README.md
In sboysel/spitcastr: An R client for Spitcast Surf Forecast API
spitcastr
An R client for the Spitcast Surf Forecast
API.
Usage
Install spitcastr
# install.packages("devtools")
devtools::install_github("sboysel/spitcastr")
library(spitcastr)
County
List the spots in a county
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
county(item = "spots", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 28 x 5
#> county latitude longitude spot_id spot_name
#> * <chr> <dbl> <dbl> <int> <chr>
#> 1 Orange County 33.7 -118. 222 Seal Beach Pier
#> 2 Orange County 33.7 -118. 602 Surfside Jetty
#> 3 Orange County 33.7 -118. 603 Anderson St
#> 4 Orange County 33.7 -118. 604 Bolsa Chica
#> 5 Orange County 33.7 -118. 220 Goldenwest
#> 6 Orange County 33.7 -118. 605 17th Street
#> 7 Orange County 33.7 -118. 221 Huntington Pier
#> 8 Orange County 33.6 -118. 643 Huntington Beach
#> 9 Orange County 33.6 -118. 219 56th Street
#> 10 Orange County 33.6 -118. 607 40th Street
#> # ... with 18 more rows
List swell forecasts for a particular county
county(item = "swell", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 25 x 24
#> date day gmt hour hst name `0.dir` `0.hs` `0.tp` `1.dir`
#> * <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Saturda… Sat 2018-4… 12AM 2.01 Orang… 109 1.74 7.4 359
#> 2 Saturda… Sat 2018-4… 1AM 2.04 Orang… 109 1.79 7.5 1
#> 3 Saturda… Sat 2018-4… 2AM 2.07 Orang… 109 1.84 7.8 1
#> 4 Saturda… Sat 2018-4… 3AM 2.09 Orang… 109 1.87 7.9 1
#> 5 Saturda… Sat 2018-4… 4AM 2.09 Orang… 109 1.87 7.9 1
#> 6 Saturda… Sat 2018-4… 5AM 2.08 Orang… 109 1.88 8 1
#> 7 Saturda… Sat 2018-4… 6AM 2.06 Orang… 108 1.86 8 1
#> 8 Saturda… Sat 2018-4… 7AM 2.03 Orang… 108 1.83 8.1 1
#> 9 Saturda… Sat 2018-4… 8AM 2 Orang… 107 1.79 8.1 1
#> 10 Saturda… Sat 2018-4… 9AM 1.97 Orang… 106 1.76 8.1 0
#> # ... with 15 more rows, and 14 more variables: `1.hs` <dbl>,
#> # `1.tp` <dbl>, `2.dir` <dbl>, `2.hs` <dbl>, `2.tp` <dbl>,
#> # `3.dir` <dbl>, `3.hs` <dbl>, `3.tp` <dbl>, `4.dir` <dbl>,
#> # `4.hs` <dbl>, `4.tp` <dbl>, `5.dir` <dbl>, `5.hs` <dbl>, `5.tp` <dbl>
Get tide predictions for a particular county
county(item = "tide", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 25 x 7
#> date day gmt hour name tide tide_meters
#> * <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Saturday Apr 28 2018 Sat 2018-4-28 7 12AM Orang… 2.58 0.786
#> 2 Saturday Apr 28 2018 Sat 2018-4-28 8 1AM Orang… 1.27 0.388
#> 3 Saturday Apr 28 2018 Sat 2018-4-28 9 2AM Orang… 0.302 0.092
#> 4 Saturday Apr 28 2018 Sat 2018-4-28 10 3AM Orang… -0.108 -0.033
#> 5 Saturday Apr 28 2018 Sat 2018-4-28 11 4AM Orang… 0.128 0.039
#> 6 Saturday Apr 28 2018 Sat 2018-4-28 12 5AM Orang… 0.935 0.285
#> 7 Saturday Apr 28 2018 Sat 2018-4-28 13 6AM Orang… 2.10 0.639
#> 8 Saturday Apr 28 2018 Sat 2018-4-28 14 7AM Orang… 3.30 1.00
#> 9 Saturday Apr 28 2018 Sat 2018-4-28 15 8AM Orang… 4.22 1.28
#> 10 Saturday Apr 28 2018 Sat 2018-4-28 16 9AM Orang… 4.61 1.41
#> # ... with 15 more rows
Get water temperature for a particular county
county(item = "temp", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 1 x 6
#> buoy_id celcius county fahrenheit recorded wetsuit
#> <int> <dbl> <chr> <int> <chr> <chr>
#> 1 46242 16.4 Orange County 61 Y2018 M4 D28 H18 3mm Wetsuit
Get wind speed predictions for a particular county
county(item = "wind", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 25 x 9
#> date day direction_degre… direction_text gmt hour name speed_kts
#> * <chr> <chr> <int> <chr> <chr> <chr> <chr> <int>
#> 1 Satu… Sat 240 SW 2018… 12AM Oran… 5
#> 2 Satu… Sat 240 SW 2018… 1AM Oran… 5
#> 3 Satu… Sat 240 SW 2018… 2AM Oran… 5
#> 4 Satu… Sat 240 SW 2018… 3AM Oran… 4
#> 5 Satu… Sat 240 SW 2018… 4AM Oran… 3
#> 6 Satu… Sat 130 SE 2018… 5AM Oran… 3
#> 7 Satu… Sat 130 SE 2018… 6AM Oran… 3
#> 8 Satu… Sat 130 SE 2018… 7AM Oran… 3
#> 9 Satu… Sat 130 SE 2018… 8AM Oran… 3
#> 10 Satu… Sat 130 SE 2018… 9AM Oran… 4
#> # ... with 15 more rows, and 1 more variable: speed_mph <dbl>
Spots
List all spots
spot(item = "all") %>%
dplyr::tbl_df() %>%
dplyr::filter(county_name == "Santa Barbara")
#> # A tibble: 22 x 5
#> county_name latitude longitude spot_id spot_name
#> <chr> <dbl> <dbl> <int> <chr>
#> 1 Santa Barbara 35.0 -121. 711 Santa Maria Rivermouth
#> 2 Santa Barbara 34.7 -121. 712 Surf Beach
#> 3 Santa Barbara 34.5 -121. 185 Jalama
#> 4 Santa Barbara 34.5 -120. 713 Tajiguas
#> 5 Santa Barbara 34.5 -120. 620 Refugio
#> 6 Santa Barbara 34.5 -120. 714 Beavers
#> 7 Santa Barbara 34.5 -120. 183 El Capitan
#> 8 Santa Barbara 34.4 -120. 182 Sands
#> 9 Santa Barbara 34.4 -120. 181 Devereux
#> 10 Santa Barbara 34.4 -120. 715 Pescaderos
#> # ... with 12 more rows
Get the forecast for a specific spot
spot(item = "forecast", spot_id = 183) %>%
dplyr::tbl_df()
#> # A tibble: 25 x 17
#> date day gmt hour latitude live longitude shape shape_full size
#> * <chr> <chr> <chr> <chr> <dbl> <int> <dbl> <chr> <chr> <int>
#> 1 Satu… Sat 2018… 12AM 34.5 0 -120. pf Poor-Fair 0
#> 2 Satu… Sat 2018… 1AM 34.5 0 -120. pf Poor-Fair 0
#> 3 Satu… Sat 2018… 2AM 34.5 0 -120. pf Poor-Fair 0
#> 4 Satu… Sat 2018… 3AM 34.5 0 -120. pf Poor-Fair 0
#> 5 Satu… Sat 2018… 4AM 34.5 0 -120. pf Poor-Fair 0
#> 6 Satu… Sat 2018… 5AM 34.5 0 -120. pf Poor-Fair 0
#> 7 Satu… Sat 2018… 6AM 34.5 0 -120. pf Poor-Fair 0
#> 8 Satu… Sat 2018… 7AM 34.5 0 -120. pf Poor-Fair 0
#> 9 Satu… Sat 2018… 8AM 34.5 0 -120. pf Poor-Fair 0
#> 10 Satu… Sat 2018… 9AM 34.5 0 -120. pf Poor-Fair 0
#> # ... with 15 more rows, and 7 more variables: size_ft <dbl>,
#> # spot_id <int>, spot_name <chr>, warnings <list>,
