This vignette covers NOAA buoy data from the National Buoy Data Center. The
main function to get data is buoy
, while buoys
can be used to
get the buoy IDs and web pages for each buoy.
library('rnoaa')
res <- buoys(dataset = "cwind")
Inspect the buoy ids, and the urls for them
head(res) #> id #> 1 41001 #> 2 41002 #> 3 41004 #> 4 41006 #> 5 41008 #> 6 41009 #> url #> 1 https://dods.ndbc.noaa.gov/thredds/catalog/data/cwind/41001/catalog.html #> 2 https://dods.ndbc.noaa.gov/thredds/catalog/data/cwind/41002/catalog.html #> 3 https://dods.ndbc.noaa.gov/thredds/catalog/data/cwind/41004/catalog.html #> 4 https://dods.ndbc.noaa.gov/thredds/catalog/data/cwind/41006/catalog.html #> 5 https://dods.ndbc.noaa.gov/thredds/catalog/data/cwind/41008/catalog.html #> 6 https://dods.ndbc.noaa.gov/thredds/catalog/data/cwind/41009/catalog.html
Or browse them on the web
browseURL(res[1, 2])
With buoy
you can get data for a particular dataset, buoy id, year, and datatype.
Get data for a buoy
if no year or datatype specified, we get the first file
buoy(dataset = 'cwind', buoyid = 46085) #> Using c2007.nc #> Dimensions (rows/cols): [33486 X 5] #> 2 variables: [wind_dir, wind_spd] #> #> # A tibble: 33,486 x 5 #> time lat lon wind_dir wind_spd #> <chr> <dbl> <dbl> <int> <dbl> #> 1 2007-05-05T02:00:00Z 55.9 -143. 331 2.80 #> 2 2007-05-05T02:10:00Z 55.9 -143. 328 2.60 #> 3 2007-05-05T02:20:00Z 55.9 -143. 329 2.20 #> 4 2007-05-05T02:30:00Z 55.9 -143. 356 2.10 #> 5 2007-05-05T02:40:00Z 55.9 -143. 360 1.5 #> 6 2007-05-05T02:50:00Z 55.9 -143. 10 1.90 #> 7 2007-05-05T03:00:00Z 55.9 -143. 10 2.20 #> 8 2007-05-05T03:10:00Z 55.9 -143. 14 2.20 #> 9 2007-05-05T03:20:00Z 55.9 -143. 16 2.10 #> 10 2007-05-05T03:30:00Z 55.9 -143. 22 1.60 #> # … with 33,476 more rows
Including year
buoy(dataset = 'cwind', buoyid = 41001, year = 1999) #> Using c1999.nc #> Dimensions (rows/cols): [52554 X 5] #> 2 variables: [wind_dir, wind_spd] #> #> # A tibble: 52,554 x 5 #> time lat lon wind_dir wind_spd #> <chr> <dbl> <dbl> <int> <dbl> #> 1 1999-01-01T00:00:00Z 34.7 -72.7 272 11.7 #> 2 1999-01-01T00:10:00Z 34.7 -72.7 260 11 #> 3 1999-01-01T00:20:00Z 34.7 -72.7 249 8.70 #> 4 1999-01-01T00:30:00Z 34.7 -72.7 247 8.40 #> 5 1999-01-01T00:40:00Z 34.7 -72.7 240 7.10 #> 6 1999-01-01T00:50:00Z 34.7 -72.7 242 7.90 #> 7 1999-01-01T01:00:00Z 34.7 -72.7 246 8.30 #> 8 1999-01-01T01:10:00Z 34.7 -72.7 297 10.9 #> 9 1999-01-01T01:20:00Z 34.7 -72.7 299 11.3 #> 10 1999-01-01T01:30:00Z 34.7 -72.7 299 11.1 #> # … with 52,544 more rows
Including year and datatype
buoy(dataset = 'cwind', buoyid = 45005, year = 2008, datatype = "c") #> Dimensions (rows/cols): [29688 X 5] #> 2 variables: [wind_dir, wind_spd] #> #> # A tibble: 29,688 x 5 #> time lat lon wind_dir wind_spd #> <chr> <dbl> <dbl> <int> <dbl> #> 1 2008-04-29T09:00:00Z 41.7 -82.4 10 9 #> 2 2008-04-29T09:10:00Z 41.7 -82.4 8 9 #> 3 2008-04-29T09:20:00Z 41.7 -82.4 5 9.30 #> 4 2008-04-29T09:30:00Z 41.7 -82.4 13 9.5 #> 5 2008-04-29T09:40:00Z 41.7 -82.4 14 9.40 #> 6 2008-04-29T09:50:00Z 41.7 -82.4 12 9.40 #> 7 2008-04-29T14:00:00Z 41.7 -82.4 341 6.5 #> 8 2008-04-29T14:10:00Z 41.7 -82.4 332 6.80 #> 9 2008-04-29T14:20:00Z 41.7 -82.4 335 6.40 #> 10 2008-04-29T14:30:00Z 41.7 -82.4 332 6.5 #> # … with 29,678 more rows
Including just datatype
buoy(dataset = 'cwind', buoyid = 45005, datatype = "c") #> Using c1996.nc #> Dimensions (rows/cols): [26784 X 5] #> 2 variables: [wind_dir, wind_spd] #> #> # A tibble: 26,784 x 5 #> time lat lon wind_dir wind_spd #> <chr> <dbl> <dbl> <int> <dbl> #> 1 1996-05-15T23:00:00Z 41.7 -82.4 337 2.20 #> 2 1996-05-15T23:10:00Z 41.7 -82.4 282 1 #> 3 1996-05-15T23:20:00Z 41.7 -82.4 282 2.20 #> 4 1996-05-15T23:30:00Z 41.7 -82.4 258 2.60 #> 5 1996-05-15T23:40:00Z 41.7 -82.4 254 3 #> 6 1996-05-15T23:50:00Z 41.7 -82.4 252 2.70 #> 7 1996-05-16T00:00:00Z 41.7 -82.4 240 2.10 #> 8 1996-05-16T00:10:00Z 41.7 -82.4 246 2.40 #> 9 1996-05-16T00:20:00Z 41.7 -82.4 251 2.70 #> 10 1996-05-16T00:30:00Z 41.7 -82.4 253 2.90 #> # … with 26,774 more rows
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