Ordering boxplots can be tricky sometimes. There I present 3 boxplots. The first one is the most classic one. The second one is the same boxplot but ordered. The third one does the work for a multifactorial data frame. The code is given below for the 3 examples separately.
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### Exemple 1 : Usual boxplot # Create data :7 varieties of wheat / 20 samples per variety / a numeric value for each sample variety=c(rep("soldur", 20) , rep("silur", 20) , rep("lloyd", 20), rep("pescadou", 20), rep("X4582", 20) , rep("Dudur", 20) , rep("Classic", 20)) note=c( sample(2:5, 20 , replace=T) , sample(6:10, 20 , replace=T), sample(1:7, 30 , replace=T), sample(3:10, 70 , replace=T) ) data=data.frame(variety, note) # Basic boxplot boxplot(data$note ~ data$variety , col="grey") |
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### Exemple 2 : Ordered boxplot # Create data :7 varieties of wheat / 20 samples per variety / a numeric value for each sample variety=c(rep("soldur", 20) , rep("silur", 20) , rep("lloyd", 20), rep("pescadou", 20), rep("X4582", 20) , rep("Dudur", 20) , rep("Classic", 20)) note=c( sample(2:5, 20 , replace=T) , sample(6:10, 20 , replace=T), sample(1:7, 30 , replace=T), sample(3:10, 70 , replace=T) ) data=data.frame(variety, note) # Create a vector named "new_order" containing the desired order, and ask to boxplot using this vector ! new_order <- with(data, reorder(variety , note, median , na.rm=T)) # And draw the boxplot boxplot(data$note ~ new_order , ylab="sickness" , col="darkgreen", boxwex=0.4 , main="") |
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### Exemple 3 : Multifactorial ordered boxplot # Create data # This time we still have several varieties of wheat. But each genotype have 20 samples grown in high temperature, # and 20 in low temperature. So we have 2 factors : variety and treatment. variety=c(rep("soldur" , 40), rep("silur" , 40), rep("lloyd" , 40), rep("pescadou" , 40) , rep("X4582" , 40) , rep("Dudur" , 40) , rep("Classic" , 40)) treatment= rep(c(rep("high" , 20) , rep("low" , 20)) , 7) note=c( rep(c(sample(0:4, 20 , replace=T) , sample(1:6, 20 , replace=T)),2), rep(c(sample(5:7, 20 , replace=T), sample(5:9, 20 , replace=T)),2), c(sample(0:4, 20 , replace=T) , sample(2:5, 20 , replace=T), rep(c(sample(6:8, 20 , replace=T) , sample(7:10, 20 , replace=T)),2) )) data=data.frame(variety, treatment , note) # I reorder the variety from the most sensible to the most resistant. (mixing low and high treatments for the calculations) new_order <- with(data, reorder(variety , note, mean , na.rm=T)) # Then I make the boxplot, asking to use the 2 factors : variety (in the good order) AND treatment : par(mar=c(3,4,3,1)) myplot=boxplot(note ~ treatment*new_order , data=data , boxwex=0.4 , ylab="sickness", main="sickness of several wheat lines" , col=c("slateblue1" , "tomato") , xaxt="n") # To add the label of x axis my_names=sapply(strsplit(myplot$names , '\\.') , function(x) x[[2]] ) my_names=my_names[seq(1 , length(my_names) , 2)] axis(1, at = seq(1.5 , 14 , 2), labels = my_names , tick=FALSE , cex=0.3) for(i in seq(0.5 , 20 , 2)){ abline(v=i,lty=1, col="grey")} # Add a legend legend("bottomright", legend = c("High treatment", "Low treatment"), col=c("slateblue1" , "tomato"), pch = 15, bty = "n", pt.cex = 3, cex = 1.2, horiz = F, inset = c(0.1, 0.1)) |
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What exactly are you doing using the rep and sampling function? I want to make a similar graph but I’m trying to understand exactly this part of the code.
Brilliant!
Hi,
I already have a table with equivalent treatments and varieties, as well as the percentage and its standard error and confidence intervals. I plot the % but I would like to add the confidence intervals on the plot so it would make a box as well as the standard error. COuld you help ? Thanks
Cool stuff. Small typo: it’s ex_a_mple
Thanks, it’s corrected now!
The graphs in this site really help! I appreciate all the codes and plots that you made, thank you!
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