haidawave is an R package to calculate wave exposure (mean fetch) based on fetch and possibly wind data. In addition to the mean_fetch, wind_weights and weighted_fetch functions, which are demonstrated below, it also provides the convert_proj function to convert latitudes and longitudes to eastings and northings in BC Albers (or a projection of the users choice); the nearest_point function to join one data frame to another based on the shortest euclidean distance between their coordinates; and the hg_fetch and hg_wind to download the complete fetch and wind datasets for Haida Gwaii.
library(haidawave)
library(magrittr)
library(ggplot2)
library(ggthemes)
library(RColorBrewer)
library(plyr)
library(dplyr)
# only consider points for Reef Island, Laskeek Bay, British Coluumbia
laskeek_fetch <- filter(haidawave::laskeek_fetch, Easting > 627000 & Northing < 890000)
# calculate mean fetch for each point
fetch <- ddply(laskeek_fetch, c("Easting", "Northing"), mean_fetch)
# Plot mean Fetch
gp <- ggplot(fetch) + geom_point(aes(x = Easting, y = Northing, color = Fetch), size = 0.5) +
coord_fixed() +
theme_few() +
scale_color_distiller(palette = "Spectral")
print(gp)
# get wind weights by station and direction
cumshewa_wind <- ddply(haidawave::cumshewa_wind, c("Station"), wind_weights) %>% as.tbl()
cumshewa_wind
#> # A tibble: 36 × 5
#> Station Direction Frequency Speed Weight
#> <chr> <dbl> <int> <dbl> <dbl>
#> 1 Cumshewa 10 762 16.91995 0.02863840
#> 2 Cumshewa 20 418 12.89234 0.02182135
#> 3 Cumshewa 30 324 11.41975 0.01932887
#> 4 Cumshewa 40 228 11.33772 0.01919002
#> 5 Cumshewa 50 208 10.53365 0.01782907
#> 6 Cumshewa 60 198 10.73737 0.01817389
#> 7 Cumshewa 70 201 12.68159 0.02146464
#> 8 Cumshewa 80 260 13.58462 0.02299308
#> 9 Cumshewa 90 290 16.53793 0.02799181
#> 10 Cumshewa 100 365 18.02466 0.03050821
#> # ... with 26 more rows
# averge fetch bearings by 10 degree increments so consistent with wind directions
laskeek_fetch %<>% mutate(Bearing = (Bearing + 5) %/% 10 * 10)
# bind wind weights
laskeek_fetch %<>% left_join(cumshewa_wind, by = c("Bearing" = "Direction"))
laskeek_fetch
#> # A tibble: 146,736 × 8
#> Easting Northing Bearing Fetch Station Frequency Speed Weight
#> <dbl> <dbl> <dbl> <dbl> <chr> <int> <dbl> <dbl>
#> 1 630768 887336 10 1 Cumshewa 762 16.91995 0.0286384
#> 2 630759 887339 10 1 Cumshewa 762 16.91995 0.0286384
#> 3 630755 887348 10 346 Cumshewa 762 16.91995 0.0286384
#> 4 630759 887357 10 338 Cumshewa 762 16.91995 0.0286384
#> 5 630766 887364 10 334 Cumshewa 762 16.91995 0.0286384
#> 6 630775 887368 10 333 Cumshewa 762 16.91995 0.0286384
#> 7 630785 887369 10 342 Cumshewa 762 16.91995 0.0286384
#> 8 630794 887367 10 1055 Cumshewa 762 16.91995 0.0286384
#> 9 630796 887358 10 1062 Cumshewa 762 16.91995 0.0286384
#> 10 630790 887350 10 1 Cumshewa 762 16.91995 0.0286384
#> # ... with 146,726 more rows
# calculate mean weighted fetch
laskeek_fetch %<>% ddply(c("Easting", "Northing"), weighted_fetch) %>% as.tbl()
print(gp %+% laskeek_fetch)
To install the latest version from GitHub
# install.packages("devtools")
devtools::install_github("sebdalgarno/haidawave")
Please report any issues.
Pull requests are always welcome.