In landscape genetics, habitat connectivity and population genetic structure have been analysed using graph‐theoretic approaches to understand how landscape features influence demography (i.e. dispersal and population size). Despite substantial advances in enhancing both genetic and landscape graph use, a software tool bringing together a large range of construction and analysis parameters for these two types of graphs was lacking in the landscape genetic toolbox. Moreover, although these two types of graphs appear complementary for answering landscape genetic questions, methods for comparing them have not been forthcoming. We have developed an R package to improve and encourage the use of these graphs. It includes functions for converting and importing genetic data and for genetic distance computing. It also implements time‐efficient geodesic and cost‐distance calculations from spatial data. A large range of parameters can be used to create genetic and landscape graphs from these data, including several graph pruning methods. We made available to R users the command‐line facilitaties of Graphab software to easily model landscape graphs in R. The package functions perform preliminary analysis to adapt methodological choices to research questions. Landscape and genetic graphs created can be analysed with node‐level metrics as well as link‐level and modularity analyses. Users can compare and visualise these graphs and export them to shapefiles to facilitate interpretation and subsequent analyses. graph4lg contributes to expanding landscape and genetic graph potential for analyzing ecological connectivity while encouraging further investigations on methodological implications related to these tools.