Black outlines for errorbar using geom_line
Hi Michael, I don't know about ggplot, but it is fairly easy to create outlined or even shadowed errorbars: require(plotrix) # display thick black errorbars dispersion(...,lwd=3) # display thin white errorbars dispersion(...,col="white") The above trick produces white errorbars outlined in black. I think it will work for any routine producing error bars, not just the "dispersion" function. You may be able to simply add two geom_errorbar components to the call in the same way as above. Jim
On Sat, May 2, 2015 at 8:18 AM, <michael.eisenring at agroscope.admin.ch> wrote:
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,
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3L, 3L), .Label = c("C", "L1", "L4"), class = "factor"), leaf = structure(c(1L,
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"leaf", "cor_average"), class = "data.frame", row.names = c(NA,
-297L))
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