The data tutorials in this series cover how to open, work with and plot vector-format spatial data (points, lines and polygons) in R. Additional topics include working with spatial metadata (extent and coordinate reference system), working with spatial attributes and plotting data by attribute. Data used in this series cover NEON Harvard Forest Field Site and are in shapefile and .csv formats. R Skill Level: Intermediate – you’ve got the basics of R down but haven’t previously worked with spatial data in R. To complete the tutorial series you will need an updated version of R and, preferably, RStudio installed on your computer. R is a programming language that specializes in statistical computing. It is a powerful tool for exploratory data analysis. To interact with R, we strongly recommend RStudio, an interactive development environment (IDE). You can chose to install packages with each lesson or you can download all of the necessary R packages now. These vector data provide information on the site characterization and infrastructure at the National Ecological Observatory Network’s Harvard Forest field site. The Harvard Forest shapefiles are from the Harvard Forest GIS & Map archives. US Country and State Boundary layers are from the US Census Bureau. The LiDAR and imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network’s Harvard Forest and San Joaquin Experimental Range field sites and processed at NEON headquarters. The entire dataset can be accessed by request from the NEON Airborne Data Request Page on the NEON website. Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets. An overview of setting the working directory in R can be found here. R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page. Working with Spatio-temporal Data in R Series: This tutorial series is part of a larger spatio-temporal tutorial series and Data Carpentry workshop. Included series are introduction to spatio-temporal data and data management, working With raster data in R, working with vector data in R and working with tabular time series in R. This spatial data tutorial explains the how to open and plot shapefiles containing point, line and polygon vector data in R. This tutorial provides an overview of how to locate and query shapefile attributes as well as subset shapefiles by specific attribute values in R. It also covers plotting multiple shapefiles by attribute and building a custom plot legend. This tutorial provides an overview of how to create a a plot of multiple shapefiles using base R plot. It also explores adding a legend with custom symbols that match your plot colors and symbols. This tutorial will cover how to identify the CRS of a spatial vector object in R. It will also explore differences in units associated with different projections and how to reproject data using spTransform in R. Spatial data need to be in the same projection in order to successfully map and process them in non-gui tools such as R. This tutorial covers how to convert a .csv file that contains spatial coordinate information into a spatial object in R. We will then export the spatial object as a Shapefile for efficient import into R and other GUI GIS applications including QGIS and ArcGIS This tutorial covers how to modify (crop) a raster extent using the extent of a vector shapefile. It also covers extracting pixel values from defined locations stored in a spatial object. Source.