R Cran Barplot Error Bars
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States Stanford Workshop in Biodemography → Plotting Error Bars in R August 24th, 2009 · 52 Comments · R One scatter plot with error bars in r common frustration that I have heard expressed about R is that there is no automatic way to plot error bars (whiskers really) on bar plots. I just encountered this issue revising a paper for https://www.r-bloggers.com/building-barplots-with-error-bars/ submission and figured I'd share my code. The following simple function will plot reasonable error bars on a bar plot. PLAIN TEXT 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 http://monkeysuncle.stanford.edu/?p=485 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", ylab="Value (arbitrary units)") error.bar(barx,y.means, 1.96*y.sd/10) Now let's say we want to create the very common plot in reporting the results of scientific experiments: adjacent bars representing the treatment and the control with 95% confidence intervals on the estimates of the means. The trick here is to create a 2 x n matrix of your bar values, where each row holds the values to be compared (e.g., treatment vs. control, male vs. female, etc.). Let's look at our same Gaussian means but now compare them to a Gaussian r.v. wi
with the R statistics environment / language and from time to time I will post solutions to everyday problems here in the blog. R is a very powerful environment for https://morevine.wordpress.com/2010/05/06/statistics-with-r-barplots-with-errorbars/ statistical data analysis but I really don't like the syntax. It really takes a while until you feel close to "home" in this language. And sometimes you run into problems where you http://cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/ think "R is for statistics so there must be an easy solution for this". But sometimes there is none and you really have to argue with code. A couple of days error bar a ago I had the problem of drawing a barplot showing some mean values and I wanted to add fancy error bars to the plot (see the result picture at the end of the post). I searched the internet but I find the howtos too difficult which is why I write a (hopefully) easier one. Maybe it will serve as a future reference with error bars for me, too. So, the problem is drawing error bars to a barplot. The barplot in R just shows numerical values (heights) as bars. The barplot function itself doesn't have any clue about the underlying data. Thus, you have to add the little whiskers for the standard error afterwards by hand. To plot the error bars we need the package Hmisc that you can download from the CRAN network. To include this library to use its functions you have call >library(Hmisc) Ok. Everything should be set up now. Let's first produce some data which we can plot: >v1 = rnorm(100, mean=8, sd=1) >v2 = rnorm(100, mean=8, sd=2) >v3 = rnorm(100, mean=8, sd=3) This will produce three vectors v1, v2 and v3, each filled with 100 normal distributed numbers; each vector has a mean of 8. All were created with different standard deviations (the parameter sd). Since we will need these means more often in the next steps, let's save them in variables: >m.v1 = mean(v1); m.v2 = mean(v2); m.v3 = mean(v3) The plot for the means will look like that: >heights = c(m.v1, m.v2, m.v3) >bp = barplot
error bars Two within-subjects variables Note about normed means Helper functions Problem You want to plot means and error bars for a dataset. Solution To make graphs with ggplot2, the data must be in a data frame, and in “long” (as opposed to wide) format. If your data needs to be restructured, see this page for more information. Sample data The examples below will the ToothGrowth dataset. Note that dose is a numeric column here; in some situations it may be useful to convert it to a factor. tg <- ToothGrowth head(tg) #> len supp dose #> 1 4.2 VC 0.5 #> 2 11.5 VC 0.5 #> 3 7.3 VC 0.5 #> 4 5.8 VC 0.5 #> 5 6.4 VC 0.5 #> 6 10.0 VC 0.5 library(ggplot2) First, it is necessary to summarize the data. This can be done in a number of ways, as described on this page. In this case, we’ll use the summarySE() function defined on that page, and also at the bottom of this page. (The code for the summarySE function must be entered before it is called here). # summarySE provides the standard deviation, standard error of the mean, and a (default 95%) confidence interval tgc <- summarySE(tg, measurevar="len", groupvars=c("supp","dose")) tgc #> supp dose N len sd se ci #> 1 OJ 0.5 10 13.23 4.459709 1.4102837 3.190283 #> 2 OJ 1.0 10 22.70 3.910953 1.2367520 2.797727 #> 3 OJ 2.0 10 26.06 2.655058 0.8396031 1.899314 #> 4 VC 0.5 10 7.98 2.746634 0.8685620 1.964824 #> 5 VC 1.0 10 16.77 2.515309 0.7954104 1.799343 #> 6 VC 2.0 10 26.14 4.797731 1.5171757 3.432090 Line graphs After the data is summarized, we can make