├── collections
├── gis_lab
│ ├── preview
│ │ ├── svgs.png
│ │ └── preview.png
│ ├── README.md
│ ├── LICENSE.txt
│ └── svg
│ │ ├── lep-shad_25k.svg
│ │ ├── wood-tray_25k.svg
│ │ └── ancient-wall_25k.svg
├── all_maki_icons
│ ├── preview
│ │ └── maki.png
│ ├── svg
│ │ ├── shelter-11.svg
│ │ ├── shelter-15.svg
│ │ ├── monument-11.svg
│ │ ├── star-15.svg
│ │ ├── religious-christian-11.svg
│ │ ├── star-11.svg
│ │ ├── building-11.svg
│ │ ├── clothing-store-11.svg
│ │ ├── monument-15.svg
│ │ ├── water-11.svg
│ │ ├── clothing-store-15.svg
│ │ ├── circle-11.svg
│ │ ├── circle-15.svg
│ │ ├── school-11.svg
│ │ ├── religious-jewish-11.svg
│ │ ├── religious-jewish-15.svg
│ │ ├── ranger-station-15.svg
│ │ ├── town-hall-11.svg
│ │ ├── building-15.svg
│ │ ├── roadblock-11.svg
│ │ ├── roadblock-15.svg
│ │ ├── suitcase-11.svg
│ │ ├── square-11.svg
│ │ ├── water-15.svg
│ │ ├── ranger-station-11.svg
│ │ ├── religious-christian-15.svg
│ │ ├── school-15.svg
│ │ ├── square-15.svg
│ │ ├── commercial-11.svg
│ │ ├── commercial-15.svg
│ │ ├── bar-11.svg
│ │ ├── airport-11.svg
│ │ ├── town-hall-15.svg
│ │ ├── campsite-15.svg
│ │ ├── suitcase-15.svg
│ │ ├── industry-11.svg
│ │ ├── information-15.svg
│ │ ├── marker-15.svg
│ │ ├── square-stroke-15.svg
│ │ ├── airfield-11.svg
│ │ ├── heart-15.svg
│ │ ├── hospital-15.svg
│ │ ├── laundry-11.svg
│ │ ├── marker-11.svg
│ │ ├── airport-15.svg
│ │ ├── bar-15.svg
│ │ ├── prison-11.svg
│ │ ├── america-football-11.svg
│ │ ├── place-of-worship-11.svg
│ │ ├── square-stroke-11.svg
│ │ ├── waste-basket-11.svg
│ │ ├── restaurant-11.svg
│ │ ├── triangle-11.svg
│ │ ├── bakery-11.svg
│ │ ├── mountain-15.svg
│ │ ├── circle-stroked-11.svg
│ │ ├── religious-muslim-11.svg
│ │ ├── place-of-worship-15.svg
│ │ ├── circle-stroked-15.svg
│ │ ├── heart-11.svg
│ │ ├── fire-station-11.svg
│ │ ├── laundry-15.svg
│ │ ├── pharmacy-11.svg
│ │ ├── airfield-15.svg
│ │ ├── industry-15.svg
│ │ ├── triangle-15.svg
│ │ ├── dentist-15.svg
│ │ ├── star-stroke-15.svg
│ │ ├── triangle-stroked-11.svg
│ │ ├── information-11.svg
│ │ ├── star-stroke-11.svg
│ │ ├── cafe-11.svg
│ │ ├── triangle-stroked-15.svg
│ │ ├── attraction-11.svg
│ │ ├── hospital-11.svg
│ │ ├── fire-station-15.svg
│ │ ├── volcano-11.svg
│ │ ├── police-11.svg
│ │ ├── prison-15.svg
│ │ ├── warehouse-15.svg
│ │ ├── drinking-water-15.svg
│ │ ├── college-15.svg
│ │ ├── dentist-11.svg
│ │ ├── rocket-11.svg
│ │ ├── drinking-water-11.svg
│ │ ├── mountain-11.svg
│ │ ├── pharmacy-15.svg
│ │ ├── picnic-site-11.svg
│ │ ├── lighthouse-15.svg
│ │ ├── cafe-15.svg
│ │ ├── lighthouse-11.svg
│ │ ├── parking-11.svg
│ │ ├── religious-muslim-15.svg
│ │ ├── cemetery-15.svg
│ │ ├── music-15.svg
│ │ ├── pitch-15.svg
│ │ ├── rail-light-11.svg
│ │ ├── restaurant-15.svg
│ │ ├── lodging-11.svg
│ │ ├── parking-15.svg
│ │ ├── lodging-15.svg
│ │ ├── pitch-11.svg
│ │ ├── police-15.svg
│ │ ├── warehouse-11.svg
│ │ ├── college-11.svg
│ │ ├── post-11.svg
│ │ ├── america-football-15.svg
│ │ ├── car-11.svg
│ │ ├── post-15.svg
│ │ ├── art-gallery-15.svg
│ │ ├── art-gallery-11.svg
│ │ ├── aquarium-15.svg
│ │ ├── cemetery-11.svg
│ │ ├── garden-15.svg
│ │ ├── ice-cream-11.svg
│ │ ├── rocket-15.svg
│ │ ├── aerialway-15.svg
│ │ ├── museum-11.svg
│ │ ├── swimming-11.svg
│ │ ├── zoo-11.svg
│ │ ├── music-11.svg
│ │ ├── golf-15.svg
│ │ ├── aerialway-11.svg
│ │ ├── rail-metro-11.svg
│ │ ├── car-15.svg
│ │ ├── campsite-11.svg
│ │ ├── rail-light-15.svg
│ │ ├── cross-15.svg
│ │ ├── hairdresser-15.svg
│ │ ├── waste-basket-15.svg
│ │ ├── attraction-15.svg
│ │ ├── picnic-site-15.svg
│ │ ├── bus-15.svg
│ │ ├── swimming-15.svg
│ │ ├── entrance-15.svg
│ │ ├── heliport-11.svg
│ │ ├── stadium-15.svg
│ │ ├── beer-15.svg
│ │ ├── cross-11.svg
│ │ ├── ice-cream-15.svg
│ │ ├── skiing-15.svg
│ │ ├── heliport-15.svg
│ │ ├── entrance-11.svg
│ │ ├── bakery-15.svg
│ │ ├── castle-15.svg
│ │ ├── telephone-11.svg
│ │ ├── beer-11.svg
│ │ ├── cinema-15.svg
│ │ ├── rail-metro-15.svg
│ │ ├── dog-park-11.svg
│ │ ├── theatre-15.svg
│ │ ├── fuel-11.svg
│ │ ├── parking-garage-15.svg
│ │ ├── harbor-11.svg
│ │ ├── stadium-11.svg
│ │ └── cinema-11.svg
│ └── README.txt
├── road_signs
│ └── preview
│ │ └── preview.png
├── z_m_styling
│ ├── preview
│ │ └── z_styling.png
│ ├── example_data
│ │ └── sample_zm_data.gpkg
│ └── README
├── qgis_rscripts
│ ├── rscripts
│ │ ├── Frequency_table.rsx
│ │ ├── qqplot.rsx
│ │ ├── Raster_histogram.rsx
│ │ ├── Kolmogrov-Smirnov_test.rsx
│ │ ├── Polygon_coordinates.rsx
│ │ ├── Histogram.rsx
│ │ ├── Histogram.rsx.help
│ │ ├── barplot.rsx
│ │ ├── qqplot.rsx.help
│ │ ├── F_function.rsx
│ │ ├── Raster_histogram.rsx.help
│ │ ├── scatterplot_regressione.rsx
│ │ ├── Minimum_convex_polygon.rsx
│ │ ├── scatterplot_types.rsx.help
│ │ ├── G_function.rsx
│ │ ├── CART.rsx.help
│ │ ├── Polygon_coordinates.rsx.help
│ │ ├── scatterplot_log.rsx.help
│ │ ├── Summarize_by_field.rsx.help
│ │ ├── Random_sampling_grid.rsx
│ │ ├── Regular_sampling_grid.rsx
│ │ ├── Alpha_shape.rsx.help
│ │ ├── Quadrat_analysis.rsx
│ │ ├── Simple_Linear_Regression.rsx
│ │ ├── Summarize_by_two_fields.rsx.help
│ │ ├── ggplot_scatterplot.rsx
│ │ ├── Extract_points_from_line.rsx.help
│ │ ├── Kolmogrov-Smirnov_test.rsx.help
│ │ ├── frequency_plot.rsx.help
│ │ ├── Monte-Carlo_spatial_randomness.rsx
│ │ ├── ggplot_scatterplot.rsx.help
│ │ ├── Density_curve.rsx.help
│ │ ├── Minimum_convex_polygon_sf.rsx
│ │ ├── ANOVA.rsx
│ │ ├── Density_curve.rsx
│ │ ├── Regular_sampling_grid.rsx.help
│ │ ├── CART.rsx
│ │ ├── PCA_var.rsx.help
│ │ ├── Multiple_Regression.rsx
│ │ ├── ternaryPlots.rsx.help
│ │ ├── Kriging.rsx.help
│ │ ├── Ripley_-_Rasson_spatial_domain.rsx
│ │ ├── scatterplot_types.rsx
│ │ ├── ACP_cercle.rsx.help
│ │ ├── PCA_cercle.rsx.help
│ │ ├── Autocor_Moran.rsx.help
│ │ ├── Frequency_table.rsx.help
│ │ ├── HCA.rsx.help
│ │ ├── Tobler.rsx.help
│ │ ├── Ripley_-_Rasson_spatial_domain.rsx.help
│ │ ├── FAMD.rsx.help
│ │ ├── ANOVA.rsx.help
│ │ ├── Extract_points_from_line.rsx
│ │ ├── Random_sampling_grid.rsx.help
│ │ ├── frequency_plot.rsx
│ │ ├── PCA_contribution.rsx.help
│ │ ├── Kriging_with_model_selection.rsx.help
│ │ ├── Simple_Linear_Regression.rsx.help
│ │ ├── Summary_statistics.rsx.help
│ │ ├── Close_neighbor.rsx.help
│ │ ├── scatterplot_log.rsx
│ │ ├── Kernel_density_estimation.rsx.help
│ │ ├── Summarize_by_field.rsx
│ │ ├── ACP_cercle.rsx
│ │ ├── PCA_cercle.rsx
│ │ ├── Multiple_Regression.rsx.help
│ │ ├── PCA_individus.rsx.help
│ │ ├── Distance.rsx.help
│ │ ├── CoA.rsx.help
│ │ ├── Advanced_raster_histogram.rsx
│ │ ├── Quadrat_analysis.rsx.help
│ │ ├── Summarize_by_two_fields.rsx
│ │ ├── barplot.rsx.help
│ │ ├── Selection_with_criterion_choice.rsx.help
│ │ ├── Summary_statistics.rsx
│ │ ├── FAMD.rsx
│ │ ├── ternaryPlots.rsx
│ │ ├── PCA_contribution.rsx
│ │ ├── Advanced_raster_histogram.rsx.help
│ │ ├── Autocor_Moran.rsx
│ │ ├── PCA_var.rsx
│ │ ├── Distance.rsx
│ │ ├── Alpha_shape.rsx
│ │ ├── raster-attribute-table.rsx.help
│ │ ├── Selection_Cp.rsx
│ │ ├── Selection_with_r2.rsx
│ │ ├── Selection_with_r2.rsx.help
│ │ ├── Selection_with_r2_adjusted.rsx
│ │ └── Relative_distribution_(distance_covariate).rsx
│ └── README.md
├── qgis_rscripts2
│ ├── rscripts
│ │ ├── Frequency_table.rsx
│ │ ├── qqplot.rsx
│ │ ├── Raster_histogram.rsx
│ │ ├── Kolmogrov-Smirnov_test.rsx
│ │ ├── Polygone.rsx
│ │ ├── Histogram.rsx
│ │ ├── Histogram.rsx.help
│ │ ├── barplots.rsx
│ │ ├── Density_curve.rsx.help
│ │ ├── qqplot.rsx.help
│ │ ├── Random_sampling_grid.rsx
│ │ ├── Regular_sampling_grid.rsx
│ │ ├── Minimum_convex_polygon.rsx
│ │ ├── F_function.rsx
│ │ ├── G_function.rsx
│ │ ├── Raster_histogram.rsx.help
│ │ ├── Polygone.rsx.help
│ │ ├── scatterplot_regressione.rsx
│ │ ├── frequency_plot.rsx.help
│ │ ├── scatterplot_types.rsx.help
│ │ ├── Quadrat_analysis.rsx
│ │ ├── CART.rsx.help
│ │ ├── scatterplot_log.rsx.help
│ │ ├── Extract_points_from_line.rsx.help
│ │ ├── Summarize_by_field.rsx.help
│ │ ├── CART.rsx
│ │ ├── Distance.rsx.help
│ │ ├── Alpha_shape.rsx.help
│ │ ├── Simple_Linear_Regression.rsx
│ │ ├── Summarize_by_two_fields.rsx.help
│ │ ├── prova_ggplot2.rsx.help
│ │ ├── ggplot_scatterplot.rsx
│ │ ├── Frequency_table.rsx.help
│ │ ├── Density_curve.rsx
│ │ ├── Kolmogrov-Smirnov_test.rsx.help
│ │ ├── ggplot_scatterplot.rsx.help
│ │ ├── ANOVA.rsx
│ │ ├── ANOVA.rsx.help
│ │ ├── Monte-Carlo_spatial_randomness.rsx
│ │ ├── Autocor_spatiale.rsx.help
│ │ ├── Regular_sampling_grid.rsx.help
│ │ ├── ACP_var.rsx.help
│ │ ├── Multiple_Regression.rsx
│ │ ├── Extract_points_from_line.rsx
│ │ ├── ternaryPlots.rsx.help
│ │ ├── Close_neighbor.rsx.help
│ │ ├── Kriging.rsx.help
│ │ ├── scatterplot_types.rsx
│ │ ├── Ripley_-_Rasson_spatial_domain.rsx
│ │ ├── ACP_cercle.rsx.help
│ │ ├── Tobler.rsx.help
│ │ ├── Ripley_-_Rasson_spatial_domain.rsx.help
│ │ ├── Douglas-Peucker.rsx.help
│ │ ├── AFDM.rsx.help
│ │ ├── CAH.rsx.help
│ │ ├── Random_sampling_grid.rsx.help
│ │ ├── frequency_plot.rsx
│ │ ├── ACP_contribution.rsx.help
│ │ ├── Kriging_with_model_selection.rsx.help
│ │ ├── Summarize_by_field.rsx
│ │ ├── Simple_Linear_Regression.rsx.help
│ │ ├── Summary_statistics.rsx.help
│ │ ├── ACP_cercle.rsx
│ │ ├── Advanced_raster_histogram.rsx
│ │ ├── scatterplot_log.rsx
│ │ ├── Douglas-Peucker_with_choice.rsx.help
│ │ ├── Alpha_shape.rsx
│ │ ├── Kernel_density_estimation.rsx.help
│ │ ├── Distance.rsx
│ │ ├── ACP_individus.rsx.help
│ │ ├── Multiple_Regression.rsx.help
│ │ ├── Summarize_by_two_fields.rsx
│ │ ├── barplots.rsx.help
│ │ ├── AFC.rsx.help
│ │ ├── Advanced_raster_histogram.rsx.help
│ │ ├── Summary_statistics.rsx
│ │ ├── Quadrat_analysis.rsx.help
│ │ ├── ACP_contribution.rsx
│ │ ├── ternaryPlots.rsx
│ │ ├── AFDM.rsx
│ │ ├── Selection_with_criterion_choice.rsx.help
│ │ ├── A-star.rsx.help
│ │ ├── ACP_var.rsx
│ │ ├── Autocor_spatiale.rsx
│ │ ├── Selection_with_r2.rsx
│ │ ├── Selection_Cp.rsx
│ │ ├── Selection_with_r2_adjusted.rsx
│ │ ├── Selection_with_Bayesian_Information_Criterion.rsx
│ │ ├── Relative_distribution_(distance_covariate).rsx
│ │ ├── CAH.rsx
│ │ ├── raster-attribute-table.rsx.help
│ │ ├── Selection_with_r2.rsx.help
│ │ └── Close_neighbor.rsx
│ └── README.md
└── osm_spatialite_googlemaps
│ └── preview
│ ├── osm_spatialite_googlemaps.png
│ └── osm_spatialite_googlemaps_lines.qml.png
└── README.md
/collections/gis_lab/preview/svgs.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/gis_lab/preview/svgs.png
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/collections/gis_lab/preview/preview.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/gis_lab/preview/preview.png
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/collections/all_maki_icons/preview/maki.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/all_maki_icons/preview/maki.png
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/collections/road_signs/preview/preview.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/road_signs/preview/preview.png
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/collections/z_m_styling/preview/z_styling.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/z_m_styling/preview/z_styling.png
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/collections/qgis_rscripts/rscripts/Frequency_table.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | >table(Layer[[Field]])
5 |
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/collections/qgis_rscripts2/rscripts/Frequency_table.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | >table(Layer[[Field]])
5 |
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/collections/qgis_rscripts/rscripts/qqplot.rsx:
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1 | ##Plots=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | qqnorm(Layer[[X]])
6 | qqline(Layer[[X]])
7 |
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/collections/qgis_rscripts2/rscripts/qqplot.rsx:
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1 | ##Plots=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | qqnorm(Layer[[X]])
6 | qqline(Layer[[X]])
7 |
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/collections/z_m_styling/example_data/sample_zm_data.gpkg:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/z_m_styling/example_data/sample_zm_data.gpkg
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/collections/gis_lab/README.md:
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1 | This download from http://gis-lab.info/qa/qgis-symbols.html includes the following:
2 |
3 | 1. LICENSE.txt.
