I am looking for a statistician who has advanced skills in R programming and can help me with time series analysis. Specifically, I want to learn how to apply ARIMA modeling to a SAS dataset called "sashelp.AIR", which contains monthly airline passenger data from 1949 to 1960. I need you to create a detailed tutorial that explains the steps and logic of the analysis, as well as the R codes that you use. You should also include comments and descriptions of the R codes to make them easy to understand and follow.

library(ggplot2)
library(fpp2)
library(forecast)
library(ggsci)
data(ausair)
ausair <- diff(log(ausair))
autoplot(ausair) + ggtitle("Log difference of Passenger data")
ggseasonplot(ausair)
ggseasonplot(ausair, type = "reversed")
ggAcf(ausair)
ggPacf(ausair)
model1 <- Arima(ausair, order = c(2, 1, 0), seasonal = list(order = c(2, 1, 0), period = 12))
model2 <- Arima(ausair, order = c(1, 1, 1), seasonal = list(order = c(1, 1, 1), period = 12))
model3 <- Arima(ausair, order = c(1, 1, 1), seasonal = list(order = c(2, 1, 0), period = 12))
model4 <- Arima(ausair, order = c(2, 1, 0), seasonal = list(order = c(1, 1, 1),