Ggplot2 Bar Chart Error Bars
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needs to be set at the layer level if you are overriding the plot defaults. data A layer specific dataset - only needed if you r calculate standard error want to override the plot defaults. stat The statistical transformation to use ggplot2 stat_summary on the data for this layer. position The position adjustment to use for overlappling points on this ggplot confidence interval 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 summaryse 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 # we need to specify how wide the objects we are dodging are dodge Mapping a variable to y and
Geom_errorbar Linetype
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 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) Map
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Error Bars In R
Packages R Built-in data sets Importing Data Preparing Files barplot with error bars r Importing txt|csv: R Base Functions Fast Importing txt|csv: readr package Importing Excel Files Exporting geom_errorbar horizontal 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 http://docs.ggplot2.org/0.9.3.1/geom_errorbar.html 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 http://www.sthda.com/english/wiki/ggplot2-error-bars-quick-start-guide-r-software-and-data-visualization 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 Met
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standard error of the mean, and a 95% confidence interval. The key step is to precalculate the statistics for ggplot2. n.per.group<-10 alpha<-0.05 # for a (1.00-alpha)=95% confidence interval # Simulate raw data for an experiment or observational study. data.raw <- data.frame( treatment=rep(c('A','B'), each=n.per.group), value=c(rnorm(n.per.group, 2), rnorm(n.per.group, 3)) ) # This data frame calculates statistics for each treatment. data.summary <- data.frame( treatment=levels(data.raw$treatment), mean=tapply(data.raw$value, data.raw$treatment, mean), n=tapply(data.raw$value, data.raw$treatment, length), sd=tapply(data.raw$value, data.raw$treatment, sd) ) # Precalculate standard error of the mean (SEM) data.summary$sem <- data.summary$sd/sqrt(data.summary$n) # Precalculate margin of error for confidence interval data.summary$me <- qt(1-alpha/2, df=data.summary$n)*data.summary$sem # Load ggplot2 library require(ggplot2) # Use ggplot to draw the bar plot using the precalculated 95% CI. png('barplot-ci.png') # Write to PNG ggplot(data.summary, aes(x = treatment, y = mean)) + geom_bar(position = position_dodge(), stat="identity", fill="blue") + geom_errorbar(aes(ymin=mean-me, ymax=mean+me)) + ggtitle("Bar plot with 95% confidence intervals") + # plot title theme_bw() + # remove grey background (because Tufte said so) theme(panel.grid.major = element_blank()) # remove x and y major grid lines (because Tufte said so) dev.off() # Close PNG # Plot one standard error (standard error of the mean/SEM) png('barplot-sem.png') ggplot(data.summary, aes(x = treatment, y = mean)) + geom_bar(position = position_dodge(), stat="identity", fill="blue") + geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem)) + ggtitle("Bar plot with standard error as error bars") + theme_bw() + theme(panel.grid.major = element_blank()) dev.off() # Plot one standard deviation png('barplot-sd.png') ggplot(data.summary, aes(x = treatment, y = mean)) + geom_bar(position = position_dodge(), stat="identity", fill="blue") + geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd)) + ggtitle("Bar plot with standard deviation as error bars") + theme_bw() + theme(panel.grid.major = element_blank()) dev.off() The plots: Tested with R 2.15.2, R 3.0.2, and ggplot2 0.9.3.1. Recent