I have ~400 individuals and >10k timepoints each (simulation results) I would like to be able to monitor as they change over the course of time. Plotting all individuals is too messy, plotting mean +-sd, min/max, or quantiles is too little information for my taste. I am wondering what other people have come up with to visualize this type of data. If there were fewer data points I would use beanplots for each timepoint, but that would not work for so many timepoints.
I would either use a smoother, such as:
or I would subsample your data and plot only every 5 individuals, and every 10 time steps (for example).
library(ggplot2) # Data looks like: # Subject Timestep Y # 1 1 0.5 # 1 2 0.6 # 1 3 0.6 # 1 4 0.7 temp=subset(data, ((as.numeric(subject)%%5)==0) & ((as.numeric(Timestep)%%10)==0)) qplot(Timestep,Y,data=temp)
These are some great suggestions. My suggestion is to use half-violin plots as shown in http://biostat.mc.vanderbilt.edu/HmiscNew using the R
summaryS function and