rnoaa v0.8.4

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'NOAA' Weather Data from R

Client for many 'NOAA' data sources including the 'NCDC' climate 'API' at <https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for each of the 'API' 'endpoints': data, data categories, data sets, data types, locations, location categories, and stations. In addition, we have an interface for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS', tornado data via the 'NOAA' storm prediction center, and more.

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:

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

Dataset Description Start Date End Date Data Coverage
GHCND Daily Summaries 1763-01-01 2018-12-09 1.00
GSOM Global Summary of the Month 1763-01-01 2018-11-01 1.00
GSOY Global Summary of the Year 1763-01-01 2018-01-01 1.00
NEXRAD2 Weather Radar (Level II) 1991-06-05 2018-12-10 0.95
NEXRAD3 Weather Radar (Level III) 1994-05-20 2018-12-07 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.

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

ncdc(datasetid = 'PRECIP_HLY', locationid = 'ZIP:28801', datatypeid = 'HPCP', limit = 5, token =  "YOUR_TOKEN")

2) 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

options(noaakey = "KEY_EMAILED_TO_YOU")

3) 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):

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

install.packages("rnoaa")

or development version from GitHub

devtools::install_github("ropensci/rnoaa")

Load rnoaa

library('rnoaa')

NCDC v2 API data

Fetch list of city locations in descending order

ncdc_locs(locationcategoryid='CITY', sortfield='name', sortorder='desc')
#> $meta
#> $meta$totalCount
#> [1] 1987
#> 
#> $meta$pageCount
#> [1] 25
#> 
#> $meta$offset
#> [1] 1
#> 
#> 
#> $data
#>       mindate    maxdate                  name datacoverage            id
#> 1  1892-08-01 2018-10-31            Zwolle, NL       1.0000 CITY:NL000012
#> 2  1901-01-01 2018-12-07            Zurich, SZ       1.0000 CITY:SZ000007
#> 3  1957-07-01 2018-12-07         Zonguldak, TU       1.0000 CITY:TU000057
#> 4  1906-01-01 2018-12-07            Zinder, NG       0.9025 CITY:NG000004
#> 5  1973-01-01 2018-12-07        Ziguinchor, SG       1.0000 CITY:SG000004
#> 6  1938-01-01 2018-12-07         Zhytomyra, UP       0.9723 CITY:UP000025
#> 7  1948-03-01 2018-12-07        Zhezkazgan, KZ       0.9302 CITY:KZ000017
#> 8  1951-01-01 2018-12-07         Zhengzhou, CH       1.0000 CITY:CH000045
#> 9  1941-01-01 2018-10-31          Zaragoza, SP       1.0000 CITY:SP000021
#> 10 1936-01-01 2009-06-17      Zaporiyhzhya, UP       1.0000 CITY:UP000024
#> 11 1957-01-01 2018-12-07          Zanzibar, TZ       0.8016 CITY:TZ000019
#> 12 1973-01-01 2018-12-07            Zanjan, IR       0.9105 CITY:IR000020
#> 13 1893-01-01 2018-12-10     Zanesville, OH US       1.0000 CITY:US390029
#> 14 1912-01-01 2017-06-19             Zahle, LE       0.9819 CITY:LE000004
#> 15 1951-01-01 2018-12-07           Zahedan, IR       0.9975 CITY:IR000019
#> 16 1860-12-01 2018-12-07            Zagreb, HR       1.0000 CITY:HR000002
#> 17 1975-08-29 2018-12-07         Zacatecas, MX       0.9306 CITY:MX000036
#> 18 1947-01-01 2018-12-07 Yuzhno-Sakhalinsk, RS       1.0000 CITY:RS000081
#> 19 1893-01-01 2018-12-10           Yuma, AZ US       1.0000 CITY:US040015
#> 20 1942-02-01 2018-12-10   Yucca Valley, CA US       1.0000 CITY:US060048
#> 21 1885-01-01 2018-12-10      Yuba City, CA US       1.0000 CITY:US060047
#> 22 1998-02-01 2018-12-07            Yozgat, TU       0.9993 CITY:TU000056
#> 23 1893-01-01 2018-12-10     Youngstown, OH US       1.0000 CITY:US390028
#> 24 1894-01-01 2018-12-10           York, PA US       1.0000 CITY:US420024
#> 25 1869-01-01 2018-12-10        Yonkers, NY US       1.0000 CITY:US360031
#> 
#> attr(,"class")
#> [1] "ncdc_locs"

