Hello, again blog readers! It's been about 60 days since my last post and it's about time I did another Fitbit step comparison - the last one I did was in April. Yikes.
Before I jump into that, let me take a minute to answer some questions you might have:
Well, since I last blogged, a few things have happened. Like I went to Chicago to present at the Stata Conference and that was pretty awesome. I've never presented at a professional conference and our work was very well received. I got to visit San Diego a few weeks ago and that was very nice. We got to see old friends and hang out with family. I loved going back home. The beach was perfect, the weather was just right and the Mexican food was delicious.
Besides that, work's good and time has gone by pretty fast. I can't believe I've been at my new job for 3 months. Seems like soon I won't be able to claim claim "new girl" status anymore. In Seattle, we've been exploring plenty of dog friendly places and we try to discover new places every weekend.
Most recently, my blog was featured on Stata News! It felt so awesome seeing my name (with an accent and everything) and my blog post mentioned there. Thank you, Stata! For those of you who left a comment in that blog post, I will respond to it shortly.
Why haven't I kept up with my blog?
Not sure I have a good answer for that one - the easiest explanation is lack of free time. Any free time I do have I spend it with my fiancé and my dog, running, doing yoga/barre, or exploring the city. I will strive for more blog posts. Really. I like blogging, I like writing and I like exploring my data and sharing it with you all.
You presented it and we you blogged about it, so where's gcharts?
Yes, I presented beta version at the conference. Will and I are working on it and hope to have it ready for public release in the next few weeks. Stay tuned.
If you have any additional questions, feel free to ask away.
Okay, now moving onto the blog post. So what has my steps data looked like from May to now? This is especially interesting as I did another half marathon in June and moved soon after. My schedule, work, and day to day activities have changed a lot since moving from San Diego.
Below you'll see my average daily steps for the last 6 months compared to my friend Will's average daily steps. Interestingly, our steps show a similar trend leading up to and right after May 2016. May was my last full month in San Diego, so my hypothesis is that the decrease in joint work break walks and half-marathon training has something to do with that decrease for the both of us.
Keeping with the tradition of posting monthly graphs, I've graphed our daily step activity for May through August below.
As I mentioned earlier, Will and I ran the Rock'n'Roll Half on June 5th in San Diego which explains that spike. That date also happens to coincide with the personal record, in terms of daily steps (42,431 steps) and half-marathon finish time, of your's truly. 1:58:32. I know! Thank you. Not bad for my second half. Although, I must say that course was a lot easier than my first one. I also moved to Seattle in June and unfortunately for Will, the asphalt around Balboa Park was pretty bad so and he tripped and scraped his knees and palms around mile 8 or 9 of the course (he needed stitches) and so those injuries are the reason for his low step count that month.
July is interesting in that there are a few days with very similar steps. A group of us, including Will, had Fitbit challenges (Work Week Hustle and Weekend Warrior Challenges - these are challenges through the app where you compete with friends in total steps during weekdays and weekends. More on that in a post to follow) and so I'm thinking that's why there are a lot of days with similar step activity, not sure though. I'll have to look into that and get back to you.
August is funny. I didn't meet goal on a Saturday during my vacation! I tried, people, but Orange County is not that close to San Diego (traffic took a while) and lounging on the beach doesn't allow for many steps. Oh well, at least I had fun in the sun, and isn't that the point? Overall, August was a pretty good month for steps.
Below, I've graphed our average hourly step count during weekdays (I also excluded holidays). I wanted to see what our graphs looked like together before and after I moved and I'm using Will's hourly step shape as a control. This data represents 60 days before and 60 days after moving. Interestingly, our hourly weekday graphs are very similar when I was in SD (March - May) and are not so similar when I moved (June-August). The similarity of our hourly graphs is due to the similarity in work schedules, meetings, lunch breaks, etc. Both of our hourly shapes changed after I moved. For me, the reason is that I walk more now that I don't drive to work and I'm allowed to bring my dog to the office so there's that difference, too. I take a couple of dog walks (about 2 a day, though, so not that many) 2-3 times a week. For Will, I'm not sure what's changed in his day-to-day, so you'll have to call and ask him. My speculation is now that our other friends/coworkers also have Fitbits/Garmin trackers maybe they take walks at different times. I'll find out.
