Mean point in error bar
To emphasize the mean of the error bar we can even add a point at the center of the error bar using the geom_point() function of the ggplot2 package.
Syntax: plot+ geom_point( aes( x, y), size, shape, fill)
where,
- size: determines the size of the point
- shape: determines the shape of the point
- fill: determines the fill color of the point
Here, is a bar plot with error bars and a point at the middle of the error bar made using the geom_errorbar() and the geom_point() function of the ggplot2 package.
R
# Create sample data set.seed (5642) sample_data <- data.frame (name = c ( "Geek1" , "Geek2" , "Geek3" , "Geek4" , "Geeek5" ) , value = c (31,12,15,28,45)) # create standard error standard_error = 5 # Load ggplot2 package library ( "ggplot2" ) # Create bar plot using ggplot() function ggplot (sample_data, aes (name,value,, color=name)) + # geom_bar function is used to plot bars of barplot geom_bar (stat = "identity" , fill= "white" )+ # geom_errorbar function is used to plot error bars # color width and size parameter are used to format # the error bars geom_errorbar ( aes (ymin=value-standard_error, ymax=value+standard_error, color=name), width=0.2, size=1, linetype=1)+ # geom_point is used to give a point at mean geom_point (mapping= aes (name, value), size=4, shape=21, fill= "white" ) |
Output:
How To Make Barplots with Error bars in ggplot2 in R?
In this article, we will discuss how to make a barplot with an error bar using ggplot2 in the R programming language.
Error Bars helps us to visualize the distribution of the data. Error Bars can be applied to any type of plot, to provide an additional layer of detail on the presented data. Often there may be uncertainty around the count values in data due to error margins and we could represent them as an error bar. Error bars are used to show the range of uncertainty around the distribution of data.
We can draw error bars to a plot using the geom_errorbar() function of the ggplot2 package of the R Language.
Syntax: plot + geom_errorbar( aes( ymin= value – standard_error, ymax= value + standard_error ))
where,
value: determines the column for mean values
standard_error: determines the mean error