Vision

This fall I moved into a new house, which I quickly learned has an extremely unreliable thermostat. As someone who likes to be cool in the evening for bedtime, but also doesn’t love wearing sweaters indoors, I wondered if there might be a better way of monitoring my house’s temperature.

Fortunately for me, I had a surplus Raspberry Pi lying around, which I quickly determined could be leveraged into serving as the brains of my thermostat, given the right input sensors. Plus, the Pi easily connects to the AWS IoT interface, which would allow me to transmit data to AWS and then process and save that data in a variety of locations for analysis.

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Background

One of my favorite things in the world (except for one particular night this March) is watching my Virginia Cavaliers play basketball. I’m particularly excited about this year’s team, which I think is one of the deepest in recent memory and is going to be highly motivated all season coming off of last year’s disappointing finish against UMBC. I’ll leave the detailed analysis of their performance to the professionals over at Streaking the Lawn, but wanted to share a bit of work I put together on our most important returning players.

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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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