Now that I've updated you on the last few months of Fitbit activity, I thought it appropriate to look at the last year just to see what that all looks like.
That's July 2015 - August 2016 average daily steps for the both of us. Cool, right? While, I only beat Will's average daily step count 4 out of 13 months, you can see how positive of an effect training for both half marathons had on our step activity. I'm tempted to sign up for another one soon, but it should definitely be held someplace warm. It's already cooling down over here and running outside is hard for this Southern Californian.
Well, there you have it. I hope you've enjoyed this latest blog post. I'll leave you with this cool picture of my Rock'N'Roll race course graph as seen through my Fitbit app. I loved saying farewell to my favorite city by running through some of my favorite parts of it. In case you're wondering, Mission Bay is my absolute favorite running spot in San Diego.
And just so you know what I'm talking about, here's a picture I took of Mission Bay on my last sunset run there. I know this isn't Instagram, so this should go without saying, but no filter was used to touch this picture up. Gorgeous, right? Until next time, blog readers.
Let's get local. I'm talking about San Diego beer. This is the third post regarding data I've downloaded and cleaned from BreweryDB. For a while now, I've been wanting to map all the breweries in San Diego County. Why? Well, for starters, San Diego is a great place for craft beer enthusiasts and I keep hearing about how many breweries there are in SD County. Secondly, why not? If you've got the data, use it. So now with BreweryDB and their brewery information, I can finally do that. See the map I made below. Note, however, that this map doesn't include ALL breweries in SD County. The data from BreweryDB that I downloaded is only for breweries that had at least one "verified" beer entry in their database. Also, I only included unique breweries, leaving out tasting rooms or additional brewery locations. This left me with 76 breweries which have been mapped below:
This visualization was made with Stata and Google Charts API using the links to brewery icons from BreweryDB. Any breweries without icons are shown with default red markers.
Of the available beers in BreweryDB for these 76 SD County breweries, the make-up of beer styles is as follows:
It's a pretty good variety of beer and it's great if you love Pale Ales, IPAs or Double IPAs as those seem to be the most common types of beers brewed in SD (within the North American Origin Ales category).
Using Stata's gpsbound command, the 2010 Census county shape files, and the latitude and longitude brewery information within the BreweryDB dataset, I summed up the number of breweries in each county and each state.
*This only includes breweries from BreweryDB that had at least one verified beer entry in the database.
California ranks first in terms of unique breweries among states and San Diego County ranks first among the counties from this dataset. San Diego is a pretty great place if you like craft beer (and even if you don't, San Diego is a pretty likable place with perfect weather). The breweries here make all kinds of beers and most tasting rooms are dog and family friendly. Go check one out if you're around. Some of my favorite SD County breweries are:
This is a continuation of the work I did in part 1 using BreweryDB data. I've cleaned most files with brewery and beer information (only 44 files could not be parsed using insheetjson in Stata, but I'm working on those separately, they will be included in my analysis at a later time). For now, this analysis only includes files which I was able to parse.
So, who makes beer? Breweries. What styles are there? Well let me tell you. There are the following styles in BreweryDB. Under each style there are up to 170 beer categories (or sub-styles?) like the ones I described in Part 1.
Looks like the unique count of breweries with North American Origin Ales style surpasses all other counts with close to 3,500 in this (somewhat complete) sample. With the explosion of micro-breweries, and all kinds of people getting into beer, I guess I'm not too surprised. So, what types of beers are contained within this style? I've summarized them here. The top 50% of the North American Origin Ales are made up by the following styles: American-Style India Pale Ale (19%), American-Style Pale Ale (15%), American-Style Amber/Red Ale (11%) and the Imperial or Double India Pale Ale (9%). See table below.
