When working with counts, having many zeros does not necessarily indicate zero inflation. I demonstrate this by simulating data from the negative binomial and generalized Poisson distributions. I then show one way to check if the data has excess zeros compared to the number of zeros expected based on the model.
Analyzing positive data with 0 values can be challenging, since a direct log transformation isn't possible. I discuss some of the things to consider when deciding on an analysis strategy for such data and then explore the effect of the value of the constant, c, when using log(y + c) as the response variable.
I currently work as an applied statistician in aviation and aeronautics. In a previous role as a consulting statistician in academia I created and taught R workshops for applied science graduate students who are just getting started in R, where my goal was to make their transition to a programming language as smooth as possible. See these workshop materials at my website.