CUOSGwiki - Recent changes [en-ca]

Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 07:12:09 GMT

QGIS Functions

File:QGISsearchalgorithmsupdated.png

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Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 07:08:13 GMT

Hillshade

File:SlopeAspectMap1.jpeg

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File:AspectSlope2.jpeg

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Utilizing Rstudio as an alternative GIS

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Laplacian Filter

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Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 03:40:45 GMT

Raster to Vector

Land Use Change Assessment in SAGA GIS

Deslaf — Sat, 10 Oct 2026 03:11:18 GMT

visualizing step 1 under 'unsupervised classification'

Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 03:10:17 GMT

Raster to Vector

File:Classifystep1.png

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New page

Land Use Change Assessment in SAGA GIS

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File:Betterindicating....png

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Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 02:42:14 GMT

Raster to Vector

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Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 01:07:59 GMT

R and RStudio

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Utilizing Rstudio as an alternative GIS

CharlesPianosi — Sat, 10 Oct 2026 00:59:33 GMT

R and RStudio

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CharlesPianosi — Sat, 10 Oct 2026 00:58:20 GMT

CharlesPianosi uploaded File:Screenshot 2026-10-09 205313.png Screenshot of page when downloading R 4.6.1

New page

== Summary ==
Screenshot of page when downloading R 4.6.1

@@ Line 96: / Line 96: @@
  qgis_search_algorithms(algorithm="clip")
 After running this line you will see a list of 7 clipping tools that have been displayed in the console.
-[[File:QGIS R algoSearch.png|500px|thumb|none| Figure 7: Clipping algorithms available for use within RStudio]]
+[[File:QGISsearchalgorithmsupdated.png|500px|thumb|none| Figure 7: Clipping algorithms available for use within RStudio]]
 We will select the GDAL clipping function '''gdal:cliprasterbyextent''' and determine which parameters we will need to be able to make use of this function in RStudio. To do this we will use the '''qgis_get_argument_specs()''' function. In order to use this function we need to use the full name of the function which is '''gdal:cliprasterbyextent'''.

@@ Line 95: / Line 95: @@
  '''#Searches for functions which contain the keyword 'clip''''
  qgis_search_algorithms(algorithm="clip")
-After running this line you will see a list of 11 clipping tools that have been displayed in the console.
+After running this line you will see a list of 7 clipping tools that have been displayed in the console.
 [[File:QGIS R algoSearch.png|500px|thumb|none| Figure 7: Clipping algorithms available for use within RStudio]]
@@ Line 105: / Line 105: @@
 Now that we know how to search for the functions we would like to use we can go on to utilize these functions to analyze DEM files.
 == Raster to Vector ==
 The first algorithm we will investigate is '''gdal:polygonize'''. This algorithm takes a raster image and converts its features into polygons. In order to use this function we will need to determine the parameters for: input, field, and output. The '''qgis_run_algorithm()''' function takes in the desired algorithm as well as that algorithms parameters, since the output of this function is very large we will be storing the output vector in the temporary files of qgisprocess. This is done leaving out the OUTPUT parameter within the arguments and then calling the '''st_as_sf()''' function on the result of the algorithim.
@@ Line 171: / Line 172: @@
  aspectfile <- file.path(path, "aspectmap.tif")
  qgis_run_algorithm("gdal:aspect", INPUT = canada_dem_clip,BAND = 1, OUTPUT = aspect_file)
  aspectmap <- raster(aspectfile)
  plot(aspectmap, col = rainbow(50), main = "Slope Aspect in degrees")
+[[File:SlopeAspectMap1.jpeg|700px|thumb|none| Figure 13: Aspect map generated in R.]]
 Remember that we can change the symbology of plotted rasters or shapefiles using R's built in plotting options!
@@ Line 181: / Line 185: @@
  aspectcolours <- cut(aspect, breaks = c(0, 45, 135, 225, 315, 360))
  plot(aspectcolours, col = c("black", "yellow", "grey", "orange", "black"), main = "Slope Aspect in Degrees")
+[[File:AspectSlope2.jpeg|700px|thumb|none| Figure 14: Aspect map generated in R, with customized symbology.]]
 The above code allows us to select which directions get which colours. We can also alter the angle and number of directions by adding or removing breaks or by changing the values.
@@ Line 194: / Line 200: @@
 Below you can see the output generated from the hillshade as well as the output generated by overlaying the DEM.
-[[File:Hillshade BC.png|500px|left|thumb|none| Figure 13: Hillshade raster of the original Canadian DEM.]]
+[[File:Hillshade BC.png|500px|left|thumb|none| Figure 15: Hillshade raster of the original Canadian DEM.]]
-[[File:HillElev BC.png|500px|center|thumb|none| Figure 14: Canadian DEM overlaying the hillshade raster.]]
+[[File:HillElev BC.png|500px|center|thumb|none| Figure 16: Canadian DEM overlaying the hillshade raster.]]
 .

