Getting Started • StreamCatTools

Installing and loading StreamCatTools

To install, currently you need to install from GitHub using devtools

library(devtools)
install_github('USEPA/StreamCatTools')

After installing load the library

library(StreamCatTools)

Background

The StreamCatTools package was designed to simplify the use of StreamCat data in R, leveraging the new API for StreamCat.

StreamCat API

We can pull data into R from the StreamCat API by simply passing a URL to extract from json. We have to hard-wire parameters though and are limited in the number of records returned through a GET request.

res <- jsonlite::fromJSON("https://api.epa.gov/StreamCat/streams/metrics?name=fert&areaOfInterest=cat&comid=179")
res$items
#>   fertws fertcat comid
#> 1  1.438   1.438   179

List API parameters

List StreamCat parameters: Get a list of available StreamCat values for certain parameters using the sc_get_params function via the API

region_params <- sc_get_params(param='areaOfInterest')

name_params <- sc_get_params(param='metric_names')

print(paste0('region parameters are: ', paste(region_params,collapse = ', ')))
#> [1] "region parameters are: cat, catrp100, other, ws, wsrp100"
print(paste0('A selection of available StreamCat metrics include: ',paste(name_params[1:10],collapse = ', ')))
#> [1] "A selection of available StreamCat metrics include: agkffact, al2o3, bankfulldepth, bankfullwidth, bfi, canaldens, cao, cbnf, chem, clay"

We can also see what metrics are available for what areas of interest and what years using the sc_get_params function (which returns a tibble of information about StreamCat metrics):

var_info <- sc_get_params(param='variable_info')
head(var_info)
#> # A tibble: 6 × 11
#>   category     metric aoi   year  short_description long_description units  dsid
#>   <chr>        <chr>  <chr> <chr> <chr>             <chr>            <chr> <dbl>
#> 1 Anthropogen… NABD_… Cat,… NA    NABD Dam Density  Density of geor… Coun…    33
#> 2 Anthropogen… NABD_… Cat,… NA    NABD NID Reservo… Volume all rese… Cubi…    33
#> 3 Anthropogen… NABD_… Cat,… NA    NABD Normal Rese… Volume all rese… Cubi…    33
#> 4 Natural      Preci… Cat,… NA    Surplus Precipit… This dataset re… Kilo…    75
#> 5 Natural      Rckde… Cat,… NA    Mean Bedrock Dep… Mean depth (cm)… Cent…    56
#> 6 Soils        agkff… Cat,… NA    Ag Soil Erodibil… Mean of STATSGO… Unit…    28
#> # ℹ 3 more variables: dataset <chr>, source_name <chr>, source_URL <chr>

We can look up the display name or names for a metric using the sc_fullname function via the API

metric='pcthbwet2011'
fullname <- sc_fullname(metric)
fullname
#> [1] "pcthbwet2011"

metric='pctdecid2019,fert'
fullname <- sc_fullname(metric)
fullname
#> [1] "pctdecid2019" "fert"

We can additionally get a data frame of state FIPS codes, abbreviations and names, and the same information for counties as well using sc_get_params:

states <- sc_get_params(param='state')
head(states)
#>   st_fips st_abbr     st_name
#> 1      01      AL     Alabama
#> 2      04      AZ     Arizona
#> 3      05      AR    Arkansas
#> 4      06      CA  California
#> 5      08      CO    Colorado
#> 6      09      CT Connecticut

counties <- sc_get_params(param='county')
head(counties)
#>    fips state    county_name
#> 1 01001    AL Autauga County
#> 2 01003    AL Baldwin County
#> 3 01005    AL Barbour County
#> 4 01007    AL    Bibb County
#> 5 01009    AL  Blount County
#> 6 01011    AL Bullock County

Get data for COMIDs

In this example we access several variables, for several areas of interest, and for several COMIDs using the sc_get_data function. Loads data into a tibble we can view.

df <- sc_get_data(metric='pcturbmd2006,damdens,tridens', aoi='rp100cat,cat,ws', comid='179,1337,1337420')
knitr::kable(df)

comid	pcturbmd2006cat	pcturbmd2006ws	damdensws
179	0.00	0.00	0.0000
1337	0.07	0.07	0.0000
1337420	0.00	0.01	0.0329

Get data for county

In this example we access a couple variables at the watershed scale for the area of interest of a county (Benton County in this case) using the sc_get_data function.

