2017 Cycle-Commuting now complete.

I've updated the cycle-commuting Tableau dashboard with the remainder of the 2017 data, including the weather data. A wet and slushy end to the year. And more business travel meant less commuting overall.

I still haven't quite worked out why there is so much quarter-to-quarter variation; 2017 showed a repeat of the pattern of 2016, so this looks like seasonality rather than randomness. I'm sure traffic, daylight and weather have something to do with it. For example, there's a lot less traffic for much of the Q3, since the schools are on holiday.

Further analysis is for another day, but ideas certainly welcome. You have the data.

I've decided that 3 years is quite enough of this sort of recording, so for cycling at least, that's all folks!

Energy - nearly 8 years

Just updated the household energy consumption Tableau - the 8th year of data will be complete next week! 

Maybe I should link it to the weather information - like the cycling dashboard, which has also been brought up-to-date. Seem not to have been in the office much over the Summer, so numbers are down. But speeds up, as usual at this time of year.

Cycling into the wind

So now I've merged the wind data that I was using with the thorn plots and the cycling data on the Tableau public dashboard. It uses an average wind speed and direction based on the two metar observations nearest to the mid-time of the ride. 

I wondered how to show wind direction, but in the end a simple grouping by quadrant was easy to do and good enough to separate the data into meaningful groups.

The effect of windspeed on the way home, when the wind is in the SW is pretty clear, I think.

I just wish I could get a sensible data between start&end calculated field in the title. Could not get this to work in Tableau.

Thorn Plot, for visualising head winds, final version

Continuing the short series on plotting headwinds, perhaps you prefer the 'thorn plot' version, in which the relative frequency of wind from a particular direction is represented both by transparency of colour and also by the width of the triangle.

For this version, I finally remembered to normalise the number of observations between locations. There are more records for some airports than others, but this is irrelevant for the graph, so I rescale.

Final daisy plot for visualising head wind when cycling.

Got there in the end. I know he guidance is that colour is not the best way to show variation, but this 'daisy plot' works for me, and is less untidy than the 'thorn' variation.

The curved line comes from those explicitly drawn by ggplot in the geom_poly statement, so repeating the start point of the poly gave me a balanced curve on both sides (which gives a clue as to how to get straight lines, but I'm sticking with this version).

Quite a lot of ggplot in the end:

ggplot(y8p , aes(x, y, group = windHead, alpha = numObs)) + 

  scale_alpha(range = c(0.3, 1), breaks = seq(0, 24000, by = 4000)) + 

  geom_polygon(fill = "blue") +

  xlab("Angle = Wind heading (deg) ") + 

  ylab("Mean wind speed (km/h)") +

  labs(alpha = "Observations") +

  coord_polar(start = -pi/8) + 

  scale_y_reverse(limits = c(maxKMH, 0)) + 

  scale_x_continuous(limits = c(-22.5,337.5), breaks = NULL) +

  geom_label(aes(x=0, y = maxKMH, label = station), colour = "black",

             show.legend = FALSE) +

  #theme(legend.position = c(1,0), legend.justification = c(1,0)) + 

  theme(strip.background = element_blank(),

        strip.text.x = element_blank()) +

  facet_wrap( ~ station)

For which, the y8p data looks like this. Note that I've created 4 lines for each real line of data - and x and y give the 4 points used in the polygon for each real point of data.

  station windHead   windKPH numObs     x         y

    <chr>   <fctr>     <dbl>  <int> <dbl>     <dbl>

1    EBBR        0  9.167982   7190   0.0  9.167982

2    EBBR        0  9.167982   7190  22.5  0.000000

3    EBBR        0  9.167982   7190 -22.5  0.000000

4    EBBR        0  9.167982   7190   0.0  9.167982

5    EBBR       45 10.177946   7358  45.0 10.177946

6    EBBR       45 10.177946   7358  67.5  0.000000

More Pies, Roses and Thorns

Continuing the hunt for ways to show headwind, rather prettier are these version, using an explicit geom_polygon to construct triangles.

In the first version, I'm using both transparency and thickness to code the number of observations (frequency that the wind was in this direction). I think this looks like thorns. Haven't worked out why the axis transform should put the twist on the triangles. Suggestions welcome!

In the second version, I've stuck to fixed width. This is more like the diaphragm of a camera, or a daisy.

Pies and Roses

On a slight tangent from my usual data, I always have the impression that there is more of a headwind when I'm cycling home, than when I go to work. Is it true?

Happily someone has kindly archived weather observations from the nearby Brussels airport, so I've been looking at the last 5 years' or so.

The question is, how to show it? For an R user, this is the fairly obvious answer - using coord_polar() in the lovely ggplot. To a trained eye, perhaps it's clear that going to work (heading North-East), typical winds are low whereas in the reverse direction the average wind is much stronger (the upper teens). 

But to me, the impact is back to front; it feels like it's easier, somehow, to cycle south-west. And there's no indication of how frequently the wind is in each direction.

So I want to reverse the scale. Now the length of the shape gives the strength of the wind, with the baseline on the outside, and the width of the base of the shape gives the frequency of the wind being in this direction.

Ok, so it looks like a messed up version of the dreaded pie chart, but is the impact of the message clearer?