ropensci/rnoaa

Name: rnoaa

Owner: rOpenSci

Description: R interface to many NOAA data APIs

Created: 2013-07-08 22:31:24.0

Updated: 2018-01-09 18:54:43.0

Pushed: 2018-01-08 15:18:26.0

Homepage: https://ropensci.org/tutorials/rnoaa_tutorial.html

Size: 102067

Language: R

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README

rnoaa

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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
Help

There is a tutorial on the rOpenSci website, and there are many tutorials in the package itself, available in your R session, or on CRAN. The tutorials:

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 unknown reason. This relates to ncdc_*() functions only.

|Dataset |Description |Start Date |End Date | Data Coverage| |:———-|:—————————|:———-|:———-|————-:| |GHCND |Daily Summaries |1763-01-01 |2017-05-01 | 1.00| |GSOM |Global Summary of the Month |1763-01-01 |2017-04-01 | 1.00| |GSOY |Global Summary of the Year |1763-01-01 |2016-01-01 | 1.00| |NEXRAD2 |Weather Radar (Level II) |1991-06-05 |2017-05-01 | 0.95| |NEXRAD3 |Weather Radar (Level III) |1994-05-20 |2017-04-07 | 0.95| |NORMAL_ANN |Normals Annual/Seasonal |2010-01-01 |2010-01-01 | 1.00| |NORMAL_DLY |Normals Daily |2010-01-01 |2010-12-31 | 1.00| |NORMAL_HLY |Normals Hourly |2010-01-01 |2010-12-31 | 1.00| |NORMAL_MLY |Normals Monthly |2010-01-01 |2010-12-01 | 1.00| |PRECIP_15 |Precipitation 15 Minute |1970-05-12 |2014-01-01 | 0.25| |PRECIP_HLY |Precipitation Hourly |1900-01-01 |2014-01-01 | 1.00|

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 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)

(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

ons(noaakey = "KEY_EMAILED_TO_YOU")

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

Installation

GDAL

You'll need GDAL 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:

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):

all.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

all.packages("rnoaa")

or development version from GitHub

ools::install_github("ropensci/rnoaa")

Load rnoaa

ary('rnoaa')
NCDC v2 API data
Fetch list of city locations in descending order
_locs(locationcategoryid='CITY', sortfield='name', sortorder='desc')
meta
meta$totalCount
1] 1980

meta$pageCount
1] 25

meta$offset
1] 1


data
     mindate    maxdate                  name datacoverage            id
  1892-08-01 2017-03-31            Zwolle, NL       1.0000 CITY:NL000012
  1901-01-01 2017-04-29            Zurich, SZ       1.0000 CITY:SZ000007
  1957-07-01 2017-04-29         Zonguldak, TU       1.0000 CITY:TU000057
  1906-01-01 2017-04-29            Zinder, NG       0.9025 CITY:NG000004
  1973-01-01 2017-04-29        Ziguinchor, SG       1.0000 CITY:SG000004
  1938-01-01 2017-04-29         Zhytomyra, UP       0.9723 CITY:UP000025
  1948-03-01 2017-04-29        Zhezkazgan, KZ       0.9302 CITY:KZ000017
  1951-01-01 2017-04-29         Zhengzhou, CH       1.0000 CITY:CH000045
  1941-01-01 2017-03-31          Zaragoza, SP       1.0000 CITY:SP000021
0 1936-01-01 2009-06-17      Zaporiyhzhya, UP       1.0000 CITY:UP000024
1 1957-01-01 2017-04-29          Zanzibar, TZ       0.8016 CITY:TZ000019
2 1973-01-01 2017-04-29            Zanjan, IR       0.9105 CITY:IR000020
3 1893-01-01 2017-05-01     Zanesville, OH US       1.0000 CITY:US390029
4 1912-01-01 2017-04-29             Zahle, LE       0.9819 CITY:LE000004
5 1951-01-01 2017-04-29           Zahedan, IR       0.9975 CITY:IR000019
6 1860-12-01 2017-04-29            Zagreb, HR       1.0000 CITY:HR000002
7 1975-08-29 2017-04-29         Zacatecas, MX       0.9306 CITY:MX000036
8 1947-01-01 2017-04-29 Yuzhno-Sakhalinsk, RS       1.0000 CITY:RS000081
9 1893-01-01 2017-05-01           Yuma, AZ US       1.0000 CITY:US040015
0 1942-02-01 2017-05-01   Yucca Valley, CA US       1.0000 CITY:US060048
1 1885-01-01 2017-05-01      Yuba City, CA US       1.0000 CITY:US060047
2 1998-02-01 2017-04-29            Yozgat, TU       1.0000 CITY:TU000056
3 1893-01-01 2017-05-01     Youngstown, OH US       1.0000 CITY:US390028
4 1894-01-01 2017-05-01           York, PA US       1.0000 CITY:US420024
5 1869-01-01 2017-05-01        Yonkers, NY US       1.0000 CITY:US360031

