Introduction to mapping and spatial analysis with R

You are here: https://heima.hafro.is/~einarhj/older/spatialr

The course announcement is here

Time and location

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Time: 2019-11-25 10:00 - 2019-11-29 14:00

Location:

Preamble

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During the course we will allocate some time for each participant to work on a personal data, doing some manipulation and producing maps. Please bring any spatial data that you are interested working with.

Prior to the course please install:

• Latest version of R: see The Comprehensive R Archive Network
  • If your platform is Windows, also install Rtools
• Latest version of RStudio: see RStudio Desktop
  • If you are adventurous install the latest developmental version

For those that want to get a head we recommend the following read:

• https://ggplot2-book.org
• R for Data Science (Wickham and Grolemund 2016)
• Robin Lovelace, Jakub Nowosad, Jannes Muenchow: Geocomputation with R
• Edzer Pebesma, Roger Bivand: Spatial Data Science
• Spatial Data Science
• r-spatial

Additional sites:

• stats545
• TCRENV2017
• The Visual Raster Cheat Sheet

All the latest things in R:

• Rweekly
• Twitter #rstats
• Twitter #rspatial

The workplan

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(Definitively a) Preliminary schedule:

• Lectures (within the time constraint above): 09:00 - 17:00
• Own work, we will be around: 17:00 - 18:00
• Lunch (definite): 13:00 - 14:00
• Coffee (plan): 11:00 - 11:20, 15:30 - 15:45

Day 1 - Monday (10:00 - 17:00)

• R & RStudio (EH)
• Spatial data in R (JB)
• Introduction to ggplot (EH)

Day 2 - Tuesday (09:00 - 17:00)

• Data transformation (EH)
• The sf class (JB)
• Reading/writing spatial data(JB)

Day 3 - Wednesday (09:00 - 17:00)

• Coordinate Reference Systems (JB)
• Working with geometries (JB)
• Reproducible document writing in R? (EH)
• Spatial operations (JB)

Day 4 - Thursday (09:00 - 17:00)

• Rasters (JB)
• Interactive maps - leaflet (EH)

Day 5 - Friday (09:00 - 13:00)

• Interpolation (JB)
• Little more on ggplot2 (EH)
• Recap
• Course evaluation and certificates

Bits and pieces

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• Categorising continuos variable
• Downloading data
• Some details on raster merging
• Function name conflicts
• Points in polygons
• Time series graphs - visualizing vms data
• Merging VMS and logbooks - solving using time
• Popups in leaflet

Datasets used

• Information of datasets used - clean up after the course

References

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Web page source is found at https://github.com/fishvice/spatialr.

Wickham, Hadley, and Garrett Grolemund. 2016. “R for Data Science.” Sebastopol, CA: O’Reilly. http://r4ds.had.co.nz.