The article introduces the R package 'dagitty', which provides access to the capabilities of the DAGitty web application within the R platform for statistical computing. The package allows users to evaluate whether a Directed Acyclic Graph (DAG) is consistent with the dataset it represents, enumerate statistically equivalent but causally different DAGs, and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality helps epidemiologists detect causal misspecifications in DAGs and make robust inferences. The package is available on CRAN and GitHub, while the web application DAGitty is licensed under the GNU General Public License (GPL) version 2. The article also discusses the implementation and usage of the package, including examples and methods for evaluating DAG-dataset consistency and identifying valid adjustment sets for statistically equivalent DAGs. The authors emphasize the importance of using DAGs to strengthen confidence in causal inference and highlight the package's potential to facilitate the use of DAGs in epidemiological research.The article introduces the R package 'dagitty', which provides access to the capabilities of the DAGitty web application within the R platform for statistical computing. The package allows users to evaluate whether a Directed Acyclic Graph (DAG) is consistent with the dataset it represents, enumerate statistically equivalent but causally different DAGs, and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality helps epidemiologists detect causal misspecifications in DAGs and make robust inferences. The package is available on CRAN and GitHub, while the web application DAGitty is licensed under the GNU General Public License (GPL) version 2. The article also discusses the implementation and usage of the package, including examples and methods for evaluating DAG-dataset consistency and identifying valid adjustment sets for statistically equivalent DAGs. The authors emphasize the importance of using DAGs to strengthen confidence in causal inference and highlight the package's potential to facilitate the use of DAGs in epidemiological research.