@@ Line 107: / Line 107: @@
 == Raster to Vector ==
 The first algorithm we will investigate is '''gdal:polygonize'''. This algorithm takes a raster image and converts its features into polygons. In order to use this function we will need to determine the parameters for: input, field, and output. The '''qgis_run_algorithm()''' function takes in the desired algorithm as well as that algorithms parameters, since the output of this function is very large we will be storing the output vector in the temporary files of qgisprocess. This is done leaving out the OUTPUT parameter within the arguments and then calling the '''st_as_sf()''' function on the result of the algorithim.
  '''#Conversion of DEM to shapefile format'''
  output <- qgis_run_algorithm("gdal:polygonize", INPUT=canada_dem, FIELD="values", BAND=1)
@@ Line 113: / Line 114: @@
-After running this code you will see a vector interpretation of the clipped study area. Here is a side by side comparison between the DEM and its shapefile counterpart.
+After running this code you will see a vector interpretation of the raster dataset.
-[[File:Canada Vectorized.png|500px|center|thumb|none| Figure 10: DEM of Canada study area displayed within the plot window.]]
 == Clipping ==
@@ Line 122: / Line 121: @@
 In order to clip raster files we will make use of the '''gdal:cliprasterbyextent''' algorithm. This algorithm takes in a raster input, the desired clipping extent, output raster type and the desired output raster file. Additionally, I have provided a clipping extent for the image so that we can test this algorithm.
  '''#Clipped version of the original Canada DEM'''
+ path = "The folder you want to save to"
- clipped_qgis_result <- qgis_run_algorithm("gdal:cliprasterbyextent", INPUT = canada_dem, PROJWIN = "-135,-115,50,60", OUTPUT = dem_path)
- canada_dem_clip <- terra::rast(dem_path)
+ qgis_run_algorithm("gdal:cliprasterbyextent", INPUT=canada_dem, PROJWIN = "-135,-115,50,60", OUTPUT=paste("path/clip_dem.tif", sep=""))
- plot(canada_dem_clip, col = grey(0:100 / 100), main = "Clipped Canada DEM")
+ canada_dem_clip<-raster(paste(path,"/clip_dem.tif", sep=""))
+ plot(canada_dem_clip,col=grey(0:100/100), main='Clipped Canada DEM')
 After running this function we plot the raster image, the result should represent the section of Canada that we specified as the extent in the algorithm.
@@ Line 152: / Line 153: @@
 == Laplacian Filter ==
+'''This section of the tutorial is not currently feasible as of October 2026, but may be again with a change of code. Newer versions of QGIS do not incorporate SAGA, and the plugin   must be installed manually by going to the ''plugins'' tab in the ribbon, and searching for SAGA in the ''manage and install plugins'' pop-up window. Currently, the SAGA plugin is not supported at all for QGIS, and there is not yet a native tool to run a Laplacian Filter.'''
 In this example we will utilize a Laplacian filter on the clipped raster version of the Canadian DEM. The Laplacian filter will be used to highlight the edges of the mountain range and gullies. To utilize a Laplacian filter within RStudio we will use a SAGA algorithm found within QGIS, '''saga:laplacianfilter'''. In order to use the Laplacian filter we need an input raster, kernel method and type, radius, and sampling method. The code to run this filter on the clipped version of the Canadian DEM is provided below.
  #Creates a new raster image depicting the filtered Canadian DEM
+ path = "The folder you want to save to"
  qgis_run_algorithm("saga:laplacianfilter", INPUT=canada_dem_clip, METHOD = 2, KERNEL_RADIUS = 1, KERNEL_TYPE = 0, RESULT=paste(path,"/laplacian.tif", sep=""))
  lap_output<-raster(paste(path,"/laplacian.tif", sep=""))
@@ Line 160: / Line 166: @@
 The resulting output is a filtered version of the original DEM. The Laplacian filter is often used for edge detection and is useful for analyzing mountain ranges as shown below.
 [[File:LaplacianFilter update.png|500px|thumb|none| Figure 12: Canadian DEM filtered by the Laplacian algorithm.]]
+== Aspect Map ==
+We can generate aspect maps using the GDAL function in QGIS, with largely the same process as we did for the other maps:
+ aspectfile <- file.path(path, "aspectmap.tif")
+ qgis_run_algorithm("gdal:aspect", INPUT = canada_dem_clip,BAND = 1, OUTPUT = aspect_file)
+ aspectmap <- raster(aspectfile)
+ plot(aspectmap, col = rainbow(50), main = "Slope Aspect in degrees")
+Remember that we can change the symbology of plotted rasters or shapefiles using R's built in plotting options!
+ #Split the aspect raster into 5 colours based on the direction
+ aspectcolours <- cut(aspect, breaks = c(0, 45, 135, 225, 315, 360))
+ plot(aspectcolours, col = c("black", "yellow", "grey", "orange", "black"), main = "Slope Aspect in Degrees")
+The above code allows us to select which directions get which colours. We can also alter the angle and number of directions by adding or removing breaks or by changing the values.
 == Hillshade ==

