With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, leaving a gap for energy measuring tools in other programming language such as R. To address this gap, we propose ``\textbf{\textit{RJoules}}'', a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of \textit{RJoules} by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at \url{https://youtu.be/yMKFuvAM-DE} and related artifacts at https://rishalab.github.io/RJoules.