Chernoff Faces of Bike Rides

Chernoff faces of 4 bike rides

Chernoff faces from 4 bike rides

I plugged some of the fit file data that I stored in a Postgres database into R to generate the image above.  Two R libraries, RpostgreSQL to connect to Postgres, and aplpack, to generate the faces were used.  Once the libraries were loaded the following R command pulls the data out of Postgres:

rs <- dbSendQuery(con, "
SELECT ride_date
, ST_Length(ST_Transform(ST_Makeline(the_geom ORDER BY ride_time),26916)) As length
, date_part('hour', max(ride_time))-date_part('hour', min(ride_time)) As ridetime
, avg(temperature) As temp, avg(speed) As avg_speed
, avg(altitude) As alt, max(altitude)-min(altitude) As alt_diff
, avg(cadence) As rpm 
FROM ride GROUP BY ride_date ORDER BY ride_date

Then fetch the data from the record set with:

rides<-fetch(rs, n=-1)

And finally plot the faces with:

faces(rides[,2:8], face.type=0, labels=rides$ride_date)

The faces function draws the faces based on the order of the variables.  The features are modified in this order:

height of face 
width of face 
structure of face 
height of mouth 
width of mouth 
height of eyes 
width of eyes 
height of hair 
width of hair 
style of hair 
height of nose 
width of nose 
width of ear 
height of ear

If you don’t have enough variables to fill this list it will wrap around and start again from the top.  For more configuration options use ?faces after you load the aplpack.

There are two small problems with the data, and they both caused by stopping the timer on your bike computer, then restarting at a later time and place.   The time ridden is calculated here by simply subtracting the minimum time from the maximum.  If you rode for 3 hours and took an hour break the query would return a time of 4 hours.  Similarly, if you ride for a mile, hop in a truck and drive 10, then ride for another mile you’d get a result of 12 miles.  There’s a way to work around this, I just haven’t figured it out yet.  I suppose the best solution is to harden up and not take breaks.

The data is stored as points in the database, to get the length ST_Makeline “connects the dots” from point to point.  An awesome feature of Postgres 9.0+ is the Order By in the middle an aggregate function.  It helped in this case because the first ride graphed doubled up and crossed over itself several times.  This lead to the query planner making some interesting decisions on where the line should go.  Forcing the process to follow the points in order the line followed my route perfectly.

  1. No trackbacks yet.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: