README.Rmd

rnoaa
=====

```{r echo=FALSE}
library("knitr")
hook_output <- knitr::knit_hooks$get("output")
knitr::knit_hooks$set(output = function(x, options) {
   lines <- options$output.lines
   if (is.null(lines)) {
     return(hook_output(x, options))  # pass to default hook
   }
   x <- unlist(strsplit(x, "\n"))
   more <- "..."
   if (length(lines)==1) {        # first n lines
     if (length(x) > lines) {
       # truncate the output, but add ....
       x <- c(head(x, lines), more)
     }
   } else {
     x <- c(if (abs(lines[1])>1) more else NULL,
            x[lines],
            if (length(x)>lines[abs(length(lines))]) more else NULL
           )
   }
   # paste these lines together
   x <- paste(c(x, ""), collapse = "\n")
   hook_output(x, options)
 })

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  fig.width = 10,
  fig.path = "inst/img/",
  cache.path = "inst/cache/"
)
```

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`rnoaa` is an R interface to many NOAA data sources. We don't cover all of them, but we include many commonly used sources, and add we are always adding new sources. We focus on easy to use interfaces for getting NOAA data, and giving back data in easy to use formats downstream. We currently don't do much in the way of plots or analysis. 

## Data sources in rnoaa

* NOAA NCDC climate data:
    * We are using the NOAA API version 2
    * Docs for the NCDC API are at http://www.ncdc.noaa.gov/cdo-web/webservices/v2
    * GHCN Daily data is available at http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/ via FTP and HTTP
* Severe weather data docs are at http://www.ncdc.noaa.gov/swdiws/
* [Sea ice data](ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/shapefiles)
* [NOAA buoy data](http://www.ndbc.noaa.gov/)
* [ERDDAP data](http://upwell.pfeg.noaa.gov/erddap/index.html)
  * Now in package [rerddap](https://github.com/ropensci/rerddap)
* Tornadoes! Data from the [NOAA Storm Prediction Center](http://www.spc.noaa.gov/gis/svrgis/)
* HOMR - Historical Observing Metadata Repository - from [NOAA NCDC](http://www.ncdc.noaa.gov/homr/api)
* Storm data - from the [International Best Track Archive for Climate Stewardship (IBTrACS)](http://www.ncdc.noaa.gov/ibtracs/index.php?name=wmo-data)
* [GHCND FTP data](ftp://ftp.ncdc.noaa.gov/pub/data/noaa) - NOAA NCDC API has some/all (not sure really) of this data, but FTP allows to get more data more quickly
* [Global Ensemble Forecast System (GEFS) data](https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-ensemble-forecast-system-gefs)
* [Extended Reconstructed Sea Surface Temperature (ERSST) data](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4)
* [Argo buoys](http://www.argo.ucsd.edu/) - a global array of more than 3,000 free-drifting profiling floats that measures thetemperature and salinity of the upper 2000 m of the ocean
* [NOAA CO-OPS - tides and currents data](http://tidesandcurrents.noaa.gov/)

## Help

There is a tutorial on the [rOpenSci website](http://ropensci.org/tutorials/rnoaa_tutorial.html), and there are many tutorials in the package itself, available in your R session, or [on CRAN](http://cran.rstudio.com/package=rnoaa). The tutorials:

* NOAA Buoy vignette
* NOAA National Climatic Data Center (NCDC) vignette (examples)
* NOAA NCDC attributes vignette
* NOAA NCDC workflow vignette
* Sea ice vignette
* Severe Weather Data Inventory (SWDI) vignette
* Historical Observing Metadata Repository (HOMR) vignette
* Storms (IBTrACS) vignette

## netcdf data

Functions to work with buoy data use netcdf files. You'll need the `ncdf` package for those functions, and those only. `ncdf` is in Suggests in this package, meaning you only need `ncdf` if you are using the buoy functions. You'll get an informative error telling you to install `ncdf` if you don't have it and you try to use the buoy functions. Installation of `ncdf` should be straightforward on Mac and Windows, but on Linux you may have issues. See http://cran.r-project.org/web/packages/ncdf/INSTALL

## NOAA NCDC Datasets

There are many NOAA NCDC datasets. All data sources work, except `NEXRAD2` and `NEXRAD3`, for an unkown reason. This relates to `ncdc_*()` functions only.

| Dataset | Description | Start date | End date |
|---------|-------------|------------|----------|
| ANNUAL | Annual Summaries | 1831-02-01 | 2013-11-01 |
| GHCND | Daily Summaries | 1763-01-01 | 2014-03-15 |
| GHCNDMS | Monthly Summaries | 1763-01-01 | 2014-01-01 |
| NORMAL_ANN | Normals Annual/Seasonal | 2010-01-01 | 2010-01-01 |
| NORMAL_DLY | Normals Daily | 2010-01-01 | 2010-12-31 |
| NORMAL_HLY | Normals Hourly | 2010-01-01 | 2010-12-31 |
| NORMAL_MLY | Normals Monthly | 2010-01-01 | 2010-12-01 |
| PRECIP_15 | Precipitation 15 Minute | 1970-05-12 | 2013-03-01 |
| PRECIP_HLY | Precipitation Hourly | 1900-01-01 | 2013-03-01 |
| NEXRAD2 | Nexrad Level II | 1991-06-05 | 2014-03-14 |
| NEXRAD3 | Nexrad Level III | 1994-05-20 | 2014-03-11 |

## NOAA NCDC Attributes

Each NOAA dataset has a different set of attributes that you can potentially get back in your search. See http://www.ncdc.noaa.gov/cdo-web/datasets for detailed info on each dataset. We provide some information on the attributes in this package; see the [vignette for attributes](inst/vign/rncdc_attributes.md) to find out more