@@ Line 105: / Line 105: @@
 Now that we know how to search for the functions we would like to use we can go on to utilize these functions to analyze DEM files.
 == Raster to Vector ==
 The first algorithm we will investigate is '''gdal:polygonize'''. This algorithm takes a raster image and converts its features into polygons. In order to use this function we will need to determine the parameters for: input, field, and output. The '''qgis_run_algorithm()''' function takes in the desired algorithm as well as that algorithms parameters, since the output of this function is very large we will be storing the output vector in the temporary files of qgisprocess. This is done leaving out the OUTPUT parameter within the arguments and then calling the '''st_as_sf()''' function on the result of the algorithim.
  '''#Conversion of DEM to shapefile format'''
+ output <- qgis_run_algorithm("gdal:polygonize", INPUT=canada_dem, FIELD="values", BAND=1)
- dem_path <- "canada_dem_clip.tif"
- clip_result <- qgis_run_algorithm("gdal:cliprasterbyextent",INPUT = canada_dem,PROJWIN = "lat, lat, lon, lon, [EPSG:4326]", OUTPUT = dem_path)
- canada_dem_clip <- dem_path
- output <- qgis_run_algorithm("gdal:polygonize", INPUT = canada_dem_clip, FIELD = "values", BAND = 1, OUTPUT = "canada_poly.gpkg")
  canada_poly <- st_as_sf(output)
  plot(canada_poly)
 After running this code you will see a vector interpretation of the clipped study area. Here is a side by side comparison between the DEM and its shapefile counterpart.
 [[File:Canada Vectorized.png|500px|center|thumb|none| Figure 10: DEM of Canada study area displayed within the plot window.]]
-.
 == Clipping ==
@@ Line 127: / Line 122: @@
 In order to clip raster files we will make use of the '''gdal:cliprasterbyextent''' algorithm. This algorithm takes in a raster input, the desired clipping extent, output raster type and the desired output raster file. Additionally, I have provided a clipping extent for the image so that we can test this algorithm.
  '''#Clipped version of the original Canada DEM'''
- qgis_run_algorithm("gdal:cliprasterbyextent", INPUT=canada_dem, PROJWIN = "-135,-115,50,60", OUTPUT=paste(path,"/clip_dem.tif", sep=""))
+ clipped_qgis_result <- qgis_run_algorithm("gdal:cliprasterbyextent", INPUT = canada_dem, PROJWIN = "-135,-115,50,60", OUTPUT = dem_path)
- canada_dem_clip<-raster(paste(path,"/clip_dem.tif", sep=""))
+ canada_dem_clip <- terra::rast(dem_path)
- plot(canada_dem_clip,col=grey(0:100/100), main='Clipped Canada DEM')
+ plot(canada_dem_clip, col = grey(0:100 / 100), main = "Clipped Canada DEM")
 After running this function we plot the raster image, the result should represent the section of Canada that we specified as the extent in the algorithm.

@@ Line 53: / Line 53: @@
 '''1.''' Click Geoprocessing > Imagery > Classification > Unsupervised > K-Means Clustering for Grids
+[[File:Classifystep1.png|600px]]
 We use the K-Means Clustering because it assumes that the number of clusters is known, whereas the ISODATA algorithm allows for different number of clusters. In order to keep things simple, we will be using K-Means Clustering. [http://www.wu.ece.ufl.edu/books/EE/communications/UnsupervisedClassification.html#:~:text=The%20ISODATA%20algorithm%20is%20similar,minimize%20the%20within%20cluster%20variability.]

@@ Line 34: / Line 34: @@
 . Once you've clicked on Landsat Import with Options, a window will pop up like the one below. In this situation you have to click on the ... beside Okay to find the data you would like to select. Once you've selected your data, press Okay.
-[[File:Opening Images2.png|500px]]
+[[File:betterindicating....png|500px]]
 . To add both images to the map, in Manager, under Data you must double click on both files, and they will appear