#> # shape_detail.swell <chr>, shape_detail.tide <chr>,
#> # shape_detail.wind <chr>
List spot near your location
spot(item = "nearby") %>%
dplyr::tbl_df()
#> # A tibble: 20 x 7
#> county_id progress spot_id spot_id_char spot_name longitude latitude
#> * <int> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 5 10 110 bolinas-bolin… Bolinas -123. 37.9
#> 2 5 10 112 fort-cronkhit… Fort Cron… -123. 37.8
#> 3 6 46 113 fort-point-sa… Fort Point -122. 37.8
#> 4 6 24 649 eagles-point-… Eagles Po… -122. 37.8
#> 5 6 25 648 deadmans-san-… Deadmans -122. 37.8
#> 6 6 44 697 kellys-cove-s… Ocean Bea… -123. 37.8
#> 7 6 100 114 north-ocean-b… Ocean Bea… -123. 37.8
#> 8 6 100 117 south-ocean-b… Ocean Bea… -123. 37.8
#> 9 7 100 120 linda-mar-pac… Linda Mar -123. 37.6
#> 10 7 27 121 montara-monta… Montara -123. 37.6
#> 11 7 100 122 mavericks-hal… Mavericks -123. 37.5
#> 12 7 45 123 princeton-jet… Princeton… -122. 37.5
#> 13 7 22 126 pomponio-stat… Pomponio … -122. 37.3
#> 14 7 26 118 ano-nuevo-pes… Ano Nuevo -122. 37.1
#> 15 1 21 593 county-line-d… County Li… -122. 37.1
#> 16 1 48 129 waddell-creek… Waddell C… -122. 37.1
#> 17 1 16 600 waddell-reefs… Waddell R… -122. 37.1
#> 18 1 29 128 scotts-creek-… Scotts Cr… -122. 37.0
#> 19 1 19 133 davenport-lan… Davenport… -122. 37.0
#> 20 1 92 131 four-mile-san… Four Mile -122. 37.0
List neighbors of a given spot
spot(item = "neighbors", spot_id = 1, direction = "above") %>%
dplyr::tbl_df()
#> # A tibble: 15 x 7
#> county_id progress spot_id spot_id_char spot_name longitude latitude
#> * <int> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 1 52 130 three-mile-san… Three Mi… -122. 37.0
#> 2 1 100 6 natural-bridge… Natural … -122. 36.9
#> 3 1 57 146 stockton-avenu… Stockton… -122. 36.9
#> 4 1 45 145 swift-street-s… Swift St… -122. 36.9
#> 5 1 64 10 getchell-santa… Getchell -122. 36.9
#> 6 1 32 144 mitchells-cove… Mitchell… -122. 37.0
#> 7 1 100 2 steamer-lane-s… Steamer … -122. 37.0
#> 8 1 100 3 cowells-santa-… Cowells -122. 37.0
#> 9 1 10 143 the-rivermouth… The Rive… -122. 37.0
#> 10 1 30 9 blacks-santa-c… Blacks -122. 37.0
#> 11 1 30 8 santa-marias-s… Santa Ma… -122. 37.0
#> 12 1 54 7 26th-ave-santa… 26th Ave… -122. 37.0
#> 13 1 31 138 little-windans… Little W… -122. 37.0
#> 14 1 61 137 rockview-santa… Rockview -122. 37.0
#> 15 1 87 5 sewer-peak-san… Sewer Pe… -122. 37.0
Search for a spot with a sequence of parameters
spot(item = "search", size_min = 3, size_max = 8, shape_min = 1) %>%
dplyr::tbl_df()
#> # A tibble: 25 x 18
#> `_id` coast_order field_data_count score spot_id spot_id_char spot_name
#> * <chr> <chr> <int> <dbl> <int> <chr> <chr>
#> 1 717-… 12-15 2 4.70 717 silver-stra… Silver S…
#> 2 106-… 04-06 1 2.13 106 russian-riv… Russian …
