Recommender systems play an essential role in our digital society as they suggest products to purchase, restaurants to visit, and even resources to support education. Recommender systems based on collaborative filtering are the most popular among the ones used in e-commerce platforms to improve user experience. Given the collaborative environment, these recommenders are more vulnerable to shilling attacks, i.e., malicious users creating fake profiles to provide fraudulent reviews, which are deliberately written to sound authentic and aim to manipulate the recommender system to promote or demote target products or simply to sabotage the system. Therefore, understanding the effects of shilling attacks and the robustness of recommender systems have gained massive attention. However, empirical analysis thus far has assessed the robustness of recommender systems via simulated attacks, and there is a lack of evidence on what is the impact of fraudulent reviews in a real-world setting. In this paper, we present the results of an extensive analysis conducted on multiple real-world datasets from different domains to quantify the effect of shilling attacks on recommender systems. We focus on the performance of various well-known collaborative filtering-based algorithms and their robustness to different types of users. Trends emerging from our analysis unveil that, in the presence of spammers, recommender systems are not uniformly robust for all types of benign users.