[R] add median value and standard deviation bar to lattice plot

Luigi Marongiu marongiu.luigi at gmail.com
Wed Mar 15 10:22:30 CET 2017

```Dear all,
I am analyzing some multivariate data that is organized like this:
1st variable = cluster (A or B)
2nd variable = target (a, b, c, d, e)
3rd variable = type (blank, negative, positive)
4th variable = sample (the actual name of the sample)
5th variable = average (the actual reading -- please not that this is the
mean of different measures with an assumed normal distribution, but the
assumption might not always be true)
6th variable = stdev (the standard deviation associated with each reading)
7th variable = ll (lower limit that is average stdev)
8th variable = ul (upper limit that is average + stdev)

I am plotting the data using lattice's stripplot and I would need to add:
1. an error bar for each measurement. the bar should be possibly coloured
in light grey and semitransparent to reduce the noise of the plot.
2. a type-based median bar to show differences in measurements between
blanks, negative and positive samples within each panel.

How would I do that?
Many thanks,
Luigi

>>>
cluster <- c(rep("A", 90), rep("B", 100))
sample <- c(
rep(c("cow-01", "cow-02", "cow-03", "cow-04", "cow-05", "cow-06",
"cow-07", "cow-08", "cow-09", "cow-10", "cow-11",
"cow-12", "cow-13", "cow-14", "cow-15", "cow-16", "cow-17",
"blank"), 5),
rep(c("cow-26", "cow-35", "cow-36", "cow-37", "cow-38", "cow-39",
"cow-40", "cow-41", "cow-42", "cow-43", "cow-44", "cow-45",
"cow-46", "cow-47", "cow-48", "cow-49", "cow-50", "cow-51",
"cow-59", "blank"), 5)
)
type <- c(
rep(c("negative", "negative", "negative", "negative", "negative",
"negative", "negative", "negative", "positive", "positive",
"positive", "positive", "positive", "positive", "positive",
"positive", "positive", "blank"), 5),
rep(c("negative", "positive", "negative", "negative", "negative",
"negative", "negative", "negative", "positive", "positive",
"positive", "positive", "positive", "positive", "positive",
"positive", "positive", "positive", "positive", "blank"), 5)
)
target <- c(
c(rep("a", 18), rep("b", 18), rep("c", 18), rep("d", 18), rep("e", 18)),
c(rep("a", 20), rep("b", 20), rep("c", 20), rep("d", 20), rep("e", 20))
)
average <- c(88.5, 49, 41, 33, 35, 45, 95, 30, 41, 64, 22, 29, 59, 71, 128, 39,
42, 47, 86, 100,
69, 44, 53, 66, 66, 71, 161, 69, 22.5, 30, 67, 99, 129, 94, 49,
33, 28, 31, 26, 23,
30, 41, 35, 23, 38, 43, 15, 21, 45, 51.5, 34, 26, 43, 32.5, 59,
58.5, 61, 62.5, 58,
59.5, 60.5, 60, 64, 110, 55, 66, 197, 83.5, 155, 76, 125, 90, 73,
84, 95.5, 62, 82, 138,
103.5, 57, 138, 149.5, 57, 54, 245.5, 191, 131, 96, 176, 45, 76,