#> 3 611-… 14-24 2 1.96 611 thalia-lagu… Thalia
#> 4 152-… 09-10 14 1.85 152 sand-dollar… Sand Dol…
#> 5 215-… 14-25 1 1.84 215 brooks-stre… Brooks S…
#> 6 743-… 14-26 2 1.82 743 agate-lagun… Agate
#> 7 229-… 15-27 527 1.66 229 blacks-beac… Blacks B…
#> 8 209-… 14-38 112 1.52 209 cottons-poi… Cottons …
#> 9 801-… 06-08 3 1.49 801 sloat-ocean… Ocean Be…
#> 10 238-… 15-01 762 1.48 238 oceanside-h… Oceansid…
#> # ... with 15 more rows, and 11 more variables: timestamp <int>,
#> # warnings <int>, average.shape <dbl>, average.size <dbl>,
#> # average.size_max <dbl>, average.size_min <dbl>, date_local.dd <int>,
#> # date_local.mm <int>, date_local.yy <int>, longitude <dbl>,
#> # latitude <dbl>
List top spots
spot(item = "top") %>%
dplyr::tbl_df()
#> # A tibble: 5 x 5
#> avg_max_size avg_min_size shape spot_id spot_name
#> * <int> <int> <chr> <int> <chr>
#> 1 6 5 Fair 229 Blacks Beach
#> 2 5 5 Fair 209 Cottons Point
#> 3 5 5 Fair 238 Oceanside Harbor
#> 4 5 5 Fair 594 Oceanside Pier
#> 5 5 4 Fair 234 Swamis
Resources
Be sure to checkout Spitcast and read the 'fine
print':
Public distribution of Spitcast API content must acknowledge Spitcast as
the content source, and provide a link to Spitcast.com.
The Spitcast API is available for non-commercial use. Commercial use is
possible by prior arrangement.
The Spitcast API is available for low request volume use. Please cache API
responses. Please also make API requests to api.spitcast.com, and NOT to
www.spitcast.com.
The Spitcast API is experimental and is currently offered on an ad hoc basis
with no guarantee of uptime or availability of continued service. We reserve
the right to disable access to external applications at any time.
sboysel/spitcastr documentation built on May 29, 2019, 3:25 p.m.
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spitcastr
An R client for the Spitcast Surf Forecast
API.
Usage
Install spitcastr
# install.packages("devtools")
devtools::install_github("sboysel/spitcastr")
library(spitcastr)
County
List the spots in a county
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
county(item = "spots", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 28 x 5
#> county latitude longitude spot_id spot_name
#> * <chr> <dbl> <dbl> <int> <chr>
#> 1 Orange County 33.7 -118. 222 Seal Beach Pier
#> 2 Orange County 33.7 -118. 602 Surfside Jetty
#> 3 Orange County 33.7 -118. 603 Anderson St
#> 4 Orange County 33.7 -118. 604 Bolsa Chica
#> 5 Orange County 33.7 -118. 220 Goldenwest
#> 6 Orange County 33.7 -118. 605 17th Street
#> 7 Orange County 33.7 -118. 221 Huntington Pier
#> 8 Orange County 33.6 -118. 643 Huntington Beach
#> 9 Orange County 33.6 -118. 219 56th Street
#> 10 Orange County 33.6 -118. 607 40th Street
#> # ... with 18 more rows
List swell forecasts for a particular county