4 | 2. A directory of all SVGS.
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/collections/qgis_rscripts/rscripts/Raster_histogram.rsx:
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1 | ##Raster processing=group
2 | ##Layer = raster
3 | ##showplots
4 | hist(as.matrix(Layer),main="Histogram",xlab="Layer")
5 |
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/collections/qgis_rscripts2/rscripts/Raster_histogram.rsx:
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1 | ##Raster processing=group
2 | ##Layer = raster
3 | ##showplots
4 | hist(as.matrix(Layer),main="Histogram",xlab="Layer")
5 |
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/collections/qgis_rscripts/rscripts/Kolmogrov-Smirnov_test.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | library(nortest)
5 | >lillie.test(Layer[[Field]])
6 |
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/collections/qgis_rscripts2/rscripts/Kolmogrov-Smirnov_test.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | library(nortest)
5 | >lillie.test(Layer[[Field]])
6 |
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/collections/qgis_rscripts2/rscripts/Polygone.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##nombre= number 10
4 | library(sp)
5 | Layer@polygons[[nombre]]@Polygons[[1]]@coords
6 |
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/collections/qgis_rscripts/rscripts/Polygon_coordinates.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector polygon
3 | ##Polygon_number=number 1
4 | library(sf)
5 | st_coordinates(Layer[Polygon_number,])
6 |
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/collections/osm_spatialite_googlemaps/preview/osm_spatialite_googlemaps.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/osm_spatialite_googlemaps/preview/osm_spatialite_googlemaps.png
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/collections/qgis_rscripts/rscripts/Histogram.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##Field=Field Layer
5 | hist(Layer[[Field]],main=paste("Histogram of",Field),xlab=paste(Field))
6 |
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/collections/qgis_rscripts2/rscripts/Histogram.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##Field=Field Layer
5 | hist(Layer[[Field]],main=paste("Histogram of",Field),xlab=paste(Field))
6 |
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/collections/osm_spatialite_googlemaps/preview/osm_spatialite_googlemaps_lines.qml.png:
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https://raw.githubusercontent.com/qgis/QGIS-Resources/HEAD/collections/osm_spatialite_googlemaps/preview/osm_spatialite_googlemaps_lines.qml.png
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/collections/qgis_rscripts/rscripts/Histogram.rsx.help:
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1 | {"Field": "A nuneric field.", "ALG_DESC": "This tool creates a histogram of the input numeric field using the hist() function.", "Layer": "A vector layer with a numeric field."}
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/collections/qgis_rscripts2/rscripts/Histogram.rsx.help:
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1 | {"Field": "A nuneric field.", "ALG_DESC": "This tool creates a histogram of the input numeric field using the hist() function.", "Layer": "A vector layer with a numeric field."}
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/collections/qgis_rscripts/rscripts/barplot.rsx:
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1 | ##Vector processing=group
2 | ##Bar plot=name
3 | ##showplots
4 | ##Layer=vector
5 | ##Field=Field Layer
6 | barplot(table(Layer[[Field]]), main = paste("Bar plot of", Field), xlab = paste(Field))
7 |
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/collections/qgis_rscripts2/rscripts/barplots.rsx:
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1 | ##Vector processing=group
2 | ##Bar plots=name
3 | ##showplots
4 | ##Layer=vector
5 | ##Field=Field Layer
6 | barplot(table(Layer[[Field]]), main = paste("Bar plot of", Field), xlab = paste(Field))
7 |
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/collections/qgis_rscripts/rscripts/qqplot.rsx.help:
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1 | {"ALG_DESC": "QQPLOT for normal distribution", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "qqplot", "ALG_HELP_CREATOR": "Matteo Ghetta", "X": "X parameter"}
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/collections/qgis_rscripts2/rscripts/Density_curve.rsx.help:
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1 | {"ALG_DESC": "This script computes the density curve.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector", "Field": "the field", "RPLOTS": "the density curve", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts2/rscripts/qqplot.rsx.help:
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1 | {"ALG_DESC": "QQPLOT for normal distribution", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "qqplot", "ALG_HELP_CREATOR": "Matteo Ghetta", "X": "X parameter"}
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/collections/qgis_rscripts2/rscripts/Random_sampling_grid.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector
3 | ##Size=number 10
4 | ##Output= output vector
5 | pts=spsample(Layer,Size,type="random")
6 | Output=SpatialPointsDataFrame(pts, as.data.frame(pts))
7 |
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/collections/qgis_rscripts2/rscripts/Regular_sampling_grid.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector
3 | ##Size=number 10
4 | ##Output= output vector
5 | pts=spsample(Layer,Size,type="regular")
6 | Output=SpatialPointsDataFrame(pts, as.data.frame(pts))
7 |
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/collections/qgis_rscripts/rscripts/F_function.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Nsim=number 10
4 | ##showplots
5 | library("maptools")
6 | library("spatstat")
7 | ppp=as(as(Layer, "Spatial"),"ppp")
8 | plot(envelope(ppp, Fest, nsim=Nsim))
9 |
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/collections/qgis_rscripts2/rscripts/Minimum_convex_polygon.rsx:
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1 | ##Home Range Analysis=group
2 | ##Layer=vector
3 | ##Percentage=number 10
4 | ##Field=Field Layer
5 | ##Home_ranges=Output vector
6 | library(adehabitatHR)
7 | Home_ranges<-mcp(Layer[,Field],percent=Percentage)
8 |
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/collections/qgis_rscripts/rscripts/Raster_histogram.rsx.help:
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1 | {"ALG_CREATOR": "Victor Olaya, volayaf(at)gmail.com", "ALG_DESC": "The script creates a raster histogram.", "Layer ": "An input raster.", "RPLOTS": "Histogram", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.co"}
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/collections/qgis_rscripts2/rscripts/F_function.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Nsim=number 10
4 | ##showplots
5 | library("maptools")
6 | library("spatstat")
7 | ppp=as(as(Layer, "SpatialPoints"),"ppp")
8 | plot(envelope(ppp, Fest, nsim=Nsim))
9 |
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/collections/qgis_rscripts2/rscripts/G_function.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Nsim=number 10
4 | ##showplots
5 | library("maptools")
6 | library("spatstat")
7 | ppp=as(as(Layer, "SpatialPoints"),"ppp")
8 | plot(envelope(ppp, Gest, nsim=Nsim))
9 |
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/collections/qgis_rscripts2/rscripts/Raster_histogram.rsx.help:
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1 | {"ALG_CREATOR": "Victor Olaya, volayaf(at)gmail.com", "ALG_DESC": "The script creates a raster histogram.", "Layer ": "An input raster.", "RPLOTS": "Histogram", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.co"}
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/collections/qgis_rscripts2/rscripts/Polygone.rsx.help:
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1 | {"ALG_CREATOR": "JEANDENANS L.", "nombre": "the number of the polygon which we want the coordinates", "ALG_DESC": "This script extrats the coordinates of polygons", "Layer": "Input vector with polygons", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts/rscripts/scatterplot_regressione.rsx:
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1 | ##My scripts=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##Title=string
7 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y, main=Title)+
8 | abline(lm(Layer[[Y]]~Layer[[X]]))
9 |
10 |
11 |
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/collections/qgis_rscripts2/rscripts/scatterplot_regressione.rsx:
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1 | ##My scripts=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##Title=string
7 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y, main=Title)+
8 | abline(lm(Layer[[Y]]~Layer[[X]]))
9 |
10 |
11 |
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/collections/qgis_rscripts/rscripts/Minimum_convex_polygon.rsx:
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1 | ##Home Range Analysis=group
2 | ##load_vector_using_rgdal
3 | ##Layer=vector
4 | ##Percentage=number 50
5 | ##Field=Field Layer
6 | ##Home_ranges=Output vector
7 | library(adehabitatHR)
8 | Home_ranges<-mcp(Layer[,Field],percent=Percentage)
9 |
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/collections/qgis_rscripts2/rscripts/frequency_plot.rsx.help:
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1 | {"ALG_DESC": "Frequency plot with mean and median lines", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "Field": "Numeric field of the vector layer", "RPLOTS": "Frequency plot", "ALG_HELP_CREATOR": "Matteo Ghetta", "ALG_VERSION": "0.1"}
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/collections/all_maki_icons/svg/shelter-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/scatterplot_types.rsx.help:
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1 | {"ALG_DESC": "Scatterplot", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "Plot in output", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "Y parameter", "X": "X parameter", "type": "Plot type (point, line, both)"}
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/collections/qgis_rscripts2/rscripts/scatterplot_types.rsx.help:
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1 | {"ALG_DESC": "Scatterplot", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "Plot in output", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "Y parameter", "X": "X parameter", "type": "Plot type (point, line, both)"}
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/collections/qgis_rscripts2/rscripts/Quadrat_analysis.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##showplots
4 | library("maptools")
5 | library("spatstat")
6 | ppp=as(as(Layer, "SpatialPoints"),"ppp")
7 | qc=quadratcount(ppp)
8 | plot(Layer)
9 | plot(qc, add=TRUE)
10 | >quadrat.test(ppp);
11 |
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/collections/all_maki_icons/svg/shelter-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/G_function.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Nsim=number 10
4 | ##showplots
5 | library("maptools")
6 | library("spatstat")
7 | #ppp=as(as(Layer, "SpatialPoints"),"ppp")
8 | ppp=as(as(Layer, "Spatial"),"ppp")
9 | plot(envelope(ppp, Gest, nsim=Nsim))
10 |
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/collections/all_maki_icons/svg/monument-11.svg:
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1 |
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/collections/all_maki_icons/svg/star-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/CART.rsx.help:
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1 | {"ALG_DESC": "This script does a Classification And Regression Trees (CART) and gives the plot with the tree.", "ALG_CREATOR": "JEANDENANS L.", "Field1": "The field", "Layer": "Input vector with at least one field.", "RPLOTS": "the tree of the CART", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/all_maki_icons/svg/religious-christian-11.svg:
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1 |
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/collections/all_maki_icons/svg/star-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/CART.rsx.help:
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1 | {"ALG_DESC": "This script does a Classification And Regression Trees (CART) and produces a plot with the tree.", "ALG_CREATOR": "JEANDENANS L.", "Field1": "The field", "Layer": "Input vector with at least one field.", "RPLOTS": "The tree of the CART", "ALG_HELP_CREATOR": "JEANDENANS L."}
2 |
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/collections/all_maki_icons/svg/building-11.svg:
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1 |
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/collections/all_maki_icons/svg/clothing-store-11.svg:
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1 |
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/collections/qgis_rscripts/README.md:
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1 | R script collection for QGIS and the Processing R Provider plugin
2 | =================================================================
3 |
4 | Origin
5 | ------
6 |
7 | R scripts from the on-line collection for QGIS 2,
8 | updated to work with QGIS 3 and the Processing R Provider
9 | plugin.