df <- sc_get_data(metric='pctwdwet2006', aoi='ws', county='41003')
knitr::kable(head(df))

comid	pctwdwet2006ws
23762961	1.11
23762985	0.84
23762915	1.14
23762959	1.14
23762967	1.10
23762761	1.72

Get all metrics for COMIDs or an Area of Interest

We can also get all StreamCat metrics for a set of COMIDs or an area of interest. Please do not request all metrics for conus in order not to overload requests to the server.

df <- sc_get_data(comid='179', aoi='cat', metric='all')
knitr::kable(head(df))

comid	pctburnarea1984cat	pctburnarea1985cat	pctburnarea1986cat	pctburnarea1987cat	pctmodsev1993cat	pcthighsev1993cat	pctincvegresp1993cat	pctnonprocmask1993cat	pctundsev1996cat	pctlowsev1996cat	pctburnarea1988cat	pctburnarea1989cat	pctburnarea1990cat	pctburnarea1991cat	pctburnarea1992cat	pctburnarea1993cat	pctburnarea1994cat	pctburnarea1995cat	pctburnarea1996cat	pctburnarea1997cat	pctburnarea1998cat	pctburnarea1999cat	pctburnarea2000cat	pctburnarea2001cat	pctburnarea2002cat	pctburnarea2003cat	pctburnarea2004cat	pctburnarea2005cat	pctburnarea2006cat	pctburnarea2007cat	pctburnarea2008cat	pctburnarea2009cat	pctburnarea2010cat	pctburnarea2011cat	pctburnarea2012cat	pctburnarea2013cat	pctburnarea2014cat	pctburnarea2015cat	pctburnarea2016cat	pctburnarea2017cat	pctburnarea2018cat	pctlowsev1993cat	pctmodsev1996cat	pcthighsev1996cat	pctincvegresp1996cat	pctnonprocmask1996cat	pctundsev1998cat	pctlowsev1998cat	pctmodsev1998cat	pcthighsev1998cat	pctincvegresp1998cat	pctnonprocmask1998cat	pctundsev2004cat	pctlowsev2004cat	pctmodsev2004cat	pcthighsev2004cat	pctincvegresp2004cat	pctnonprocmask2004cat	pctundsev2007cat	pctlowsev2007cat	pctmodsev2007cat	pcthighsev2007cat	pctincvegresp2007cat	pctnonprocmask2007cat	pctundsev2008cat	pctlowsev2008cat	pctmodsev2008cat	pcthighsev2008cat	pctincvegresp2008cat	pctnonprocmask2008cat	pctundsev2010cat	pctlowsev2010cat	pctmodsev2010cat	pcthighsev2010cat	pctincvegresp2010cat	pctnonprocmask2010cat	pctundsev2011cat	pctlowsev2011cat	pctmodsev2011cat	pcthighsev2011cat	pctincvegresp2011cat	pctnonprocmask2011cat	pctundsev2016cat	pctlowsev2016cat	pctundsev1984cat	pctlowsev1984cat	pctmodsev1984cat	pcthighsev1984cat	pctincvegresp1984cat	pctnonprocmask1984cat	septiccat	pctundsev1986cat	pctlowsev1986cat	pctmodsev1986cat	pcthighsev1986cat	pctincvegresp1986cat	pctnonprocmask1986cat	pctundsev1989cat	pctlowsev1989cat	pctmodsev1989cat	pcthighsev1989cat	pctincvegresp1989cat	pctnonprocmask1989cat	pctundsev1994cat	pctlowsev1994cat	pctmodsev1994cat	pcthighsev1994cat	pctincvegresp1994cat	pctnonprocmask1994cat	pctundsev1997cat	pctlowsev1997cat	pctmodsev1997cat	pcthighsev1997cat	pctincvegresp1997cat	pctnonprocmask1997cat	pctundsev2001cat	pctlowsev2001cat	pctmodsev2001cat	