@@ Line 36: / Line 36: @@
 Now we can download '''RStudio''', similar to R we will be utilizing all default paths and options. '''RStudio''' can be downloaded [https://posit.co/download/rstudio-desktop/#download here]. At the time of writing RStudio version 2026.09.0-174 is the most current update, and requires an R installation of 3.3.0+ or above.
-[[File:RStudioDownload 2023.png|600px|thumb|none| Figure 4: RStudio version 2026.09.0-174 installer.]]
+[[File:RStudiodownload.png|600px|thumb|none| Figure 4: RStudio version 2026.09.0-174 installer. For Windows, select the first option.]]
 == RStudio setup ==
@@ Line 60: / Line 60: @@
 *'''qgisprocess''': Creates a link between the IDE (RStudio) and functions found within QGIS
 *'''raster''': Provides the user to read, write and manipulate raster images within RStudio
+NOTE: You may have to run the code "qgis_enable_plugins()" to enable the processing plugin.
 == Data Acquisition ==
@@ Line 76: / Line 79: @@
 We will import the dataset that used in this tutorial into our workspace and display it within the RStudio '''plot''' frame. We will be downloading a DEM of Canada from the '''geodata''' package. Due to the high resolution of the raster we will also use a function from the '''raster''' package that will aggregate the raster cells by a factor of 10 to make processing easier.
- '''Digital Elevation Model (DEM) of Canada'''
+ '''#Digital Elevation Model (DEM) of Canada'''
  geodata_path("geodata")
  canada_dem <- elevation_30s(country = "CAN", mask = TRUE)
@@ Line 104: / Line 107: @@
 == Raster to Vector ==
 The first algorithm we will investigate is '''gdal:polygonize'''. This algorithm takes a raster image and converts its features into polygons. In order to use this function we will need to determine the parameters for: input, field, and output. The '''qgis_run_algorithm()''' function takes in the desired algorithm as well as that algorithms parameters, since the output of this function is very large we will be storing the output vector in the temporary files of qgisprocess. This is done leaving out the OUTPUT parameter within the arguments and then calling the '''st_as_sf()''' function on the result of the algorithim.
  '''#Conversion of DEM to shapefile format'''
- output <- qgis_run_algorithm("gdal:polygonize", INPUT=canada_dem_clip, FIELD="values", BAND=1)
+ dem_path <- "canada_dem_clip.tif"
+ clip_result <- qgis_run_algorithm("gdal:cliprasterbyextent",INPUT = canada_dem,PROJWIN = "-112, -111, 50, 51 [EPSG:4326]", OUTPUT = dem_path)
+ canada_dem_clip <- dem_path
+ output <- qgis_run_algorithm("gdal:polygonize", INPUT = canada_dem_clip, FIELD = "values", BAND = 1, OUTPUT = "canada_poly.gpkg")
  canada_poly <- st_as_sf(output)
  plot(canada_poly)

@@ Line 19: / Line 19: @@
 We start by creating a fresh install of QGIS in order to download the functions that we will be exploring in this tutorial. QGIS can be installed from https://www.qgis.org/en/site/forusers/download.html, for the purposes of this tutorial we will be installing the 64-bit version for Windows.  For other operating systems, follow directions at the QGIS website.
-[[File:QGISinstall.PNG|600px|thumb|none| Figure 1: QGIS 64-bit installer.]]
+[[File:2026downloadpageforqgis.png|600px|thumb|none| Figure 1: QGIS 64-bit installer.]]
 *After downloading the installer, open it, select '''Advanced install''' then click next
@@ Line 35: / Line 35: @@
 [[File:Screenshot 2026-10-09 205313.png|1200px|thumb|none| Figure 3: R 4.6.1 installer.]]
-Now we can download '''RStudio''', similar to R we will be utilizing all default paths and options. '''RStudio''' can be downloaded [https://posit.co/download/rstudio-desktop/#download here]. At the time of writing RStudio version 2023.09.0+463 is the most current update, and requires an R installation of 3.3.0+ or above.
+Now we can download '''RStudio''', similar to R we will be utilizing all default paths and options. '''RStudio''' can be downloaded [https://posit.co/download/rstudio-desktop/#download here]. At the time of writing RStudio version 2026.09.0-174 is the most current update, and requires an R installation of 3.3.0+ or above.
-[[File:RStudioDownload 2023.png|600px|thumb|none| Figure 4: RStudio version 2023.09.0+463 installer.]]
+[[File:RStudioDownload 2023.png|600px|thumb|none| Figure 4: RStudio version 2026.09.0-174 installer.]]
 == RStudio setup ==

@@ Line 33: / Line 33: @@
 In order to utilize RStudio we first need to download '''R'''. This can be downloaded for Windows from https://cran.r-project.org/bin/windows/base/. For this installer we will be accepting all of the default paths and options.
-[[File:Rdownload 2023.png|600px|thumb|none| Figure 3: R 4.6.1 installer.]]
+[[File:Screenshot 2026-10-09 205313.png|1200px|thumb|none| Figure 3: R 4.6.1 installer.]]
 Now we can download '''RStudio''', similar to R we will be utilizing all default paths and options. '''RStudio''' can be downloaded [https://posit.co/download/rstudio-desktop/#download here]. At the time of writing RStudio version 2023.09.0+463 is the most current update, and requires an R installation of 3.3.0+ or above.