Cran Barplot Error Bars
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R Cran Barplot
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Adding Standard Error Bars In R
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Error Bar In R
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Calculate Standard Error In R
Curve for the United States Stanford Workshop in Biodemography → Plotting Error Bars in R August 24th, 2009 · summaryse r 52 Comments · R One 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 https://www.r-bloggers.com/building-barplots-with-error-bars/ encountered this issue revising a paper for 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. http://monkeysuncle.stanford.edu/?p=485 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", 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. c
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 statistical data analysis https://morevine.wordpress.com/2010/05/06/statistics-with-r-barplots-with-errorbars/ 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 think "R is http://strata.uga.edu/6370/rtips/barPlotErrorbars.html 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 a ago I had the error bar 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 for me, too. So, the problem is drawing barplot with error 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(heights, ylim=c(0,10)) Means of v1, v2 and v3 For creating the errorbars we use the errba
a bar plot is one of those things. The segments() command lets you draw line segments, provided you specify the coordinates of the beginning and end of the segments. Since an error bar is just a line segment, the x coordinates for the start and the end are the center of the top of the bar, and the y coordinates are the top of the bar plus or minus the length of the error bar. I assumed that the centers of the bars would be integers, that is, if you had three bars, that their centers would be at 1, 2, and 3. That is not true - the centers of the bars are not integers, which makes finding their locations trickier. You could use the locator() function to find the centers of the bars, but clicking on points can be imprecise. It is also impractical when there are many bars. The easy solution to finding the bar centers is in the barplot() command itself: in addition to plotting the graph, the function also returns a vector of the centers of each bar. Here is how it all works. First, I will use the mean lengths of three small mammals, with the standard errors of those lengths. names <- c("squirrel", "rabbit", "chipmunk") means <- c(23, 28, 19) standardErrors <- c(1.2, 1.7, 0.9) Because the top of the plot is scaled to the tallest bar, the error bars will get clipped if I add them. To prevent this, I calculate the top of the highest bar; here, the error bars I am using are plus or minus two standard errors. plotTop <- max(means+standardErrors*2) First, I will plot the graph, with the bars filled with gray, with y-axis labels rotated (las=1), and with the limits on the y-axis expanded so they will include the error bars. I assign the barplot() command to barCenters, because the command returns a vector of the centers of the bars. barCenters <- barplot(means, names.arg=names, col="gray", las=1, ylim=c(0,plotTop)) Finally, I add the error bars using segments(), where the x coordinates for the beginning and end of each bar are saved in barCenters. The y coordinate of the bottom of each bar is the mean minus two standard errors, and the y coordinate of the top of each bar is the mean plus two standard errors. To make the bars bolder, I double the line wid