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?