# Relearn boxplot and label the outliers

[This article was first published on

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

**StaTEAstics.**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

This stackoverflow post was where I found how the outliers and whiskers of the Tukey box plots are defined in R and ggplot2:

In ggplot2, what do the end of the boxplot lines represent?

and this post on how to label the outliers using base graphics.

How to label all the outliers in a boxplot

Since the use of ggplot2 is required for this task, I have written some basic hack code to label the outliers for ggplot2.

Here are the codes:

## Install the FAOSTAT package to obtain the data if(!is.element("FAOSTAT", .packages())) install.packages("FAOSTAT") library(FAOSTAT) ## Download data on Cassava production cp.lst = getFAOtoSYB(name = "cassava_production", domainCode = "QC", itemCode = 125, elementCode = 5510) ## Use the country level data, and take only data for 2011 and remove the NA's cp.df = cp.lst$entity[!is.na(cp.lst$entity$cassava_production) & cp.lst$entity$Year == 2011, ] ## Merge with the country profile to obtain the country names for labelling ccp.df = merge(cp.df, FAOcountryProfile[, c("FAOST_CODE", "ABBR_FAO_NAME")], all.x = TRUE) ## Merge with the regional pofile to obtain the UNSD M49 macro region ## composition for multiple boxplot. rcp.df = merge(ccp.df, FAOregionProfile[, c("FAOST_CODE", "UNSD_MACRO_REG")], all.x = TRUE) ## Compute the quantile qrcp.df = ddply(.data = rcp.df, .variables = .(UNSD_MACRO_REG), transform, lQntl = quantile(cassava_production, probs = 0.25, na.rm = TRUE), uQntl = quantile(cassava_production, probs = 0.75, na.rm = TRUE)) ## Compute the lower and upper bound which defines the outlier brcp.df = ddply(.data = qrcp.df, .variables = .(UNSD_MACRO_REG), transform, lBound = lQntl - 1.5 * (uQntl - lQntl), uBound = uQntl + 1.5 * (uQntl - lQntl)) ## Remove the country names if it is within the bounds with(brcp.df, { brcp.df[cassava_production <= uBound & cassava_production >= lBound, "ABBR_FAO_NAME"] <<- NA }) ## Plot the data set.seed(587) ggplot(data = brcp.df, aes(x = UNSD_MACRO_REG, y = cassava_production)) + geom_boxplot(outlier.colour = NA) + geom_text(aes(label = ABBR_FAO_NAME), size = 2, position = position_jitter(width = 0.1)) + labs(x = NULL, y = NULL, title = "Production of Cassava by region")Here is the final product, to avoid over-plotting of texts I have used

*position_jitter.*Which is not an elegant solution but I just can not find any algorithm that works well in general.

To

**leave a comment**for the author, please follow the link and comment on their blog:**StaTEAstics.**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.