Get info on a station by specifying a dataset, locationtype, location, and station

ncdc_stations(datasetid='GHCND', locationid='FIPS:12017', stationid='GHCND:USC00084289')
#> $meta
#> NULL
#> 
#> $data
#>   elevation    mindate    maxdate latitude                  name
#> 1      12.2 1899-02-01 2018-12-09  28.8029 INVERNESS 3 SE, FL US
#>   datacoverage                id elevationUnit longitude
#> 1            1 GHCND:USC00084289        METERS  -82.3126
#> 
#> attr(,"class")
#> [1] "ncdc_stations"

Search for data

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

See a data.frame

head( out$data )
#> # A tibble: 6 x 5
#>   date                datatype        station           value fl_c 
#>   <chr>               <chr>           <chr>             <int> <chr>
#> 1 2010-05-01T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   652 S    
#> 2 2010-05-02T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   655 S    
#> 3 2010-05-03T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   658 S    
#> 4 2010-05-04T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   661 S    
#> 5 2010-05-05T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   663 S    
#> 6 2010-05-06T00:00:00 DLY-TMAX-NORMAL GHCND:USW00014895   666 S

Plot data, super simple, but it’s a start

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.

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

ncdc_datasets()
#> $meta
#> $meta$offset
#> [1] 1
#> 
#> $meta$count
#> [1] 11
#> 
#> $meta$limit
#> [1] 25
#> 
#> 
#> $data
#>                     uid    mindate    maxdate                        name
#> 1  gov.noaa.ncdc:C00861 1763-01-01 2018-12-09             Daily Summaries
#> 2  gov.noaa.ncdc:C00946 1763-01-01 2018-11-01 Global Summary of the Month
#> 3  gov.noaa.ncdc:C00947 1763-01-01 2018-01-01  Global Summary of the Year
#> 4  gov.noaa.ncdc:C00345 1991-06-05 2018-12-10    Weather Radar (Level II)
#> 5  gov.noaa.ncdc:C00708 1994-05-20 2018-12-07   Weather Radar (Level III)
#> 6  gov.noaa.ncdc:C00821 2010-01-01 2010-01-01     Normals Annual/Seasonal
#> 7  gov.noaa.ncdc:C00823 2010-01-01 2010-12-31               Normals Daily
#> 8  gov.noaa.ncdc:C00824 2010-01-01 2010-12-31              Normals Hourly
#> 9  gov.noaa.ncdc:C00822 2010-01-01 2010-12-01             Normals Monthly
#> 10 gov.noaa.ncdc:C00505 1970-05-12 2014-01-01     Precipitation 15 Minute
#> 11 gov.noaa.ncdc:C00313 1900-01-01 2014-01-01        Precipitation Hourly
#>    datacoverage         id
#> 1          1.00      GHCND
#> 2          1.00       GSOM
#> 3          1.00       GSOY
#> 4          0.95    NEXRAD2
#> 5          0.95    NEXRAD3
#> 6          1.00 NORMAL_ANN
#> 7          1.00 NORMAL_DLY
#> 8          1.00 NORMAL_HLY
#> 9          1.00 NORMAL_MLY
#> 10         0.25  PRECIP_15
#> 11         1.00 PRECIP_HLY
#> 
#> attr(,"class")
#> [1] "ncdc_datasets"

Get data category data and metadata

ncdc_datacats(locationid = 'CITY:US390029')
#> $meta
#> $meta$totalCount
#> [1] 38
#> 
#> $meta$pageCount
#> [1] 25
#> 
#> $meta$offset
#> [1] 1
#> 
#> 
#> $data
#>                     name            id
#> 1    Annual Agricultural        ANNAGR
#> 2     Annual Degree Days         ANNDD
#> 3   Annual Precipitation       ANNPRCP
#> 4     Annual Temperature       ANNTEMP
#> 5    Autumn Agricultural         AUAGR
#> 6     Autumn Degree Days          AUDD
#> 7   Autumn Precipitation        AUPRCP
#> 8     Autumn Temperature        AUTEMP
#> 9               Computed          COMP
#> 10 Computed Agricultural       COMPAGR
#> 11           Degree Days            DD
#> 12      Dual-Pol Moments DUALPOLMOMENT
#> 13             Echo Tops       ECHOTOP
#> 14      Hydrometeor Type   HYDROMETEOR
#> 15            Miscellany          MISC
#> 16                 Other         OTHER
#> 17               Overlay       OVERLAY
#> 18         Precipitation          PRCP
#> 19          Reflectivity  REFLECTIVITY
#> 20    Sky cover & clouds           SKY
#> 21   Spring Agricultural         SPAGR
#> 22    Spring Degree Days          SPDD
#> 23  Spring Precipitation        SPPRCP
#> 24    Spring Temperature        SPTEMP
#> 25   Summer Agricultural         SUAGR
#> 
#> attr(,"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.