pcthighsev2001cat	pctincvegresp2001cat	pctnonprocmask2001cat	pctundsev2009cat	pctlowsev2009cat	pctmodsev2009cat	pcthighsev2009cat	pctincvegresp2009cat	pctnonprocmask2009cat	pctundsev2012cat	pctlowsev2012cat	pctmodsev2012cat	pcthighsev2012cat	pctincvegresp2012cat	pctnonprocmask2012cat	pctundsev2018cat	pctlowsev2018cat	pctmodsev2018cat	pcthighsev2018cat	pctincvegresp2018cat	pctnonprocmask2018cat	pctagslphigh2006cat	pctagslpmid2008cat	pctagslpmid2004cat	pctagslphigh2004cat	pctagslpmid2006cat	pctagslphigh2008cat	pctagslpmid2011cat	pctagslphigh2011cat	pctagdrainagecat	nsurpcat	nanicat	pctbl2008cat	pctconif2008cat	pctcrop2008cat	pctdecid2008cat	pctgrs2008cat	pcthay2008cat	pcthbwet2008cat	pctice2008cat	pctmxfst2008cat	pctow2008cat	pctshrb2008cat	pcturbhi2008cat	pcturblo2008cat	pcturbmd2008cat	pcturbop2008cat	pctwdwet2008cat	pctundsev2006cat	pctlowsev2006cat	pctmodsev2006cat	pcthighsev2006cat	pctincvegresp2006cat	pctnonprocmask2006cat	rockncat	pctundsev2015cat	pctlowsev2015cat	pctmodsev2015cat	pcthighsev2015cat	pctincvegresp2015cat	pctnonprocmask2015cat	wdrw_ldcat	pctagslpmid2001cat	pctagslphigh2001cat	pctimp2001cat	pctimp2004cat	pctimp2006cat	pctimp2008cat	pctimp2011cat	pctimp2013cat	pctimp2016cat	pctimp2019cat	pctbl2004cat	pctconif2004cat	pctcrop2004cat	pctdecid2004cat	pctgrs2004cat	pcthay2004cat	pcthbwet2004cat	pctice2004cat	pctmxfst2004cat	pctow2004cat	pctshrb2004cat	pcturbhi2004cat	pcturblo2004cat	pcturbmd2004cat	pcturbop2004cat	pctwdwet2004cat	pctbl2019cat	pctconif2019cat	pctcrop2019cat	pctdecid2019cat	pctgrs2019cat	pcthay2019cat	pcthbwet2019cat	pctice2019cat	pctmxfst2019cat	pctow2019cat	pctshrb2019cat	pcturbhi2019cat	pcturblo2019cat	pcturbmd2019cat	pcturbop2019cat	pctwdwet2019cat	wetindexcat	pctundsev1987cat	pctlowsev1987cat	pctmodsev1987cat	pcthighsev1987cat	pctincvegresp1987cat	pctnonprocmask1987cat	pctundsev1990cat	pctlowsev1990cat	pctmodsev1990cat	pcthighsev1990cat	pctincvegresp1990cat	pctnonprocmask1990cat	pctundsev1991cat	pctlowsev1991cat	pctmodsev1991cat	pcthighsev1991cat	pctincvegresp1991cat	pctnonprocmask1991cat	pctundsev1995cat	pctlowsev1995cat	pctmodsev1995cat	pcthighsev1995cat	pctincvegresp1995cat	pctnonprocmask1995cat	pctundsev1999cat	pctlowsev1999cat	pctmodsev1999cat	pcthighsev1999cat	pctincvegresp1999cat	pctnonprocmask1999cat	pctundsev2002cat	pctlowsev2002cat	pctmodsev2002cat	pcthighsev2002cat	pctincvegresp2002cat	pctnonprocmask2002cat	pctundsev2013cat	pctlowsev2013cat	pctmodsev2013cat	pcthighsev2013cat	pctincvegresp2013cat	pctnonprocmask2013cat	pctundsev2014cat	pctlowsev2014cat	pctundsev2003cat	pctlowsev2003cat	pctmodsev2003cat	pcthighsev2003cat	pctincvegresp2003cat	pctnonprocmask2003cat	pctundsev2005cat	pctlowsev2005cat	pctmodsev2005cat	