SC1.46

R is probably the most important statistical computing language in academia. With more than 10,000 packages it has been extended in many directions, including a huge support for geospatial data (see https://cran.r-project.org/web/views/Spatial.html and Bivand, Pebesma, and Gómez-Rubio 2013). R’s flexibility and statistical capabilities have made it attractive for people working in Earth, planetary and space sciences and a need for geographic data science.

This course will introduce the audience to R’s geographical capabilities, building on the book Geocomputation with R (https://geocompr.robinlovelace.net/) by the workshop authors (Lovelace, Nowosad, and Muenchow 2018). It will cover four topics and provide a solid foundation for attendees to apply R to a range of geographic data:

1. R’s implementation of the two most important spatial data models - vector (Pebesma 2018) and raster (Hijmans 2017).
2. Spatial data visualization with R.
3. Bridges to dedicated GIS software such as QGIS.
4. Statistical learning with geographic data.

Understanding data models is vital for working with geographic data in R. Maps, based on the data, can display complex information in a beautiful way while allowing for first inferences about spatial relationships and patterns. R has already become a Geographic Information System (GIS) (Bivand, Pebesma, and Gómez-Rubio 2013) - a system for the analysis, manipulation and visualization of geographic data (Longley et al. 2015). However, R was not designed as a GIS, and therefore computing large amounts of geographic data in R can be cumbersome. Even more important, R is missing hundreds of geoalgorithms which are readily available in common Desktop GIS. To deal with these shortcomings R packages have been developed allowing R to interface with GIS software. As an example, we will introduce the RQGIS package (Muenchow, Schratz, and Brenning 2017) for this purpose but also comment on other R-GIS bridges such as RSAGA (Brenning, Bangs, and Becker 2018) and rgrass7 (Bivand 2017). We will use RQGIS to compute terrain attributes (catchment area, catchment slope, SAGA wetness index, etc.) which we will subsequently use to model and predict spatially landslide susceptibility with the help of statistical learning techniques such as GLMs, GAMs and random forests (James et al. 2013). Hence, we show by example how to combine the best of two worlds: the geoprocessing power of a GIS and the (geo-)statistical data science power of R. The short course will consist of a mixture of presentations, live code demos and short interactive exercises if time allows.

Learning objectives
By the end of this workshop, the participants should:

- Know how to handle the two spatial data models (vector and raster) in R.
- Import/export different geographic data formats.
- Know the importance of coordinate reference systems.
- Be able to visualize geographic data in a compelling fashion.
- Know about geospatial software interfaces and how they are integrated with R (GEOS, GDAL, QGIS, GRASS, SAGA).
- Know about the specific challenges when modeling geographic data.

Software requirements
1. Latest version of R and RStudio
2. R packages: sf, raster, RQGIS, RSAGA, spData, tmap, tidyverse, mlr
3. QGIS (including SAGA and GRASS), please follow our installation guide (http://jannes-m.github.io/RQGIS/articles/install_guide.html) to make sure that RQGIS can work with QGIS

References
Bivand, Roger. 2017. Rgrass7: Interface Between GRASS 7 Geographical Information System and R. https://CRAN.R-project.org/package=rgrass7.

Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. New York: Springer.

Brenning, Alexander, Donovan Bangs, and Marc Becker. 2018. RSAGA: SAGA Geoprocessing and Terrain Analysis. https://CRAN.R-project.org/package=RSAGA.

Hijmans, Robert J. 2017. Raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani, eds. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics 103. New York: Springer.

Longley, Paul, Michael Goodchild, David Maguire, and David Rhind. 2015. Geographic Information Science & Systems. Fourth edition. Hoboken, NJ: Wiley.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2018. Geocomputation with R. The R Series. CRC Press.

Muenchow, Jannes, Patrick Schratz, and Alexander Brenning. 2017. “RQGIS: Integrating R with QGIS for Statistical Geocomputing.” The R Journal 9 (2): 409–28.

Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html.