county(item = "swell", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 25 x 24
#> date day gmt hour hst name `0.dir` `0.hs` `0.tp` `1.dir`
#> * <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Saturda… Sat 2018-4… 12AM 2.01 Orang… 109 1.74 7.4 359
#> 2 Saturda… Sat 2018-4… 1AM 2.04 Orang… 109 1.79 7.5 1
#> 3 Saturda… Sat 2018-4… 2AM 2.07 Orang… 109 1.84 7.8 1
#> 4 Saturda… Sat 2018-4… 3AM 2.09 Orang… 109 1.87 7.9 1
#> 5 Saturda… Sat 2018-4… 4AM 2.09 Orang… 109 1.87 7.9 1
#> 6 Saturda… Sat 2018-4… 5AM 2.08 Orang… 109 1.88 8 1
#> 7 Saturda… Sat 2018-4… 6AM 2.06 Orang… 108 1.86 8 1
#> 8 Saturda… Sat 2018-4… 7AM 2.03 Orang… 108 1.83 8.1 1
#> 9 Saturda… Sat 2018-4… 8AM 2 Orang… 107 1.79 8.1 1
#> 10 Saturda… Sat 2018-4… 9AM 1.97 Orang… 106 1.76 8.1 0
#> # ... with 15 more rows, and 14 more variables: `1.hs` <dbl>,
#> # `1.tp` <dbl>, `2.dir` <dbl>, `2.hs` <dbl>, `2.tp` <dbl>,
#> # `3.dir` <dbl>, `3.hs` <dbl>, `3.tp` <dbl>, `4.dir` <dbl>,
#> # `4.hs` <dbl>, `4.tp` <dbl>, `5.dir` <dbl>, `5.hs` <dbl>, `5.tp` <dbl>
Get tide predictions for a particular county
county(item = "tide", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 25 x 7
#> date day gmt hour name tide tide_meters
#> * <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Saturday Apr 28 2018 Sat 2018-4-28 7 12AM Orang… 2.58 0.786
#> 2 Saturday Apr 28 2018 Sat 2018-4-28 8 1AM Orang… 1.27 0.388
#> 3 Saturday Apr 28 2018 Sat 2018-4-28 9 2AM Orang… 0.302 0.092
#> 4 Saturday Apr 28 2018 Sat 2018-4-28 10 3AM Orang… -0.108 -0.033
#> 5 Saturday Apr 28 2018 Sat 2018-4-28 11 4AM Orang… 0.128 0.039
#> 6 Saturday Apr 28 2018 Sat 2018-4-28 12 5AM Orang… 0.935 0.285
#> 7 Saturday Apr 28 2018 Sat 2018-4-28 13 6AM Orang… 2.10 0.639
#> 8 Saturday Apr 28 2018 Sat 2018-4-28 14 7AM Orang… 3.30 1.00
#> 9 Saturday Apr 28 2018 Sat 2018-4-28 15 8AM Orang… 4.22 1.28
#> 10 Saturday Apr 28 2018 Sat 2018-4-28 16 9AM Orang… 4.61 1.41
#> # ... with 15 more rows
Get water temperature for a particular county
county(item = "temp", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 1 x 6
#> buoy_id celcius county fahrenheit recorded wetsuit
#> <int> <dbl> <chr> <int> <chr> <chr>
#> 1 46242 16.4 Orange County 61 Y2018 M4 D28 H18 3mm Wetsuit
Get wind speed predictions for a particular county
county(item = "wind", county_name = "Orange County") %>%
dplyr::tbl_df()
#> # A tibble: 25 x 9
#> date day direction_degre… direction_text gmt hour name speed_kts
#> * <chr> <chr> <int> <chr> <chr> <chr> <chr> <int>
#> 1 Satu… Sat 240 SW 2018… 12AM Oran… 5
#> 2 Satu… Sat 240 SW 2018… 1AM Oran… 5
#> 3 Satu… Sat 240 SW 2018… 2AM Oran… 5
#> 4 Satu… Sat 240 SW 2018… 3AM Oran… 4
#> 5 Satu… Sat 240 SW 2018… 4AM Oran… 3
#> 6 Satu… Sat 130 SE 2018… 5AM Oran… 3
#> 7 Satu… Sat 130 SE 2018… 6AM Oran… 3
