The noaaoceans package is designed to facilitate access to various National Oceanic and Atmospheric Administration (NOAA) data sources. The current version focuses on accessing data from the CO-OPS API. The package also facilitates the collection of basic metadata for each of the stations that collect the data available in the API.
library(noaaoceans)
library(dplyr)
library(ggplot2)
library(maps)
library(mapdata)
# Get a list of all the stations.
station_df <- list_coops_stations()
# Filter to stations in Washington with Water Temp Sensor
wa_station <- station_df %>%
filter(station_state == 'WA' & water_temp == '1')
wa_station %>% dplyr::as_tibble(.) %>% head()
#> # A tibble: 6 x 15
#> station_id station_names station_state station_lat station_long
#> <chr> <chr> <chr> <chr> <chr>
#> 1 9440422 Longview WA 46.1061 -122.9542
#> 2 9440910 Toke Point WA 46.7075 -123.9669
#> 3 9441102 Westport WA 46.9043 -124.1051
#> 4 9442396 La Push, Qui… WA 47.913 -124.6369
#> 5 9443090 Neah Bay WA 48.3703 -124.6019
#> 6 9444090 Port Angeles WA 48.1247 -123.4411
#> # ... with 10 more variables: date_established <chr>, water_level <chr>,
#> # winds <chr>, air_temp <chr>, water_temp <chr>, air_pressure <chr>,
#> # conductivity <chr>, visibility <chr>, humidity <chr>, air_gap <chr># Create an empty storage data frame.
water_temp <- data.frame()
# Loop through the station and call the API for each station
for (i in wa_station$station_id) {
query_df <- query_coops_data(station_id = i,
start_date = '20181201',
end_date = '20181231',
data_product = 'water_temperature',
interval = 'h') # hourly readings
# Add current station results to the storage data frame
water_temp <- water_temp %>% bind_rows(., query_df)
}
water_temp %>% as_tibble(.) %>% head()
#> # A tibble: 6 x 4
#> t v f station
#> <chr> <chr> <chr> <chr>
#> 1 2018-12-01 00:00 48.2 0,0,0 9440422
#> 2 2018-12-01 01:00 48.2 0,0,0 9440422
#> 3 2018-12-01 02:00 48.0 0,0,0 9440422
#> 4 2018-12-01 03:00 48.0 0,0,0 9440422
#> 5 2018-12-01 04:00 48.0 0,0,0 9440422
#> 6 2018-12-01 05:00 48.0 0,0,0 9440422# Correct data types.
water_temp <- water_temp %>%
mutate(v = as.numeric(v), t = as.POSIXct(t))
water_temp %>%
group_by(t) %>%
# Compute the hourly average.
summarise(avg_temp = mean(v, na.rm = TRUE)) %>%
# Plot the hourly average.
ggplot(aes(x = t, y = avg_temp)) +
geom_path() +
labs(x = "Date",
y = 'Average Water Temperature',
title = 'Average Hourly Water Temperature In Washington During December 2018')