shp <- tornadoes()
#> OGR data source with driver: ESRI Shapefile 
#> Source: "/home/jose/.cache/rnoaa/tornadoes/torn", layer: "torn"
#> with 62520 features
#> It has 21 fields
library('sp')
plot(shp)

HOMR metadata

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

homr(qid = 'COOP:046742')
#> $`20002078`
#> $`20002078`$id
#> [1] "20002078"
#> 
#> $`20002078`$head
#>                  preferredName latitude_dec longitude_dec precision
#> 1 PASO ROBLES MUNICIPAL AP, CA      35.6697     -120.6283   DDddddd
#>             por.beginDate por.endDate
#> 1 1949-10-05T00:00:00.000     Present
#> 
#> $`20002078`$namez
#>                         name  nameType
#> 1   PASO ROBLES MUNICIPAL AP      COOP
#> 2   PASO ROBLES MUNICIPAL AP PRINCIPAL
#> 3 PASO ROBLES MUNICIPAL ARPT       PUB
#> 
#> $`20002078`$identifiers
#>      idType          id
#> 1     GHCND USW00093209
#> 2   GHCNMLT USW00093209
...

Storm data

Get storm data for the year 2010

storm_data(year = 2010)
#> # A tibble: 2,787 x 200
#>    serial_num season   num basin sub_basin name  iso_time nature latitude
#>    <chr>       <int> <int> <chr> <chr>     <chr> <chr>    <chr>     <dbl>
#>  1 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"      -9.5
#>  2 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -10.2
#>  3 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -11.1
#>  4 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -11.9
#>  5 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -12.5
#>  6 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -12.8
#>  7 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -12.9
#>  8 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -12.9
#>  9 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -13  
#> 10 2009317S1…   2010     1 " SI" " MM"     ANJA  2009-11… " TS"     -13.1
#> # ... with 2,777 more rows, and 191 more variables: 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.

res <- gefs("Total_precipitation_surface_6_Hour_Accumulation_ens", lat = 46.28125, lon = -116.2188)
head(res$data)
#>   Total_precipitation_surface_6_Hour_Accumulation_ens lon lat ens time2
#> 1                                                0.45 244  46   0     6
#> 2                                                0.40 244  46   1     6
#> 3                                                0.18 244  46   2     6
#> 4                                                0.30 244  46   3     6
#> 5                                                0.60 244  46   4     6
#> 6                                                0.13 244  46   5     6

Argo buoys data

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

# 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)

coops_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] "41.7597"
#> 
#> $metadata$lon
#> [1] "-81.2811"
#> 
#> 
#> $data
#>            t       v   f
#> 1 2015-09-27 174.430 0,0
#> 2 2015-09-28 174.422 0,0

Contributors

Meta

  • Please report any issues or bugs.
  • 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. By participating in this project you agree to abide by its terms.