10 |
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/collections/qgis_rscripts/rscripts/Polygon_coordinates.rsx.help:
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1 | {"ALG_CREATOR": "H. Tveite", "Polygon_number": "The number of the polygon for which we want the coordinates", "ALG_DESC": "This script extracts the coordinates of a specified polygon from a polygon layer", "Layer": "Input polygon vector layer", "ALG_HELP_CREATOR": "H. Tveite"}
2 |
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/collections/qgis_rscripts/rscripts/scatterplot_log.rsx.help:
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1 | {"ALG_DESC": "Scatterplot with different axis scales", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "Output plot", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "X parameter", "X": "X parameter", "type": "Scale type (normal, logarithmic)"}
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/collections/qgis_rscripts2/rscripts/scatterplot_log.rsx.help:
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1 | {"ALG_DESC": "Scatterplot with different axis scales", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "Output plot", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "X parameter", "X": "X parameter", "type": "Scale type (normal, logarithmic)"}
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/collections/all_maki_icons/svg/monument-15.svg:
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1 |
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/collections/all_maki_icons/svg/water-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Summarize_by_field.rsx.help:
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1 | {"ALG_DESC": "", "ALG_CREATOR": "Filipe S. Dias", "Layer": "This script summarizes a numeric field by a factor field. It requires packages \"doBy\",\"foreign\" and \"plyr\".", "Numeric_field": "Numeric field", "Factor": "Factor field", "Folder": "Output folder. This is NOT optional."}
2 |
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/collections/qgis_rscripts2/rscripts/Extract_points_from_line.rsx.help:
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1 | {"ALG_DESC": "This script extracts coordinates of all points of a line", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector with lines", "ALG_HELP_CREATOR": "JEANDENANS L.", "nombre": "number of the line which we want the coordinates", "Output": "a shapefile with these points"}
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/collections/qgis_rscripts2/rscripts/Summarize_by_field.rsx.help:
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1 | {"ALG_DESC": "", "ALG_CREATOR": "Filipe S. Dias", "Layer": "This script summarizes a numeric field by a factor field. It requires packages \"doBy\",\"foreign\" and \"plyr\".", "Numeric_field": "Numeric field", "Factor": "Factor field", "Folder": "Output folder. This is NOT optional."}
2 |
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/collections/all_maki_icons/svg/clothing-store-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Random_sampling_grid.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector polygon
3 | ##Size=number 10
4 | ##Output= output vector
5 | spatpoly = as(Layer, "Spatial")
6 |
7 | pts=spsample(spatpoly,Size,type="random")
8 | spdf=SpatialPointsDataFrame(pts, as.data.frame(pts))
9 |
10 | Output=st_as_sf(spdf)
11 |
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/collections/qgis_rscripts/rscripts/Regular_sampling_grid.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector polygon
3 | ##Size=number 10
4 | ##Output= output vector
5 | spatpoly = as(Layer, "Spatial")
6 |
7 | pts=spsample(spatpoly,Size,type="regular")
8 | spdf=SpatialPointsDataFrame(pts, as.data.frame(pts))
9 |
10 | Output=st_as_sf(spdf)
11 |
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/collections/all_maki_icons/svg/circle-11.svg:
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1 |
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/collections/all_maki_icons/svg/circle-15.svg:
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1 |
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/collections/all_maki_icons/svg/school-11.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/CART.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##showplots
5 | library(cluster)
6 | library(rpart)
7 | arbre <- rpart(Layer[[Field1]]~.,Layer)
8 | arbre
9 | plot(arbre, main="Arbre", branch=1, compress=T, margin=0.1)
10 | text(arbre,splits=T, fancy=T, use.n=F, pretty=0, all=T)
11 |
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/collections/qgis_rscripts2/rscripts/Distance.rsx.help:
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1 | {"Layer2": "a line", "ALG_DESC": "This script calculates the distance between two geometries: a line and points.", "ALG_CREATOR": "JEANDENANS L.", "Layer1": "a set of data (points)", "Field": "identification of points", "ALG_HELP_CREATOR": "JEANDENANS L.", "output": "a shapfile with points and the distance."}
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/collections/qgis_rscripts/rscripts/Alpha_shape.rsx.help:
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1 | {"ALG_DESC": "This script computes a concave hull with the method of alpha-shape and gives a shapfile with lines of the concave hull.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector with points", "ALG_HELP_CREATOR": "JEANDENANS L.", "alpha": "a number >0", "Output": "a shapefile with the concave hull"}
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/collections/qgis_rscripts/rscripts/Quadrat_analysis.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##showplots
4 | library("maptools")
5 | library("spatstat")
6 | #ppp=as(as(Layer, "SpatialPoints"),"ppp")
7 | ppp=as(as(Layer, "Spatial"),"ppp")
8 | qc=quadratcount(ppp)
9 | plot(Layer)
10 | plot(qc, add=TRUE)
11 | >quadrat.test(ppp);
12 |
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/collections/qgis_rscripts2/rscripts/Alpha_shape.rsx.help:
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1 | {"ALG_DESC": "This script computes a concave hull with the method of alpha-shape and gives a shapfile with lines of the concave hull.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector with points", "ALG_HELP_CREATOR": "JEANDENANS L.", "alpha": "a number >0", "Output": "a shapefile with the concave hull"}
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/collections/all_maki_icons/svg/religious-jewish-11.svg:
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1 |
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/collections/all_maki_icons/svg/religious-jewish-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Simple_Linear_Regression.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##showplots
6 | test<-lm(Layer[[Field1]] ~Layer[[Field2]] )
7 | par(mfrow=c(2,2))
8 | plot(test,which=1)
9 | plot(test,which=2)
10 | plot(test,which=3)
11 | plot(test,which=4)
12 | summary(test)
13 |
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/collections/qgis_rscripts/rscripts/Summarize_by_two_fields.rsx.help:
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1 | {"ALG_DESC": "This script summarizes a numeric field by two factor fields. It requires packages \"doBy\",\"foreign\" and \"plyr\".", "Factor1": "Factor field", "Factor2": "Factor field", "ALG_CREATOR": "Filipe S. Dias ", "Layer": "Layer", "Numeric_field": "Numeric field", "Folder": "Output folder"}
2 |
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/collections/qgis_rscripts2/rscripts/Simple_Linear_Regression.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##showplots
6 | test<-lm(Layer[[Field1]] ~Layer[[Field2]] )
7 | par(mfrow=c(2,2))
8 | plot(test,which=1)
9 | plot(test,which=2)
10 | plot(test,which=3)
11 | plot(test,which=4)
12 | summary(test)
13 |
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/collections/qgis_rscripts2/rscripts/Summarize_by_two_fields.rsx.help:
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1 | {"ALG_DESC": "This script summarizes a numeric field by two factor fields. It requires packages \"doBy\",\"foreign\" and \"plyr\".", "Factor1": "Factor field", "Factor2": "Factor field", "ALG_CREATOR": "Filipe S. Dias ", "Layer": "Layer", "Numeric_field": "Numeric field", "Folder": "Output folder"}
2 |
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/collections/qgis_rscripts2/rscripts/prova_ggplot2.rsx.help:
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1 | {"ALG_DESC": "Simple scatterplot with ggplot (be sure ggplot2 is installed on your computer!)", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer in input", "ALG_VERSION": "1.0", "RPLOTS": "Plot in output", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "Y paramter", "X": "X paramter", "Z": "Grouping variable"}
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/collections/all_maki_icons/svg/ranger-station-15.svg:
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1 |
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/collections/all_maki_icons/svg/town-hall-11.svg:
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1 |
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/collections/all_maki_icons/svg/building-15.svg:
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1 |
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/collections/all_maki_icons/svg/roadblock-11.svg:
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1 |
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/collections/all_maki_icons/svg/roadblock-15.svg:
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1 |
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/collections/all_maki_icons/svg/suitcase-11.svg:
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1 |
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/collections/all_maki_icons/svg/square-11.svg:
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1 |
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/collections/all_maki_icons/svg/water-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/ggplot_scatterplot.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##Group=Field Layer
7 | require(ggplot2)
8 | ggplot()+
9 | geom_point(aes(x=Layer[[X]],y=Layer[[Y]],
10 | color=as.factor(Layer[[Group]])))+
11 | theme(legend.title = element_blank())+
12 | xlab(X)+
13 | ylab(Y)
14 |
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/collections/all_maki_icons/svg/ranger-station-11.svg:
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1 |
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/collections/all_maki_icons/svg/religious-christian-15.svg:
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1 |
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/collections/all_maki_icons/svg/school-15.svg:
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1 |
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/collections/all_maki_icons/svg/square-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/ggplot_scatterplot.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##Group=Field Layer
7 | require(ggplot2)
8 | ggplot()+
9 | geom_point(aes(x=Layer[[X]],y=Layer[[Y]],
10 | color=as.factor(Layer[[Group]])))+
11 | theme(legend.title = element_blank())+
12 | xlab(X)+
13 | ylab(Y)
14 |
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/collections/all_maki_icons/svg/commercial-11.svg:
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1 |
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/collections/all_maki_icons/svg/commercial-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Frequency_table.rsx.help:
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1 | {"ALG_DESC": "This tool builds a frequency table using the table() function.", "R_CONSOLE_OUTPUT": "Frequency table.", "ALG_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com ", "Layer": "A vector layer with a numeric or string field.", "Field": "A string or numeric field.", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com "}
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/collections/qgis_rscripts/rscripts/Extract_points_from_line.rsx.help:
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1 | {"ALG_DESC": "This script creates a point layer from the points of a specified line in a line layer", "ALG_CREATOR": "H Tveite", "Layer": "Input line vector layer", "Line_number": "The number of the line we want to extract the points from", "Output": "A point vector layer with the extracted points", "ALG_HELP_CREATOR": "H Tveite"}
2 |
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/collections/qgis_rscripts/rscripts/Kolmogrov-Smirnov_test.rsx.help:
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1 | {"Field": "A numeric field.", "ALG_DESC": "This script performs the Lilliefors (Kolmogrov-Smirnov) test for the composite hypothesis of normality.\n\nR dependencies: library \"nortest\"\n\n", "R_CONSOLE_OUTPUT": "The results of the Lilliefors (Kolmogrov-Smirnov) test.", "Layer": "A vector containing at least one numeric field.", "null": ""}
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/collections/qgis_rscripts2/README.md:
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1 | R script collection for QGIS
2 | ============================
3 |
4 | Origin
5 | ------
6 |
7 | The first version of this collection is a copy of the on-line
8 | R scripts that were available for QGIS 2.
9 | Found in the currently archived github repository
10 | qgis / QGIS-Processing
11 | (https://github.com/qgis/QGIS-Processing/tree/master/rscripts).
12 |
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/collections/all_maki_icons/svg/bar-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/frequency_plot.rsx.help:
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1 | {"ALG_DESC": "Frequency plot with mean and median lines",
2 | "ALG_VERSION": "0.1",
3 | "ALG_CREATOR": "Matteo Ghetta",
4 | "ALG_HELP_CREATOR": "Matteo Ghetta",
5 | "Layer": "Input vector layer",
6 | "Field": "A numeric field of the input vector layer",
7 | "RPLOTS": "(Specify) The location and name for the output HTML file with the plot."}
8 |
9 |
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/collections/qgis_rscripts2/rscripts/Density_curve.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | ##showplots
5 | Densite <- density(Layer[[Field]])
6 | plot(Densite$x, Densite$y, type="b")
7 | abline(v = mean(Layer[[Field]]), col = "red")
8 | abline(v = mean(Layer[[Field]])+2*sd(Layer[[Field]]), col = "green")
9 | abline(v = mean(Layer[[Field]])-2*sd(Layer[[Field]]), col = "green")
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/collections/qgis_rscripts2/rscripts/Kolmogrov-Smirnov_test.rsx.help:
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1 | {"Field": "A numeric field.", "ALG_DESC": "This script performs the Lilliefors (Kolmogrov-Smirnov) test for the composite hypothesis of normality.\n\nR dependencies: library \"nortest\"\n\n", "R_CONSOLE_OUTPUT": "The results of the Lilliefors (Kolmogrov-Smirnov) test.", "Layer": "A vector containing at least one numeric field.", "null": ""}
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/collections/all_maki_icons/svg/airport-11.svg:
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1 |
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/collections/all_maki_icons/svg/town-hall-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Monte-Carlo_spatial_randomness.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Simulations=number 100
4 | ##Plot_name=string 'K function'
5 | ##showplots
6 | library(spatstat)
7 | library(maptools)
8 |
9 | sp <- as(Layer, "Spatial")
10 | sp <- as(sp, "ppp")
11 |
12 | e <- envelope(sp, Kest, nsim = Simulations)
13 | plot(e, main = Plot_name)
14 |
15 |
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/collections/qgis_rscripts/rscripts/ggplot_scatterplot.rsx.help:
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1 | {"ALG_DESC": "Simple scatterplot (with grouping option) with ggplot.\n\nNote: be sure ggplot2 package is installed in your computer!", "Group": "Grouping variable", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Input vector layer", "ALG_VERSION": "1.0", "RPLOTS": "ggplot graph", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "X parameter", "X": "X parameter"}
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/collections/qgis_rscripts2/rscripts/ggplot_scatterplot.rsx.help:
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1 | {"ALG_DESC": "Simple scatterplot (with grouping option) with ggplot.\n\nNote: be sure ggplot2 package is installed in your computer!", "Group": "Grouping variable", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Input vector layer", "ALG_VERSION": "1.0", "RPLOTS": "ggplot graph", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "X parameter", "X": "X parameter"}
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/collections/all_maki_icons/svg/campsite-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Density_curve.rsx.help:
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1 | {"ALG_DESC": "Computes the density curve for the given field.",
2 | "ALG_CREATOR": "JEANDENANS L.", "ALG_HELP_CREATOR": "JEANDENANS L.", "Layer": "Input vector layer",
3 | "Field": "The (numeric) attribute/field of the input layer to use for the curve",
4 | "RPLOTS": "(Specify) The location and name for the output HTML file with the plot (optional)."}
5 |
6 |
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/collections/qgis_rscripts/rscripts/Minimum_convex_polygon_sf.rsx:
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1 | ##Home Range Analysis=group
2 | ##Layer=vector
3 | ##Percentage=number 90
4 | ##Field=Field Layer
5 | ##Home_ranges=Output vector
6 | library(adehabitatHR)
7 |
8 | LayerXY = st_zm(Layer)
9 | SpatialLayerXY = as(LayerXY, "Spatial")
10 |
11 | HR<-mcp(SpatialLayerXY[,Field],percent=Percentage)
12 |
13 | Home_ranges<-st_as_sf(HR)
14 |
15 |
16 |
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/collections/qgis_rscripts2/rscripts/ANOVA.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##Field5=Field Layer
8 | test1<-lm(Layer[[Field1]]~Layer[[Field2]]+Layer[[Field3]]+Layer[[Field4]]+Layer[[Field5]] )
9 | test2<-lm(Layer[[Field1]]~Layer[[Field2]]+Layer[[Field4]]+Layer[[Field5]] )
10 | anova(test1,test2)
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/collections/qgis_rscripts2/rscripts/ANOVA.rsx.help:
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1 | {"ALG_DESC": "This script does an ANalysis Of VAriance (ANOVA) and gives the summary of the test.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 5 numerics fields.", "Field4": "The fourth field", "Field5": "The fifth field", "Field2": "The second field", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts2/rscripts/Monte-Carlo_spatial_randomness.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Simulations=number 100
4 | ##Optional_plot_name=string
5 | ##showplots
6 | library(spatstat)
7 | library(maptools)
8 | sp <- as(Layer, "SpatialPoints")
9 | sp <- as(sp, "ppp")
10 | e <- envelope(sp, Kest, nsim = Simulations)
11 | >e
12 | plot(e, main = Optional_plot_name)
13 |
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/collections/all_maki_icons/svg/suitcase-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/ANOVA.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##Field5=Field Layer
8 | test1<-lm(Layer[[Field1]]~Layer[[Field2]]+Layer[[Field3]]+Layer[[Field4]]+Layer[[Field5]])
9 | test2<-lm(Layer[[Field1]]~Layer[[Field2]]+Layer[[Field4]]+Layer[[Field5]])
10 | anova(test1,test2)
11 |
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/collections/all_maki_icons/svg/industry-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Density_curve.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | ##showplots
5 | Density <- density(Layer[[Field]])
6 | plot(Density$x, Density$y, type="b")
7 | abline(v = mean(Layer[[Field]]), col = "red")
8 | abline(v = mean(Layer[[Field]])+2*sd(Layer[[Field]]), col = "green")
9 | abline(v = mean(Layer[[Field]])-2*sd(Layer[[Field]]), col = "green")
10 |
11 |
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/collections/qgis_rscripts2/rscripts/Autocor_spatiale.rsx.help:
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1 | {"ALG_DESC": "This script calculates the spatial corelation with the Moran's test ", "nb_vois": "number of neighboors in the moran's test", "nb_simul": "number of simulation in the moran's test", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector with at least one field", "Field": "the field", "RPLOTS": "histogram of residues", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/all_maki_icons/svg/information-15.svg:
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1 |
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/collections/all_maki_icons/svg/marker-15.svg:
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1 |
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/collections/all_maki_icons/svg/square-stroke-15.svg:
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1 |
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/collections/all_maki_icons/svg/airfield-11.svg:
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1 |
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/collections/all_maki_icons/svg/heart-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Regular_sampling_grid.rsx.help:
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1 | {"Output": "Sampled points", "ALG_DESC": "This scripts samples point location within a given polygon(s) using a regular (systematically aligned) sampling method. The methods used assume that the geometry used is not spherical, so objects should be in planar coordinates.", "Layer": "A vector layer containing polygons.\n", "Size": "Number of sample points to be generated.\n"}
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/collections/qgis_rscripts2/rscripts/Regular_sampling_grid.rsx.help:
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1 | {"Output": "Sampled points", "ALG_DESC": "This scripts samples point location within a given polygon(s) using a regular (systematically aligned) sampling method. The methods used assume that the geometry used is not spherical, so objects should be in planar coordinates.", "Layer": "A vector layer containing polygons.\n", "Size": "Number of sample points to be generated.\n"}
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/collections/all_maki_icons/svg/hospital-15.svg:
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1 |
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/collections/all_maki_icons/svg/laundry-11.svg:
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1 |
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/collections/all_maki_icons/svg/marker-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/CART.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##showplots
5 | ##load_vector_using_rgdal
6 | library(cluster)
7 | library(rpart)
8 | tree <- rpart(Layer[[Field1]]~., Layer, control=rpart.control(minsplit=2, minbucket=2, cp=0.001))
9 | tree
10 | plot(tree, main="Tree", branch=1, compress=T, margin=0.1)
11 | text(tree, splits=T, fancy=T, use.n=F, pretty=0, all=T)
12 |
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/collections/qgis_rscripts/rscripts/PCA_var.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) and gives the representation of variables.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "the plot of variables of the PCA", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts2/rscripts/ACP_var.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) and gives the representation of variables.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "the plot of variables of the PCA", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/all_maki_icons/svg/airport-15.svg:
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1 |
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/collections/all_maki_icons/svg/bar-15.svg:
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1 |
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/collections/all_maki_icons/svg/prison-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Multiple_Regression.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##Field5=Field Layer
8 | ##Field6=Field Layer
9 | ##Field7=Field Layer
10 | library(ade4)
11 | test<-lm(Layer[[Field1]]~Layer[[Field2]]+Layer[[Field3]]+Layer[[Field4]]+Layer[[Field5]]+Layer[[Field6]]+Layer[[Field7]])
12 | summary(test)
13 |
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/collections/qgis_rscripts2/rscripts/Multiple_Regression.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##Field5=Field Layer
8 | ##Field6=Field Layer
9 | ##Field7=Field Layer
10 | library(ade4)
11 | test<-lm(Layer[[Field1]]~Layer[[Field2]]+Layer[[Field3]]+Layer[[Field4]]+Layer[[Field5]]+Layer[[Field6]]+Layer[[Field7]])
12 | summary(test)
13 |
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/collections/qgis_rscripts/rscripts/ternaryPlots.rsx.help:
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1 | {"ALG_DESC": "You can easily creat ternary plots in QGIS with ggplot2 and ggtern package!\n\nrequires ggplot2 and ggtern packages", "Group": "Optional grouping Field", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer you want to use to calculate the ternary plot", "ALG_VERSION": "0.1", "RPLOTS": "ternary plot", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "Y Field", "X": "X Field", "Z": "Z Field"}
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/collections/qgis_rscripts2/rscripts/Extract_points_from_line.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##nombre=number 10
4 | ##Output= output vector
5 | library(sp)
6 | Coord<-Layer@lines[[nombre]]@Lines[[1]]@coords
7 | x<-as.numeric(Coord[,1])
8 | y<-as.numeric(Coord[,2])
9 | X<-cbind(x,y)
10 | matrix<-as.matrix(X)
11 | matrix<-SpatialPointsDataFrame(matrix, as.data.frame(Coord),proj4string=CRS("+init=epsg:2154"))
12 | Output=matrix
13 |
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/collections/qgis_rscripts2/rscripts/ternaryPlots.rsx.help:
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1 | {"ALG_DESC": "You can easily creat ternary plots in QGIS with ggplot2 and ggtern package!\n\nrequires ggplot2 and ggtern packages", "Group": "Optional grouping Field", "ALG_CREATOR": "Matteo Ghetta", "Layer": "Vector layer you want to use to calculate the ternary plot", "ALG_VERSION": "0.1", "RPLOTS": "ternary plot", "ALG_HELP_CREATOR": "Matteo Ghetta", "Y": "Y Field", "X": "X Field", "Z": "Z Field"}
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/collections/all_maki_icons/svg/america-football-11.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Close_neighbor.rsx.help:
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1 | {"distance": "a number >0", "ALG_DESC": "This script selects points which are separated by a distance equal or lower than a distance d and gives a shapefile with points selected.", "ALG_CREATOR": "JEANDENANS L.", "Field1": "the name of this points in order to identify objects", "Layer": "Input vector with points", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a shapefile with data which are selected"}
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/collections/all_maki_icons/svg/place-of-worship-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Kriging.rsx.help:
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1 | {"ALG_DESC": "This script does a kriging with the best variogram which is selecd with the function auto.Krige and gives a raster of the spatial interpolation. Fields can be numerics or not because in the script, there is a coding to numeric.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector", "Field": "the field", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster of the kriging", "by": "the pixel size"}
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/collections/qgis_rscripts/rscripts/Ripley_-_Rasson_spatial_domain.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Output=output vector
4 | library("spatstat")
5 | library("maptools")
6 |
7 | crs=st_crs(Layer)
8 | spatpoints = as(Layer, "Spatial")
9 |
10 | ripras=ripras(as(spatpoints,"ppp"))
11 | polyg=as(ripras,"SpatialPolygons")
12 | SPDF=SpatialPolygonsDataFrame(polyg, data.frame(1))
13 |
14 | Output=st_set_crs(st_as_sf(SPDF), crs)
15 |
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/collections/qgis_rscripts/rscripts/scatterplot_types.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##type=selection point;lines;point&lines
7 | if (type == 0) {
8 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y)
9 | } else if (type == 1) {
10 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y, type="l")
11 | } else if (type == 2) {
12 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y, type="b")
13 | }
14 |
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/collections/qgis_rscripts2/rscripts/Kriging.rsx.help:
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1 | {"ALG_DESC": "This script does a kriging with the best variogram which is selecd with the function auto.Krige and gives a raster of the spatial interpolation. Fields can be numerics or not because in the script, there is a coding to numeric.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector", "Field": "the field", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster of the kriging", "by": "the pixel size"}
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/collections/qgis_rscripts2/rscripts/scatterplot_types.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##type=selection point;lines;point&lines
7 | if (type == 0) {
8 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y)
9 | } else if (type == 1) {
10 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y, type="l")
11 | } else if (type == 2) {
12 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y, type="b")
13 | }
14 |
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/collections/all_maki_icons/svg/square-stroke-11.svg:
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1 |
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/collections/all_maki_icons/svg/waste-basket-11.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Ripley_-_Rasson_spatial_domain.rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Output=output vector
4 | library("spatstat")
5 | library("maptools")
6 | proj4string(Layer)->crs
7 | spatpoints = as(Layer,"SpatialPoints")
8 | ripras=ripras(as(spatpoints,"ppp"))
9 | polyg=as(ripras,"SpatialPolygons")
10 | Output1= SpatialPolygonsDataFrame(polyg, data.frame(1))
11 | proj4string(Output1)<-crs
12 | Output<-Output1
13 |
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/collections/all_maki_icons/svg/restaurant-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/ACP_cercle.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) with 4 fields and gives the circle of correlations.", "Field2": "The second field", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "ALG_CREATOR": "JEANDENANS L.", "RPLOTS": "the plot of the map of a correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L.", "null": ""}
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/collections/qgis_rscripts/rscripts/PCA_cercle.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) with 4 fields and gives the circle of correlations.", "Field2": "The second field", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "ALG_CREATOR": "JEANDENANS L.", "RPLOTS": "the plot of the map of a correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L.", "null": ""}
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/collections/qgis_rscripts2/rscripts/ACP_cercle.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) with 4 fields and gives the circle of correlations.", "Field2": "The second field", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "ALG_CREATOR": "JEANDENANS L.", "RPLOTS": "the plot of the map of a correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L.", "null": ""}
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/collections/all_maki_icons/svg/triangle-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Autocor_Moran.rsx.help:
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1 | {"ALG_DESC": "This script calculates the spatial correlation with the Moran's test ", "nb_neighb": "Number of neighboors (nearest) considered", "nb_simulations": "Number of simulations", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector point layer with at least one field", "Field": "The field for which the spatial autocorrelation is calculated", "RPLOTS": "Histogram of residuals", "ALG_HELP_CREATOR": "JEANDENANS L."}
2 |
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/collections/qgis_rscripts/rscripts/Frequency_table.rsx.help:
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1 | {"ALG_DESC": "This tool builds a frequency table using the table() function.",
2 | "ALG_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com ",
3 | "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com",
4 | "Layer": "A vector layer with a numeric or string field.",
5 | "Field": "A string or numeric field.",
6 | "R_CONSOLE_OUTPUT": "(Specify) The location and name for the output HTML file with the table."}
7 |
8 |
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/collections/qgis_rscripts/rscripts/HCA.rsx.help:
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1 | {"ALG_DESC": "This script does a Hierarchical Cluster Analysis (HCA) and plots the dendogram.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector with at least 5 fields.", "Field1": "The first field", "Field2": "The second field", "Field3": "The third field", "Field4": "The fourth field", "Field5": "The fifth field", "RPLOTS": "the plot of the dendrogram", "ALG_HELP_CREATOR": "JEANDENANS L.", "method": "HCA method - choice"}
2 |
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/collections/qgis_rscripts/rscripts/Tobler.rsx.help:
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1 | {"ALG_DESC": "This script is a Tobler\u2019s Hiking Function and gives a shapefile with the best way.", "y_depart": "coordinate Y of the starting point", "ALG_CREATOR": "JEANDENANS L.", "x_depart": "coordinate X of the starting point", "Layer": "a raster with elevation", "y_arrivee": "coordinate Y of the end point", "x_arrivee": "coordinate X of the end point", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a vector with the way"}
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/collections/qgis_rscripts2/rscripts/Tobler.rsx.help:
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1 | {"ALG_DESC": "This script is a Tobler\u2019s Hiking Function and gives a shapefile with the best way.", "y_depart": "coordinate Y of the starting point", "ALG_CREATOR": "JEANDENANS L.", "x_depart": "coordinate X of the starting point", "Layer": "a raster with elevation", "y_arrivee": "coordinate Y of the end point", "x_arrivee": "coordinate X of the end point", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a vector with the way"}
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/collections/all_maki_icons/svg/bakery-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Ripley_-_Rasson_spatial_domain.rsx.help:
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1 | {"ALG_CREATOR": "Victor Olaya, volayaf(at)gmail.com", "Output": "Riplay-Rasson estimate of the spatial domain.", "ALG_DESC": "This script computes the Ripley-Rasson estimate of the spatial domain from which an observed pattern of points came.\n\nR dependencies: library \"maptools\" and \"spatstat\"", "Layer": "A vector layer contain a point pattern.", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com"}
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/collections/qgis_rscripts2/rscripts/Ripley_-_Rasson_spatial_domain.rsx.help:
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1 | {"ALG_CREATOR": "Victor Olaya, volayaf(at)gmail.com", "Output": "Riplay-Rasson estimate of the spatial domain.", "ALG_DESC": "This script computes the Ripley-Rasson estimate of the spatial domain from which an observed pattern of points came.\n\nR dependencies: library \"maptools\" and \"spatstat\"", "Layer": "A vector layer contain a point pattern.", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com"}
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/collections/all_maki_icons/README.txt:
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1 | This download from mapbox.com/maki-icons/ includes the following:
2 |
3 | 1. LICENSE.txt.
4 | 2. A directory of all your styled icons in svgs/. These icons can be used for any purpose, but the tool was primarily designed to be used with mapbox.com/studio/styles/. Open a style and drag the contents of "svgs" onto the style.
5 | 3. an iconset-.js file. Use this file to import your iconset back into the maki icon editor at mapbox.com/maki-icons/#editable.
