Tuesday, February 5, 2013

Relearn boxplot and label the outliers

Despite the fact that box plot is used almost every where and taught at undergraduate statistic classes, I recently had to re-learn the box plot in order to know how to label the outliers.

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.

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