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‎The Principal Components Analysis is one of the popular exploratory approaches to reduce the dimension and to describe the main source of variation among data‎. ‎Despite many benefits‎, ‎it is encountered with some problems in multivariate analysis‎. ‎Having outliers among data significantly influences the results of this method and it sounds a robust version of PCA is beneficial  in this case‎. ‎In addition‎, ‎having moderate loadings in the final results makes the interpretation of principal components rather difficult‎. ‎One can consider a version of sparse components in this case‎. ‎We study a hybrid approach consisting of joint robust and sparse components and conduct some simulations to evaluate and compare it with other traditional methods‎. ‎The proposed technique is implemented in a real-life example dealing with the crime rate in the USA‎.