parplot: different color for byvars when using by() option

[Chen Samulsion]

Dear Stata users,

I have a question about -parplot- command (SSC). This command can produce parallel coordinates plot. Using the classical "iris data", I have produced a parplot. Everything goes well, however, I want to take a step further. That is to say, I want to assign different colors to each speicies, for example, blue color for setosa, red color for versicolor, and yellow color for virginica, and what's more, I want these assignments can be copied in the last "total" plot. It seems that the parplot only allow one color for all species (i.e. the byvars of by() option). I wonder is there any way to work around this problem? I dismiss the over() option because I want to display each sepecies separately. Thank you.

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(species sepallen sepalwid petallen petalwid)
1 5.1 3.5 1.4  .2
1 4.9   3 1.4  .2
1 4.7 3.2 1.3  .2
1 4.6 3.1 1.5  .2
1   5 3.6 1.4  .2
1 5.4 3.9 1.7  .4
1 4.6 3.4 1.4  .3
1   5 3.4 1.5  .2
1 4.4 2.9 1.4  .2
1 4.9 3.1 1.5  .1
1 5.4 3.7 1.5  .2
1 4.8 3.4 1.6  .2
1 4.8   3 1.4  .1
1 4.3   3 1.1  .1
1 5.8   4 1.2  .2
1 5.7 4.4 1.5  .4
1 5.4 3.9 1.3  .4
1 5.1 3.5 1.4  .3
1 5.7 3.8 1.7  .3
1 5.1 3.8 1.5  .3
1 5.4 3.4 1.7  .2
1 5.1 3.7 1.5  .4
1 4.6 3.6   1  .2
1 5.1 3.3 1.7  .5
1 4.8 3.4 1.9  .2
1   5   3 1.6  .2
1   5 3.4 1.6  .4
1 5.2 3.5 1.5  .2
1 5.2 3.4 1.4  .2
1 4.7 3.2 1.6  .2
1 4.8 3.1 1.6  .2
1 5.4 3.4 1.5  .4
1 5.2 4.1 1.5  .1
1 5.5 4.2 1.4  .2
1 4.9 3.1 1.5  .2
1   5 3.2 1.2  .2
1 5.5 3.5 1.3  .2
1 4.9 3.6 1.4  .1
1 4.4   3 1.3  .2
1 5.1 3.4 1.5  .2
1   5 3.5 1.3  .3
1 4.5 2.3 1.3  .3
1 4.4 3.2 1.3  .2
1   5 3.5 1.6  .6
1 5.1 3.8 1.9  .4
1 4.8   3 1.4  .3
1 5.1 3.8 1.6  .2
1 4.6 3.2 1.4  .2
1 5.3 3.7 1.5  .2
1   5 3.3 1.4  .2
2   7 3.2 4.7 1.4
2 6.4 3.2 4.5 1.5
2 6.9 3.1 4.9 1.5
2 5.5 2.3   4 1.3
2 6.5 2.8 4.6 1.5
2 5.7 2.8 4.5 1.3
2 6.3 3.3 4.7 1.6
2 4.9 2.4 3.3   1
2 6.6 2.9 4.6 1.3
2 5.2 2.7 3.9 1.4
2   5   2 3.5   1
2 5.9   3 4.2 1.5
2   6 2.2   4   1
2 6.1 2.9 4.7 1.4
2 5.6 2.9 3.6 1.3
2 6.7 3.1 4.4 1.4
2 5.6   3 4.5 1.5
2 5.8 2.7 4.1   1
2 6.2 2.2 4.5 1.5
2 5.6 2.5 3.9 1.1
2 5.9 3.2 4.8 1.8
2 6.1 2.8   4 1.3
2 6.3 2.5 4.9 1.5
2 6.1 2.8 4.7 1.2
2 6.4 2.9 4.3 1.3
2 6.6   3 4.4 1.4
2 6.8 2.8 4.8 1.4
2 6.7   3   5 1.7
2   6 2.9 4.5 1.5
2 5.7 2.6 3.5   1
2 5.5 2.4 3.8 1.1
2 5.5 2.4 3.7   1
2 5.8 2.7 3.9 1.2
2   6 2.7 5.1 1.6
2 5.4   3 4.5 1.5
2   6 3.4 4.5 1.6
2 6.7 3.1 4.7 1.5
2 6.3 2.3 4.4 1.3
2 5.6   3 4.1 1.3
2 5.5 2.5   4 1.3
2 5.5 2.6 4.4 1.2
2 6.1   3 4.6 1.4
2 5.8 2.6   4 1.2
2   5 2.3 3.3   1
2 5.6 2.7 4.2 1.3
2 5.7   3 4.2 1.2
2 5.7 2.9 4.2 1.3
2 6.2 2.9 4.3 1.3
2 5.1 2.5   3 1.1
2 5.7 2.8 4.1 1.3
3 6.3 3.3   6 2.5
3 5.8 2.7 5.1 1.9
3 7.1   3 5.9 2.1
3 6.3 2.9 5.6 1.8
3 6.5   3 5.8 2.2
3 7.6   3 6.6 2.1
3 4.9 2.5 4.5 1.7
3 7.3 2.9 6.3 1.8
3 6.7 2.5 5.8 1.8
3 7.2 3.6 6.1 2.5
3 6.5 3.2 5.1   2
3 6.4 2.7 5.3 1.9
3 6.8   3 5.5 2.1
3 5.7 2.5   5   2
3 5.8 2.8 5.1 2.4
3 6.4 3.2 5.3 2.3
3 6.5   3 5.5 1.8
3 7.7 3.8 6.7 2.2
3 7.7 2.6 6.9 2.3
3   6 2.2   5 1.5
3 6.9 3.2 5.7 2.3
3 5.6 2.8 4.9   2
3 7.7 2.8 6.7   2
3 6.3 2.7 4.9 1.8
3 6.7 3.3 5.7 2.1
3 7.2 3.2   6 1.8
3 6.2 2.8 4.8 1.8
3 6.1   3 4.9 1.8
3 6.4 2.8 5.6 2.1
3 7.2   3 5.8 1.6
3 7.4 2.8 6.1 1.9
3 7.9 3.8 6.4   2
3 6.4 2.8 5.6 2.2
3 6.3 2.8 5.1 1.5
3 6.1 2.6 5.6 1.4
3 7.7   3 6.1 2.3
3 6.3 3.4 5.6 2.4
3 6.4 3.1 5.5 1.8
3   6   3 4.8 1.8
3 6.9 3.1 5.4 2.1
3 6.7 3.1 5.6 2.4
3 6.9 3.1 5.1 2.3
3 5.8 2.7 5.1 1.9
3 6.8 3.2 5.9 2.3
3 6.7 3.3 5.7 2.5
3 6.7   3 5.2 2.3
3 6.3 2.5   5 1.9
3 6.5   3 5.2   2
3 6.2 3.4 5.4 2.3
3 5.9   3 5.1 1.8
end
label values species species
label def species 1 "setosa", modify
label def species 2 "versicolor", modify
label def species 3 "virginica", modify

