To be useful, scientific results must be reproducible and trustworthy. Data provenance-the history of data and how it was computed-underlies reproducibility of, and trust in, data analyses. Our work focuses on collecting data provenance from R scripts and providing tools that use the provenance to increase the reproducibility of and trust in analyses done in R. Specifically, our "End-toend provenance tools" ("E2ETools") use data provenance to: document the computing environment and inputs and outputs of a script's execution; support script debugging and exploration; and explain differences in behavior across repeated executions of the same script. Use of these tools can help both the original author and later users of a script reproduce and trust its results.