#> 8 Satu… Sat 130 SE 2018… 7AM Oran… 3
#> 9 Satu… Sat 130 SE 2018… 8AM Oran… 3
#> 10 Satu… Sat 130 SE 2018… 9AM Oran… 4
#> # ... with 15 more rows, and 1 more variable: speed_mph <dbl>
Spots
List all spots
spot(item = "all") %>%
dplyr::tbl_df() %>%
dplyr::filter(county_name == "Santa Barbara")
#> # A tibble: 22 x 5
#> county_name latitude longitude spot_id spot_name
#> <chr> <dbl> <dbl> <int> <chr>
#> 1 Santa Barbara 35.0 -121. 711 Santa Maria Rivermouth
#> 2 Santa Barbara 34.7 -121. 712 Surf Beach
#> 3 Santa Barbara 34.5 -121. 185 Jalama
#> 4 Santa Barbara 34.5 -120. 713 Tajiguas
#> 5 Santa Barbara 34.5 -120. 620 Refugio
#> 6 Santa Barbara 34.5 -120. 714 Beavers
#> 7 Santa Barbara 34.5 -120. 183 El Capitan
#> 8 Santa Barbara 34.4 -120. 182 Sands
#> 9 Santa Barbara 34.4 -120. 181 Devereux
#> 10 Santa Barbara 34.4 -120. 715 Pescaderos
#> # ... with 12 more rows
Get the forecast for a specific spot
spot(item = "forecast", spot_id = 183) %>%
dplyr::tbl_df()
#> # A tibble: 25 x 17
#> date day gmt hour latitude live longitude shape shape_full size
#> * <chr> <chr> <chr> <chr> <dbl> <int> <dbl> <chr> <chr> <int>
#> 1 Satu… Sat 2018… 12AM 34.5 0 -120. pf Poor-Fair 0
#> 2 Satu… Sat 2018… 1AM 34.5 0 -120. pf Poor-Fair 0
#> 3 Satu… Sat 2018… 2AM 34.5 0 -120. pf Poor-Fair 0
#> 4 Satu… Sat 2018… 3AM 34.5 0 -120. pf Poor-Fair 0
#> 5 Satu… Sat 2018… 4AM 34.5 0 -120. pf Poor-Fair 0
#> 6 Satu… Sat 2018… 5AM 34.5 0 -120. pf Poor-Fair 0
#> 7 Satu… Sat 2018… 6AM 34.5 0 -120. pf Poor-Fair 0
#> 8 Satu… Sat 2018… 7AM 34.5 0 -120. pf Poor-Fair 0
#> 9 Satu… Sat 2018… 8AM 34.5 0 -120. pf Poor-Fair 0
#> 10 Satu… Sat 2018… 9AM 34.5 0 -120. pf Poor-Fair 0
#> # ... with 15 more rows, and 7 more variables: size_ft <dbl>,
#> # spot_id <int>, spot_name <chr>, warnings <list>,
#> # shape_detail.swell <chr>, shape_detail.tide <chr>,
#> # shape_detail.wind <chr>
List spot near your location
spot(item = "nearby") %>%
dplyr::tbl_df()
#> # A tibble: 20 x 7
#> county_id progress spot_id spot_id_char spot_name longitude latitude
#> * <int> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 5 10 110 bolinas-bolin… Bolinas -123. 37.9
#> 2 5 10 112 fort-cronkhit… Fort Cron… -123. 37.8
#> 3 6 46 113 fort-point-sa… Fort Point -122. 37.8
#> 4 6 24 649 eagles-point-… Eagles Po… -122. 37.8
#> 5 6 25 648 deadmans-san-… Deadmans -122. 37.8
#> 6 6 44 697 kellys-cove-s… Ocean Bea… -123. 37.8
#> 7 6 100 114 north-ocean-b… Ocean Bea… -123. 37.8
#> 8 6 100 117 south-ocean-b… Ocean Bea… -123. 37.8
#> 9 7 100 120 linda-mar-pac… Linda Mar -123. 37.6
#> 10 7 27 121 montara-monta… Montara -123. 37.6
#> 11 7 100 122 mavericks-hal… Mavericks -123. 37.5
#> 12 7 45 123 princeton-jet… Princeton… -122. 37.5
#> 13 7 22 126 pomponio-stat… Pomponio … -122. 37.3
#> 14 7 26 118 ano-nuevo-pes… Ano Nuevo -122. 37.1
