Error Bar R Graph
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11 How can I generate the 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 standard deviation bar graph question edited Oct 23 '12 at 15:10 Roland 73.2k463102 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 err
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|| is.character(x)) "" else as.character(substitute(y)), add=FALSE, lty=1, type='p', ylim=NULL, lwd=1, pch=16, Type=rep(1, length(y)), ...) Arguments x vector of numeric x-axis values (for vertical error bars) or a factor or character variable (for horizontal error bars, x representing the group labels) http://svitsrv25.epfl.ch/R-doc/library/Hmisc/html/errbar.html y vector of y-axis values. yplus vector of y-axis values: the tops of the error bars. yminus vector of y-axis values: the bottoms of the error bars. cap the width of the little lines at the tops and http://moderndata.plot.ly/easy-error-bars-with-r-and-plotly/ bottoms of the error bars in units of the width of the plot. Defaults to 0.015. main a main title for the plot, see also title. sub a sub title for the plot. xlab optional x-axis error bar labels if add=FALSE. ylab optional y-axis labels if add=FALSE. Defaults to blank for horizontal charts. add set to TRUE to add bars to an existing plot (available only for vertical error bars) lty type of line for error bars type type of point. Use type="b" to connect dots. ylim y-axis limits. Default is to use range of y, yminus, and yplus. For horizonal charts, ylim is really the x-axis range, excluding differences. lwd line error bar graph width for line segments (not main line) pch character to use as the point. Type used for horizontal bars only. Is an integer vector with values 1 if corresponding values represent simple estimates, 2 if they represent differences. ... other parameters passed to all graphics functions. Details errbar adds vertical error bars to an existing plot or makes a new plot with error bars. It can also make a horizontal error bar plot that shows error bars for group differences as well as bars for groups. For the latter type of plot, the lower x-axis scale corresponds to group estimates and the upper scale corresponds to differences. The spacings of the two scales are identical but the scale for differences has its origin shifted so that zero may be included. If at least one of the confidence intervals includes zero, a vertical dotted reference line at zero is drawn. Author(s) Charles Geyer, University of Chicago. Modified by Frank Harrell, Vanderbilt University, to handle missing data, to add the parameters add and lty, and to implement horizontal charts with differences. Examples set.seed(1) x <- 1:10 y <- x + rnorm(10) delta <- runif(10) errbar( x, y, y + delta, y - delta ) # Show bootstrap nonparametric CLs for 3 group means and for # pairwise differences on same graph group <- sampl
dataset, and are easy to graph with Plotly and R! Error bars can be used to visualize standard deviations, standard errors or confidence intervals (just don't forget to specify which measure the error bar in the graph represents). Below are two examples that demonstrate how to graph a variety of error bars. The complete R script and data used to create these 2 graphs are available here! To create vertical error bars, like on the Snow line in the graph below, set error_y = list(type = "data", array = c(YOUR_VALUES)) 1 error_y = list(type = "data", array = c(YOUR_VALUES))
It is also possible to calculate and plot error bars with a percent value, like on the Rain line below. Set: error_y = list(type = "percent", value = CHOOSE_%_VALUE) 1 error_y = list(type = "percent", value = CHOOSE_%_VALUE) To create horizontal error bars use error_x. Furthermore, it's easy to graph asymmetrical error bars. Just set symmetric = FALSE and add an arrayminus array like this: error_x = list( type = "data", symmetric = FALSE, array = c(YOUR_HIGH_VALUES), arrayminus = c(YOUR_LOW_VALUES)) 12345 error_x = list(type = "data",symmetric = FALSE,array = c(YOUR_HIGH_VALUES),arrayminus = c(YOUR_LOW_VALUES)) Creating dashboards or visualizations at your company? Consider Plotly Enterprise for modern intracompany graph and data sharing. chelsea Tags: confidence interval, Error bars, Plotly, R, RStudio, standard deviation, standard error Post navigation Previous Post 3d surface plots with RStudio and PlotlyNext Post Using R, Python, & Plotly With Tableau Search for: Search Recent Posts nteract: Revolutionizing the Notebook Experience Simple REST APIs for charts and datasets Upgrading to plotly 4.0 (and above) Radial Stacked Area Chart in R using Plotly Analyzing Plotly’s Python package downloads R Upgrading to plotly 4.0 (and above) Radial Stacked Area Chart in R using Plotly Using cranlogs in R with Plotly New feature: Dropdown menus in Plotly and R PLOTCON 2016 - Speakers and topics in R Blog roll R-Bloggers