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/collections/qgis_rscripts2/rscripts/Douglas-Peucker.rsx.help:
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1 | {"distance": "degree of simplification: the limit distance between the simplificated line and points of polygon.", "ALG_DESC": "This script is the algorithm of Douglas-Peucker and gives simplificated lines.", "Output2": "a shapfile with the simplification of outline", "Output1": "a shapfile with the simplification of border", "Layer": "Input vector with polygons", "ALG_CREATOR": "JEANDENANS L.", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/all_maki_icons/svg/mountain-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/FAMD.rsx.help:
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1 | {"Field1 ": "The first field", "ALG_DESC": "This script does a Factor Analysis of Mixed Data (FAMD) and gives the circle of correlations.", "ALG_CREATOR": "JEANDENANS L.", "Field3 ": "The third field", "Layer": "Input vector with at least 6 fields.", "Field2 ": "The second field", "RPLOTS": "plot of the correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L.", "Field6 ": "The sixth field", "Field5 ": "The fifth field", "Field4 ": "The fourth field"}
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/collections/qgis_rscripts2/rscripts/AFDM.rsx.help:
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1 | {"Field1 ": "The first field", "ALG_DESC": "This script does a Factor Analysis of Mixed Data (FAMD) and gives the circle of correlations.", "ALG_CREATOR": "JEANDENANS L.", "Field3 ": "The third field", "Layer": "Input vector with at least 6 fields.", "Field2 ": "The second field", "RPLOTS": "plot of the correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L.", "Field6 ": "The sixth field", "Field5 ": "The fifth field", "Field4 ": "The fourth field"}
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/collections/qgis_rscripts/rscripts/ANOVA.rsx.help:
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1 | {"ALG_DESC": "This script does an ANalysis Of VAriance (ANOVA) and gives the summary of the test. The third field is removed in the second lm",
2 | "ALG_CREATOR": "JEANDENANS L.",
3 | "ALG_HELP_CREATOR": "JEANDENANS L.",
4 | "Layer": "Input vector layer with at least five numerical fields.",
5 | "Field1": "The first field", "Field2": "The second field",
6 | "Field3": "The third field", "Field4": "The fourth field",
7 | "Field5": "The fifth field"}
8 |
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/collections/qgis_rscripts/rscripts/Extract_points_from_line.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector line
3 | ##Line_number=number 1
4 | ##Output=output vector
5 | ##load_vector_using_rgdal
6 | library(sp)
7 | Coord<-Layer@lines[[Line_number]]@Lines[[1]]@coords
8 | x<-as.numeric(Coord[,1])
9 | y<-as.numeric(Coord[,2])
10 | X<-cbind(x,y)
11 | matrix<-as.matrix(X)
12 | matrix<-SpatialPointsDataFrame(matrix, as.data.frame(Coord), proj4string=CRS(proj4string(Layer)))
13 | Output=matrix
14 |
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/collections/qgis_rscripts/rscripts/Random_sampling_grid.rsx.help:
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1 | {"ALG_DESC": "This scripts samples point location within a given polygon(s) using a random sampling method. The used methods assume that the geometry used is not spherical, so objects should be in planar coordinates.\n\n", "ALG_CREATOR": "Victor Olaya", "Layer": "A vector layer containing polygons.", "ALG_HELP_CREATOR": "Filipe Dias", "Output": "Randomly generated random points.", "Size": "Number of sample points to be randomly generated"}
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/collections/qgis_rscripts2/rscripts/CAH.rsx.help:
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1 | {"ALG_DESC": "This script does a Hierarchical Cluster Analysis (HCA) and gives the dendogram.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 5 fields.", "Field4": "The fourth field", "Field5": "The fifth field", "Field2": "The second field", "RPLOTS": "the plot of the dendrogram of the analysis", "ALG_HELP_CREATOR": "JEANDENANS L.", "method": "choice of the method of HCA."}
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/collections/all_maki_icons/svg/circle-stroked-11.svg:
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1 |
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/collections/all_maki_icons/svg/religious-muslim-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/frequency_plot.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##Field=Field Layer
5 | plot(ecdf(Layer[[Field]]), verticals=T, pch=46, main="Frequency plot", xlab=Field) +
6 | abline(v=median(Layer[[Field]]), col="red") +
7 | text(median(Layer[[Field]]), 0.2, "median", col="red", srt=90, adj=c(-0.1, -0.1)) +
8 | abline(v=mean(Layer[[Field]]), col="green") +
9 | text(mean(Layer[[Field]]), 0.2, "mean", col="green", srt=90, adj=c(2, -0.1))
10 |
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/collections/qgis_rscripts2/rscripts/Random_sampling_grid.rsx.help:
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1 | {"ALG_DESC": "This scripts samples point location within a given polygon(s) using a random sampling method. The used methods assume that the geometry used is not spherical, so objects should be in planar coordinates.\n\n", "ALG_CREATOR": "Victor Olaya", "Layer": "A vector layer containing polygons.", "ALG_HELP_CREATOR": "Filipe Dias", "Output": "Randomly generated random points.", "Size": "Number of sample points to be randomly generated"}
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/collections/qgis_rscripts2/rscripts/frequency_plot.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##Field=Field Layer
5 | plot(ecdf(Layer[[Field]]), verticals=T, pch=46, main="Frequency plot", xlab=Field) +
6 | abline(v=median(Layer[[Field]]), col="red") +
7 | text(median(Layer[[Field]]), 0.2, "median", col="red", srt=90, adj=c(-0.1, -0.1)) +
8 | abline(v=mean(Layer[[Field]]), col="green") +
9 | text(mean(Layer[[Field]]), 0.2, "mean", col="green", srt=90, adj=c(2, -0.1))
10 |
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/collections/all_maki_icons/svg/place-of-worship-15.svg:
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1 |
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/README.md:
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1 | # QGIS Resources Sharing Repository
2 |
3 | This is the official repository for QGIS styles, icons and other resource types that can be shared with the QGIS Resource Sharing Plugin: https://plugins.qgis.org/plugins/qgis_resource_sharing/
4 |
5 |
6 | If you want to share a resource collection with the QGIS community, or just plan to use the QGIS Resource Sharing Plugin to browse the available collections, please have a look to the online manual: http://qgis-contribution.github.io/QGIS-ResourceSharing/
7 |
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/collections/all_maki_icons/svg/circle-stroked-15.svg:
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1 |
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/collections/all_maki_icons/svg/heart-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/PCA_contribution.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) and gives the plot of the contribution of each field to an axis.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "The plot of the contribution of fields to the construction of the axis of PCA.", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts2/rscripts/ACP_contribution.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) and gives the plot of the contribution of each field to an axis.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "The plot of the contribution of fields to the construction of the axis of PCA.", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/all_maki_icons/svg/fire-station-11.svg:
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1 |
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/collections/all_maki_icons/svg/laundry-15.svg:
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1 |
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/collections/all_maki_icons/svg/pharmacy-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Kriging_with_model_selection.rsx.help:
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1 | {"ALG_DESC": "This script does a kriging and we can select the model of the variogram; It gives a raster of the spatial interpolation. Fields can be numerics or not because in the script, there is a coding to numeric.", "Selection": "the list of differents models of variogram", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector", "Field": "The field", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster of the kriging", " by": "the pixel size"}
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/collections/all_maki_icons/svg/airfield-15.svg:
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1 |
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/collections/all_maki_icons/svg/industry-15.svg:
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1 |
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/collections/all_maki_icons/svg/triangle-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Kriging_with_model_selection.rsx.help:
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1 | {"ALG_DESC": "This script does a kriging and we can select the model of the variogram; It gives a raster of the spatial interpolation. Fields can be numerics or not because in the script, there is a coding to numeric.", "Selection": "the list of differents models of variogram", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector", "Field": "The field", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster of the kriging", " by": "the pixel size"}
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/collections/all_maki_icons/svg/dentist-15.svg:
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1 |
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/collections/all_maki_icons/svg/star-stroke-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Summarize_by_field.rsx:
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1 | ##Layer=vector
2 | ##Numeric_field=Field Layer
3 | ##Factor=Field Layer
4 | ##Folder=folder
5 | library(doBy)
6 | library(plyr)
7 | library(foreign)
8 | setwd(Folder)
9 | table1<-data.frame(num=Layer[[Numeric_field]],f=Layer[[Factor]])
10 | output<-summaryBy(num~f, data=table1,FUN=c(sum,mean,var))
11 | output1<-rename(output,c("num.sum"="Sum","num.mean"="Mean","num.var"="Variance","f"=Factor))
12 | write.dbf(output1,file=paste("summarize_output", ".dbf", sep = ""))
13 |
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/collections/all_maki_icons/svg/triangle-stroked-11.svg:
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1 |
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/collections/all_maki_icons/svg/information-11.svg:
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1 |
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/collections/all_maki_icons/svg/star-stroke-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Simple_Linear_Regression.rsx.help:
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1 | {"ALG_DESC": "This script computes a Simple Linear Regression Y~X and gives plots of a regression linear:(1) Residuals vs Fitted, (2) Normal Q-Q, (3) Scale-Location, (4) Cook's distance", "ALG_CREATOR": "JEANDENANS L.", "Field1": "The first field.", "Layer": "Input vector with at least 2 fields.", "Field2": "The second field.", "RPLOTS": "plots of a regression linear:\n- Residuals vs Fitted\n- Normal Q-Q\n- Scale-Location\n- Cook's distance", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts/rscripts/Summary_statistics.rsx.help:
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1 | {"ALG_DESC": "This tool calculates the following summary statistics for a numeric field: (1) Sum, (2) Count, (3) Unique values, (4) Minimum value, (5) Maximum value, (6) Range, (7) Mean, (8) Median and (9) Standard deviation.\n\n", "R_CONSOLE_OUTPUT": "Summary statistics table", "ALG_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com ", "Layer": "Input vector with at least one numeric field", "Field": "Numeric field", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com "}
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/collections/qgis_rscripts2/rscripts/Simple_Linear_Regression.rsx.help:
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1 | {"ALG_DESC": "This script computes a Simple Linear Regression Y~X and gives plots of a regression linear:(1) Residuals vs Fitted, (2) Normal Q-Q, (3) Scale-Location, (4) Cook's distance", "ALG_CREATOR": "JEANDENANS L.", "Field1": "The first field.", "Layer": "Input vector with at least 2 fields.", "Field2": "The second field.", "RPLOTS": "plots of a regression linear:\n- Residuals vs Fitted\n- Normal Q-Q\n- Scale-Location\n- Cook's distance", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts2/rscripts/Summary_statistics.rsx.help:
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1 | {"ALG_DESC": "This tool calculates the following summary statistics for a numeric field: (1) Sum, (2) Count, (3) Unique values, (4) Minimum value, (5) Maximum value, (6) Range, (7) Mean, (8) Median and (9) Standard deviation.\n\n", "R_CONSOLE_OUTPUT": "Summary statistics table", "ALG_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com ", "Layer": "Input vector with at least one numeric field", "Field": "Numeric field", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com "}
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/collections/all_maki_icons/svg/cafe-11.svg:
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1 |
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/collections/all_maki_icons/svg/triangle-stroked-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Close_neighbor.rsx.help:
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1 | {"ALG_DESC": "This script selects points which are separated by a distance lower than or equal to a given distance and returns a dataset with the selected points.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input point vector layer", "Distance": "A number (>0), specifying the minimum distance", "Name_field": "A name that can identify the objects", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "The selected points with the following attributes: coordinates (x, y, ...) and name (Name field)"}
2 |
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/collections/all_maki_icons/svg/attraction-11.svg:
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1 |
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/collections/all_maki_icons/svg/hospital-11.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/ACP_cercle.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##showplots
8 | library(ade4)
9 | library(rpanel)
10 | library(spatstat)
11 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
12 | donne<-na.exclude(donne)
13 | donne<-as.data.frame(donne)
14 | names(donne)<- c(Field1, Field2, Field3, Field4)
15 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
16 | s.corcircle(acp$co)
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/collections/all_maki_icons/svg/fire-station-15.svg:
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1 |
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/collections/all_maki_icons/svg/volcano-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/scatterplot_log.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##type=selection No logarithm;logarithm X;logarithmY;logarithm X and Y
7 | if (type == 0) {
8 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y)
9 | }
10 | if (type == 1) {
11 | plot(log(Layer[[X]]), Layer[[Y]], xlab=X, ylab=Y)
12 | } else if (type == 2) {
13 | plot(Layer[[X]], log(Layer[[Y]]), xlab=X, ylab=Y)
14 | } else if (type == 3) {
15 | plot(log(Layer[[X]]), log(Layer[[Y]]), xlab=X, ylab=Y)
16 | }
17 |
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/collections/qgis_rscripts2/rscripts/Advanced_raster_histogram.rsx:
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1 | ##Layer=raster
2 | ##Raster processing=group
3 | ##Dens_or_Hist=string Hist
4 | ##showplots
5 | library(rpanel)
6 | library(rasterVis)
7 | str <- Dens_or_Hist
8 | if (str !='Dens' & str != 'Hist'){
9 | rp.messagebox('you must enter "Dens" or "Hist"', title = 'oops!')
10 | } else {
11 | if (nbands(Layer) == 1) {
12 | Layer <- as.matrix(Layer)
13 | Layer <- raster(Layer)
14 | }
15 | if (str == 'Dens') {
16 | densityplot(Layer)
17 | } else if (str == 'Hist') {
18 | histogram(Layer)
19 | }
20 | }
21 |
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/collections/qgis_rscripts2/rscripts/scatterplot_log.rsx:
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1 | ##Vector processing=group
2 | ##showplots
3 | ##Layer=vector
4 | ##X=Field Layer
5 | ##Y=Field Layer
6 | ##type=selection No logarithm;logarithm X;logarithmY;logarithm X and Y
7 | if (type == 0) {
8 | plot(Layer[[X]], Layer[[Y]], xlab=X, ylab=Y)
9 | }
10 | if (type == 1) {
11 | plot(log(Layer[[X]]), Layer[[Y]], xlab=X, ylab=Y)
12 | } else if (type == 2) {
13 | plot(Layer[[X]], log(Layer[[Y]]), xlab=X, ylab=Y)
14 | } else if (type == 3) {
15 | plot(log(Layer[[X]]), log(Layer[[Y]]), xlab=X, ylab=Y)
16 | }
17 |
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/collections/qgis_rscripts2/rscripts/Douglas-Peucker_with_choice.rsx.help:
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1 | {"distance": "degree of simplification: the limit distance between the simplificated line and points of polygon.", "ALG_DESC": "This script is the algorithm of Douglas-Peucker with the choice of the polygon which is simplified and gives a shapefile with this polygon.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "a set of data (polygons)", "numero": "the number of the polygon which we want simplify", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a shafile with the simplification of the polygon's border"}
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/collections/qgis_rscripts/rscripts/Kernel_density_estimation.rsx.help:
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1 | {"Layer2": "Input vector of polygons on the same area.", "ALG_DESC": "This script computes the Kernel density estimation and gives a raster with estimations. We can choose if we want the correction for a possible frontier bias (method 1) or not (method 0).", "ALG_CREATOR": "JEANDENANS L.", "Layer1": "Input vector with field which we want to estimate.", "methode": "a number (1 : without correction or 0: with correction)", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster with the kernel density estimation"}
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/collections/qgis_rscripts2/rscripts/Alpha_shape.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##alpha=number 10
4 | ##Output= output vector
5 | library(rgdal)
6 | library(alphahull)
7 | library(maptools)
8 | alpha_points = ashape(coordinates(Layer),alpha=alpha)
9 | l <- list()
10 | for (i in 1:nrow(alpha_points$edges)) {
11 | l[[i]] <- Line(rbind(alpha_points$edges[i, 3:4], alpha_points$edges[i, 5:6]))
12 | }
13 | l <- list(Lines(l, as.character("1")))
14 | sldf <- SpatialLinesDataFrame(SpatialLines(l), data.frame(name ="ashape"), match.ID = FALSE)
15 | Output=sldf
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/collections/qgis_rscripts2/rscripts/Kernel_density_estimation.rsx.help:
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1 | {"Layer2": "Input vector of polygons on the same area.", "ALG_DESC": "This script computes the Kernel density estimation and gives a raster with estimations. We can choose if we want the correction for a possible frontier bias (method 1) or not (method 0).", "ALG_CREATOR": "JEANDENANS L.", "Layer1": "Input vector with field which we want to estimate.", "methode": "a number (1 : without correction or 0: with correction)", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster with the kernel density estimation"}
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/collections/all_maki_icons/svg/police-11.svg:
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1 |
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/collections/all_maki_icons/svg/prison-15.svg:
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1 |
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/collections/all_maki_icons/svg/warehouse-15.svg:
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1 |
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/collections/gis_lab/LICENSE.txt:
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1 | EN:
2 |
3 | You are allowed to use this set of symbols for QGIS under conditions of CC-BY 3.0 unported license (http://creativecommons.org/licenses/by/3.0/).
4 |
5 | Authors: Yury Ryabov (riabovvv@gmail.com), Pavel Sergeev (aka old_Bibigon).
6 |
7 |
8 |
9 | RU:
10 |
11 | Вы можете пользоваться данным набором символов для QGIS на условиях, предусмотренных непортированной лицензией CC-BY 3.0 (http://creativecommons.org/licenses/by/3.0/deed.ru).
12 |
13 | Авторы: Юрий Рябов (riabovvv@gmail.com), Павел Сергеев (aka old_Bibigon).