Interesting. To be honest, I don't like IPAs, pale ales, or IIPAs. They're just too hoppy for my taste. Pass me a Belgian instead. On that note, here are the counts of breweries who make Belgian & French Origin Ales.
A lot lower than the North American Origin Ales, but with the IPAs growing out of control (or so it feels like they are here in San Diego), I guess that makes sense. Plus, while there are fewer breweries that make Belgian and French Ales these types of beer seem to have been around longer. For example, the earliest established brewery with Belgian and French Ales is in 1121 by Leffe versus 1471 for the earliest North American Ale which interestingly enough corresponds to the beer style "Golden or a Blonde Ale" made by none other than a Belgian Brewery: Hetanker. Aren't Belgian breweries just the best?
I've mapped the breweries that make Belgian and French Origin Ales below that are located in California, Texas, North Carolina, New York, and D.C. Why these states? These are the states where most of my site's visitors are from :)
Like I said in the first post, there's a lot of data and I've only shown you a little bit of it! Look forward to more posts that use additional variables that I haven't even mentioned and maybe cooler maps. Cheers!
A few days ago, I attended the San Diego Economic Roundtable at the University of San Diego which included a panel of experts discussing the economic outlook for San Diego County. My favorite speakers were Marc Martin, VP of Beer, from Karl Strauss and Navrina Singh, Director Product Management, from Qualcomm. Singh had a lot to say about data, technology, innovation and start ups in San Diego County. Did you know that there are 27 coworking spaces, accelerators, and incubators in San Diego? I sure didn't. Martin's discussion of beer, all the data he showed, along with some cool maps, sparked this blog post which has been a long time coming. In case you don't know, I'm quite the craft beer enthusiast! Allow me to nerd out as two of my favorite things come together: data and craft beer.
Martin's talk focused on the growing number of microbreweries and craft beer data. Here are some cool facts I came away with from his presentation that are worth mentioning again:
On to my blog post: While searching for beer data for this blog post, I stumbled across a gold mine: BreweryDB.com. I got access to their data using API. In the last few days, I've looped through over 750 requests using Stata's shell command and Will's helpful post on Stata & cURL.
In the table below I've detailed the number of beers (listed as results) under each style ID in BreweryDB's database. There are a total of 48,841 beers as of January 17, 2016. When filtering for the word "Belgian" in the style name, I got a total of 5,883 beers. Can you guess what my favorite type of beer is? :) I made the table below using Google Charts API table visualization. There are a total of 170 beer style IDs under BreweryDB and I've summed up the number of beers under each style. You can sort by ID, Beer Style or Results by clicking on whichever column title you'd like.
Disclaimer: This product uses the BreweryDB API but is not endorsed or certified by PintLabs.
Seeing as BreweryDB's data is extensive and I'm oh-so excited to share with you some of my findings, I've decided to make a series of blog posts about this. This is why this is part 1. This is only the tip of the iceberg, my friends, and I'm not sure how big of an iceberg I'll be uncovering, but stay tuned for more.
So today we decided we wanted to go shop at an outlet mall, but the question was, which one to choose? We could either go to Carlsbad Premium Outlets up in North County or Las Americas Premium Outlets right by the border.
To help make the decision, we looked up what stores the outlets had. I searched online for a solid comparison of the two outlets but results were slim and the only things I found were old threads on Yelp! or TripAdvisor comparing the quality of the two. The Simon websites do have a list of the stores at each outlet, but it was hard to go through the list and switch windows to see differences/similarities in store listings. See store directories for Las Americas and Carlsbad.
Here is an easy to read table that you can SORT by clicking on the outlet names. Cool, huh?
This list also includes restaurants and kiosks. Carlsbad has a total of 101 shops, and Las Americas has a grand total of 169 shops. So you could probably guess where I went :)
I made the table above by copying the store listings to text files, importing them to Stata, cleaning up the variables, renaming the stores to have proper() case before merging the two datasets. Finally, I used Google Charts API to display the results.
My name is Belen, I like to play with data using Stata during work hours and in my free time. I like blogging about my Fitbit, Stata, and random musings.