ttr(,"class")
1] "ncdc_locs"
Get info on a station by specifying a dataset, locationtype, location, and station
_stations(datasetid='GHCND', locationid='FIPS:12017', stationid='GHCND:USC00084289')
meta
ULL

data
 elevation    mindate    maxdate latitude                  name
      12.2 1899-02-01 2017-04-30  28.8029 INVERNESS 3 SE, FL US
 datacoverage                id elevationUnit longitude
            1 GHCND:USC00084289        METERS  -82.3126

ttr(,"class")
1] "ncdc_stations"
Search for data
<- ncdc(datasetid='NORMAL_DLY', stationid='GHCND:USW00014895', datatypeid='dly-tmax-normal', startdate = '2010-05-01', enddate = '2010-05-10')
See a data.frame
( out$data )
                date        datatype           station value fl_c
 2010-05-01T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   652    S
 2010-05-02T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   655    S
 2010-05-03T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   658    S
 2010-05-04T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   661    S
 2010-05-05T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   663    S
 2010-05-06T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   666    S
Plot data, super simple, but it's a start
<- ncdc(datasetid='GHCND', stationid='GHCND:USW00014895', datatypeid='PRCP', startdate = '2010-05-01', enddate = '2010-10-31', limit=500)
_plot(out, breaks="1 month", dateformat="%d/%m")

plot of chunk unnamed-chunk-13

More plotting

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

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

plot of chunk unnamed-chunk-14

Get table of all datasets
_datasets()
meta
meta$offset
1] 1

meta$count
1] 11

meta$limit
1] 25


data
                   uid    mindate    maxdate                        name
  gov.noaa.ncdc:C00861 1763-01-01 2017-05-01             Daily Summaries
  gov.noaa.ncdc:C00946 1763-01-01 2017-04-01 Global Summary of the Month
  gov.noaa.ncdc:C00947 1763-01-01 2016-01-01  Global Summary of the Year
  gov.noaa.ncdc:C00345 1991-06-05 2017-05-01    Weather Radar (Level II)
  gov.noaa.ncdc:C00708 1994-05-20 2017-04-07   Weather Radar (Level III)
  gov.noaa.ncdc:C00821 2010-01-01 2010-01-01     Normals Annual/Seasonal
  gov.noaa.ncdc:C00823 2010-01-01 2010-12-31               Normals Daily
  gov.noaa.ncdc:C00824 2010-01-01 2010-12-31              Normals Hourly
  gov.noaa.ncdc:C00822 2010-01-01 2010-12-01             Normals Monthly
0 gov.noaa.ncdc:C00505 1970-05-12 2014-01-01     Precipitation 15 Minute
1 gov.noaa.ncdc:C00313 1900-01-01 2014-01-01        Precipitation Hourly
  datacoverage         id
          1.00      GHCND
          1.00       GSOM
          1.00       GSOY
          0.95    NEXRAD2
          0.95    NEXRAD3
          1.00 NORMAL_ANN
          1.00 NORMAL_DLY
          1.00 NORMAL_HLY
          1.00 NORMAL_MLY
0         0.25  PRECIP_15
1         1.00 PRECIP_HLY

ttr(,"class")
1] "ncdc_datasets"
Get data category data and metadata
_datacats(locationid = 'CITY:US390029')
meta
meta$totalCount
1] 38

meta$pageCount
1] 25

meta$offset
1] 1


data
                   name            id
    Annual Agricultural        ANNAGR
     Annual Degree Days         ANNDD
   Annual Precipitation       ANNPRCP
     Annual Temperature       ANNTEMP
    Autumn Agricultural         AUAGR
     Autumn Degree Days          AUDD
   Autumn Precipitation        AUPRCP
     Autumn Temperature        AUTEMP
               Computed          COMP
0 Computed Agricultural       COMPAGR
1           Degree Days            DD
2      Dual-Pol Moments DUALPOLMOMENT
3             Echo Tops       ECHOTOP
4      Hydrometeor Type   HYDROMETEOR
5            Miscellany          MISC
6                 Other         OTHER
7               Overlay       OVERLAY
8         Precipitation          PRCP
9          Reflectivity  REFLECTIVITY
0    Sky cover & clouds           SKY
1   Spring Agricultural         SPAGR
2    Spring Degree Days          SPDD
3  Spring Precipitation        SPPRCP
4    Spring Temperature        SPTEMP
5   Summer Agricultural         SUAGR

ttr(,"class")
1] "ncdc_datacats"
Tornado data

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

<- tornadoes()
GR data source with driver: ESRI Shapefile
ource: "/Users/sacmac/Library/Caches/rnoaa/tornadoes/torn", layer: "torn"
ith 60114 features
t has 22 fields
nteger64 fields read as strings:  om yr mo dy tz stf stn mag inj fat wid fc
ary('sp')
(shp)

plot of chunk unnamed-chunk-17

HOMR metadata

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

(qid = 'COOP:046742')
`20002078`
`20002078`$id
1] "20002078"

`20002078`$head
                preferredName latitude_dec longitude_dec precision
 PASO ROBLES MUNICIPAL AP, CA      35.6697     -120.6283   DDddddd
           por.beginDate por.endDate
 1949-10-05T00:00:00.000     Present

`20002078`$namez
                       name  nameType
   PASO ROBLES MUNICIPAL AP      COOP
   PASO ROBLES MUNICIPAL AP PRINCIPAL
 PASO ROBLES MUNICIPAL ARPT       PUB

`20002078`$identifiers
    idType          id
     GHCND USW00093209
   GHCNMLT USW00093209
Storm data

Get storm data for the year 2010

m_data(year = 2010)
 A tibble: 2,855 × 195
     serial_num season   num basin sub_basin  name            iso_time
          <chr>  <int> <int> <chr>     <chr> <chr>               <chr>
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-13 06:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-13 12:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-13 18:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 00:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 06:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 12:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-14 18:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-15 00:00:00
  2009317S10073   2010     1    SI        MM  ANJA 2009-11-15 06:00:00
0 2009317S10073   2010     1    SI        MM  ANJA 2009-11-15 12:00:00
 ... with 2,845 more rows, and 188 more variables: nature <chr>,
   latitude <dbl>, longitude <dbl>, wind.wmo. <dbl>, pres.wmo. <dbl>,
   center <chr>, wind.wmo..percentile <dbl>, pres.wmo..percentile <dbl>,
   track_type <chr>, latitude_for_mapping <dbl>,
   longitude_for_mapping <dbl>, current.basin <chr>,
   hurdat_atl_lat <dbl>, hurdat_atl_lon <dbl>, hurdat_atl_grade <dbl>,
   hurdat_atl_wind <dbl>, hurdat_atl_pres <dbl>, td9636_lat <dbl>,
GEFS data

Get forecast for a certain variable.

<- gefs("Total_precipitation_surface_6_Hour_Accumulation_ens", lat = 46.28125, lon = -116.2188)
(res$data)
 Total_precipitation_surface_6_Hour_Accumulation_ens lon lat ens time2
                                                   0 244  46   0     6
                                                   0 244  46   1    12
                                                   0 244  46   2    18
                                                   0 244  46   3    24
                                                   0 244  46   4    30
                                                   0 244  46   5    36
Argo buoys data

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

atial search - by bounding box
_search("coord", box = c(-40, 35, 3, 2))

me based search
_search("coord", yearmin = 2007, yearmax = 2009)

ta quality based search
_search("coord", pres_qc = "A", temp_qc = "A")

arch on partial float id number
_qwmo(qwmo = 49)

t data
(dac = "meds", id = 4900881, cycle = 127, dtype = "D")
CO-OPS data

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

s_search(station_name = 9063053, begin_date = 20150927, end_date = 20150928,
         product = "daily_mean", datum = "stnd", time_zone = "lst")
metadata
metadata$id
1] "9063053"

metadata$name
1] "Fairport"

metadata$lat
1] "41.7598"

metadata$lon
1] "-81.2811"


data
          t       v   f
 2015-09-27 174.430 0,0
 2015-09-28 174.422 0,0
Contributors
Meta

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This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.