Thank you very much Rui!
--
Dr. Ivan Calandra
Imaging lab
RGZM - MONREPOS Archaeological Research Centre
Schloss Monrepos
56567 Neuwied, Germany
+49 (0) 2631 9772-243
https://www.researchgate.net/profile/Ivan_Calandra
On 04/09/2021 18:22, Rui Barradas wrote:
Hello,
The problem is that you have two grouping aesthetics, color and shape.
In geom_point make the group explicit:
p <- ggplot(my_data, aes(x = Diet, y = value, color = Software))
p <- p + geom_boxplot(outlier.shape = NA)
p + geom_point(
mapping = aes(shape = NMP_cat, group = Software),
position = position_jitterdodge()
)
Hope this helps,
Rui Barradas
Às 09:54 de 02/09/21, Ivan Calandra escreveu:
Dear useRs,
I'm having a problem to combine geom_boxplot and geom_point with
jitter. It is difficult to explain but the code and result should
make it clear (the example dataset is long so I copy it at the end of
the email):
p <- ggplot(my_data, aes(x = Diet, y = value, color = Software))
p <- p + geom_boxplot(outlier.shape = NA)
p <- p + geom_point(mapping = aes(shape = NMP_cat), position =
position_jitterdodge())
print(p)
As you can see in the resulting plot, the points with different
shapes are dodged across the boxplot categories (colors). I'd like
the three shapes per color to be restricted in one boxplot color,
with jitter of course to better visualize the points.
Does that make sense?
I have played with the arguments of position_jitterdodge(), but it
seems to me that the problem is that the shape aesthetic is not in
the geom_boxplot() call (but I don't want it there, see below).
For background information, the column used for shape gives some sort
of "quality" to the points; that's why I want to show the points
differently, so that it can easily be seen whether "good" points plot
in the same area as the "bad" points.
Because I'm doing facet plots with other variables, I do not want to
separate these categories in the boxplots - the resulting plots would
be overcrowded.
Thank you for the help.
Ivan
---
my_data <- structure(list(Diet = c("Dry lucerne", "Dry lucerne", "Dry
lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne",
"Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry
lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne",
"Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry
lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne",
"Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry
lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne",
"Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne",
"Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry
lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne", "Dry lucerne",
"Dry lucerne", "Dry grass", "Dry grass", "Dry grass", "Dry grass",
"Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
grass", "Dry grass", "Dry grass", "Dry grass", "Dry grass", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry bamboo", "Dry
bamboo",
"Dry bamboo", "Dry bamboo"), Software = c("ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax",
"ConfoMap", "Toothfrax", "ConfoMap", "Toothfrax", "ConfoMap",
"Toothfrax", "ConfoMap", "Toothfrax"), NMP_cat = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L), .Label = c("0-5%", "5-10%", "10-20%", "20-100%"), class =
c("ordered", "factor")), name = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label =
c("Asfc", "Smfc", "HAsfc9", "HAsfc81", "epLsar", "NewEplsar"), class
= "factor"), value = c(16.00716636, 12.925787, 14.05932485,
11.999816, 15.12321532, 12.711474, 12.79565826, 10.900949,
15.90481161, 12.836045, 16.22778102, 13.565995, 14.71354945,
12.384152, 16.61354777, 13.714165, 15.91399496, 12.983796,
19.44739619, 15.173215, 16.13761798, 12.932798, 14.7332952, 12.10277,
10.78710961, 8.762726, 10.16027362, 8.040399, 14.53444662, 11.527896,
17.38120685, 13.78922, 11.26840546, 9.426558, 24.01797992, 18.398553,
13.7435699, 11.44385, 14.391873, 10.757141, 22.39390393, 18.176262,
11.60322022, 9.969118, 11.6099975, 10.059618, 11.86282935, 10.280864,
16.22473644, 13.562839, 12.46350165, 10.629406, 23.9347534,
19.062174, 19.58121507, 15.910959, 13.99145447, 11.352648,
14.38942328, 11.821431, 23.4733371, 18.549503, 13.08142223,
10.735494, 17.09293046, 13.012834, 28.80020878, 22.447105,
25.74460885, 19.76834, 14.29106582, 12.233774, 12.03005024,
10.364224, 12.58953574, 10.30257, 18.07111578, 14.416143,
20.85562751, 16.524047, 21.06132234, 15.744758, 15.24052683,
11.891487, 11.62446752, 9.14325, 11.75704705, 10.358542, 13.65568703,
11.766129, 16.98137759, 12.594787, 11.6560954, 10.32073, 15.46708251,
13.199232, 13.20110131, 11.060226, 16.13986173, 13.564802,
25.45656859, 20.071231, 24.84006178, 19.335892, 14.4723856,
11.994841, 12.07940958, 9.470493, 13.93630412, 11.489685,
21.84464295, 17.806018, 17.4383111, 14.478338, 20.55074297,
16.254467, 30.15238714, 24.193768, 32.8541897, 25.769585,
32.06966759, 24.507185, 20.53975772, 15.951186, 11.54494952,
9.676342, 13.56490524, 11.456356, 13.58242208, 10.919419,
13.55260161, 11.356056, 38.58113502, 31.087536, 23.6753536,
18.749955, 26.38707155, 20.877856, 26.18252748, 20.758242)),
row.names = c(NA, -140L), class = c("tbl_df", "tbl", "data.frame"))
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