#> 15 1 21 593 county-line-d… County Li… -122. 37.1
#> 16 1 48 129 waddell-creek… Waddell C… -122. 37.1
#> 17 1 16 600 waddell-reefs… Waddell R… -122. 37.1
#> 18 1 29 128 scotts-creek-… Scotts Cr… -122. 37.0
#> 19 1 19 133 davenport-lan… Davenport… -122. 37.0
#> 20 1 92 131 four-mile-san… Four Mile -122. 37.0
List neighbors of a given spot
spot(item = "neighbors", spot_id = 1, direction = "above") %>%
dplyr::tbl_df()
#> # A tibble: 15 x 7
#> county_id progress spot_id spot_id_char spot_name longitude latitude
#> * <int> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 1 52 130 three-mile-san… Three Mi… -122. 37.0
#> 2 1 100 6 natural-bridge… Natural … -122. 36.9
#> 3 1 57 146 stockton-avenu… Stockton… -122. 36.9
#> 4 1 45 145 swift-street-s… Swift St… -122. 36.9
#> 5 1 64 10 getchell-santa… Getchell -122. 36.9
#> 6 1 32 144 mitchells-cove… Mitchell… -122. 37.0
#> 7 1 100 2 steamer-lane-s… Steamer … -122. 37.0
#> 8 1 100 3 cowells-santa-… Cowells -122. 37.0
#> 9 1 10 143 the-rivermouth… The Rive… -122. 37.0
#> 10 1 30 9 blacks-santa-c… Blacks -122. 37.0
#> 11 1 30 8 santa-marias-s… Santa Ma… -122. 37.0
#> 12 1 54 7 26th-ave-santa… 26th Ave… -122. 37.0
#> 13 1 31 138 little-windans… Little W… -122. 37.0
#> 14 1 61 137 rockview-santa… Rockview -122. 37.0
#> 15 1 87 5 sewer-peak-san… Sewer Pe… -122. 37.0
Search for a spot with a sequence of parameters
spot(item = "search", size_min = 3, size_max = 8, shape_min = 1) %>%
dplyr::tbl_df()
#> # A tibble: 25 x 18
#> `_id` coast_order field_data_count score spot_id spot_id_char spot_name
#> * <chr> <chr> <int> <dbl> <int> <chr> <chr>
#> 1 717-… 12-15 2 4.70 717 silver-stra… Silver S…
#> 2 106-… 04-06 1 2.13 106 russian-riv… Russian …
#> 3 611-… 14-24 2 1.96 611 thalia-lagu… Thalia
#> 4 152-… 09-10 14 1.85 152 sand-dollar… Sand Dol…
#> 5 215-… 14-25 1 1.84 215 brooks-stre… Brooks S…
#> 6 743-… 14-26 2 1.82 743 agate-lagun… Agate
#> 7 229-… 15-27 527 1.66 229 blacks-beac… Blacks B…
#> 8 209-… 14-38 112 1.52 209 cottons-poi… Cottons …
#> 9 801-… 06-08 3 1.49 801 sloat-ocean… Ocean Be…
#> 10 238-… 15-01 762 1.48 238 oceanside-h… Oceansid…
#> # ... with 15 more rows, and 11 more variables: timestamp <int>,
#> # warnings <int>, average.shape <dbl>, average.size <dbl>,
#> # average.size_max <dbl>, average.size_min <dbl>, date_local.dd <int>,
#> # date_local.mm <int>, date_local.yy <int>, longitude <dbl>,
#> # latitude <dbl>
List top spots
spot(item = "top") %>%
dplyr::tbl_df()
#> # A tibble: 5 x 5
#> avg_max_size avg_min_size shape spot_id spot_name
#> * <int> <int> <chr> <int> <chr>
#> 1 6 5 Fair 229 Blacks Beach
#> 2 5 5 Fair 209 Cottons Point
#> 3 5 5 Fair 238 Oceanside Harbor
#> 4 5 5 Fair 594 Oceanside Pier
#> 5 5 4 Fair 234 Swamis
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