33, 37, 51, 44, 50, 54,
66, 49, 90, 66.5, 42.5, 67, 56, 54, 50, 45, 99, 50, 51.5, 212, 40,
68, 121, 80, 57,
81.5, 128, 77, 119.5, 126, 184, 101, 103, 88, 100, 140, 186, 297,
32, 184, 36, 45, 45, 44,
86, 65, 61, 76, 62, 136, 84, 80, 56, 109, 116, 54, 59,
79, 34, 74.5,
54, 49, 55, 56,
59, 56, 56, 57, 67, 65, 63, 52, 58, 59, 56, 54, 66, 92, 87, 59,
33, 58, 51, 54,
52, 47, 45, 42, 52, 57, 79, 42, 45.5, 47, 47, 36, 50, 53, 49 )
stdev <- c(17.85, 6.31, 3.42, 1.04, 0.51, 6.04, 38.43, 2.78, 5.55, 26.72, 1.83,
9.92, 4.59, 19, 7.96,
7.5, 1.06, 9.66, 75.94, 36.79, 50.45, 9.79, 1.55, 11.42, 64.12,
0.79, 15.14, 16.15, 8.12, 4.04, 92.57, 35.35,
42.28, 52.96, 7.06, 4.97, 1.15, 4.77, 6.59, 7.27, 0.75, 4.25,
9, 0.1, 1.14, 4.17, 6.73, 3.81, 3.27,
97.44, 9.74, 0.45, 8.14, 5.91, 13.1, 98.22, 8.92, 72.62, 70.26,
59.46, 29.89, 56.35, 91.25, 49.94, 20.65, 62.04,
95.13, 35.89, 99.64, 29.44, 33.12, 45.91, 96.69, 9.05, 38.56,
3.09, 0.6, 8.69, 16.95, 74.03, 84.05, 39.87, 15.52,
27.92, 35.72, 80.26, 71.93, 66.73, 87.8, 5.43, 98.3, 7.41, 9.86,
63.64, 0.36, 5.84, 1.58, 20.1, 4.21, 82.12,
19.29, 9.02, 22.12, 54.08, 74.95, 3.24, 9.67, 67.98,
9.92, 40.69,
6.24, 8.76, 74.25, 46.34, 25.69, 90.63, 83.71,
73.53, 57.88, 15.84, 82.07, 67.45, 47.39, 98.77, 75.1,
64.9, 3.71,
87.44, 61.06, 4.77, 57.54, 7.68, 4.54, 6.15,
3.32, 60.39, 33.78, 66.22, 18.67, 76.53, 63.54, 47.06, 38.47,
88.15, 18.25, 4.26, 67.19, 88.87, 29.65, 7.33, 68.18,
28.03, 6.91, 77.82, 22.23, 73.23, 95.21, 27.11, 37.01, 34.88,
28.15, 11.27, 15.67, 96.08, 89.52, 28.6, 8.22, 23.55,
59.2, 36.38, 41.38, 0.4, 56.82, 32.35, 20.6, 18.13, 8.15, 1.08,
9.85, 1.07, 37.75, 97.6, 7.16, 8.51, 4.42,
0.15, 1.28, 7.42, 71.15, 9.39)
ll <- c(70.65, 42.69, 37.58, 31.96, 34.49, 38.96, 56.57, 27.22, 35.45,
37.28, 20.17, 19.08, 54.41, 52, 120.04, 31.5, 40.94, 37.34,
10.06, 63.21, 18.55, 34.21, 51.45, 54.58, 1.88, 70.21, 145.86,
52.85, 14.38, 25.96, -25.57, 63.65, 86.72, 41.04, 41.94, 28.03,
26.85, 26.23, 19.41, 15.73, 29.25, 36.75, 26, 22.9, 36.86, 38.83,
8.27, 17.19, 41.73, -45.94, 24.26, 25.55, 34.86, 26.59, 45.9,
-39.72, 52.08, -10.12, -12.26, 0.0399999999999991, 30.61, 3.65,
-27.25, 60.06, 34.35, 3.96, 101.87, 47.61, 55.36, 46.56, 91.88, 44.09,
-23.69, 74.95, 56.94, 58.91, 81.4, 129.31, 86.55, -17.03, 53.95,
109.63, 41.48, 26.08, 209.78, 110.74, 59.07, 29.27, 88.2, 39.57,
-22.3, 25.59, 27.14, -12.64, 43.64, 44.16, 52.42, 45.9, 44.79, 7.88,
47.21, 33.48, 44.88, 1.92, -20.95, 46.76, 35.33, 31.02,
40.08, 10.81, 205.76, 31.24, -6.25, 74.66, 54.31, -33.63,
-2.20999999999999, 54.47, 19.12, 103.66, 43.93, 116.55, 53.61, 4.23,
12.9, 35.1, 136.29, 98.56, 235.94, 27.23, 126.46, 28.32, 40.46,
