We present a unified and tractable framework for distributionally robust optimization that could encompass a variety of statistical information including, among others things, constraints on expectation, scenario-wise expectations, Wasserstein metric, and uncertain probabilities defined by phi-divergence. To address a distributionally robust optimization problem with recourse, we introduce the scenario wise linear decision rule, which is based on the classical linear decision rule and can also be applied in situations where the recourse decisions are discrete. Based in this format, we has also developed a new Matlab based algebraic modeling language to model and solve distributionally robust optimization problems with recourse.