14 |
15 |
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/collections/qgis_rscripts/rscripts/Summarize_by_field.rsx:
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1 | ##Layer=vector
2 | ##Numeric_field=Field Layer
3 | ##Factor=Field Layer
4 | ##Folder=folder
5 | ##Summary_table=output table
6 | library(doBy)
7 | library(plyr)
8 | library(foreign)
9 | setwd(Folder)
10 | table1<-data.frame(num=Layer[[Numeric_field]],f=Layer[[Factor]])
11 | output<-summaryBy(num~f, data=table1,FUN=c(sum,mean,var))
12 | Summary_table<-rename(output,c("num.sum"="Sum","num.mean"="Mean","num.var"="Variance","f"=Factor))
13 | write.dbf(Summary_table,file=paste("summarize_output", ".dbf", sep = ""))
14 |
15 |
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/collections/all_maki_icons/svg/drinking-water-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Distance.rsx:
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1 | ##Basic statistics=group
2 | ##Layer1=vector
3 | ##Layer2=vector
4 | ##Field=Field Layer1
5 | ##output= output vector
6 | library(geosphere)
7 | library(rgeos)
8 | line<-gLineMerge(Layer2, byid=FALSE, id = NULL)
9 | x<-coordinates(Layer1)
10 | X<-dist2Line(x, line, distfun=distHaversine)
11 | matrix<-as.matrix(X[,2:3])
12 | X<-cbind(X, as.data.frame(Layer1[[Field]]))
13 | result<-SpatialPointsDataFrame(matrix, as.data.frame(X, row.names=NULL))
14 | proj4string(Layer1)->crs
15 | proj4string(result)<-crs
16 | output<-result
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/collections/all_maki_icons/svg/college-15.svg:
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1 |
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/collections/all_maki_icons/svg/dentist-11.svg:
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1 |
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/collections/all_maki_icons/svg/rocket-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/ACP_cercle.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##load_vector_using_rgdal
8 | ##showplots
9 | library(ade4)
10 | library(rpanel)
11 | library(spatstat)
12 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
13 | donne<-na.exclude(donne)
14 | donne<-as.data.frame(donne)
15 | names(donne)<- c(Field1, Field2, Field3, Field4)
16 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
17 | s.corcircle(acp$co)
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/collections/qgis_rscripts/rscripts/PCA_cercle.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##load_vector_using_rgdal
8 | ##showplots
9 | library(ade4)
10 | library(rpanel)
11 | library(spatstat)
12 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
13 | donne<-na.exclude(donne)
14 | donne<-as.data.frame(donne)
15 | names(donne)<- c(Field1, Field2, Field3, Field4)
16 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
17 | s.corcircle(acp$co)
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/collections/all_maki_icons/svg/drinking-water-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Multiple_Regression.rsx.help:
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1 | {"ALG_DESC": "This script does a multiple regression with 6 explicatives variables and gives the summary of the test.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The second explicative field.", "Field1": "The explained field.", "Field6": "The fifth explicative field.", "Layer": "Input vector with at least 7 fields.", "Field4": "The third explicative field.", "Field5": "The fourth explicative field.", "Field2": "The first explicative field.", "Field7": "The sixth explicative field.", "ALG_HELP_CREATOR": "JEANDENANS L.", "null": ""}
2 |
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/collections/qgis_rscripts/rscripts/PCA_individus.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) and gives a representation of persons.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "The plot of persons of the PCA with two persons selected", "ALG_HELP_CREATOR": "JEANDENANS L.", "Individu2": "a number of a person who we want to see on the plot", "Individu1": "a number of a person who we want to see on the plot"}
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/collections/qgis_rscripts2/rscripts/ACP_individus.rsx.help:
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1 | {"ALG_DESC": "This script does a Principal Component Analysis (PCA) and gives a representation of persons.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields.", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "The plot of persons of the PCA with two persons selected", "ALG_HELP_CREATOR": "JEANDENANS L.", "Individu2": "a number of a person who we want to see on the plot", "Individu1": "a number of a person who we want to see on the plot"}
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/collections/qgis_rscripts2/rscripts/Multiple_Regression.rsx.help:
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1 | {"ALG_DESC": "This script does a multiple regression with 6 explicatives variables and gives the summary of the test.", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The second explicative field.", "Field1": "The explained field.", "Field6": "The fifth explicative field.", "Layer": "Input vector with at least 7 fields.", "Field4": "The third explicative field.", "Field5": "The fourth explicative field.", "Field2": "The first explicative field.", "Field7": "The sixth explicative field.", "ALG_HELP_CREATOR": "JEANDENANS L.", "null": ""}
2 |
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/collections/all_maki_icons/svg/mountain-11.svg:
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1 |
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/collections/all_maki_icons/svg/pharmacy-15.svg:
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1 |
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/collections/all_maki_icons/svg/picnic-site-11.svg:
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1 |
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/collections/all_maki_icons/svg/lighthouse-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Summarize_by_two_fields.rsx:
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1 | ##Layer=vector
2 | ##Numeric_field=Field Layer
3 | ##Factor1=Field Layer
4 | ##Factor2=Field Layer
5 | ##Folder=folder
6 | library(doBy)
7 | library(plyr)
8 | library(foreign)
9 | setwd(Folder)
10 | table1<-data.frame(num=Layer[[Numeric_field]],f1=Layer[[Factor1]],f2=Layer[[Factor2]])
11 | output<-summaryBy(num~f1+f2, data=table1,FUN=c(sum,mean,var))
12 | output1<-rename(output,c("num.sum"="Sum","num.mean"="Mean","num.var"="Variance","f1"=Factor1,"f2"=Factor2))
13 | write.dbf(output1,file=paste("summarize_output", ".dbf", sep = ""))
14 |
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/collections/all_maki_icons/svg/cafe-15.svg:
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1 |
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/collections/all_maki_icons/svg/lighthouse-11.svg:
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1 |
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/collections/all_maki_icons/svg/parking-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Distance.rsx.help:
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1 | {"ALG_DESC": "For each point in the input point layer, the script finds the nearest location on the line layer and calculates the distance. Coordinates have to be in the latitude / longitude ranges.",
2 | "ALG_CREATOR": "The QGIS community",
3 | "ALG_HELP_CREATOR": "The QGIS community",
4 | "Input_Point_Layer": "A set of points (lat/lon)",
5 | "Line_Layer": "A line layer",
6 | "ID_Field": "ID field for the input points",
7 | "Nearest": "The output point layer (containing the nearest point on the line for each input point) with input point ID and the distance."}
8 |
9 |
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/collections/qgis_rscripts2/rscripts/barplots.rsx.help:
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1 | {"ALG_DESC": "Creates a bar plot with vertical bars using barplot().",
2 |
3 | "ALG_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
4 |
5 | "ALG_HELP_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
6 |
7 | "ALG_VERSION": "1.0-0",
8 |
9 | "Layer": "Input point vector layer.",
10 |
11 | "Field": "Field of the attribute table of the point vector layer passed to Layer holding data of type String for plotting."
12 | }
13 |
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/collections/all_maki_icons/svg/religious-muslim-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/CoA.rsx.help:
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1 | {"ALG_DESC": "This script does a Correspondence Analysis (CA) and gives differents plots:(1) a plot with the percentage of variance, (2) a plot of the factorail design, (3) a plot with the correlation circle", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "differents plots:\n- a plot with the percentage of variance\n- a plot of the factorail design\n- a plot with the correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/qgis_rscripts2/rscripts/AFC.rsx.help:
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1 | {"ALG_DESC": "This script does a Correspondence Analysis (CA) and gives differents plots:(1) a plot with the percentage of variance, (2) a plot of the factorail design, (3) a plot with the correlation circle", "ALG_CREATOR": "JEANDENANS L.", "Field3": "The third field", "Field1": "The first field", "Layer": "Input vector with at least 4 fields", "Field4": "The fourth field", "Field2": "The second field", "RPLOTS": "differents plots:\n- a plot with the percentage of variance\n- a plot of the factorail design\n- a plot with the correlation circle", "ALG_HELP_CREATOR": "JEANDENANS L."}
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/collections/all_maki_icons/svg/cemetery-15.svg:
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1 |
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/collections/all_maki_icons/svg/music-15.svg:
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1 |
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/collections/all_maki_icons/svg/pitch-15.svg:
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1 |
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/collections/all_maki_icons/svg/rail-light-11.svg:
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1 |
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/collections/all_maki_icons/svg/restaurant-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Advanced_raster_histogram.rsx.help:
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1 | {"ALG_CREATOR": "", "ALG_DESC": "This algorithm generates a histogram or a density plot for the given raster. NOTE that you should not use this algorithm to process large rasters.\n\nR dependencies: rpanel, rasterVis. If you are using Linux you need to install \"tcktk\" and \"BWidget\" from your package master.", "Dens_or_Hist": "Use 'hist' to produce histogram of the raster values (separate plots for each band) and 'dens' if you want to create a density plot (single plot for all bands).", "RPLOTS": "Raster histogram.", "Layer": "A single- or multi-band raster."}
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/collections/all_maki_icons/svg/lodging-11.svg:
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1 |
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/collections/all_maki_icons/svg/parking-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/Summary_statistics.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | Summary_statistics<-data.frame(rbind(sum(Layer[[Field]]),
5 | length(Layer[[Field]]),
6 | length(unique(Layer[[Field]])),
7 | min(Layer[[Field]]),
8 | max(Layer[[Field]]),
9 | max(Layer[[Field]])-min(Layer[[Field]]),
10 | mean(Layer[[Field]]),
11 | median(Layer[[Field]]),
12 | sd(Layer[[Field]])),row.names=c("Sum:","Count:","Unique values:","Minimum value:","Maximum value:","Range:","Mean value:","Median value:","Standard deviation:"))
13 | colnames(Summary_statistics)<-c(Field)
14 | >Summary_statistics
15 |
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/collections/all_maki_icons/svg/lodging-15.svg:
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1 |
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/collections/all_maki_icons/svg/pitch-11.svg:
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1 |
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/collections/all_maki_icons/svg/police-15.svg:
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1 |
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/collections/all_maki_icons/svg/warehouse-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Advanced_raster_histogram.rsx:
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1 | ##Raster_layer=raster
2 | ##Raster processing=group
3 | ##Dens_or_Hist=string Hist
4 | ##showplots
5 | library(rpanel)
6 | library(rasterVis)
7 | str <- Dens_or_Hist
8 | if (str !='Dens' & str != 'Hist'){
9 | rp.messagebox('you must enter "Dens" or "Hist"', title = 'oops!')
10 | } else {
11 | if (nbands(Raster_layer) == 1) {
12 | Raster_layer <- as.matrix(Raster_layer)
13 | Raster_layer <- raster(Raster_layer)
14 | }
15 | if (str == 'Dens') {
16 | densityplot(Raster_layer)
17 | } else if (str == 'Hist') {
18 | histogram(Raster_layer)
19 | }
20 | }
21 |
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/collections/qgis_rscripts/rscripts/Quadrat_analysis.rsx.help:
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1 | {"ALG_DESC": "The script divides the window into quadrats and counts the numbers of points in each quadrat. Then it performs a test of Complete Spatial Randomness based on the quadrat counts.\n\nR dependencies: library \"maptools\" and \"spatstat\"", "R_CONSOLE_OUTPUT": "The results of the Chi-squared test of complete spatial randomness (CSR) using quadrat counts.\n", "ALG_CREATOR": "Victor Olaya, volayaf(at)gmail.com", "Layer": "A vector containg a point pattern.", "RPLOTS": "A display containing the number of points per quadrat.", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com"}
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/collections/qgis_rscripts/rscripts/Summarize_by_two_fields.rsx:
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1 | ##Layer=vector
2 | ##Numeric_field=Field Layer
3 | ##Factor1=Field Layer
4 | ##Factor2=Field Layer
5 | ##Folder=folder
6 | ##Summary_Table=output table
7 | library(doBy)
8 | library(plyr)
9 | library(foreign)
10 | setwd(Folder)
11 | table1<-data.frame(num=Layer[[Numeric_field]],f1=Layer[[Factor1]],f2=Layer[[Factor2]])
12 | output<-summaryBy(num~f1+f2, data=table1,FUN=c(sum,mean,var))
13 | Summary_Table<-rename(output,c("num.sum"="Sum","num.mean"="Mean","num.var"="Variance","f1"=Factor1,"f2"=Factor2))
14 | write.dbf(Summary_Table,file=paste("summarize_output", ".dbf", sep = ""))
15 |
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/collections/qgis_rscripts2/rscripts/Quadrat_analysis.rsx.help:
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1 | {"ALG_DESC": "The script divides the window into quadrats and counts the numbers of points in each quadrat. Then it performs a test of Complete Spatial Randomness based on the quadrat counts.\n\nR dependencies: library \"maptools\" and \"spatstat\"", "R_CONSOLE_OUTPUT": "The results of the Chi-squared test of complete spatial randomness (CSR) using quadrat counts.\n", "ALG_CREATOR": "Victor Olaya, volayaf(at)gmail.com", "Layer": "A vector containg a point pattern.", "RPLOTS": "A display containing the number of points per quadrat.", "ALG_HELP_CREATOR": "Filipe S. Dias, filipesdias(at)gmail.com"}
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/collections/all_maki_icons/svg/college-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/barplot.rsx.help:
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1 | {"ALG_DESC": "Creates a bar plot with vertical bars using barplot().",
2 | "ALG_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
3 | "ALG_HELP_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
4 | "ALG_VERSION": "1.0-0",
5 | "Layer": "Input point vector layer.",
6 | "Field": "Field of the attribute table of the point vector layer passed to Layer holding data of type String for plotting.",
7 | "RPLOTS": "(Specify) The location and name for the output HTML file with the plot"}
8 |
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/collections/all_maki_icons/svg/post-11.svg:
