What statistical analysis to run for count data in R? I am working with deer count data across three years where counts were taken in the spring and fall seasons. Basically, I want to compare counts taken in spring to counts taken in the fall, to see if the number of deer observed differs between the seasons. The count surveys were all done at the same location and multiple surveys were done in each season of each year. The program I'm using is R.
This sounds like a relatively simple stats question to me, but I'm new to R and am not sure what kind of a statistical analysis I should run. Any advice would be greatly appreciated!
Edit for clarity: There is only one location. All surveys were conducted on the same ranch along the same stretch of road. 
 A: I'd recommend starting off using a Poisson regression model, which is well suited for count models.  Since you seem to have multiple counts at different locations, you will need to use a method that takes into account the correlation of these observations within their clusters?  I would suggest using a Generalized Estimating Equations (GEE) approach or a mixed model approach.  If you aren't interested in examining differences between measurement sites, then I'd recommend going with GEE since it offers population average estimates.  There are plenty of posts on Cross-Validated that describe GEE and mixed models.
A: There is a debate in biology and ecology studies about transformation (e.g. log-transform) or model reformation (GLM) in dealing with count data. It really depends on the outcome you want to achieve and the cost (error) you can bear. If you want to visualize the data using the boxplot and avoid outliers, I would recommend transforming the data. If you want to develop statistical models, GLM models are recommended. Please keep in mind count data are nonparametric data, a t-test is not recommended. Please also refer to this paper and this page. It has a good description of the history of the debate I mentioned at the beginning.
A: A basic statistical test you can do is the unpaired t-test. This will give you t-value and a confidence interval (default 95%) to determine if there is a "true" difference between the number of deer in spring versus fall. R code below:
t.test(spring_deer_count, fall_deer_count)

If your t-value falls outside the confidence interval, you can safely reject the null hypothesis and accept the alternative hypothesis. 
