Personally I usually prefer to go for the simpler model i.e. the model with fewer parameters. I would then rather go for the ARIMA(1,0,1) therefore. This is of course given that the residuals are white noise (if you are following the Box & Jenkins procedure).
You do this by using
But as @StepahanKolassa mentioned, you can use
auto.arima in the
forecast library. Just remember to give your undifferenced data to
auto.arima since it will determine the order of differencing for you. More info on the
forecast package here.
If you want to know more on choosing the order of ARIMA models by the Box & Jenkins approach, you might find this post helpful.