Dear,
Ok...I thought a hint would have been sufficient, but I'm still stuck.
based on the 'Data' dataframe:
Data
mod_names err_type value spec_comp sign.value pos
1 mod1 var 2.970681e-03 ID - 1.485341e-03
2 mod1 mMSE 1.881598e-01 ID 9.705057e-02
3 mod2 var 2.999713e-02 ID - 1.499856e-02
4 mod2 mMSE 3.055899e-01 ID 1.827921e-01
5 mod3 var 1.570650e-01 ID + 7.853252e-02
6 mod3 mMSE 2.430458e-01 ID 2.785880e-01
7 mod4 var 2.439812e-02 ID + 1.219906e-02
8 mod4 mMSE 1.439277e-01 ID 9.636199e-02
9 mod1 var 6.476435e-01 DU - 3.238218e-01
10 mod1 mMSE 1.575243e+00 DU 1.435265e+00
11 mod2 var 1.394583e-02 DU - 6.972916e-03
12 mod2 mMSE 1.269346e+01 DU 6.360675e+00
13 mod3 var 1.338403e+00 DU + 6.692017e-01
14 mod3 mMSE 8.775683e+00 DU 5.726245e+00
15 mod4 var 5.583281e-02 DU - 2.791641e-02
16 mod4 mMSE 3.591430e+00 DU 1.851548e+00
17 mod1 var 2.810587e-01 SY + 1.405294e-01
18 mod1 mMSE 8.157692e-01 SY 6.889433e-01
19 mod2 var 1.747732e-01 SY + 8.738661e-02
20 mod2 mMSE 9.926385e-01 SY 6.710925e-01
21 mod3 var 1.113529e-01 SY + 5.567645e-02
22 mod3 mMSE 6.747922e-01 SY 4.487490e-01
23 mod4 var 2.017077e+00 SY + 1.008539e+00
24 mod4 mMSE 1.231198e+00 SY 2.632676e+00
25 mod1 bias 7.167534e+01 LT + 3.583767e+01
26 mod1 var 3.287647e-06 LT - 7.167534e+01
27 mod1 mMSE 2.113895e+00 LT 7.273229e+01
28 mod2 bias 5.979827e+01 LT + 2.989914e+01
29 mod2 var 8.464560e-04 LT - 5.979869e+01
30 mod2 mMSE 9.425981e-01 LT 6.027042e+01
31 mod3 bias 5.604628e+00 LT + 2.802314e+00
32 mod3 var 4.698450e-02 LT - 5.628121e+00
33 mod3 mMSE 6.434771e-01 LT 5.973351e+00
34 mod4 bias 1.614153e+02 LT + 8.070763e+01
35 mod4 var 4.131887e+00 LT + 1.634812e+02
36 mod4 mMSE 5.797821e+00 LT 1.684461e+02
I run the command:
ggplot(Data, aes(x=mod_names, y=value,
fill=err_type))+geom_bar(stat='identity', position='stack') +
facet_wrap(~spec_comp, nrow=1,
scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5,
hjust=1.)) +
theme(axis.title.x = element_blank())+ ylab(units) +
geom_text ( aes(label=sign.value, y=pos)) +....
where err_type = c('bias', mMSe', 'var'). My questions are
1. how to color-code only the mMSE portion of the bars based on a range of
values 'r' from -1 to 1 (basically the correlation coefficient):
r
ID DU SY LT
mod1 -0.5 0.5 0.4 -0.1
mod2 0.6 0.2 -0.2 0.9
mod3 0.5 0.5 0.9 -1.0
mod4 0.4 0.3 0.2 0.0
2. and how to color-code the x-labels of the last panel (the LT one) based on
the values of 'bias'.
Thanks a lot for your help.
On 22/02/2016 17:00, Jeff Newmiller wrote:
> By failing to provide a reproducible example and framing the desired
> answer as 'any hint' you are effectively limiting yourself to 'any
> hint' rather than a working example.
>
> The 'any hint' is that err_type should be a factor with levels in your
> desired order, and you can then use any of the scale_fill_* functions
> including scale_colour_manual to specify the colors in the same order
> as the levels in the factor.
> --
> Sent from my phone. Please excuse my brevity.
>
> On February 22, 2016 7:24:17 AM PST, efisio solazzo
> <efisio.sola...@jrc.ec.europa.eu> wrote:
>
> Dear,
> I wonder if there is a way to 'play' with colors in facet ggplot and
> geom_bar.
>
> With reference to the attached figure, I'd like to
> - color the green portion based on a numerical variable (say 1 to 10) on
> all of the four panels and
> - color-code the y labels based on the values of the 'bias' (red portion
> of bars) only on the fourth panel.
>
> the code to produce the plot is:
>
> ggplot(Data, aes(x=mod_names, y=value,
> fill=err_type))+geom_bar(stat='identity', position='stack') +
> facet_wrap(~spec_comp, nrow=1,
> scales="free") +
> theme(axis.text.x = element_text(angle = 90, vjust = 0.5,
> hjust=1.)) +
> theme(axis.title.x = element_blank())+ ylab(units) +
> geom_text ( aes(label=sign.value, y=pos)) +....
>
> where err_type = c('bias', mMSe', 'var')
>
> thanks for any hint.
>
--
Efisio SOLAZZO, Ph.D.
European Commission, Joint Research Centre,
Institute for Environment and Sustainability,
TP123, Via E. Fermi, 2749 I-21027 Ispra (VA), Italy
Tel: +390332789944 Fax: +390332785837
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