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1 |
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/collections/all_maki_icons/svg/america-football-15.svg:
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1 |
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/collections/all_maki_icons/svg/car-11.svg:
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1 |
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/collections/all_maki_icons/svg/post-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Selection_with_criterion_choice.rsx.help:
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1 | {"ALG_DESC": "This script does a selection of variables. The following models are available: exhaustive search, forward or backward stepwise, or sequential replacement. And the selection criterion can be chosen.", "ALG_CREATOR": "JEANDENANS L.", "Layer": "Input vector.", "Field1": "The variable which is explained by the others", "ID_field": "The field that is used to identify the objects.", "nbr_var": "The number of variables which we want in the best model", "Method": "Choose from the list of methods", "Criterion": "Choose from a list of criterions", "ALG_HELP_CREATOR": "JEANDENANS L."}
2 |
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/collections/qgis_rscripts/rscripts/Summary_statistics.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | ##load_vector_using_rgdal
5 | Summary_statistics<-data.frame(rbind(sum(Layer[[Field]]),
6 | length(Layer[[Field]]),
7 | length(unique(Layer[[Field]])),
8 | min(Layer[[Field]]),
9 | max(Layer[[Field]]),
10 | max(Layer[[Field]])-min(Layer[[Field]]),
11 | mean(Layer[[Field]]),
12 | median(Layer[[Field]]),
13 | sd(Layer[[Field]])),row.names=c("Sum:","Count:","Unique values:","Minimum value:","Maximum value:","Range:","Mean value:","Median value:","Standard deviation:"))
14 | colnames(Summary_statistics)<-c(Field)
15 | >Summary_statistics
16 |
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/collections/all_maki_icons/svg/art-gallery-15.svg:
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1 |
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/collections/all_maki_icons/svg/art-gallery-11.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/ACP_contribution.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##showplots
8 | library(ade4)
9 | library(rpanel)
10 | library(spatstat)
11 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
12 | donne<-na.exclude(donne)
13 | donne<-as.data.frame(donne)
14 | names(donne)<- c(Field1, Field2, Field3, Field4)
15 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
16 | contribution<-inertia.dudi(acp,col.inertia = T)$col.abs
17 | plot(contribution)
18 | text(contribution ,row.names(contribution))
19 |
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/collections/all_maki_icons/svg/aquarium-15.svg:
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1 |
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/collections/all_maki_icons/svg/cemetery-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/FAMD.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##Field5=Field Layer
8 | ##Field6=Field Layer
9 | ##load_vector_using_rgdal
10 | ##showplots
11 | library(FactoMineR)
12 | library(ade4)
13 | Layer<-as.data.frame(Layer)
14 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]], Layer[[Field5]], Layer[[Field6]])
15 | colnames(donne)<-c(Field1, Field2, Field3, Field4, Field5, Field6)
16 | X<-FAMD(donne, ncp=5, graph=FALSE, sup.var=NULL, ind.sup=NULL, axes=c(1,2), row.w=NULL)
17 | s.corcircle(X$var$coord[,1:2])
18 |
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/collections/qgis_rscripts/rscripts/ternaryPlots.rsx:
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1 | ##ggplot=group
2 | ##showplots
3 | ##Layer=Vector
4 | ##X= Field Layer
5 | ##Y=Field Layer
6 | ##Z=Field Layer
7 | ##Group=optional Field Layer
8 | library("ggplot2")
9 | library("ggtern")
10 | if (is.null(Group)){
11 | ggplot()+
12 | geom_point(aes(Layer[[X]],Layer[[Y]], Layer[[Z]]))+
13 | coord_tern() +
14 | xlab(X)+
15 | ylab(Y) +
16 | zlab(Z) +
17 | theme_showarrows()
18 | } else {
19 | ggplot()+
20 | geom_point(aes(Layer[[X]],Layer[[Y]], Layer[[Z]], color=as.factor(Layer[[Group]])))+
21 | coord_tern() +
22 | xlab(X)+
23 | ylab(Y) +
24 | zlab(Z) +
25 | theme_showarrows()+
26 | theme(legend.title=element_blank())
27 | }
28 |
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/collections/qgis_rscripts2/rscripts/ternaryPlots.rsx:
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1 | ##ggplot=group
2 | ##showplots
3 | ##Layer=Vector
4 | ##X= Field Layer
5 | ##Y=Field Layer
6 | ##Z=Field Layer
7 | ##Group=optional Field Layer
8 | library("ggplot2")
9 | library("ggtern")
10 | if (is.null(Group)){
11 | ggplot()+
12 | geom_point(aes(Layer[[X]],Layer[[Y]], Layer[[Z]]))+
13 | coord_tern() +
14 | xlab(X)+
15 | ylab(Y) +
16 | zlab(Z) +
17 | theme_showarrows()
18 | } else {
19 | ggplot()+
20 | geom_point(aes(Layer[[X]],Layer[[Y]], Layer[[Z]], color=as.factor(Layer[[Group]])))+
21 | coord_tern() +
22 | xlab(X)+
23 | ylab(Y) +
24 | zlab(Z) +
25 | theme_showarrows()+
26 | theme(legend.title=element_blank())
27 | }
28 |
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/collections/all_maki_icons/svg/garden-15.svg:
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1 |
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/collections/all_maki_icons/svg/ice-cream-11.svg:
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1 |
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/collections/all_maki_icons/svg/rocket-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/AFDM.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1 = Field Layer
4 | ##Field2 = Field Layer
5 | ##Field3 = Field Layer
6 | ##Field4 = Field Layer
7 | ##Field5 = Field Layer
8 | ##Field6 = Field Layer
9 | ##showplots
10 | library(FactoMineR)
11 | library(ade4)
12 | Layer<-as.data.frame(Layer)
13 | donne<-cbind(Layer[[Field1]],Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]],Layer[[Field6]])
14 | colnames(donne)<-c(Field1, Field2, Field3,Field4,Field5,Field6)
15 | X<-FAMD (donne, ncp = 5, graph =FALSE, sup.var = NULL,
16 | ind.sup = NULL, axes = c(1,2), row.w = NULL, tab.comp = NULL)
17 | s.corcircle(X$var$coord[,1:2])
18 |
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/collections/all_maki_icons/svg/aerialway-15.svg:
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1 |
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/collections/all_maki_icons/svg/museum-11.svg:
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1 |
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/collections/all_maki_icons/svg/swimming-11.svg:
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1 |
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/collections/all_maki_icons/svg/zoo-11.svg:
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1 |
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/collections/all_maki_icons/svg/music-11.svg:
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1 |
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/collections/qgis_rscripts/rscripts/PCA_contribution.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##load_vector_using_rgdal
8 | ##showplots
9 | library(ade4)
10 | library(rpanel)
11 | library(spatstat)
12 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
13 | donne<-na.exclude(donne)
14 | donne<-as.data.frame(donne)
15 | names(donne)<- c(Field1, Field2, Field3, Field4)
16 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
17 | contribution<-inertia.dudi(acp,col.inertia = T)$col.abs
18 | plot(contribution)
19 | text(contribution ,row.names(contribution))
20 |
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/collections/qgis_rscripts2/rscripts/Selection_with_criterion_choice.rsx.help:
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1 | {"Field1 ": "The explained field", "ALG_DESC": "This script does a selection of variables. We can choose the model of selection between: exhaustive serach, forward or backward stepwise, or sequential replacement. And we can select the criterion used in the selection.", "ALG_CREATOR": "JEANDENANS L.", "Criteres": "a list of criterions", "Layer": "Input vector.", " Field1 ": "the variable which is explained by the others", "nbr_var ": "a number which is the number of variables which we want in the best model", "Methode": "list of methods", "ALG_HELP_CREATOR": "JEANDENANS L.", "Field2 ": "The field which be used to identify the objects."}
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/collections/gis_lab/svg/lep-shad_25k.svg:
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1 |
2 |
3 |
4 |
25 |
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/collections/all_maki_icons/svg/golf-15.svg:
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1 |
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/collections/all_maki_icons/svg/aerialway-11.svg:
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1 |
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/collections/all_maki_icons/svg/rail-metro-11.svg:
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1 |
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/collections/all_maki_icons/svg/car-15.svg:
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1 |
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/collections/all_maki_icons/svg/campsite-11.svg:
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1 |
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/collections/all_maki_icons/svg/rail-light-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/A-star.rsx.help:
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1 | {"ALG_DESC": "This script is the algorithm A-star and gives a raster with the best way. We choose the limit of elevation in order to put obstacles and we have to choose the number of directions between 8 and 4 (with or not diagonals) .", "Layer0": "a raster of the study area.", "ALG_CREATOR": "JEANDENANS L.", "xA": "coordinate X of the starting point", "direction": "number of directions between 4 or 8", "xB": "coordinate X of the end point", "yA": "coordinate Y of the starting point", "ALG_HELP_CREATOR": "JEANDENANS L.", "Output": "a raster of the same area with the way", "yB": "coordinate Y of the end point", "hauteur": "the limit of elevation. Coordinates with a higher elevation become an obstacle."}
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/collections/all_maki_icons/svg/cross-15.svg:
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1 |
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/collections/all_maki_icons/svg/hairdresser-15.svg:
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1 |
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/collections/all_maki_icons/svg/waste-basket-15.svg:
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1 |
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/collections/z_m_styling/README:
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1 | author : Régis Haubourg
2 | 2016 01 03
3 | ----------
4 | # z value styling in QGIS
5 | This collection aims at sharing styling tips for being able to visualize Z or M values for 3DM geometries.
6 | That can be useful for checking editing mistakes, or improving 2.5 D styling for roofs slopes for instance.
7 |
8 | # Caveats and warnings:
9 | Z values are supposed to be in a cartesian system (ie meters) like layer's CRS units.
10 | This style won't work with reprojected datasets or on-the-fly projection set to a non projected CRS like WGS84 because z offset is not reprojected to decimal degrees.
11 | So if you work in lat lon system, Z values should be edited using decimal degrees unit (which is quite uncommon).
12 |
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/collections/all_maki_icons/svg/attraction-15.svg:
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1 |
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/collections/all_maki_icons/svg/picnic-site-15.svg:
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1 |
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/collections/qgis_rscripts/rscripts/Advanced_raster_histogram.rsx.help:
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1 | {"ALG_DESC": "This algorithm generates histograms or density plots for the given raster. NOTE that you should not use this algorithm to process large rasters.\n\nR dependencies: rpanel, rasterVis. If you are using Linux you need to install \"tcktk\" and \"BWidget\" from your package master.",
2 | "ALG_CREATOR": "The QGIS Community",
3 | "ALG_HELP_CREATOR": "The QGIS Community",
4 | "Raster layer": "A single- or multi-band raster.",
5 | "Dens_or_Hist": "Use 'Hist' to produce a histogram of the raster values (separate plots for each band) and 'Dens' if you want to create a density plot.",
6 | "RPLOTS": "(Specify) The location and name for the output HTML file with the plots / histograms (optional)."}
7 |
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/collections/all_maki_icons/svg/bus-15.svg:
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1 |
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/collections/all_maki_icons/svg/swimming-15.svg:
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1 |
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/collections/qgis_rscripts2/rscripts/ACP_var.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##showplots
8 | library(ade4)
9 | library(rpanel)
10 | library(spatstat)
11 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
12 | donne<-na.exclude(donne)
13 | donne<-as.data.frame(donne)
14 | names(donne)<- c(Field1, Field2, Field3, Field4)
15 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
16 | cl1<-acp$li[,1]
17 | cc1<-acp$co[,1]
18 | cl2<-acp$li[,2]
19 | cc2<-acp$co[,2]
20 | plot(cc1,cc2,type="n", main="Les variables", xlim=c(-1,1), ylim=c(-1,1), asp=1, ylab= "",xlab= "")
21 | abline(h=0,v=0)
22 | text(cc1,cc2,row.names(acp$co))
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/collections/qgis_rscripts2/rscripts/Autocor_spatiale.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field=Field Layer
4 | ##nb_simul=number 100
5 | ##nb_vois=number 10
6 | ##showplots
7 | library(spdep)
8 | coords<-coordinates(Layer)
9 | Y<-knearneigh(coords, k=nb_vois,longlat = T)
10 | Y<-knn2nb(Y)
11 | X<-nb2listw(Y, style="B", zero.policy=T)
12 | if (class(Layer[[Field]])=="factor"){
13 | Layer[[Field]]<-as.numeric(Layer[[Field]])
14 | }
15 | if(class(Layer[[Field]])=="character"){
16 | Layer[[Field]]<-as.factor(as.numeric(Layer[[Field]]))
17 | }
18 | moran.test(x = Layer[[Field]], listw = X)
19 | test<-moran.mc(x = Layer[[Field]], listw = X,nsim=nb_simul)
20 | test
21 | hist(test$res, freq=TRUE, breaks=20, xlab="Simulatec Moran's I")
22 | abline(v=0,col="red")
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/collections/all_maki_icons/svg/entrance-15.svg:
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1 |
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/collections/all_maki_icons/svg/heliport-11.svg:
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1 |
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/collections/all_maki_icons/svg/stadium-15.svg:
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1 |
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/collections/all_maki_icons/svg/beer-15.svg:
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/collections/qgis_rscripts2/rscripts/Selection_with_r2.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1 = Field Layer
4 | ##Field2 = Field Layer
5 | ##Field3 = Field Layer
6 | ##Field4 = Field Layer
7 | ##Field5 = Field Layer
8 | ##Field6 = Field Layer
9 | ##Methode=Selectionexhaustive;backward;forward;seqrep
10 | ##Nombre_var = number 5
11 | ##showplots
12 | library(leaps)
13 | X<-cbind(Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]],Layer[[Field6]])
14 | colnames(X)<-c(Field2, Field3, Field4, Field5, Field6)
15 | method=c("exhaustive", "backward", "forward", "seqrep")
16 | methode<-method[Methode + 1]
17 | test1<-regsubsets(Layer[[Field1]]~X,data=Layer,really.big=T,nbest=1,nvmax=Nombre_var, intercept=F,method=methode)
18 | plot(test1, scale="r2")
19 |
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/collections/qgis_rscripts/rscripts/Autocor_Moran.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector point
3 | ##Field=Field Layer
4 | ##nb_simulations=number 100
5 | ##nb_neighb=number 10
6 | ##showplots
7 | library(spdep)
