knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE )
In this example we access a single variable for the Calapooia River using sc_get_data
function. We then use the nhdplusTools
library to grab flowlines and watershed for the Calapooia, plot the selected StreamCat metric for the Calapooia River and show the watershed.
library(StreamCatTools) start_comid = 23763517 nldi_feature <- list(featureSource = "comid", featureID = start_comid) flowline_nldi <- nhdplusTools::navigate_nldi(nldi_feature, mode = "UT", data_source = "flowlines", distance=5000) # get StreamCat metrics comids <- paste(as.integer(flowline_nldi$UT_flowlines$nhdplus_comid), collapse=",",sep="") df <- sc_get_data(metric='pctimp2011', aoi='cat', comid=comids) flowline_nldi <- flowline_nldi$UT_flowlines flowline_nldi$PCTIMP2011CAT <- df$pctimp2011cat[match(flowline_nldi$nhdplus_comid, df$comid)] basin <- nhdplusTools::get_nldi_basin(nldi_feature = nldi_feature)
library(mapview) mapview::mapviewOptions(fgb=FALSE) mapview::mapview(basin, alpha.regions=.08) + mapview::mapview(flowline_nldi, zcol = "PCTIMP2011CAT", legend = TRUE)
In this example we demonstrate a data 'mashup' by grabbing NRSA data from the EPA National Aquatic Resource Surveys (NARS) website directly in R, pull particular StreamCat metrics for sites using sc_get_data
, and compare landscape metrics with other NRSA metrics
nrsa <- readr::read_csv("https://www.epa.gov/sites/production/files/2015-09/siteinfo_0.csv") dplyr::glimpse(nrsa) # Promote data frame to sf spatial points data frame nrsa_sf <- sf::st_as_sf(nrsa, coords = c("LON_DD83", "LAT_DD83"), crs = 4269) # Get COMIDs using nhdplusTools package # nrsa$COMID<- NA # for (i in 1:nrow(nrsa_sf)){ # print (i) # nrsa_sf[i,'COMID'] <- discover_nhdplus_id(nrsa_sf[i,c('geometry')]) # } load(system.file("extdata", "sample_nrsa_data.rda", package="StreamCatTools")) # get particular StreamCat data for all these NRSA sites # nrsa_sf$COMID <- as.character(nrsa_sf$COMID) comids <- nrsa_sf$COMID comids <- comids[!is.na(comids)] comids <- comids[c(1:700)] comids <- paste(comids,collapse=',') df <- sc_get_data(metric='pctcrop2006', aoi='ws', comid=comids) # glimpse(df) df$COMID <- as.integer(df$comid) nrsa_sf <- dplyr::left_join(nrsa_sf, df, by='COMID')
# download mmi from NARS web page library(dplyr) library(ggplot2) mmi <- readr::read_csv("https://www.epa.gov/sites/production/files/2015-09/bentcond.csv") # dplyr::glimpse(mmi) # join mmi to NARS info data frame with StreamCat PctCrop metric nrsa_sf <- dplyr::left_join(nrsa_sf, mmi[,c('SITE_ID','BENT_MMI_COND')], by='SITE_ID') bxplt <- nrsa_sf %>% tidyr::drop_na(BENT_MMI_COND) %>% ggplot2::ggplot(aes(x=pctcrop2006ws, y=BENT_MMI_COND))+ ggplot2::geom_boxplot() suppressWarnings(print(bxplt))
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