[R] Black outlines for errorbar using geom_line
michael.eisenring at agroscope.admin.ch
michael.eisenring at agroscope.admin.ch
Sat May 2 00:18:44 CEST 2015
Hi there,
I would like to create black outlines around my errorbars in order to get especially the white errorbar better visible.
Is that even possible with ggplot2 and if yes how?
I would be very grateful if anyone could help me. I added my code and a dput() of my data,
Thank you very much,
Michael
#my code:--------------------------------------------------
#load packages
library(compute.es<http://compute.es/>);library(ggplot2);library(multcomp);library(pastecs);library(WRS)
library(pastecs);library(Hmisc)
library (car)
# Comput CI manually and produce error plots
#CI manually computed with formula summarySE
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na<http://is.na/>(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
#summarySE provides the standard deviation, standard error of the mean, and a (default 95%) confidence interval
CI <- summarySE(data, measurevar="cor_average", groupvars=c("damage","leaf"))
CI
#plotting in ggplot
# To prevent that errorbars overlap use position_dodge to move them horizontally
pd <- position_dodge(0.4) # move them .05 to the left and right
#Create errorbar using 95% CI
ggplot(CI, aes(x=leaf, y=cor_average, colour=damage)) +
geom_errorbar(aes(ymin=cor_average-ci, ymax=cor_average+ci), width=.3, size=1,position=pd) +
geom_line(position=pd) +
scale_colour_manual(values=c("white","black","gray65"))+
geom_point(position=pd,size=3)
#my data set: ---------------------------------------------------------------------------------------------------------------
> dput(data<-read.csv("Exp1.csv",sep=",",header=T))
structure(list(damage = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("C", "L1", "L4"), class = "factor"), leaf = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("Cot", "L1", "L2",
"L3", "L4", "L5", "L6", "L7", "L8"), class = "factor"), cor_average = c(587.6666667,
595.3333333, 858, 516, 542.6666667, 455, 530.3333333, 546.6666667,
215.1111111, 666.6666667, 680, 2361.666667, 2880, 2760, 2210,
2142, 2508, 1551.666667, 1856, 498.7777778, 2634.666667, 2380,
2338.333333, 2006.666667, 3296.666667, 2945.333333, 2720.666667,
2436, 2033, 2579.333333, 455.1111111, 2123.333333, 2033.333333,
3660.333333, 2765, 3752, 3150, 3747.333333, 3572, 3536, 3593.333333,
1178.777778, 3476, 3291.666667, 3927.333333, 3705, 4636.666667,
3720, 4080, 4828, 5280, 4495, 711.1111111, 4340, 4173, 4785,
5070, 6324, 3360, 5150, 6262.666667, 5624, 5595.333333, 1626.777778,
5869.333333, 5080, 6888, 7561.333333, 7280, 3610, 5926.666667,
6600, 6375.333333, 7831.666667, 4181.777778, 8023, 6077.333333,
14640, 13501.33333, 15383.33333, 6193, 10030, 8756.333333, 10291.66667,
16450, 33611.11111, 15138.66667, 10948, 6600, 7785, 10944, 11712,
17490, 14370, 10353, 8814.333333, 742.5, 605, 520, 728.5, 418,
650, 468.6666667, 506.6666667, 512, 518, 646, 1974, 2408, 2494,
1931.666667, 1243.666667, 2024, 1805, 2166.666667, 2408, 1613.333333,
2573, 2102.333333, 2470, 2701, 2566.666667, 2960, 2300, 2579.666667,
1785, 2223.333333, 2046, 3336.666667, 3737, 4529.666667, 3718,
3192, 4110, 3198, 4116, 2645, 3288, 3315, 4850, 4293.333333,
4972, 4641, 4370, 4933.333333, 3853.333333, 4658, 3720, 4340,
3882.666667, 5577, 7800, 7646.333333, 4200, 5418, 5324.666667,
4564, 5085, 5060, 7093.333333, 6923.333333, 7700, 14575, 14725,
6650, 12154.66667, 8254.666667, 5800, 7663.333333, 6640, 12661.66667,
12982.33333, 14025, 17148, 21960, 10418.33333, 25850, 12636,
8470, 17152, 20603.33333, 22078, 20086.66667, 24219, 18544, 13475,
23389, 22374, 10246.5, 13695, 12220, 487.5, 645, 623.3333333,
493.3333333, 555, 532, 658.6666667, 522, 492, 528, 604.3333333,
528, 1998.333333, 2016.666667, 1971.666667, 2376, 1780, 1729,
2026.666667, 2186.666667, 1860, 2112, 2300, 2054.666667, 2520,
2532.333333, 2181.666667, 2960.333333, 2220, 2472, 2555, 1392,
2826.666667, 1457, 2600, 2026.666667, 3720, 4275, 4389, 3014,
2875, 3920, 4094, 2712, 3853.333333, 2926, 4766.666667, 3948.333333,
5625, 6303.333333, 4783.666667, 4770, 3530.333333, 4840, 3760,
4656.666667, 3600, 3923.333333, 5458.333333, 3600, 5115, 5950,
5662.666667, 6075, 4307.333333, 5960, 5425, 4978, 6292, 4477.333333,
5568, 5364.333333, 8824.666667, 8520, 8000, 11293.33333, 5133.333333,
7973.333333, 5786.666667, 6006, 6766.666667, 7000, 7566, 15555,
14299.33333, 19716.66667, 24408, 11349, 17400, 13362, 19525,
23902, 15364, 16875, 10450, 17605, 22950, 19920, 12412, 21187.33333,
20425, 20710, 10280.5, 11063, 21498.66667, 22333.33333)), .Names = c("damage",
"leaf", "cor_average"), class = "data.frame", row.names = c(NA,
-297L))
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