pcthighsev2005cat	pctincvegresp2005cat	pctnonprocmask2005cat	pctmodsev2014cat	pcthighsev2014cat	pctincvegresp2014cat	pctnonprocmask2014cat	pctmodsev2016cat	pcthighsev2016cat	pctincvegresp2016cat	pctnonprocmask2016cat	pctundsev2017cat	pctlowsev2017cat	pctmodsev2017cat	pcthighsev2017cat	pctincvegresp2017cat	pctnonprocmask2017cat	waterinputcat	wwtpmajordenscat	wwtpminordenscat	wwtpalldenscat	pctimpslphigh2001cat	pctimpslphigh2004cat	pctimpslphigh2006cat	pctimpslphigh2008cat	pctimpslphigh2011cat	pctimpslphigh2013cat	pctimpslphigh2016cat	pctimpslphigh2019cat	pctimpslpmid2001cat	pctimpslpmid2004cat	pctimpslpmid2006cat	pctimpslpmid2008cat	pctimpslpmid2011cat	pctimpslpmid2013cat	pctimpslpmid2016cat	pctimpslpmid2019cat	pctundsev2000cat	pctlowsev2000cat	pctmodsev2000cat	pcthighsev2000cat	pctincvegresp2000cat	pctnonprocmask2000cat	sio2cat	compstrgthcat	pctfire2001cat	agkffactcat	pctwdwet2011cat	coalminedenscat	elevcat	pcturbop2006cat	pcturbhi2001cat	hydcat	no32008cat	pctalluvcoastcat	fe2o3cat	pcturblo2011cat	cbnfcat	pctconif2006cat	pctgrs2001cat	pctfire2008cat	pctsallakecat	pctcoastcrscat	ncat	pctfire2003cat	pctglactilloamcat	pctfire2010cat	omcat	pctwatercat	pctow2011cat	pctice2011cat	pctwdwet2001cat	tmean2008cat	pctfire2000cat	pctcrop2001cat	pctextruvolcat	pcturbmd2011cat	pctfire2006cat	pctow2001cat	pcthbwet2001cat	na2ocat	pctglactilcrscat	tmean2009cat	damdenscat	pctdecid2011cat	sedcat	chemcat	manurecat	pctow2006cat	pctbl2001cat	sn2008cat	pctnonagintrodmanagvegcat	pcthydriccat	pctfire2005cat	pctconif2011cat	pctcarbresidcat	tmean8110cat	caocat	pctbl2011cat	kffactcat	pctglaclakecrscat	pctmxfst2011cat	k2ocat	pctice2006cat	pcteolfinecat	pcturbhi2006cat	precip8110cat	pcthay2006cat	pctsiliciccat	pctwdwet2006cat	pctfire2007cat	rdcrscat	nabd_denscat	pctice2001cat	damnidstorcat	precip2009cat	pcturbmd2006cat	tridenscat	pctglactilclaycat	wtdepcat	pestic1997cat	pcteolcrscat	pctmxfst2001cat	permcat	tempcat	popden2010cat	pcturbhi2011cat	al2o3cat	pcturblo2001cat	bficat	pctmxfst2006cat	conncat	pcthay2011cat	tmax8110cat	inorgnwetdep2008cat	nabd_nrmstorcat	huden2010cat	scat	p2o5cat	pctshrb2006cat	damnrmstorcat	nh42008cat	mgocat	pctgrs2011cat	pctalkintruvolcat	pctglaclakefinecat	pcturblo2006cat	pctshrb2011cat	canaldenscat	pcturbmd2001cat	habtcat	nabd_nidstorcat	pctbl2006cat	fertcat	npdesdenscat	tmin8110cat	pcthay2001cat	minedenscat	pctconif2001cat	superfunddenscat	pcturbop2011cat	pctfire2002cat	pcthbwet2011cat	rdcrsslpwtdcat	pctdecid2006cat	pctshrb2001cat	precip2008cat	sandcat	pctfire2009cat	claycat	pcturbop2001cat	pctdecid2001cat	pctcrop2006cat	pctnoncarbresidcat	runoffcat	pctgrs2006cat	pctcolluvsedcat	rckdepcat	hydrlcondcat	pcthbwet2006cat	rddenscat	pctcrop2011cat	pctconif2016cat	