38.85, 40.68, 25.61, 31.22, -5.22, 57.33, -14.53, 72.46, 36.94,
41.53, -32.15, 90.75, 111.74, -13.19, -29.87, 49.35, 26.67,
6.31999999999999, 25.97, 42.09, -22.82, 33.77, -14.23, -39.21, 28.89,
19.99, 32.12, 36.85, 51.73, 36.33, -38.08, -30.52, 27.4, 45.78,
42.45, 32.8, 50.62, 17.62, 32.6, 1.18, 18.65, 33.4, 33.87, 38.85,
43.92, 32.15, 50.93, 19.25, -18.6, 34.84, 36.99, 42.58, 46.85,
34.72, 42.58, -18.15, 39.61)
ul <- c(106.35, 55.31, 44.42, 34.04, 35.51, 51.04, 133.43, 32.78, 46.55,
90.72, 23.83, 38.92, 63.59, 90, 135.96, 46.5, 43.06, 56.66,
161.94, 136.79, 119.45, 53.79, 54.55, 77.42, 130.12, 71.79, 176.14,
85.15, 30.62, 34.04, 159.57, 134.35, 171.28, 146.96, 56.06, 37.97,
29.15, 35.77, 32.59, 30.27, 30.75, 45.25, 44, 23.1, 39.14, 47.17,
21.73, 24.81, 48.27, 148.94, 43.74, 26.45, 51.14, 38.41, 72.1,
156.72, 69.92, 135.12, 128.26, 118.96, 90.39, 116.35, 155.25,
159.94, 75.65, 128.04, 292.13, 119.39, 254.64, 105.44, 158.12, 135.91,
169.69,
93.05, 134.06, 65.09, 82.6, 146.69, 120.45, 131.03, 222.05, 189.37,
72.52, 81.92, 281.22, 271.26, 202.93, 162.73, 263.8, 50.43, 174.3,
40.41, 46.86, 114.64, 44.36, 55.84, 55.58, 86.1, 53.21, 172.12,
85.79, 51.52, 89.12, 110.08, 128.95, 53.24, 54.67, 166.98, 59.92,
92.19, 218.24, 48.76, 142.25, 167.34, 105.69, 147.63, 165.21,
201.53, 134.88, 135.34, 208.07, 251.45, 148.39, 201.77, 163.1, 164.9,
143.71,
273.44, 358.06, 36.77, 241.54, 43.68, 49.54, 51.15, 47.32, 146.39,
98.78, 127.22, 94.67, 138.53, 199.54, 131.06, 118.47, 144.15, 127.25,
120.26, 121.19, 147.87, 108.65, 41.33, 142.68, 82.03, 55.91, 132.82,
78.23, 132.23, 151.21, 83.11, 94.01, 101.88, 93.15, 74.27, 67.67,
154.08, 148.52, 84.6, 62.22, 89.55, 151.2, 123.38, 100.38, 33.4,
114.82, 83.35, 74.6, 70.13, 55.15, 46.08, 51.85, 53.07, 94.75, 176.6,
49.16, 54.01, 51.42, 47.15, 37.28, 57.42, 124.15, 58.39)
my.data <- data.frame(cluster, type, target, sample, average, stdev, ll,
ul, stringsAsFactors = FALSE)

library(lattice)
library(latticeExtra)
useOuterStrips(
strip = strip.custom(par.strip.text = list(cex = 0.75)),
strip.left = strip.custom(par.strip.text = list(cex = 0.75)),
stripplot(
average ~ type|target+cluster,
my.data,
groups = type,
pch=1,
jitter.data = TRUE,
main = "Group-wise",
xlab = expression(bold("Target")), ylab = expression(bold("Reading")),
col = c("grey", "green", "red"),
par.settings = list(strip.background = list(col=c("paleturquoise",
"grey"))),
scales = list(alternating = FALSE, x=list(draw=FALSE)),
key = list(
space = "top",
columns = 3,
text = list(c("Blank", "Negative", "Positive"), col="black"),
rectangles = list(col=c("grey", "green", "red"))
)
)
)

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