8 | coords<-st_coordinates(Layer)
9 | Y<-knearneigh(coords, k=nb_neighb, longlat = T)
10 | Y<-knn2nb(Y)
11 | X<-nb2listw(Y, style="B", zero.policy=T)
12 | if (class(Layer[[Field]])=="factor"){
13 | Layer[[Field]]<-as.numeric(Layer[[Field]])
14 | }
15 | if(class(Layer[[Field]])=="character"){
16 | Layer[[Field]]<-as.factor(as.numeric(Layer[[Field]]))
17 | }
18 | moran.test(x = Layer[[Field]], listw = X)
19 | test<-moran.mc(x = Layer[[Field]], listw = X,nsim=nb_simulations)
20 | test
21 | hist(test$res, freq=TRUE, breaks=20, xlab="Moran's I, residuals")
22 | abline(v=0,col="red")
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/collections/all_maki_icons/svg/skiing-15.svg:
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/collections/qgis_rscripts/rscripts/PCA_var.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##load_vector_using_rgdal
8 | ##showplots
9 | library(ade4)
10 | library(rpanel)
11 | library(spatstat)
12 | donne<-cbind(Layer[[Field1]], Layer[[Field2]], Layer[[Field3]], Layer[[Field4]])
13 | donne<-na.exclude(donne)
14 | donne<-as.data.frame(donne)
15 | names(donne)<- c(Field1, Field2, Field3, Field4)
16 | acp<- dudi.pca(donne, center = T, scale = T, scannf = F)
17 | cl1<-acp$li[,1]
18 | cc1<-acp$co[,1]
19 | cl2<-acp$li[,2]
20 | cc2<-acp$co[,2]
21 | plot(cc1,cc2,type="n", main="Les variables", xlim=c(-1,1), ylim=c(-1,1), asp=1, ylab= "",xlab= "")
22 | abline(h=0,v=0)
23 | text(cc1,cc2,row.names(acp$co))
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/collections/qgis_rscripts2/rscripts/Selection_Cp.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1 = Field Layer
4 | ##Field2 = Field Layer
5 | ##Field3 = Field Layer
6 | ##Field4 = Field Layer
7 | ##Field5 = Field Layer
8 | ##Field6 = Field Layer
9 | ##Methode=Selectionexhaustive;backward;forward;seqrep
10 | ##Nombre_var = number 5
11 | ##showplots
12 | library(leaps)
13 | X<-cbind(Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]],Layer[[Field6]])
14 | colnames(X)<-c(Field2, Field3, Field4, Field5, Field6)
15 | method=c("exhaustive", "backward", "forward", "seqrep")
16 | methode<-method[Methode + 1]
17 | test1<-regsubsets(Layer[[Field1]]~X,data=Layer,really.big=T,nbest=1,nvmax=Nombre_var, intercept=F,method=methode)
18 | plot(test1, scale="Cp")
19 | summary(test1)
20 |
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/collections/qgis_rscripts2/rscripts/Selection_with_r2_adjusted.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1 = Field Layer
4 | ##Field2 = Field Layer
5 | ##Field3 = Field Layer
6 | ##Field4 = Field Layer
7 | ##Field5 = Field Layer
8 | ##Field6 = Field Layer
9 | ##Methode=Selectionexhaustive;backward;forward;seqrep
10 | ##Nombre_var = number 5
11 | ##showplots
12 | library(leaps)
13 | X<-cbind(Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]],Layer[[Field6]])
14 | colnames(X)<-c(Field2, Field3, Field4, Field5, Field6)
15 | method=c("exhaustive", "backward", "forward", "seqrep")
16 | methode<-method[Methode + 1]
17 | test1<-regsubsets(Layer[[Field1]]~X,data=Layer,really.big=T,nbest=1,nvmax=Nombre_var, intercept=F,method=methode)
18 | plot(test1, scale="adjr2")
19 |
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/collections/all_maki_icons/svg/heliport-15.svg:
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/collections/qgis_rscripts2/rscripts/Selection_with_Bayesian_Information_Criterion.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1 = Field Layer
4 | ##Field2 = Field Layer
5 | ##Field3 = Field Layer
6 | ##Field4 = Field Layer
7 | ##Field5 = Field Layer
8 | ##Field6 = Field Layer
9 | ##Methode=Selectionexhaustive;backward;forward;seqrep
10 | ##Nombre_var = number 5
11 | ##showplots
12 | library(leaps)
13 | X<-cbind(Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]],Layer[[Field6]])
14 | colnames(X)<-c(Field2, Field3, Field4, Field5, Field6)
15 | method=c("exhaustive", "backward", "forward", "seqrep")
16 | methode<-method[Methode + 1]
17 | test1<-regsubsets(Layer[[Field1]]~X,data=Layer,really.big=T,nbest=1,nvmax=Nombre_var, intercept=F, method=methode)
18 | plot(test1, scale="bic")
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/collections/all_maki_icons/svg/entrance-11.svg:
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/collections/qgis_rscripts2/rscripts/Relative_distribution_(distance_covariate).rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Covariate=vector
4 | ##Covariate_name=string mandatory_covariate_name_(no_spaces)
5 | ##x_label=string
6 | ##Plot_name=string
7 | ##Legend_position=string float
8 | library(spatstat)
9 | library(maptools)
10 | library(rpanel)
11 | if (Covariate_name == "") {
12 | rp.messagebox('Covariate name must not be emply!', title = 'oops!')}
13 | else {
14 | S <- Layer
15 | SP <- as(S, "SpatialPoints")
16 | P <- as(SP, "ppp")
17 | r <- Covariate
18 | rp <- as.psp(r)
19 | rdist <- distfun(rp)
20 | plot(rhohat(P, rdist, covname=Covariate_name), xlab= x_label,
21 | legendpos = Legend_position,
22 | legendargs=list(bg="transparent"),
23 | main = Plot_name)
24 | ##showplots
25 | }
26 |
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/collections/all_maki_icons/svg/bakery-15.svg:
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/collections/qgis_rscripts2/rscripts/CAH.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##Field2=Field Layer
5 | ##Field3=Field Layer
6 | ##Field4=Field Layer
7 | ##Field5=Field Layer
8 | ##method= Selection ward;average;single;complete;ward
9 | ##showplots
10 | library(cluster)
11 | Data<-cbind(Layer[[Field1]],Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]])
12 | methodes <-c("ward","average","single","complete","ward")
13 | methode<-methodes[method+1]
14 | methode
15 | cahCSP <- agnes(Data, metric = "euclidean", method = methode)
16 | sortedHeight <- sort(cahCSP$height, decreasing = TRUE)
17 | par(mfrow=c(2,1))
18 | plot(sortedHeight, type = "h", xlab = "Noeuds", ylab = "Niveau d'agregation")
19 | dendroCSP <- as.dendrogram(cahCSP)
20 | plot(dendroCSP, leaflab = "none")
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/collections/qgis_rscripts/rscripts/Distance.rsx:
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1 | ##Basic statistics=group
2 | ##Input_Point_Layer=vector
3 | ##Line_Layer=vector
4 | ##ID_Field=Field Input_Point_Layer
5 | ##Nearest=output vector
6 | library(geosphere)
7 | library(rgeos)
8 |
9 | # Remove the z and m dimensions
10 | PointsXY=st_zm(Input_Point_Layer)
11 | LinesXY=st_zm(Line_Layer)
12 | # Change format
13 | SpatPointsXY=as(PointsXY, "Spatial")
14 | SpatLinesXY=as(LinesXY, "Spatial")
15 |
16 | line=gLineMerge(SpatLinesXY, byid=FALSE, id = NULL)
17 | x=coordinates(SpatPointsXY)
18 | X=dist2Line(x, line, distfun=distHaversine)
19 | matrix=as.matrix(X[,2:3])
20 | X=cbind(X, as.data.frame(SpatPointsXY[[ID_Field]]))
21 | result=SpatialPointsDataFrame(matrix, as.data.frame(X, row.names=NULL))
22 | Nearest=st_set_crs(st_as_sf(result),st_crs(Input_Point_Layer))
23 |
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/collections/qgis_rscripts/rscripts/Alpha_shape.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector point
3 | ##alpha=number 10
4 | ##Output=output vector
5 | # ##load_vector_using_rgdal
6 | library(alphahull)
7 | library(maptools)
8 | alpha_points = ashape(st_coordinates(Layer),alpha=alpha)
9 | # alpha_points = ashape(coordinates(Layer),alpha=alpha)
10 | if (nrow(alpha_points$edges) > 0)
11 | {
12 | l <- list()
13 | for (i in 1:nrow(alpha_points$edges)) {
14 | l[[i]] <- Line(rbind(alpha_points$edges[i, 3:4], alpha_points$edges[i, 5:6]))
15 | }
16 | l <- list(Lines(l, as.character("1")))
17 | sldf <- SpatialLinesDataFrame(SpatialLines(l), data.frame(name ="ashape edges"), match.ID = FALSE)
18 | sf_alpha = st_as_sf(sldf)
19 | Output = sf_alpha
20 | } else {
21 | print("Too small alpha to produce any edges")
22 | Output = Layer
23 | }
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/collections/qgis_rscripts/rscripts/raster-attribute-table.rsx.help:
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1 | {"ALG_DESC": "Create and add an attribute table to a raster layer.",
2 |
3 | "ALG_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
4 |
5 | "ALG_HELP_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
6 |
7 | "ALG_VERSION": "1.0-0",
8 |
9 | "Layer": "An input raster layer.",
10 |
11 | "Levels": "An optional string with the names that you want to be attributed to the levels of the raster passed to Layer. Level names must be separated by a single space, i.e. level1 level2 level3. If left NULL, then level names will be identical to the integer level values.",
12 |
13 | "Output": "An output raster layer."
14 | }
15 |
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/collections/qgis_rscripts2/rscripts/raster-attribute-table.rsx.help:
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1 | {"ALG_DESC": "Create and add an attribute table to a raster layer.",
2 |
3 | "ALG_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
4 |
5 | "ALG_HELP_CREATOR": "Alessandro Samuel-Rosa (alessandrosamuelrosa at gmail.com)",
6 |
7 | "ALG_VERSION": "1.0-0",
8 |
9 | "Layer": "An input raster layer.",
10 |
11 | "Levels": "An optional string with the names that you want to be attributed to the levels of the raster passed to Layer. Level names must be separated by a single space, i.e. level1 level2 level3. If left NULL, then level names will be identical to the integer level values.",
12 |
13 | "Output": "An output raster layer."
14 | }
15 |
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/collections/qgis_rscripts2/rscripts/Selection_with_r2.rsx.help:
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1 | {"Field1 ": "The explained field", "ALG_DESC": "This script does a selection of variables with 6 fields and with the criterion r2. And we can choose the model between: exhaustive serach, forward or backward stepwise, or sequential replacement.\nIt gives the plot of the selection with the criterion r2", "Nombre_var ": "a number which is the number of variables which we want in the best model", "Field3 ": "The second explicative field", "Layer": "Input vector", "Field2 ": "The first explicative field", "ALG_CREATOR": "JEANDENANS L.", "Methode": "a list of methods", "RPLOTS": "the plot of the selection with the criterion r2", "ALG_HELP_CREATOR": "JEANDENANS L.", "Field6 ": "The fifth explicative field", "Field5 ": "The fourth explicative field", "Field4 ": "The third explicative field"}
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/collections/all_maki_icons/svg/dog-park-11.svg:
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/collections/all_maki_icons/svg/theatre-15.svg:
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/collections/qgis_rscripts/rscripts/Selection_Cp.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Explained_field=Field Layer
4 | ##Field1=Field Layer
5 | ##Field2=Field Layer
6 | ##Field3=Field Layer
7 | ##Field4=Field Layer
8 | ##Field5=Field Layer
9 | ##Number_of_variables=number 5
10 | ##Method=selection exhaustive;backward;forward;seqrep
11 | ##showplots
12 | ##load_vector_using_rgdal
13 | library(leaps)
14 | X<-cbind(Layer[[Field1]],Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]])
15 | colnames(X)<-c(Field1, Field2, Field3, Field4, Field5)
16 | methods=c("exhaustive", "backward", "forward", "seqrep")
17 | meth<-methods[Method + 1]
18 | test1<-regsubsets(Layer[[Explained_field]]~X,data=Layer,really.big=T,nbest=1,nvmax=Number_of_variables, intercept=F,method=meth)
19 | plot(test1, scale="Cp")
20 | summary(test1)
21 |
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/collections/qgis_rscripts/rscripts/Selection_with_r2.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Explained_field=Field Layer
4 | ##Field1=Field Layer
5 | ##Field2=Field Layer
6 | ##Field3=Field Layer
7 | ##Field4=Field Layer
8 | ##Field5=Field Layer
9 | ##Number_of_variables=number 5
10 | ##Method=selection exhaustive;backward;forward;seqrep
11 | ##showplots
12 | ##load_vector_using_rgdal
13 | library(leaps)
14 | X<-cbind(Layer[[Field1]],Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]])
15 | colnames(X)<-c(Field1, Field2, Field3, Field4, Field5)
16 | methods=c("exhaustive", "backward", "forward", "seqrep")
17 | meth<-methods[Method + 1]
18 | test1<-regsubsets(Layer[[Explained_field]]~X,data=Layer,really.big=T,nbest=1,nvmax=Number_of_variables, intercept=F,method=meth)
19 | plot(test1, scale="r2")
20 | summary(test1)
21 |
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/collections/qgis_rscripts2/rscripts/Close_neighbor.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Field1=Field Layer
4 | ##distance= number 10
5 | ##Output= output vector
6 | library(spdep)
7 | library(sp)
8 | coordi <- as.matrix(coordinates(Layer))
9 | tram_nb <- dnearneigh(coordi, d1 = 0, d2 = distance, row.names=Layer[[Field1]])
10 | tram_nb<-as.data.frame(card(tram_nb))
11 | tram<-cbind(as.vector(Layer[[Field1]]), tram_nb)
12 | Coord<-cbind()
13 | n<-length(Layer[[Field1]])
14 | for(i in 1:n){
15 | if (tram[i,2]!=0 ){ Coord <-rbind(Coord,cbind(coordi[i,1], coordi[i,2],as.vector(Layer[[Field1]])[i]))
16 | }
17 | }
18 | x<-as.numeric(Coord[,1])
19 | y<-as.numeric(Coord[,2])
20 | X<-cbind(x,y)
21 | matrix<-as.matrix(X)
22 | matrix<-SpatialPointsDataFrame(matrix, as.data.frame(Coord),proj4string=CRS("+init=epsg:2154"))
23 | Output=matrix
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/collections/all_maki_icons/svg/fuel-11.svg:
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/collections/qgis_rscripts/rscripts/Selection_with_r2.rsx.help:
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1 | {"ALG_DESC": "This script does a selection of variables with six fields and with the criterion r2. The following models are available: exhaustive search, forward or backward stepwise, or sequential replacement.\nIt produces a plot of the selection with the criterion r2", "Layer": "Input vector layer", "Explained_field": "The explained field", "Field1": "The first explicative field", "Field2": "The second explicative field", "Field3": "The third explicative field", "Field4": "The fourth explicative field", "Field5": "The fifth explicative field", "Number_of_variables": "The number of variables which we want in the best model", "ALG_CREATOR": "JEANDENANS L.", "Method": "Select from the list of methods", "RPLOTS": "the plot of the selection with the criterion r2", "ALG_HELP_CREATOR": "JEANDENANS L."}
2 |
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/collections/qgis_rscripts/rscripts/Selection_with_r2_adjusted.rsx:
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1 | ##Basic statistics=group
2 | ##Layer=vector
3 | ##Explained_field=Field Layer
4 | ##Field1=Field Layer
5 | ##Field2=Field Layer
6 | ##Field3=Field Layer
7 | ##Field4=Field Layer
8 | ##Field5=Field Layer
9 | ##Number_of_variables=number 5
10 | ##Method=selection exhaustive;backward;forward;seqrep
11 | ##showplots
12 | ##load_vector_using_rgdal
13 | library(leaps)
14 | X<-cbind(Layer[[Field1]],Layer[[Field2]],Layer[[Field3]],Layer[[Field4]],Layer[[Field5]])
15 | colnames(X)<-c(Field1, Field2, Field3, Field4, Field5)
16 | methods=c("exhaustive", "backward", "forward", "seqrep")
17 | meth<-methods[Method + 1]
18 | test1<-regsubsets(Layer[[Explained_field]]~X,data=Layer,really.big=T,nbest=1,nvmax=Number_of_variables, intercept=F,method=meth)
19 | plot(test1, scale="adjr2")
20 | summary(test1)
21 |
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/collections/all_maki_icons/svg/stadium-11.svg:
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/collections/qgis_rscripts/rscripts/Relative_distribution_(distance_covariate).rsx:
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1 | ##Point pattern analysis=group
2 | ##Layer=vector point
3 | ##Covariate=vector line
4 | ##Covariate_name=string covariate_name
5 | ##x_label=string
6 | ##Plot_name=string
7 | ##Legend_position=string float
8 | ##load_vector_using_rgdal
9 | ##showplots
10 | library(spatstat)
11 | library(maptools)
12 | library(rpanel)
13 | if (Covariate_name == ''){
14 | rp.messagebox('Covariate name must not be emply!', title = 'oops!')
15 | } else {
16 | S <- Layer
17 | SP <- as(S, "SpatialPoints")
18 | P <- as(SP, "ppp")
19 | r <- Covariate
20 | rp <- as.psp(r)
21 | rdist <- distfun(rp)
22 | plot(rhohat(P, rdist, covname=Covariate_name), xlab= x_label,
23 | legendpos = Legend_position,
24 | legendargs=list(bg="transparent"),
25 | main = Plot_name)
26 | }
27 |
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