In this paper, the effect analysis of data dropout on iterative learning control (ILC) for linear discrete-time systems is considered. Using the lifting technique to ILC, the conditions of tracking error for both asymptotic stability and monotonic convergence are first given, and the relationship between convergence speed and data dropout rate is also presented. It is shown that the convergent speed gets slower as dropout rate increases. To attenuate iteration-varying disturbances for ILC system with data dropout, a robust iterative learning controller design is proposed. The controller can be derived in terms of linear matrix inequalities (LMIs) that can be solved by using existing numerical techniques. Some examples are also given to validate the theoretical results and the effectiveness of the proposed robust ILC scheme.