added political boundaries
adjusted data dir added steps and example to readme
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@ -3,7 +3,7 @@ library(nhdplusTools)
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library(sf)
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library(sf)
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# set data dir
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# set data dir
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nhdplusTools_data_dir(dir = "data")
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nhdplusTools_data_dir(dir = "data/hydrologic")
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# load extent of map
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# load extent of map
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extent <- read.csv(file = "extent.csv")
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extent <- read.csv(file = "extent.csv")
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@ -1,9 +1,16 @@
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# load libraries
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# load libraries
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library(sf)
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library(sf)
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# load and crop data
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# load and crop hydrologic data
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data <- list(NULL)
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data <- list(NULL)
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sf_use_s2(FALSE)
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sf_use_s2(FALSE)
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for (layer in layers){
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for (layer in layers){
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data[[layer]] <- st_crop(st_read(paste0(nhdplusTools_data_dir(),"/data.gpkg"), layer = layer), y = extent_bbox)
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data[[layer]] <- st_crop(st_read(paste0(nhdplusTools_data_dir(),"/data.gpkg"), layer = layer), y = extent_bbox)
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}
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}
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# load political boundaries
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political <- list(NULL)
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political_boundaries <- list.files(path = "data/political_boundaries")
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for (boundary in political_boundaries) {
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political[[boundary]] <- st_read(paste0("data/political_boundaries/", boundary))
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}
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@ -42,6 +42,5 @@ plot(sf::st_geometry(data$NHDFlowline),
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xlim = c(extent$longitude_min, extent$longitude_max),
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xlim = c(extent$longitude_min, extent$longitude_max),
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ylim = c(extent$latitude_min, extent$latitude_max),
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ylim = c(extent$latitude_min, extent$latitude_max),
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add = TRUE)
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add = TRUE)
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# finish saving figure
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# finish saving figure
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dev.off()
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dev.off()
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10
README.md
10
README.md
@ -1,3 +1,11 @@
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# pretty_rivers
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# pretty_rivers
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Downloads USGS data for a given location and makes a pretty map of the rivers. The width of the rivers is proportional to the area of land that the section of river drains.
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Downloads USGS data for a given location and makes a pretty map of the rivers. The width of the rivers is proportional to the area of land that the section of river drains.
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## To run :
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1. Clone the repository
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2. Edit the extent of the map you want to produce in `extent.csv`. Include lat/long coordinates of two opposite corners (i.e. top-left and bottom-right).
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3. Run the scripts. Downloading the data and loading the data will take the longest. The whole of Wisconsin downloads ~12GB of data
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## Example figure
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