Description: Data analysis and visualization is an integral part of scientific discovery. Boasting a vast collection of open-source (free) libraries for diverse data operations, visualization and statistical analyses, R has become a sought-after skill for researchers, data analysts and researchers alike. This two-part workshop will provide hands-on exercises on data visualization using R. In Part-I, we will discuss plotting basics, data manipulation, ggplot. Introduction to spatial data mapping and interactive plots using leaflets will be explored. In Part-II, we will demonstrate geospatial operations like projections, resampling, spatial extraction, cropping, masking etc. using rasters, shapefiles, and spatial data frames. Conversion from/to different data formats like data frames, matrices, rasters, and structured data like NetCDF will also be discussed. Advanced topics will include working with data cubes (raster stack/ brick), layer-wise operations on data cubes, cell-wise operations on raster time series by implementing user-defined functions.
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Course requirements: Computer with installed and working R and RStudio. Basic experience in R is required.

Course Materials: Github Repository for LGAR: https://github.com/Vinit-Sehgal/SampleData

Part 1 (1:00- 3:00 pm): Introduction to data visualization in R:
This part of the workshop will cover basics of data visualization in R. The following topics will be covered:
• Plotting basics in R: Primer on basic plots using R.
• Data visualization using ggplot: Data transformation for use in ggplot, plotting examples using sample data, combining and exporting plots
• Spatial data visualization: Plotting spatial data points, spatial polygons and raster data visualization, using ggplot for spatial data visualization
• Introduction to Leaflets: Interactive spatial data visualization using leaflets.


Part 2 (3:15- 5:15 pm): Large-scale geospatial data analysis
This part of the workshop will focus on various advanced GIS-type operations on geospatial data in R using sample global satellite data. We will explore working with data cubes (raster stack/ brick), structured data like NetCDF/ H5 data. Application for cell-wise and layer-wise operations on spatial data will be covered.    
• Raster data and spatial polygons: Spatial data projection, cropping, resampling, data extraction and summary statistics based on raster/ polygon classes. Data conversion to-and-from x-y-z, raster and spatial polygons.  
• Working with data cubes: Creating raster stack/brick. Geospatial operations on raster stacks/ bricks. Layer-wise and cell-wise operations. Spatial correlation and summary statistics of data cubes.
• NetCDF/ H5 dataset: Importing and visualizing NetCDF/ H5 dataset, conversion to raster stack/ brick.  
• Custom cell/ layer-wise operations on data cubes: Using stackapply and cellstats function for cellwise and layer-wise operations on data cubes.  

If this is your first time using Zoom, please review information at https://it.tamu.edu/services/audio-video-and-telecommunication/audio-video/zoom/ for instructions on downloading the needed software/apps.

This workshop will be on Thursday, November 12, 1:00 p.m. to 5:15 p.m..

Registration is required to provide Zoom connection information.