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Functions in rnoaa

Name Description
gefs Get GEFS ensemble forecast data for a specific lat/lon.
erddap_datasets This function is defunct.
coops Get NOAA co-ops data
caching Clear cached files
noaa_datacats This function is defunct.
erddap_search This function is defunct.
ersst NOAA Extended Reconstructed Sea Surface Temperature (ERSST) data
erddap_table This function is defunct.
check_response_swdi Check response from NOAA SWDI service, including status codes, server error messages, mime-type, etc.
ghcnd Get all GHCND data from a single weather site
noaa_plot This function is defunct.
ncdc_datacats Get possible data categories for a particular datasetid, locationid, stationid, etc.
deg2rad Convert from degrees to radians
ncdc_datasets Search NOAA datasets
erddap_data This function is defunct.
ncdc_leg_variables This function is defunct.
meteo_distance Find all monitors within a radius of a location
ghcnd_states Get meta-data on the GHCND daily data
noaa_locs_cats This function is defunct.
meteo_nearby_stations Find weather monitors near locations
bsw Blended sea winds (BSW)
rnoaa-defunct Defunct functions in rnoaa
isd Get and parse NOAA ISD/ISH data
storm_events NOAA Storm Events data
meteo_process_geographic_data Calculate the distances between a location and all available stations
cpc_prcp Precipitation data from NOAA Climate Prediction Center (CPC)
ghcnd_stations Get information on the GHCND weather stations
theme_ice ggplot2 map theme
fipscodes FIPS codes for US states.
isd_stations_search Search for NOAA ISD/ISH station data from NOAA FTP server.
meteo_show_cache Show the meteo cache directory
ncdc_locs Get metadata about NOAA NCDC locations.
storm_names NOAA storm names from IBTrACS
ncdc_plot Plot NOAA climate data.
noaa This function is defunct.
ghcnd_search Get a cleaned version of GHCND data from a single weather site
noaa_seaice This function is defunct.
check_response Check response from NOAA, including status codes, server error messages, mime-type, etc.
ncdc_locs_cats Get metadata about NOAA location categories.
ncdc_theme Theme for plotting NOAA data
autoplot.meteo_coverage autoplot method for meteo_coverage objects
homr_definitions Historical Observing Metadata Repository (HOMR) station metadata - definitions
erddap_clear_cache This function is defunct.
ncdc_datatypes Get possible data types for a particular dataset
ncdc_leg_data This function is defunct.
is.ncdc_data Check object class
ncdc_stations Get metadata about NOAA NCDC stations.
ncdc Search for and get NOAA NCDC data
arc2 Arc2 - Africa Rainfall Climatology version 2
meteo_clear_cache Clear meteo cached files
swdi Get NOAA data for the severe weather data inventory (swdi).
argo Get Argo buoy data
ghcnd_splitvars Split variables in data returned from ghcnd
isd_read Read NOAA ISD/ISH local file
isd_stations Get NOAA ISD/ISH station data from NOAA FTP server.
noaa_datatypes This function is defunct.
meteo_coverage Determine the "coverage" for a station data frame
meteo_spherical_distance Calculate the distance between two locations
seaice Get sea ice data.
readshpfile Function to read shapefiles
rnoaa-package rnoaa
erddap_info This function is defunct.
erddap_grid This function is defunct.
storm_shp Get NOAA wind storm tabular data, metadata, or shp files from IBTrACS
tornadoes Get NOAA tornado data.
noaa_locs This function is defunct.
meteo_tidy_ghcnd Create a tidy GHCND dataset from a single monitor
homr Historical Observing Metadata Repository (HOMR) station metadata
meteo_tidy_ghcnd_element Restructure element of ghcnd_search list
meteo_pull_monitors Pull GHCND weather data for multiple weather monitors
ncdc_combine Coerce multiple outputs to a single data.frame object.
ncdc_leg_site_info This function is defunct.
vis_miss Visualize missingness in a dataframe
storm_columns NOAA storm column descriptions for data from IBTrACS
noaa_stations This function is defunct.
ncdc_leg_sites This function is defunct.
noaa_combine This function is defunct.
type_summ Type summary
buoy Get NOAA buoy data from the National Buoy Data Center
lcd Local Climitalogical Data from NOAA
noaa_datasets This function is defunct.
seaiceeurls Make all urls for sea ice data
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Vignettes of rnoaa

Name
buoy_vignette.Rmd
homr_vignette.Rmd
ncdc_attributes.Rmd
ncdc_vignette.Rmd
ncdc_workflow.Rmd
rnoaa_ropenaq.Rmd
seaice_vignette.Rmd
storms_vignette.Rmd
swdi_vignette.Rmd
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Details

License MIT + file LICENSE
Encoding UTF-8
Language en-US
URL https://github.com/ropensci/rnoaa
BugReports https://github.com/ropensci/rnoaa/issues
LazyData true
VignetteBuilder knitr
RoxygenNote 6.1.1
X-schema.org-applicationCategory Climate
X-schema.org-keywords earth, science, climate, precipitation, temperature, storm, buoy, NCDC, NOAA, tornadoe, sea ice, ISD
X-schema.org-isPartOf https://ropensci.org
NeedsCompilation no
Packaged 2019-01-14 22:39:02 UTC; sckott
Repository CRAN
Date/Publication 2019-01-14 23:10:03 UTC

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