# Getting Started

## 1 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)

## 2 Background

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

## 3 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

## 4 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 × 13
    #>   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 5 more variables: dataset <chr>, source_name <chr>, source_URL <chr>,
    #> #   ACTIVE <dbl>, DSNAME <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] "Herbaceous Wetland Percentage 2011"

    metric='pctdecid2019,fert'
    fullname <- sc_fullname(metric)
    fullname
    #> character(0)

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

## 5 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 | damdenscat | damdensws | tridensws | tridenscat | pcturbmd2006cat | pcturbmd2006ws
--- | --- | --- | --- | --- | --- | ---
179 | 0 | 0.0000 | 0 | 0 | 0.00 | 0.00
1337 | 0 | 0.0000 | 0 | 0 | 0.07 | 0.07
1337420 | 0 | 0.0329 | 0 | 0 | 0.00 | 0.01

## 6 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

## 7 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 metric=‘all’ and aoi=‘conus’ in order not to overload requests to the server. Requesting metric=‘all’ for a state or multiple states or hydroregions will also take a long time to process.

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

comid | pctimpslphigh2001cat | pctimpslphigh2004cat | pctimpslphigh2006cat | pctimpslphigh2008cat | pctimpslphigh2011cat | pctimpslphigh2013cat | pctimpslphigh2016cat | pctimpslphigh2019cat | sw_fluxcat | pctconif2016cat | pctcrop2016cat | pctdecid2016cat | pctgrs2016cat | pcthay2016cat | pcthbwet2016cat | pctice2016cat | pctmxfst2016cat | pctow2016cat | pctshrb2016cat | pcturbhi2016cat | pcturblo2016cat | pcturbmd2016cat | pcturbop2016cat | pctwdwet2016cat | pctbl2016cat | pctundsev2003cat | pctlowsev2003cat | pctmodsev2003cat | pcthighsev2003cat | pctincvegresp2003cat | pctnonprocmask2003cat | pctmodsev1993cat | pcthighsev1993cat | pctincvegresp1993cat | pctnonprocmask1993cat | pctlowsev1993cat | pctundsev1993cat | pctundsev2005cat | pctlowsev2005cat | pctmodsev2005cat | pcthighsev2005cat | pctincvegresp2005cat | pctnonprocmask2005cat | pctundsev2014cat | pctlowsev2014cat | pctmodsev2014cat | pcthighsev2014cat | pctincvegresp2014cat | pctnonprocmask2014cat | pctagdrainagecat | pctagslpmid2004cat | pctagslphigh2004cat | pctagslpmid2008cat | pctagslphigh2008cat | pctundsev1984cat | pctlowsev1984cat | pctmodsev1984cat | pcthighsev1984cat | pctincvegresp1984cat | pctnonprocmask1984cat | rockncat | pctundsev1986cat | pctlowsev1986cat | pctmodsev1986cat | pcthighsev1986cat | pctincvegresp1986cat | pctnonprocmask1986cat | pctundsev1989cat | pctlowsev1989cat | pctmodsev1989cat | pcthighsev1989cat | pctincvegresp1989cat | pctnonprocmask1989cat | pctundsev1994cat | pctlowsev1994cat | pctmodsev1994cat | pcthighsev1994cat | pctincvegresp1994cat | pctnonprocmask1994cat | pctundsev1997cat | pctlowsev1997cat | pctmodsev1997cat | pcthighsev1997cat | pctincvegresp1997cat | pctnonprocmask1997cat | pctundsev2000cat | pctlowsev2000cat | pctmodsev2000cat | pcthighsev2000cat | pctincvegresp2000cat | pctnonprocmask2000cat | pctundsev2001cat | pctlowsev2001cat | pctmodsev2001cat | pcthighsev2001cat | pctincvegresp2001cat | pctnonprocmask2001cat | pctundsev2009cat | pctlowsev2009cat | pctmodsev2009cat | pcthighsev2009cat | pctincvegresp2009cat | pctnonprocmask2009cat | pctundsev2012cat | pctlowsev2012cat | pctmodsev2012cat | pcthighsev2012cat | pctincvegresp2012cat | pctnonprocmask2012cat | pctundsev2015cat | pctlowsev2015cat | pctmodsev2015cat | pcthighsev2015cat | pctincvegresp2015cat | pctnonprocmask2015cat | pctagslpmid2011cat | pctagslphigh2011cat | nsurpcat | nanicat | septiccat | pctbl2013cat | pctconif2013cat | pctcrop2013cat | pctdecid2013cat | pctgrs2013cat | pcthay2013cat | pcthbwet2013cat | pctice2013cat | pctmxfst2013cat | pctow2013cat | pctshrb2013cat | pcturbhi2013cat | pcturblo2013cat | pcturbmd2013cat | pcturbop2013cat | pctwdwet2013cat | pctundsev1985cat | pctlowsev1985cat | pctmodsev1985cat | pcthighsev1985cat | pctincvegresp1985cat | pctnonprocmask1985cat | pctundsev1987cat | pctlowsev1987cat | pctmodsev1987cat | pcthighsev1987cat | pctincvegresp1987cat | pctnonprocmask1987cat | pctundsev1988cat | pctlowsev1988cat | pctmodsev1988cat | pcthighsev1988cat | pctincvegresp1988cat | pctnonprocmask1988cat | pctundsev1990cat | pctlowsev1990cat | pctmodsev1990cat | pcthighsev1990cat | pctincvegresp1990cat | pctnonprocmask1990cat | pctundsev1995cat | pctlowsev1995cat | pctmodsev1995cat | pcthighsev1995cat | pctincvegresp1995cat | pctnonprocmask1995cat | pctundsev1999cat | pctlowsev1999cat | pctmodsev1999cat | pcthighsev1999cat | pctincvegresp1999cat | pctnonprocmask1999cat | pctundsev2002cat | pctlowsev2002cat | pctmodsev2002cat | pcthighsev2002cat | pctincvegresp2002cat | pctnonprocmask2002cat | pctundsev2007cat | pctlowsev2007cat | pctmodsev2007cat | pcthighsev2007cat | pctincvegresp2007cat | pctnonprocmask2007cat | pctundsev2008cat | pctlowsev2008cat | pctmodsev2008cat | pcthighsev2008cat | pctincvegresp2008cat | pctnonprocmask2008cat | pctundsev2010cat | pctlowsev2010cat | pctmodsev2010cat | pcthighsev2010cat | pctincvegresp2010cat | pctnonprocmask2010cat | pctundsev2011cat | pctlowsev2011cat | pctmodsev2011cat | 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pcturbhi2008cat | pcturblo2008cat | pcturbmd2008cat | pcturbop2008cat | pctwdwet2008cat | pctbl2019cat | pctconif2019cat | pctcrop2019cat | pctdecid2019cat | pctgrs2019cat | pcthay2019cat | pcthbwet2019cat | pctice2019cat | pctmxfst2019cat | pctow2019cat | pctshrb2019cat | pcturbhi2019cat | pcturblo2019cat | pcturbmd2019cat | pcturbop2019cat | pctwdwet2019cat | wdrw_ldcat | wetindexcat | pctundsev1996cat | pctlowsev1996cat | pctmodsev1996cat | pcthighsev1996cat | pctincvegresp1996cat | pctnonprocmask1996cat | pctundsev2013cat | pctlowsev2013cat | pctmodsev2013cat | pcthighsev2013cat | pctincvegresp2013cat | pctnonprocmask2013cat | agkffactcat | kffactcat | pctalluvcoastcat | pctsallakecat | pctcoastcrscat | pctglactilloamcat | pctwatercat | pctextruvolcat | pctglactilcrscat | pcthydriccat | pctcarbresidcat | pctglaclakecrscat | pcteolfinecat | pctsiliciccat | pctglactilclaycat | pcteolcrscat | pctalkintruvolcat | pctglaclakefinecat | pctnoncarbresidcat | pctcolluvsedcat | minedenscat | nabd_denscat | nabd_nrmstorcat | nabd_nidstorcat | no32008cat | sn2008cat | inorgnwetdep2008cat | nh42008cat | pcturbhi2001cat | pctgrs2001cat | pctwdwet2001cat | pctcrop2001cat | pctow2001cat | pcthbwet2001cat | pctbl2001cat | pctice2001cat | pctmxfst2001cat | pcturblo2001cat | pcturbmd2001cat | pcthay2001cat | pctconif2001cat | pctshrb2001cat | pcturbop2001cat | pctdecid2001cat | pcturbop2006cat | pctconif2006cat | pctow2006cat | pctice2006cat | pcturbhi2006cat | pcthay2006cat | pctwdwet2006cat | pcturbmd2006cat | pctmxfst2006cat | pctshrb2006cat | pcturblo2006cat | pctbl2006cat | pctdecid2006cat | pctcrop2006cat | pctgrs2006cat | pcthbwet2006cat | pctwdwet2011cat | pcturblo2011cat | pctow2011cat | pctice2011cat | pcturbmd2011cat | pctdecid2011cat | pctconif2011cat | pctbl2011cat | pctmxfst2011cat | pcturbhi2011cat | pcthay2011cat | pctgrs2011cat | pctshrb2011cat | pcturbop2011cat | pcthbwet2011cat | pctcrop2011cat | pctnonagintrodmanagvegcat | pestic1997cat | tmean2008cat | tmean2009cat | precip2009cat | precip2008cat | tmean8110cat | precip8110cat | tmax8110cat | tmin8110cat | rddenscat | rdcrscat | rdcrsslpwtdcat | runoffcat | sandcat | claycat | omcat | wtdepcat | permcat | rckdepcat | popden2010cat | huden2010cat | pctimpslpmid2001cat | pctimpslpmid2004cat | pctimpslpmid2006cat | pctimpslpmid2008cat | pctimpslpmid2011cat | pctimpslpmid2013cat | pctimpslpmid2016cat | pctimpslpmid2019cat | pctbl2004cat | pctconif2004cat | pctcrop2004cat | pctdecid2004cat | pctgrs2004cat | pcthay2004cat | pcthbwet2004cat | pctice2004cat | pctmxfst2004cat | pctow2004cat | pctshrb2004cat | pcturbhi2004cat | pcturblo2004cat | pcturbmd2004cat | pcturbop2004cat | pctwdwet2004cat | pctburnarea1984cat | pctburnarea1985cat | pctburnarea1986cat | pctburnarea1987cat | 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 | pctundsev1991cat | pctlowsev1991cat | pctmodsev1991cat | pcthighsev1991cat | pctincvegresp1991cat | pctnonprocmask1991cat | pctundsev1992cat | pctlowsev1992cat | pctmodsev1992cat | pcthighsev1992cat | pctincvegresp1992cat | pctnonprocmask1992cat | pctundsev1998cat | pctlowsev1998cat | pctmodsev1998cat | pcthighsev1998cat | pctincvegresp1998cat | pctnonprocmask1998cat | pctundsev2004cat | pctlowsev2004cat | pctmodsev2004cat | pcthighsev2004cat | pctincvegresp2004cat | pctnonprocmask2004cat | pctundsev2006cat | pctlowsev2006cat | pctmodsev2006cat | pcthighsev2006cat | pctincvegresp2006cat | pctnonprocmask2006cat | pctundsev2016cat | pctlowsev2016cat | pctmodsev2016cat | pcthighsev2016cat | pctincvegresp2016cat | pctnonprocmask2016cat | pctundsev2017cat | pctlowsev2017cat | pctmodsev2017cat | pcthighsev2017cat | pctincvegresp2017cat | pctnonprocmask2017cat | pctundsev2018cat | pctlowsev2018cat | pctmodsev2018cat | pcthighsev2018cat | pctincvegresp2018cat | pctnonprocmask2018cat | pctagslpmid2001cat | pctagslphigh2001cat | pctagslphigh2006cat | pctagslpmid2006cat | precip9120cat | tmax9120cat | tmean9120cat | tmin9120cat | pctagslpmid2013cat | pctagslphigh2013cat | pctagslpmid2016cat | pctagslphigh2016cat | pctagslpmid2019cat | pctagslphigh2019cat | pctfrstloss2001cat | pctfrstloss2002cat | pctfrstloss2003cat | pctfrstloss2004cat | pctfrstloss2005cat | pctfrstloss2006cat | pctfrstloss2007cat | pctfrstloss2008cat | pctfrstloss2009cat | pctfrstloss2010cat | pctfrstloss2011cat | pctfrstloss2012cat | pctfrstloss2013cat
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
179 | NA | NA | NA | NA | NA | NA | NA | NA | 133875 | 4.91 | 1.54 | 3.87 | 0.05 | 1.09 | 0.71 | 0 | 49.67 | 0 | 0.25 | 0 | 0 | 0 | 0 | 37.9 | 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 | 0 | 3.17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88.2995 | 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 | 3.17 | 0 | 2102.561 | 1675.837 | 1.117 | 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 | 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 | 0.0506 | 0 | 1.438 | 52 | 0 | 0 | 0 | 0 | 0 | 196.1233 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 64.3313 | 5.5091 | 1.0772 | 7.9453 | 1.0557 | 10.276 | 0.0161 | 0.1545 | 2.2883 | 8e-04 | 0.2044 | 169.6366 | 0.9958425 | 0.9983122 | 0.7883752 | 0.9983122 | 1 | 0.997275 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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 | 4.91 | 1.54 | 3.7 | 0.35 | 1.09 | 0.71 | 0 | 49.54 | 0 | 0.25 | 0 | 0 | 0 | 0 | 37.9 | 0.4413 | 878.2494 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.3799 | 342.1452 | 2.4653 | 1.3213 | 0 | 0.23 | 36.89 | 1.52 | 0 | 1.95 | 0 | 0 | 48.76 | 0 | 0 | 1.22 | 4.89 | 0.73 | 0 | 3.82 | 0 | 4.89 | 0.08 | 0 | 0 | 1.09 | 37.29 | 0 | 49.01 | 1.01 | 0 | 0 | 3.8 | 1.52 | 0.08 | 1.24 | 37.57 | 0 | 0.1 | 0 | 0 | 3.8 | 4.89 | 0 | 49.16 | 0 | 1.09 | 0.13 | 0.81 | 0 | 0.94 | 1.52 | 0 | 40.52718 | 5.032428 | 4.852966 | 1108.386 | 1254.539 | 4.885117 | 1059.078 | 10.59572 | -0.8263614 | 0.5276 | 0 | 0 | 646 | 22.33772 | 14.35371 | 12.28701 | 49.21784 | 5.130848 | 134.9983 | 8.874548 | 5.910425 | 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 | 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 | 3.17 | 0 | 0 | 3.17 | 1152.678 | 10.56534 | 5.015274 | -0.5362276 | 3.17 | 0 | 3.17 | 0 | 3.17 | 0 | 0.71 | 0 | 0 | 0.35 | 0.03 | 0 | 0 | 0 | 0 | 0 | 0 | 0.23 | 0

