working with buoy data
Scott Chamberlain
2020-07-27
Source:vignettes/buoy_vignette.Rmd
buoy_vignette.RmdThis 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.
Find out what buoys are available in a dataset
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.htmlOr browse them on the web
browseURL(res[1, 2])Get buoy data
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 rowsIncluding 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 rowsIncluding 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 rowsIncluding 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