seen the scooters flying around - taking businessmen to work, tourists to monuments, and local kids around the neighborhood. These dockless electric scooters can be unlocked from any smartphone, riden for a low fee depending on duration, and parked anywhere in the city. They are intended to be used as a last-mile transit tool, helping commuters to and from their homes, offices, and local Metro/subway stops.

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Earlier this week I put together a series of graphics tracing Donald Trump’s approval rating by voter group and party affiliation, even throwing in the generic congressional ballot for good measure. While cool to have a point-in-time snapshot, I figured this could be a lot more useful if built into a live dashboard, with data updating automatically (as opposed to whenever I chose to save the charts).

To make that happen, I chose to create a Shiny Dashboard that integrates all of my existing charts and provides a nice structure for designing interactive web apps. Once I set up the scaffolding for how I wanted the page to look, it didn’t take too long to start filling in the boxes with different charts and scores.

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See the green line in the middle that starts up, wins a championship, and then drops off the map? That’s my grandmother.

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How popular is Donald Trump?

This question, however simple, carries long-reaching implications for America’s future. Trump’s political and moral standing come 2020 will likely define the path of the presidential election, which seems to contend either continued darkening of American politics or a great reversal of the current political environment.

As they have done for the many presidents before him, and will do for the many after him, pollsters have taken the nation’s political temperature constantly throughout the Trump administration. Luckily for us, the Huffington Post has an API which allows us to query all of these polls in aggregate and interrogate the results for specific questions.

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While I was working on my last post about NFL salary cap allocation, I struggled with how best to present the information. I knew I wanted the focus to be on outliers, but was finding a standard pie chart to be dull, and bar chart to be hard to compare. Fortunately, I stumbled upon the circumplex chart and realized this was exactly what I was looking for.

I found a number of good resources to help me on my way, most notably this post by Learning R, but ultimately ended up heavily customizing the charts until they felt right. Let’s take a look at what it takes to build and customize one of these charts!

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