R Plot With Error Bars
Contents |
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this scatter plot with error bars in r site About Us Learn more about Stack Overflow the company Business Learn more
Error.bar Function R
about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x
Error Bars In R Barplot
Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Add error
Errbar R
bars to show standard deviation on a plot in R up vote 23 down vote favorite 10 For each X-value I calculated the average Y-value and the standard deviation (sd) of each Y-value x = 1:5 y = c(1.1, 1.5, 2.9, 3.8, 5.2) sd = c(0.1, 0.3, 0.2, 0.2, 0.4) plot (x, y) How can I use the standard deviation to add error bars r summaryse to each datapoint of my plot? r plot statistics standard-deviation share|improve this question edited Oct 16 '14 at 3:43 Craig Finch 11417 asked Feb 25 '13 at 8:59 John Garreth 4572413 also see plotrix::plotCI –Ben Bolker Feb 25 '13 at 15:13 add a comment| 5 Answers 5 active oldest votes up vote 16 down vote accepted A Problem with csgillespie solution appears, when You have an logarithmic X axis. The you will have a different length of the small bars on the right an the left side (the epsilon follows the x-values). You should better use the errbar function from the Hmisc package: d = data.frame( x = c(1:5) , y = c(1.1, 1.5, 2.9, 3.8, 5.2) , sd = c(0.2, 0.3, 0.2, 0.0, 0.4) ) ##install.packages("Hmisc", dependencies=T) library("Hmisc") # add error bars (without adjusting yrange) plot(d$x, d$y, type="n") with ( data = d , expr = errbar(x, y, y+sd, y-sd, add=T, pch=1, cap=.1) ) # new plot (adjusts Yrange automatically) with ( data = d , expr = errbar(x, y, y+sd, y-sd, add=F, pch=1, cap=.015, log="x") ) share|improve this answer answered Sep 6 '13 at 14:21 R
by over 573 bloggers. There are many ways to follow us - By e-mail: On Facebook: If you are an R blogger yourself you are invited to add your own R content feed r arrows to this site (Non-English R bloggers should add themselves- here) Jobs for R-usersStatistical Analyst @ ggplot2 error bars Rostock, Mecklenburg-Vorpommern, GermanyData EngineerData Scientist – Post-Graduate Programme @ Nottingham, EnglandDirector, Real World Informatics & Analytics Data Science @ Northbrook, Illinois, U.S.Junior statistician/demographer plot mean and standard deviation in r for UNICEF Popular Searches web scraping heatmap twitter maps time series animation boxplot shiny hadoop ggplot2 how to import image file to R trading finance latex eclipse rstudio excel SQL ggplot quantmod knitr googlevis PCA market research rattle http://stackoverflow.com/questions/15063287/add-error-bars-to-show-standard-deviation-on-a-plot-in-r regression map tutorial coplot rcmdr Recent Posts Election 2016: Tracking Emotions with R and Python Data science for executives and managers The Worlds Economic Data, Shiny Apps and all you want to know about Propensity Score Matching! August Package Picks Slack all the things! Warsaw R-Ladies Notes from the Kölner R meeting, 14 October 2016 anytime 0.0.4: New features and fixes 2016-13 ‘DOM’ Version 0.3 Building a package automatically The new R Graph Gallery Network Analysis https://www.r-bloggers.com/building-barplots-with-error-bars/ Part 3 Exercises Annotated Facets with ggplot2 Paper published: mlr - Machine Learning in R a grim knight [cont’d] Other sites Jobs for R-users SAS blogs Building Barplots with Error Bars August 17, 2015By Chris Wetherill (This article was first published on DataScience+, and kindly contributed to R-bloggers) Bar charts are a pretty common way to represent data visually, but constructing them isn't always the most intuitive thing in the world. One way that we can construct these graphs is using R's default packages. Barplots using base R Let's start by viewing our dataframe: here we will be finding the mean miles per gallon by number of cylinders and number of gears. View(mtcars) We begin by aggregating our data by cylinders and gears and specify that we want to return the mean, standard deviation, and number of observations for each group: myData <- aggregate(mtcars$mpg, by = list(cyl = mtcars$cyl, gears = mtcars$gear), FUN = function(x) c(mean = mean(x), sd = sd(x), n = length(x))) After this, we'll need to do a little manipulation since the previous function returned matrices instead of vectors myData <- do.call(data.frame, myData) And now let's compute the standard error for each group. We can then rename the columns just for ease of use. myData$se <- myData$x.sd / sqrt(myData$x.n) colnames(myData) <- c("cyl", "gears", "mean", "sd", "n", "se") myData$names <- c(paste(myData$cyl, "cyl /", myData$gears
needs to be set at the layer level if you are overriding the plot defaults. data A layer specific dataset - only needed if you want to override the plot defaults. stat The statistical transformation to use on the data for this layer. position The http://docs.ggplot2.org/0.9.3.1/geom_errorbar.html position adjustment to use for overlappling points on this layer ... other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details. Description Error http://monkeysuncle.stanford.edu/?p=485 bars. Aesthetics geom_errorbar understands the following aesthetics (required aesthetics are in bold): x ymax ymin alpha colour linetype size width Examples # Create a simple example dataset df # Because the bars and errorbars have different widths # error bars we need to specify how wide the objects we are dodging are dodge Mapping a variable to y and also using stat="bin". With stat="bin", it will attempt to set the y value to the count of cases in each group. This can result in unexpected behavior and will not be allowed in a future version of ggplot2. If you want y to represent counts of cases, use stat="bin" and don't map a variable to y. If you r plot with want y to represent values in the data, use stat="identity". See ?geom_bar for examples. (Deprecated; last used in version 0.9.2) p Mapping a variable to y and also using stat="bin". With stat="bin", it will attempt to set the y value to the count of cases in each group. This can result in unexpected behavior and will not be allowed in a future version of ggplot2. If you want y to represent counts of cases, use stat="bin" and don't map a variable to y. If you want y to represent values in the data, use stat="identity". See ?geom_bar for examples. (Deprecated; last used in version 0.9.2) p + geom_bar(position=dodge) + geom_errorbar(limits, position=dodge, width=0.25) Mapping a variable to y and also using stat="bin". With stat="bin", it will attempt to set the y value to the count of cases in each group. This can result in unexpected behavior and will not be allowed in a future version of ggplot2. If you want y to represent counts of cases, use stat="bin" and don't map a variable to y. If you want y to represent values in the data, use stat="identity". See ?geom_bar for examples. (Deprecated; last used in version 0.9.2) p p + geom_pointrange(limits) p + geom_crossbar(limits, width=0.2) # If we want to draw lines, we need to manually set the # groups which define the lines - here the groups in th
Diet & Nutrition (28) Education (1) Evolution (35) Human Ecology (75) Infectious Disease (66) LaTeX (5) Primates (9) R (12) science (17) Social Network Analysis (17) Statistics (16) Teaching (10) Uncategorized (28) Meta Log in Entries RSS Comments RSS WordPress.org ← Latest Swine Flu Epidemic Curve for the United States Stanford Workshop in Biodemography → Plotting Error Bars in R August 24th, 2009 · 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 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. 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. control, male vs. female, etc.). Let's look at our same Gaussian means but now compare them to a Gaussian r.v. with mean 1.1 and unit variance. PLAIN TEXT R: y1 <- rnorm(500, mean=1.1) y1 <- mat