## NCDC Authentication

You'll need an API key to use the NOAA NCDC functions (those starting with `ncdc*()`) in this package (essentially a password). Go to http://www.ncdc.noaa.gov/cdo-web/token to get one. *You can't use this package without an API key.*

Once you obtain a key, there are two ways to use it.

a) Pass it inline with each function call (somewhat cumbersome)

```{r eval=FALSE}
ncdc(datasetid = 'PRECIP_HLY', locationid = 'ZIP:28801', datatypeid = 'HPCP', limit = 5, token =  "YOUR_TOKEN")
```

b) Alternatively, you might find it easier to set this as an option, either by adding this line to the top of a script or somewhere in your `.rprofile`

```{r eval=FALSE}
options(noaakey = "KEY_EMAILED_TO_YOU")
```

c) You can always store in permamently in your `.Rprofile` file.


## Installation

__GDAL__

You'll need [GDAL](http://www.gdal.org/) installed first. You may want to use GDAL >= `0.9-1` since that version or later can read TopoJSON format files as well, which aren't required here, but may be useful. Install GDAL:

* OSX - From http://www.kyngchaos.com/software/frameworks
* Linux - run `sudo apt-get install gdal-bin` [reference](https://www.mapbox.com/tilemill/docs/guides/gdal/#linux)
* Windows - From http://trac.osgeo.org/osgeo4w/

Then when you install the R package `rgdal` (`rgeos` also requires GDAL), you'll most likely need to specify where you're `gdal-config` file is on your machine, as well as a few other things. I have an OSX Mavericks machine, and this works for me (there's no binary for Mavericks, so install the source version):

```{r eval=FALSE}
install.packages("http://cran.r-project.org/src/contrib/rgdal_0.9-1.tar.gz", repos = NULL, type="source", configure.args = "--with-gdal-config=/Library/Frameworks/GDAL.framework/Versions/1.10/unix/bin/gdal-config --with-proj-include=/Library/Frameworks/PROJ.framework/unix/include --with-proj-lib=/Library/Frameworks/PROJ.framework/unix/lib")
```

The rest of the installation should be easy. If not, let us know.

__Stable version from CRAN__

```{r eval=FALSE}
install.packages("rnoaa")
```

__or development version from GitHub__

```{r eval=FALSE}
devtools::install_github("ropensci/rnoaa")
```

__Load rnoaa__

```{r}
library('rnoaa')
```

## NCDC v2 API data

###  Fetch list of city locations in descending order

```{r}
ncdc_locs(locationcategoryid='CITY', sortfield='name', sortorder='desc')
```

### Get info on a station by specifcying a dataset, locationtype, location, and station

```{r}
ncdc_stations(datasetid='GHCND', locationid='FIPS:12017', stationid='GHCND:USC00084289')
```


### Search for data

```{r}
out <- ncdc(datasetid='NORMAL_DLY', stationid='GHCND:USW00014895', datatypeid='dly-tmax-normal', startdate = '2010-05-01', enddate = '2010-05-10')
```

### See a data.frame

```{r}
head( out$data )
```

### Plot data, super simple, but it's a start

```{r}
out <- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-05-01', enddate = '2010-10-31', limit=500)
ncdc_plot(out, breaks="1 month", dateformat="%d/%m")
```

### More plotting

You can pass many outputs from calls to the `noaa` function in to the `ncdc_plot` function.

```{r}
out1 <- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-03-01', enddate = '2010-05-31', limit=500)
out2 <- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-09-01', enddate = '2010-10-31', limit=500)
ncdc_plot(out1, out2, breaks="45 days")
```

### Get table of all datasets

```{r}
ncdc_datasets()
```

### Get data category data and metadata

```{r}
ncdc_datacats(locationid = 'CITY:US390029')
```

## Tornado data

The function `tornadoes()` simply gets __all the data__. So the call takes a while, but once done, is fun to play with.

```{r cache=TRUE}
shp <- tornadoes()
library('sp')
plot(shp)
```

## HOMR metadata

In this example, search for metadata for a single station ID

```{r output.lines=1:20}
homr(qid = 'COOP:046742')
```

## Storm data

Get storm data for the year 2010

```{r output.lines=1:20}
storm_data(year = 2010)
```

## GEFS data

Get forecast for a certain variable.

```{r cache = TRUE}
res <- gefs("Total_precipitation_surface_6_Hour_Accumulation_ens", lat = 46.28125, lon = -116.2188)
head(res$data)
```

## Argo buoys data

There are a suite of functions for Argo data, a few egs:

```{r eval=FALSE}
# Spatial search - by bounding box
argo_search("coord", box = c(-40, 35, 3, 2))

# Time based search
argo_search("coord", yearmin = 2007, yearmax = 2009)

# Data quality based search
argo_search("coord", pres_qc = "A", temp_qc = "A")

# Search on partial float id number
argo_qwmo(qwmo = 49)

# Get data
argo(dac = "meds", id = 4900881, cycle = 127, dtype = "D")
```

## CO-OPS data

Get daily mean water level data at Fairport, OH (9063053)

```{r}
coops_search(station_name = 9063053, begin_date = 20150927, end_date = 20150928,
             product = "daily_mean", datum = "stnd", time_zone = "lst")
```

## Meta

* Please [report any issues or bugs](https://github.com/ropensci/rnoaa/issues).
* License: MIT
* Get citation information for `rnoaa` in R doing `citation(package = 'rnoaa')`
* Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.

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