I don't think you can easily do what you want to do with RM-ANOVA since number of the repetitions are not the same for all subjects. Running mixed-effects models is very easy in R. In fact, by investing a little time to learn the fundamentals and the commands, it will open a lot of possibilities to you. I also find mixed-modeling much simpler to use and more flexible and almost never need to do RM-ANOVA directly. Finally, consider that with mixed modeling you can also account for the covariance structure of the residuals (RM-ANOVA simply assumes a diagonal structure) which can be important for many applications.
There are two main packages for linear mixed modeling in R:
lme4 packages is the more modern one which is great for large datasets and also for the cases you deal with clustered data.
Nlme is the older package and is mostly deprecated in favor of
lme4. However, for repeated measures designs it is still better than
lme4 since only
nlme allows you to model the covariance structure of the residuals. The basic syntax of
nlme is very simple. For example:
fit.1 <- lme(dv ~ x + t, random=~1|subject, cor=corCompSymm())
Here I'm modeling the relationship between a dependent variable
dv and a factor
x and time-related covariate
Subject is a random effect and I have used a compound symmetry structure for the covariance of the residuals. Now you can easily get the infamous p-values by:
Finally, I can suggest you to read more about nlme using its definitive reference guide, Mixed Effects Models in S and S-Plus. Another good reference for beginners is Linear Mixed Models - a Practical Guide Using Statistical Software which compiles lots of examples of different applications of mixed modeling with code in R, SAS, SPSS, etc.