Diverse physical measurements can be modeled by X-ray transforms. While X-ray tomography is the canonical example, reference structure tomography (RST) and coded aperture snapshot spectral imaging (CASSI) are examples of physically unrelated but mathematically equivalent sensor systems. Historically, most x-ray transform based systems sample continuous distributions and apply analytical inversion processes. On the other hand, RST and CASSI generate discrete multiplexed measurements implemented with coded apertures. This multiplexing of coded measurements allows for compression of measurements from a compressed sensing perspective. Compressed sensing (CS) is a revelation that if the object has a sparse representation in some basis, then a certain number, but typically much less than what is prescribed by Shannon's sampling rate, of random projections captures enough information for a highly accurate reconstruction of the object. This paper investigates the role of coded apertures in x-ray transform measurement systems (XTMs) in terms of data efficiency and reconstruction fidelity from a CS perspective. To conduct this, we construct a unified analysis using RST and CASSI measurement models. Also, we propose a novel compressive x-ray tomography measurement scheme which also exploits coding and multiplexing, and hence shares the analysis of the other two XTMs. Using this analysis, we perform a qualitative study on how coded apertures can be exploited to implement physical random projections by "regularizing" the measurement systems. Numerical studies and simulation results demonstrate several examples of the impact of coding.