Error Bars In R Barplots
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Summaryse R
error.bar <- function(x, y, upper, lower=upper, length=0.1,...){ if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper)) stop("vectors must be same length") arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...) } Now let's use it. First, I'll create 5 means drawn from a Gaussian random variable with unit mean and variance. I want to point out another mild annoyance with the way that R handles bar plots, and how to fix it. By default, barplot() suppresses the X-axis. Not sure why. If you want the axis to show up with the same line style as the Y-axis, include the argument axis.lty=1, as below. By creating an object to hold your bar plot, you capture the midpoints of the bars along the abscissa that can later be used to plot the error bars. PLAIN TEXT R: y <- rnorm(500, mean=1) y <- matrix(y,100,5) y.means <- apply(y,2,mean) y.sd <- apply(y,2,sd) barx <- barplot(y.means, names.arg=1:5,ylim=c(0,1.5), col="blue", axis.lty=1, xlab="Replicates", yla
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Errbar R
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here for http://stackoverflow.com/questions/29768219/grouped-barplot-in-r-with-error-bars a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings https://onlinecourses.science.psu.edu/stat485/node/65 and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers error bar or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it barplot with error only takes a minute: Sign up Grouped barplot in R with error bars up vote 4 down vote favorite 1 Dear Stackoverflow users, I would like to draw a grouped barplot with error bars. Here is the kind of figure I have been able to get up to now, and this is ok for what I need: And here is my script: #create dataframe Gene<-c("Gene1","Gene2","Gene1","Gene2") count1<-c(12,14,16,34) count2<-c(4,7,9,23) count3<-c(36,22,54,12) count4<-c(12,24,35,23) Species<-c("A","A","B","B") df<-data.frame(Gene,count1,count2,count3,count4,Species) df mean1<-mean(as.numeric(df[1,][c(2,3,4,5)])) mean2<-mean(as.numeric(df[2,][c(2,3,4,5)])) mean3<-mean(as.numeric(df[3,][c(2,3,4,5)])) mean4<-mean(as.numeric(df[4,][c(2,3,4,5)])) Gene1SpeciesA.stdev<-sd(as.numeric(df[1,][c(2,3,4,5)])) Gene2SpeciesA.stdev<-sd(as.numeric(df[2,][c(2,3,4,5)])) Gene1SpeciesB.stdev<-sd(as.numeric(df[3,][c(2,3,4,5)])) Gene2SpeciesB.stdev<-sd(as.numeric(df[4,][c(2,3,4,5)])) ToPlot<-c(mean1,mean2,mean3,mean4) #plot barplot plot<-matrix(ToPlot,2,2,byrow=TRUE) #with 2 being replaced by the number of genes! tplot<-t(plot) BarPlot <- barplot(tplot, beside=TRUE,ylab="count", names.arg=c("Gene1","Gene2"),col=c("blue","red")) #add legend legend("topright", legend = c("SpeciesA","SpeciesB"), fill = c("blue","red")) #add error bars ee<-matrix(c(Gene1SpeciesA.stdev,Gene2SpeciesA.stdev,Gene1SpeciesB.stdev,Gene2SpeciesB.stdev),2,2,byrow=TRUE)*1.96/sqrt(4) tee<-t(ee) error.bar(BarPlot,tplot,tee) The problem is t
add errorbars to a barplot, and learn the trick to getting the errorbars to plot on the bars. ‹ 17.4 - Multipanel Boxplot Example up 17.6 - Creating a Polished Barplot › Login or register to post comments Navigation Start Here! Welcome to STAT 485 - Intermediate Topics in R Statistical Language! Faculty login (PSU Access Account) Lessons Introduction to the Course Chapter 11: Linear Regression Chapter 12: ANOVA Chapter 13: More Linear Models Chapter 14: Advanced Documentation Chapter 15: Visualizing Data I - Enhancing Scatter Plots Chapter 16: Visualizing Data II - Errorbars and Ribbons Chapter 17: Visualizing Data III - Boxplots and Barplots17.1 - Basic Boxplots 17.2 - Adding Polish to a Boxplot 17.3 - Adding Mean and SE to Boxplots 17.4 - Multipanel Boxplot Example 17.5 - Adding Errorbars to Barplots 17.6 - Creating a Polished Barplot 17.7 - Creating a Multipanel Barplot Chapter 18: Mixed Effects Models Chapter 19: Non-linear Models Chapter 20: Writing Functions and Packages Copyright © 2016 The Pennsylvania State University Privacy and Legal Statements Contact the Department of Statistics Online Programs