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Co-organized as BG1.73/ESSI1.19/GM12.4/NH10.5/NP10.7
Convener: Jannes Muenchow | Co-conveners: Robin Lovelace, Jakub Nowosad
Wed, 10 Apr, 08:30–10:15
 
Room -2.62
R is probably the most important statistical computing language in academia. With more than 10,000 packages it has been extended in many directions, including a huge support for geospatial data (see https://cran.r-project.org/web/views/Spatial.html and Bivand, Pebesma, and Gómez-Rubio 2013). R’s flexibility and statistical capabilities have made it attractive for people working in Earth, planetary and space sciences and a need for geographic data science.

This course will introduce the audience to R’s geographical capabilities, building on the book Geocomputation with R (https://geocompr.robinlovelace.net/) by the workshop authors (Lovelace, Nowosad, and Muenchow 2018). It will cover four topics and provide a solid foundation for attendees to apply R to a range of geographic data:

1. R’s implementation of the two most important spatial data models - vector (Pebesma 2018) and raster (Hijmans 2017).
2. Spatial data visualization with R.
3. Bridges to dedicated GIS software such as QGIS.
4. Statistical learning with geographic data.

Understanding data models is vital for working with geographic data in R. Maps, based on the data, can display complex information in a beautiful way while allowing for first inferences about spatial relationships and patterns. R has already become a Geographic Information System (GIS) (Bivand, Pebesma, and Gómez-Rubio 2013) - a system for the analysis, manipulation and visualization of geographic data (Longley et al. 2015). However, R was not designed as a GIS, and therefore computing large amounts of geographic data in R can be cumbersome. Even more important, R is missing hundreds of geoalgorithms which are readily available in common Desktop GIS. To deal with these shortcomings R packages have been developed allowing R to interface with GIS software. As an example, we will introduce the RQGIS package (Muenchow, Schratz, and Brenning 2017) for this purpose but also comment on other R-GIS bridges such as RSAGA (Brenning, Bangs, and Becker 2018) and rgrass7 (Bivand 2017). We will use RQGIS to compute terrain attributes (catchment area, catchment slope, SAGA wetness index, etc.) which we will subsequently use to model and predict spatially landslide susceptibility with the help of statistical learning techniques such as GLMs, GAMs and random forests (James et al. 2013). Hence, we show by example how to combine the best of two worlds: the geoprocessing power of a GIS and the (geo-)statistical data science power of R. The short course will consist of a mixture of presentations, live code demos and short interactive exercises if time allows.

Learning objectives
By the end of this workshop, the participants should:

- Know how to handle the two spatial data models (vector and raster) in R.
- Import/export different geographic data formats.
- Know the importance of coordinate reference systems.
- Be able to visualize geographic data in a compelling fashion.
- Know about geospatial software interfaces and how they are integrated with R (GEOS, GDAL, QGIS, GRASS, SAGA).
- Know about the specific challenges when modeling geographic data.

Software requirements
1. Latest version of R and RStudio
2. R packages: sf, raster, RQGIS, RSAGA, spData, tmap, tidyverse, mlr
3. QGIS (including SAGA and GRASS), please follow our installation guide (http://jannes-m.github.io/RQGIS/articles/install_guide.html) to make sure that RQGIS can work with QGIS

References
Bivand, Roger. 2017. Rgrass7: Interface Between GRASS 7 Geographical Information System and R. https://CRAN.R-project.org/package=rgrass7.

Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. New York: Springer.

Brenning, Alexander, Donovan Bangs, and Marc Becker. 2018. RSAGA: SAGA Geoprocessing and Terrain Analysis. https://CRAN.R-project.org/package=RSAGA.

Hijmans, Robert J. 2017. Raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani, eds. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics 103. New York: Springer.

Longley, Paul, Michael Goodchild, David Maguire, and David Rhind. 2015. Geographic Information Science & Systems. Fourth edition. Hoboken, NJ: Wiley.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2018. Geocomputation with R. The R Series. CRC Press.

Muenchow, Jannes, Patrick Schratz, and Alexander Brenning. 2017. “RQGIS: Integrating R with QGIS for Statistical Geocomputing.” The R Journal 9 (2): 409–28.

Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html.