pctfire2004cat	pctcrop2016cat	pctdecid2016cat	pctgrs2016cat	pcthay2016cat	pcthbwet2016cat	pctice2016cat	pctmxfst2016cat	pctow2016cat	pctshrb2016cat	pcturbhi2016cat	pcturblo2016cat	pcturbmd2016cat	pcturbop2016cat	pctwdwet2016cat	sw_fluxcat	pctundsev1985cat	pctlowsev1985cat	pctmodsev1985cat	pcthighsev1985cat	pctincvegresp1985cat	pctnonprocmask1985cat	pctundsev1992cat	pctlowsev1992cat	pctmodsev1992cat	pcthighsev1992cat	pctincvegresp1992cat	pctnonprocmask1992cat	pctundsev1988cat	pctlowsev1988cat	pctmodsev1988cat	pcthighsev1988cat	pctbl2013cat	pctconif2013cat	pctcrop2013cat	pctdecid2013cat	pctgrs2013cat	pcthay2013cat	pcthbwet2013cat	pctice2013cat	pctmxfst2013cat	pctow2013cat	pctshrb2013cat	pcturbhi2013cat	pcturblo2013cat	pcturbmd2013cat	pcturbop2013cat	pctwdwet2013cat	pctbl2016cat	pctincvegresp1988cat	pctnonprocmask1988cat	pctundsev1993cat	pctfrstloss2001cat	pctfrstloss2002cat	pctfrstloss2003cat	pctfrstloss2004cat	pctfrstloss2005cat	pctfrstloss2006cat	pctfrstloss2007cat	pctfrstloss2008cat	pctfrstloss2009cat	pctfrstloss2010cat	pctfrstloss2011cat	pctfrstloss2012cat	pctfrstloss2013cat	precip9120cat	tmax9120cat	tmean9120cat	tmin9120cat	pctagslpmid2013cat	pctagslphigh2013cat	pctagslpmid2016cat	pctagslphigh2016cat	pctagslpmid2019cat	pctagslphigh2019cat
179	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1.117	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3.17	3.17	0	3.17	0	3.17	0	0	2102.561	1675.837	0	4.89	1.52	3.87	0.05	1.09	1.04	0	49.06	0.05	0.91	0	0	0	0	37.52	0	0	0	0	0	0	88.2995	0	0	0	0	0	0	0.4413	3.17	0	0	0	0	0	0	0	0	0	0	4.89	1.52	3.85	0.13	1.09	1.09	0	49.04	0.13	0.89	0	0	0	0	37.39	0	4.91	1.54	3.7	0.35	1.09	0.71	0	49.54	0	0.25	0	0	0	0	37.9	878.2494	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1.34e-05	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	64.3313	169.6366	0	NA	37.57	0	196.1233	0	0	0.998	6.3799	0	5.5091	0	0.0506	4.89	0.23	0	0	0	8e-04	0	100	0	12.28701	0	0.1	0	36.89	5.032428	0	1.52	0	0	0	0	1.95	1.0772	0	4.852966	0	3.8	0.9958425	0.972	0	0.08	0	342.1452	0	0	0	4.89	0	4.885117	7.9453	0	NA	0	49.16	1.0557	0	0	0	1059.078	1.09	0	37.29	0	0	0	0	0	1108.386	0	0	0	49.21784	40.52718	0	48.76	5.130848	0.9983122	8.874548	0	10.276	0	52	49.01	1	1.09	10.59572	2.4653	0	5.910425	0.0161	0.1545	1.01	0	1.3213	2.2883	0.13	0	0	0	0.81	0	0	0.997	0	0	1.438	0	-0.8263614	1.22	0	4.89	0	0	0	0.94	0	3.8	0.73	1254.539	22.33772	0	14.35371	0	3.82	1.52	0	646	0.08	0	134.9983	0.2044	1.24	0.5276	1.52	4.91	0	1.54	3.87	0.05	1.09	0.71	0	49.67	0	0.25	0	0	0	0	37.9	133875	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4.89	1.52	3.77	0.05	1.09	0.76	0	49.7	0	0.38	0	0	0	0	37.85	0	0	0	0	0.71	0	0	0.35	0.03	0	0	0	0	0	0	0.23	0	1152.678	10.56534	5.015274	-0.5362276	3.17	0	3.17	0	3.17	0

