In utah-dwq/wasteloadR: Tools for developing wasteloads

Libraries

library(wqTools)
library(wasteloadR)
library(dataRetrieval)
library(dplyr)
library(writexl)
library(DT)
library(knitr)

Find and extract data

Select a permit ID (optional, this is used to help set the view for finding sites)

Use the permit selection gadget, then update permit_id. If you already know your permit ID, you can skip selectPermit().

selectPermit()
pid="UT0021741"
permit_location=subset(wasteloadR::permits_coords, permit_id==pid)
permit_location

Find and select sites

Use the findSites() gadget to find and select permit, monitoring location, and

qsites=findSites()
knitr::kable(qsites)

Location map

map_sites=within(qsites, {
    lab=paste0(
        '<p>',
        '<br />', "Site ID: ", site_id,
        '<br />', "Site name: ", site_name,
        '<br />', "Site type: ", type,
        '<br />', "Visit count: ", visit_count)
    color=NA
    color[type=="Monitoring location"]="purple"
    color[type=="Permit"]="orange"
    color[type=="Gauge"]="blue"
})
baseMap() %>%
    addCircleMarkers(data=map_sites, lat=~lat, lng=~long, options = pathOptions(pane = "markers"), group="Sites",
        color = ~color, opacity=0.8, layerId=~site_id,
        label = lapply(map_sites$lab, HTML) # crashes if only one site is selected for lapply
    ) %>%  
    addLegend("topright", colors=c("purple","orange","blue"), labels=c("Monitoring location", "Permit", "USGS gauge")) %>%
    fitBounds(min(map_sites$long), min(map_sites$lat), max(map_sites$long), max(map_sites$lat))

Read data

echo_data=readECHO_ec(p_id="UT0021741", start_date="01/01/2000", end_date="08/05/2020", print=F)

wqp_sites=readWQP(type="sites", siteid=subset(qsites, type=="Monitoring location")$site_id)
wqp_res=readWQP(type="result", siteid=subset(qsites, type=="Monitoring location")$site_id)
wqp_data=merge(wqp_sites, wqp_res)


whatNWISdata(siteNumbers="10171000", service="uv")
gauge_data=readNWISdv(siteNumbers="10171000", parameterCd="00060") %>% rename(discharge_cfs=X_00060_00003, disch_code=X_00060_00003_cd)# Parameter code 00060 = discharge in cfs. see also ?readNWISuv() to read high frequency values

See https://cran.r-project.org/web/packages/dataRetrieval/vignettes/dataRetrieval.html#surface-water-measurement-data for more info on reading NWIS data

Write data to Excel (optional)

writexl::write_xlsx(
    list(
        'echo_data' = echo_data,
        'wqp_data' = wqp_data,
        'gauge_data' = gauge_data
    ),
    path = "C:\\Users\\jvander\\Documents\\R\\exported_data.xlsx", format_headers=F, col_names=T
)

Water quality summary statistics (thinking of also making a gadget option(s) for this)

Subset data to desired sites, dates, parameters, etc.

summ_data=subset(wqp_data, 
    MonitoringLocationIdentifier %in% c("UTAHDWQ_WQX-4990060", "UTAHDWQ_WQX-4990070", "UTAHDWQ_WQX-4990080") & 
    CharacteristicName %in% c("Total suspended solids","Phosphate-phosphorus","Total dissolved solids","pH","Temperature, water")
)

Check parameters, units, fractions

Table of params, activity types, units, & fractions.

table_data=unique(data.frame(summ_data[,c("CharacteristicName","ActivityTypeCode","ResultSampleFractionText","ResultMeasure.MeasureUnitCode")]))
knitr::kable(table_data[order(table_data$CharacteristicName),])

Convert units if necessary. Cleanup stuff (e.g. remove lab pH values). Assign seasons.

Fill non-detects

knitr::kable(table(summ_data$ResultDetectionConditionText, exclude=NULL))
summ_data=within(summ_data, {
    ResultMeasureValue=ifelse(ResultDetectionConditionText %in% "Not Detected", DetectionQuantitationLimitMeasure.MeasureValue/2, ResultMeasureValue)
})

Summary stats (using dplyr group_by() %>% summarize() pattern)

wasteload_stats=summ_data %>% 
    group_by(MonitoringLocationIdentifier, MonitoringLocationName, SampleCollectionMethod.MethodName, CharacteristicName, ResultSampleFractionText, ResultMeasure.MeasureUnitCode) %>%
    summarize(min=min(ResultMeasureValue, na.rm=T), median=median(ResultMeasureValue, na.rm=T), mean=mean(ResultMeasureValue, na.rm=T), max=max(ResultMeasureValue, na.rm=T), 
        pctile20=quantile(ResultMeasureValue, 0.20, na.rm=T), pctile80=quantile(ResultMeasureValue, 0.80, na.rm=T), samp_count=dplyr::n(), .groups="drop")

DT::datatable(data.frame(lapply(wasteload_stats, as.factor)),
    selection='none', rownames=FALSE, filter="top",
    options = list(dom = 't', paging = FALSE)
) %>%
DT::formatRound(columns=c("min","pctile20","median","mean","pctile80","max"), digits=2) %>%
DT::formatRound(columns=c("samp_count"), digits=0)

Low flow statistics (7Q10)

Figures???

utah-dwq/wasteloadR documentation built on Oct. 19, 2024, 11:04 p.m.