---
title: "Working with LakeCat data"
output: 
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    highlighted: default 
    toc: yes
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vignette: >
  %\VignetteIndexEntry{Working with LakeCat data}
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---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", 
  warning = FALSE, 
  message = FALSE
)
```

## Accessing and working with [LakeCat](https://www.epa.gov/national-aquatic-resource-surveys/lakecat-dataset)
The `StreamCatTools` package was designed to simplify the use of [StreamCat](https://www.epa.gov/national-aquatic-resource-surveys/streamcat-dataset) data in R, leveraging the new [API for StreamCat](https://api.epa.gov/StreamCat/streams/metrics).  We now have functionality in the [StreamCat API](https://api.epa.gov/StreamCat/streams/metrics) for accessing and working with LakeCat data and have added functions in `StreamCatTools` to access LakeCata data in R using the API.

We can actually pull data into R from LakeCat by simply passing a URL to extract from json. We have to hard-wire parameters and are limited in the number of records returned through a `GET` request.
```{r, message=FALSE, eval=FALSE}
res <- jsonlite::fromJSON("https://api.epa.gov/StreamCat/lakes/metrics?name=pcturbmd2006&areaOfInterest=cat&comid=22538788")
res$items
```

## List LakeCat API parameters

List LakeCat parameters: Get a list of available LakeCat values for certain parameters using the `lc_get_params` function (right now just metric names and areas of interest for this function) via the API

```{r name_params}
library(StreamCatTools)
region_params <- lc_get_params(param='areaOfInterest')

name_params <- lc_get_params(param='metric_names')

print(paste0('region parameters are: ', paste(region_params,collapse = ', ')))
print(paste0('A selection of available LakrCat metrics include: ',paste(name_params[1:10],collapse = ', ')))
```

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):
```{r params2}
var_info <- lc_get_params(param='variable_info')
head(var_info)
```

Look up the display name or names for a metric using the `lc_fullname` function via the API
```{r fullnames}
metric='pcthbwet2016'
fullname <- lc_fullname(metric)
fullname
```

```{r fullnames2}
metric='pctdecid2019,fert'
fullname <- lc_fullname(metric)
fullname
```

## Get Waterbody COMIDs
In this example we use the `lc_get_comid` function to find COMIDs for a set of example lake locations we load into R.`lc_get_comid` is just a simple wrapper for `get_waterbodies` in the [nhdplusTools](https://doi-usgs.github.io/nhdplusTools/) R package. We can then use the COMIDs we derive for our lake locations to get LakeCat metrics for these lakes as we show in after this.
```{r comids, warning=FALSE, message=FALSE}
dd <- data.frame(x = c(-89.198,-114.125,-122.044),
                 y = c(45.502,47.877,43.730)) |> 
  sf::st_as_sf(coords = c('x', 'y'), crs = 4326)
    
comids <- lc_get_comid(dd)
```

## Get data for COMIDs

In this example we access several variables, for several areas of interest, and for several COMIDs using the `lc_get_data` function. We'll show using both the COMIDS we derived with `lc_get_comid` function in previous chunk as well as with known COMIDS for NHDPlus waterbodies. Loads data into a tibble we can view.

```{r get_data}
df <- lc_get_data(metric='pcturbmd2006,damdens', aoi='cat,ws', comid=comids)
knitr::kable(df)
```

```{r get_data2}
df <- lc_get_data(metric='pcturbmd2006,damdens', aoi='cat,ws', comid='23783629,23794487,23812618')
knitr::kable(df)
```

## 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. 
```{r countydata}
df <- lc_get_data(metric='pctwdwet2006', aoi='ws', county='41003')
knitr::kable(head(df))
```

## Get NLCD data

In this example we access National Land Cover Dataset (NLCD) data for 2019, just at the catchment level for several COMIDs using the `lc_nlcd` function. Loads data into a tibble we can view.

```{r get_nlcd}
df <- lc_nlcd(comid='23783629,23794487,23812618', year='2019', aoi='ws')
knitr::kable(df)
```