View source: R/ch_get_url_data.R
ch_get_url_data | R Documentation |
Accesses data sets, via a url the first time, saves them locally, then accesses them locally after the first time the script is executed.
ch_get_url_data(gd_url, gd_filename, quiet = FALSE)
gd_url |
url for accessing data set |
gd_filename |
name of file on local drive, including full path |
quiet |
Optional. If |
Returns a data frame (from a .csv file), a raster
object (from a .tif file),
or an sf
object (from a GeoJSON file).
Dan Moore
# example not tested automatically as multiple large data files are downloaded # Tested using files in the Upper Penticton Creek # zenodo repository https://zenodo.org/record/4781469 library(ggplot2) library(raster) # create directory to store data sets dir_name <- tempdir(check = FALSE) if (!dir.exists(dir_name)) { dir.create(dir_name) } # test with soil moisture data in csv format sm_fn <- file.path(dir_name, "sm_data.csv") sm_url <- "https://zenodo.org/record/4781469/files/sm_data.csv" sm_data <- ch_get_url_data(sm_url, sm_fn) head(sm_data) # test with tif/tiff file containing a dem ra_fn <- file.path(dir_name, "gs_dem25.tif") ra_url <- "https://zenodo.org/record/4781469/files/gs_dem25.tif" ra_data <- ch_get_url_data(ra_url, ra_fn) plot(ra_data) # test with GeoJSON gs_fn <- file.path(dir_name, "gs_soilmaps.GeoJSON") gs_url <- "https://zenodo.org/record/4781469/files/gs_soilmaps.GeoJSON" gs_data <- ch_get_url_data(gs_url, gs_fn) ggplot(gs_data) + geom_sf(aes(fill = new_key)) + labs(fill = "Soil class", x = "UTM Easting (m)", y = "UTM Northing (m)") + coord_sf(datum = 32611) + theme_bw()
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