Plot Error Bar In R
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 site About Us Learn more about Stack error.bar function r Overflow the company Business Learn more about hiring developers or posting ads with us Stack error bars in r barplot Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community scatter plot with error bars in r of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Scatter plot with error bars up vote 21 down vote favorite 11 How can I generate the
Errbar R
following plot in R? Points, shown in the plot are the averages, and their ranges correspond to minimal and maximal values. I have data in two files (below is an example). x y 1 0.8773 1 0.8722 1 0.8816 1 0.8834 1 0.8759 1 0.8890 1 0.8727 2 0.9047 2 0.9062 2 0.8998 2 0.9044 2 0.8960 .. ... r plot share|improve this question edited Oct 23 '12 at 15:10 Roland ggplot2 error bars 74.1k463103 asked Oct 23 '12 at 14:29 sherlock85 1521313 Since you clearly don't want a boxplot, I changed the title of your question in order to reflect what you really want. –Roland Oct 23 '12 at 15:11 1 also plotrix::plotCI, gplots::plotCI, library("sos"); findFn("{error bar}") –Ben Bolker Oct 23 '12 at 17:29 add a comment| 5 Answers 5 active oldest votes up vote 52 down vote accepted First of all: it is very unfortunate and surprising that R cannot draw error bars "out of the box". Here is my favourite workaround, the advantage is that you do not need any extra packages. The trick is to draw arrows (!) but with little horizontal bars instead of arrowheads (!!!). This not-so-straightforward idea comes from the R Wiki Tips and is reproduced here as a worked-out example. Let's assume you have a vector of "average values" avg and another vector of "standard deviations" sdev, they are of the same length n. Let's make the abscissa just the number of these "measurements", so x <- 1:n. Using these, here come the plotting commands: plot(x, avg, ylim=range(c(avg-sdev, avg+sdev)), pch=19, xlab="Measurements", ylab="Mean +/- SD", main="Scatter plot with std.dev error bars" ) # hack: we draw arrows but with very special "arrowheads" arrows(x, avg-sdev, x, avg+sdev, l
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 summaryse r (16) Teaching (10) Uncategorized (28) Meta Log in Entries RSS Comments RSS
R Arrows
WordPress.org ← Latest Swine Flu Epidemic Curve for the United States Stanford Workshop in Biodemography → Plotting Error
Calculate Standard Error In R
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 http://stackoverflow.com/questions/13032777/scatter-plot-with-error-bars 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 http://monkeysuncle.stanford.edu/?p=485 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 estim
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 http://docs.ggplot2.org/0.9.3.1/geom_errorbar.html transformation to use on the data for this layer. position The position adjustment to use for overlappling points on this layer ... other arguments passed on to layer. This can include http://www.sthda.com/english/wiki/ggplot2-error-bars-quick-start-guide-r-software-and-data-visualization aesthetics whose values you want to set, not map. See layer for more details. Description Error bars. Aesthetics geom_errorbar understands the following aesthetics (required aesthetics are in bold): x ymax ymin alpha error bar colour linetype size width Examples # Create a simple example dataset df # Because the bars and errorbars have different widths # 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 error bars 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 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
Installing R/RStudio Running R/RStudio R Programming Basics Getting Help Installing R Packages R Built-in data sets Importing Data Preparing Files Importing txt|csv: R Base Functions Fast Importing txt|csv: readr package Importing Excel Files Exporting Data Exporting to txt|csv Files: R Base Functions Fast Exporting to txt|csv Files: readr package Exporting to Excel Files Saving Data into RDATA and RDS Formats Word Document Word Document from Template Add Table into Word Document Powerpoint Document Editable Graph From R to Powerpoint Reshaping Data Data Manipulation Data Visualization R Base Graphs Lattice Graphs Ggplot2 3D Graphics How to Choose Great Colors? Basic Statistics Descriptive Statistics and Graphics Normality Test in R Statistical Tests and Assumptions Correlation Analysis Correlation Test Between Two Variables in R Correlation Matrix: Analyze, Format & Visualize Visualize Correlation Matrix using Correlogram Elegant Correlation Table using xtable R Package Correlation Matrix : An R Function to Do All You Need Comparing Means One-Sample vs Standard Known Mean One-Sample T-test (parametric) One-Sample Wilcoxon Test (non-parametric) Two Independent Groups Unpaired Two Samples T-test (parametric) Unpaired Two-Samples Wilcoxon Test (non-parametric) Paired Samples Paired Samples T-test (parametric) Paired Samples Wilcoxon Test (non-parametric) More Than Two Groups One-Way ANOVA Test in R Two-Way ANOVA Test in R MANOVA: Multivariate ANOVA Kruskal-Wallis (non-parametric) Comparing Variances F-Test: Compare Two Variances Compare Multiple Sample Variances Comparing Proportions One-Proportion Z-Test Two-Proportions Z-Test Chi-Square Goodness of Fit Test Chi-Square Test of Independence Cluster Analysis Overview Distance Measures Basic Clustering Partitionning Methods Hierarchical Clustering Clustering Evaluation & Validation Clustering Tendency Optimal Number of Clusters Validation Statistics Compare Clustering Algorithms p-value for Hierarchical Clustering Quick Guide for Cluster Analysis Clustering Visualization Visual Enhancement of Clustering Beautiful Dendrograms Static and Interactive Heatm