## 8 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 | pcturbhi2001cat | pctgrs2001cat | pctwdwet2001cat | pctcrop2001cat | pctow2001cat | pcthbwet2001cat | pctbl2001cat | pctice2001cat | pctmxfst2001cat | pcturblo2001cat | pcturbmd2001cat | pcthay2001cat | pctconif2001cat | pctshrb2001cat | pcturbop2001cat | pctdecid2001cat
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
179 | 0 | 0.23 | 36.89 | 1.52 | 0.0 | 1.95 | 0.00 | 0 | 48.76 | 0.00 | 0 | 1.22 | 4.89 | 0.73 | 0.00 | 3.82
1337 | 0 | 1.60 | 20.75 | 0.00 | 0.1 | 0.56 | 0.28 | 0 | 51.55 | 0.24 | 0 | 2.83 | 5.65 | 0.66 | 3.98 | 11.79
1337420 | 0 | 0.87 | 0.95 | 0.00 | 0.0 | 1.64 | 0.00 | 0 | 3.77 | 0.40 | 0 | 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 | pctice2019ws | pctmxfst2019ws | pctow2019ws | pctshrb2019ws | pcturbhi2019ws | pcturblo2019ws | pcturbmd2019ws | pcturbop2019ws | pctwdwet2019ws | pctice2006ws | 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 | 0 | 7.32 | 0.08 | 3.99 | 0.10 | 1.69 | 0.30 | 3.46 | 2.46 | 0 | 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 | 0 | 9.14 | 0.04 | 6.06 | 0.05 | 1.46 | 0.24 | 4.60 | 1.18 | 0 | 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 | 0 | 18.61 | 0.05 | 7.45 | 0.01 | 0.60 | 0.14 | 4.65 | 1.33 | 0 | 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 | 0 | 7.24 | 0.09 | 3.95 | 0.10 | 1.68 | 0.30 | 3.43 | 2.51 | 0 | 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 | 0 | 7.53 | 0.09 | 4.30 | 0.09 | 1.65 | 0.29 | 3.40 | 2.47 | 0 | 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 | 0 | 20.77 | 0.01 | 6.65 | 0.00 | 0.31 | 0.07 | 4.89 | 1.76 | 0 | 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

## 9 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))

## 10 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

## 11 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))