#Ggplot boxplot code#

# Create basic ggplot graph: ggplot(data = chloride,Īes(x = month, y = result_va)) + geom_boxplot() + xlab( 'Month') + ylab(cl_name) + labs(title = cl_site) # Pull out the official parameter and site names for labels: cl_name <- attr(chloride, 'variableInfo')]Ĭl_site <- attr(chloride, 'siteInfo')] # Add a month column: chloride $month <- month.abb Ĭhloride $month <- factor(chloride $month, levels = month.abb) # Get chloride data using dataRetrieval: chloride <- readNWISqw( '04085139', '00940') We’ll use the package dataRetrieval to get the data (see this tutorial for more information on dataRetrieval), and plot a simple boxplot by month using ggplot2:

#Ggplot boxplot code#

Here we’ll use chloride data (parameter code “00940”) measured at a USGS station on the Fox River in Green Bay, WI (station ID “04085139”). Features in this post take advantage of enhancements to ggplot2 in version 3.0.0 or later.įirst, let’s get some data that might be typically plotted in a USGS report using a boxplot. Some additional goals here are to create boxplots that come close to USGS style.

Therefore, this post breaks down the calculations into (hopefully!) easy-to-follow chunks of code for you to make your own box plot legend if necessary. The help file for this function is very informative, but it’s often non-R users asking what exactly the plot means. The base R function to calculate the box plot limits is boxplot.stats. A question that comes up is what exactly do the box plots represent? The ggplot2 box plots follow standard Tukey representations, and there are many references of this online and in standard statistical text books. Guides(colour=guide_legend(override.aes=list(size=2.Boxplots are often used to show data distributions, and ggplot2 is often used to visualize data. #Change the size of the icons/symbols in the legend Title=element_text(size=12)) + #Title text size Legend.text=element_text(size=12), #Text size =unit(1, 'cm'), #Sets overall area/size of the legend

Legend.key=element_blank(), #removes the border #Modify various aspects of the plot text and legend Geom_jitter(position=position_jitter(width=0.3), size=3.0, colour='black') + #Add the scatter points (treats outliers same as 'inliers')

Scale_fill_manual(values=c('red', 'royalblue')) + #for boxplot #scale_color_manual(values=c('red3', 'white', 'blue')) + #for scatter plot dots #Choose which colours to use otherwise, ggplot2 choose automatically Geom_boxplot(position=position_dodge(width=0.5), outlier.shape=17, lour='red', outlier.size=0.1, aes(fill=group)) + require(reshape2)Ĭolnames(ex) <- c('gene','group','exprs') That said, if you can master ggplot, then you can produce very nice graphics for publications. Many functions redundant in the sense that they do the same thing as other but have different names, and conflicts frequently arise. I do agree that ggplot can be difficult to work with. Here's a boxplot with scatterplot overlay for anyone else arriving here from Google. Devon got there before me but as he mentioned the id.vars needs to be set to 'gene'