parplot sepallen sepalwid petallen petalwid, by(species, total note("") compact) plotregion(margin(large))

[Nick Cox]

parplot doesn't support that directly at present, but consider

parplot sep* pet*, over(species) ms(Oh plus X)

Incidentally a simple plot of petal measures

* ssc install sepscatter 
sepscatter pet*, sep(species) aspect(1) legend(order(3 2 1) ring(0) col(1) pos(11))

works better here. Examples like these lead me to think that parallel coordinates plot have been a little oversold.

[Chen Samulsion]

Thank you very much Nick Cox. The iris data does show good cluster in scatter plot above. But parallel coordinate plot can transform high-dimension efficiently to 2D patterns, although every so often overplotting can make it impossible to see anything.

[Chen Samulsion]

I have hacked -parplot- command written by Nick Cox. The knack is replacing twoway connected with sepscatter (SSC), and setting default connect() to "L" (ascending). And I get the following parallel coordinate plot.

[Nick Cox]

Neat solution.

[Georgia Richards]

Hi there,
I'm also using the parplot command (SSC) and would like to change the color for each "by" variable as per Chen's graph in #4 - Chen Samulsion could you please share your code for your above graph in #4?

At present, my code is:

parplot meanop_pop_mg OpioidEML if OpioidEML!=. & meanop_pop_mg!=., ///
graphregion(fcolor(white)) msize(small) ///
by(Region2, total compact note(" ")) ///
plotregion(margin(large)) ///
xla(1 "mg/person" 2 "opioids on EMLs", ang(25))

My output is:
parplot_op_emls.tif

I also want to:
1) remove the blue background as per Chen's graph - I've tried using "graphregion(fcolor(white))" but this didn't work; and
2) I want the y-axis to include values rather than "high" and "low" - is this possible?

Thank you.

[Nick Cox]

1) There are many excellent graph schemes around which do this as one of many style choices. I always

set scheme s1color

which is one of StataCorp's own schemes. Several other schemes have similar effects.

2) The option tr(raw) turns off the default scaling whereby values are scaled between low and high.

I can't share Chen's code because I don't have a copy.

[Georgia Richards]

Great, thanks Nick.
Regarding 2) this is for the left-sided y-axis. Is there also an option for the right-sided y-axis?

[Nick Cox]

I don't understand #8. Scaling applies to all axes. You can have a right-hand axis too but it must be consistent with the left-hand axis.