Solved – What are good ways of plotting distributions over time using R

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) 

or both.

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