Get NLCD data

In this example we access National Land Cover Dataset (NLCD) data for 2001, just at the catchment level for several COMIDs using the sc_nlcd function. Loads data into a tibble we can view.

df <- sc_nlcd(year='2001', aoi='cat',
              comid='179,1337,1337420')
knitr::kable(df)

comid	pctgrs2001cat	pctwdwet2001cat	pctcrop2001cat	pctow2001cat	pcthbwet2001cat	pctbl2001cat	pctmxfst2001cat	pcturblo2001cat	pcthay2001cat	pctconif2001cat	pctshrb2001cat	pcturbop2001cat	pctdecid2001cat
179	0.23	36.89	1.52	0.0	1.95	0.00	48.76	0.00	1.22	4.89	0.73	0.00	3.82
1337	1.60	20.75	0.00	0.1	0.56	0.28	51.55	0.24	2.83	5.65	0.66	3.98	11.79
1337420	0.87	0.95	0.00	0.0	1.64	0.00	3.77	0.40	0.11	4.06	3.38	1.77	83.06

We can also pass a couple years for a different area of interest for another region like a county.

df <- sc_nlcd(year='2006, 2019', aoi='ws',
              county='41003')
knitr::kable(head(df))

comid	pctbl2019ws	pctconif2019ws	pctcrop2019ws	pctdecid2019ws	pctgrs2019ws	pcthay2019ws	pcthbwet2019ws	pctmxfst2019ws	pctow2019ws	pctshrb2019ws	pcturbhi2019ws	pcturblo2019ws	pcturbmd2019ws	pcturbop2019ws	pctwdwet2019ws	pctmxfst2006ws	pctcrop2006ws	pcthay2006ws	pcturbop2006ws	pctbl2006ws	pctconif2006ws	pctwdwet2006ws	pctgrs2006ws	pcturbhi2006ws	pcturbmd2006ws	pctshrb2006ws	pcthbwet2006ws	pctdecid2006ws	pcturblo2006ws	pctow2006ws
23762961	0.01	36.68	4.65	1.03	1.51	33.28	3.44	7.32	0.08	3.99	0.10	1.69	0.30	3.46	2.46	5.85	3.00	35.58	3.44	0.01	26.93	1.11	5.74	0.09	0.23	10.78	4.79	0.66	1.67	0.11
23762985	0.00	51.52	1.58	1.03	2.21	20.06	0.84	9.14	0.04	6.06	0.05	1.46	0.24	4.60	1.18	7.16	1.05	21.16	4.62	0.01	37.51	0.84	8.44	0.04	0.19	15.78	1.22	0.51	1.44	0.04
23762915	0.01	55.83	0.01	1.58	2.74	6.60	0.40	18.61	0.05	7.45	0.01	0.60	0.14	4.65	1.33	19.10	0.01	6.87	4.63	0.01	49.77	1.14	4.71	0.01	0.12	11.15	0.58	1.27	0.58	0.05
23762959	0.01	36.29	4.66	1.02	1.50	33.72	3.51	7.24	0.09	3.95	0.10	1.68	0.30	3.43	2.51	5.79	3.02	36.01	3.41	0.01	26.64	1.14	5.68	0.09	0.23	10.66	4.88	0.66	1.66	0.12
23762967	0.01	38.81	3.54	1.04	1.60	31.71	3.49	7.53	0.09	4.30	0.09	1.65	0.29	3.40	2.47	5.98	2.42	33.49	3.39	0.01	28.48	1.10	6.20	0.08	0.23	11.36	4.86	0.64	1.64	0.13
23762761	0.00	57.44	0.40	1.90	3.08	2.45	0.27	20.77	0.01	6.65	0.00	0.31	0.07	4.89	1.76	19.15	0.05	2.84	4.91	0.00	38.13	1.72	7.95	0.00	0.04	23.53	0.31	1.06	0.29	0.01

Get COMIDs

In this example we use the sc_get_comid function to find COMIDs for USGS stream gages we load into R. We use a .csv file with coordinate columns and a known coordinate reference system.

gages = readr::read_csv(system.file("extdata","Gages_flowdata.csv", package = "StreamCatTools"),show_col_types = FALSE)
# we'll just grab a few variables to keep things simple
gages <- gages[,c('SOURCE_FEA','STATION_NM','LON_SITE','LAT_SITE')]
gages_coms <- sc_get_comid(gages, xcoord='LON_SITE',                   ycoord='LAT_SITE', crsys=4269)

# Add the COMID we found back to gages data frame
gages$COMID <- strsplit(gages_coms, ",")[[1]]
df <- sc_get_data(metric='huden2010', aoi='ws', comid=gages_coms)
df$COMID <- as.character(df$comid)
gages <- dplyr::left_join(gages, df, by='COMID')
knitr::kable(head(gages))

Get data for a hydroregion

In this example we access a couple watershed-only metrics for a particular NHDPlus hydroregion using the sc_get_data function.

df <- sc_get_data(metric='pctwdwet2006', aoi='ws', region='Region17')
knitr::kable(head(df))

comid	pctwdwet2006ws
22988611	0.99
24114311	0.29
24114309	0.27
24116239	0.42
22988639	0.00
22988711	0.02

Get data for CONUS

In this example we access a metric for conus using the sc_get_data function - this is shown for demonstration but not run as it takes a bit of time

# df <- sc_get_data(metric='om', aoi='ws', conus='true')
# knitr::kable(head(df))