Updated 2023: Taking examples from global satellite data in gridded/ raster format, we will demonstrate several common geospatial operations like projections, resampling, spatial extraction, cropping, masking etc. using rasters, shapefiles, and spatial data frames. For a seamless analysis across different data types and platforms, conversion from/to different data formats like data frames, matrices, rasters, and structured data like NetCDF/HDF will be discussed. Advanced topics will include working with data cubes, layer-wise operations on data cubes, cell-wise operations on raster time series by implementing user-defined functions in space and time.

Updated 2021: Taking examples from global satellite data in gridded/ raster format, we will demonstrate several common geospatial operations like projections, resampling, spatial extraction, cropping, masking etc. using rasters, shapefiles, and spatial data frames. For a seamless analysis across different data types and platforms, conversion from/to different data formats like data frames, matrices, rasters, and structured data like NetCDF/HDF will be discussed. Advanced topics will include working with data cubes (RasterStack/ RasterBrick), layer-wise operations on data cubes, cell-wise operations on raster time series by implementing user-defined functions with stackApply.

Updated 2021: Statistical computing is essential for scientific inquiry, discovery, and storytelling. With R, there are endless possibilities for assembling, transforming, querying, analyzing, and ultimately visualizing data. In this session, we will give you the tools to get you started.

Taking examples from global satellite data in gridded/ raster format, we will demonstrate several common geospatial operations like projections, resampling, spatial extraction, cropping, masking etc. using rasters, shapefiles, and spatial data frames. For a seamless analysis across different data types and platforms, conversion from/to different data formats like data frames, matrices, rasters, and structured data like NetCDF/HDF will be discussed. Advanced topics will include working with data cubes (RasterStack/ RasterBrick), layer-wise operations on data cubes, cell-wise operations on raster time series by implementing user-defined functions with stackApply.

Statistical computing is essential for scientific inquiry, discovery, and storytelling. With R, there are endless possibilities for assembling, transforming, querying, analyzing, and ultimately visualizing data. In this session, we will give you the tools to get you started.