README.md
In BigelowLab/maree: maree
maree
maree is a small R package that wraps around a local installation of xtide. maree
provides fuctionality to generate and read both raw and plain output modes.
The maree package doesn't add any new functionality to xtide.
It is important to note there already exists nice alternatives including
rtide. We have found it handy to
have programmatic access to a local installation of the xtide application.
Requirements
Installation
Install from github.
devtools::install_github("BigelowLab\maree")
Usage
Listing available stations
Station (locations) are either reference (harmonic) or substations. Each has a
identifier and name.
library(maree)
# list all of the available stations
locs <- fetch_locations()
# # A tibble: 5,382 x 5
# `Station ID` Name Type Latitude Longitude
# <chr> <chr> <chr> <dbl> <dbl>
# 1 PCT3896_1 0.2 mile off Flat Point, Taku Inlet, Stephe… Sub 58.3 -134.
# 2 PCT3901_1 0.2 mile off Taku Point, Taku Inlet, Stephe… Sub 58.4 -134.
# 3 ACT8491_1 0.5 mile southeast of, Pinellas Point, Tamp… Sub 27.7 -82.6
# 4 8537731 0.8 n.mi. above entrance, Alloway Creek, Ne… Sub 39.5 -75.5
# 5 8537535 1 n.mi. above entrance, Mad Horse Creek, Ne… Sub 39.4 -75.4
# 6 ACT8496_1 1.9 miles SE of, Pinellas Point, Tampa Bay,… Sub 27.7 -82.6
# 7 8677833 2.5 miles above mouth, Little Satilla River… Sub 31.1 -81.5
# 8 8537753 2.5 n.mi. above entrance, Alloway Creek, Ne… Sub 39.5 -75.5
# 9 ACT8486_1 2.6 miles south of, Pinellas Point, Tampa B… Sub 27.7 -82.6
# 10 PCT0101_1 28th St. Pier (San Diego), 0.35 nmi. SW (De… Sub 32.7 -117.
# # … with 5,372 more rows
# or fetch a subset
locs <- fetch_locations(pattern = glob2rx("*Maine*"))
# # A tibble: 126 x 5
# `Station ID` Name Type Latitude Longitude
# <chr> <chr> <chr> <dbl> <dbl>
# 1 8417377 Androscoggin River entrance, Kennebec River, Mai… Sub 44.0 -69.9
# 2 8417144 Augusta, MCRR Bridge, Kennebec River, Maine Ref 44.3 -69.8
# 3 8418175 Back Cove, Casco Bay, Maine Sub 43.7 -70.3
# 4 8416961 Back River, Sheepscot River, Maine Sub 44.0 -69.7
# 5 8414612 Bangor, Penobscot River, Maine Ref 44.8 -68.8
# 6 8414612 Bangor, Penobscot River, Maine (sub) Sub 44.8 -68.8
# 7 8413320 Bar Harbor, Frenchman Bay, Maine Ref 44.4 -68.2
# 8 8413651 Bass Harbor, Mount Desert Island, Maine Sub 44.2 -68.4
# 9 8417227 Bath, Kennebec River, Maine Ref 43.9 -69.8
# 10 8417227 Bath, Kennebec River, Maine (sub) Sub 43.9 -69.8
# # … with 116 more rows
Fetch the tide heights at a single location
You can specify the time interval and the step increment. Note that locations
are made by simple pattern matching. Where multiple matches are found the first
encountered is returned. Use increasing specificity to match a particular location.
Portland <- get_raw_mode(location = "Portland",
time = as.POSIXct(c("1995-12-16 00:00:00", "1995-12-20 23:59:59"), tz = "UTC"),
step = '01:00',
ofile = "~/1995-12-16-rawtide.csv",
compress = TRUE)
# $location
# [1] "Portland"
#
# $file
# [1] "/home/btupper/tide.csv.gz"
#
# $data
# # A tibble: 120 x 2
# datetime height
# <dttm> <dbl>
# 1 1995-12-16 05:00:00 1.47
# 2 1995-12-16 06:00:00 2.84
# 3 1995-12-16 07:00:00 4.70
# 4 1995-12-16 08:00:00 6.67
# 5 1995-12-16 09:00:00 8.14
# 6 1995-12-16 10:00:00 8.72
# 7 1995-12-16 11:00:00 8.43
# 8 1995-12-16 12:00:00 7.34
# 9 1995-12-16 13:00:00 5.61
# 10 1995-12-16 14:00:00 3.64
# # … with 110 more rows
The above provides the tide height every hour. It is interesting to compare with
a higher sampling rate, say 10 minutes.
library(ggplot2)
Portland10 <- get_raw_mode(location = "Portland",
time = as.POSIXct(c("1995-12-16 00:00:00", "1995-12-20 23:59:59"), tz = "UTC"),
step = '00:10',
ofile = "~/1995-12-16-10minute-rawtide.csv",
compress = TRUE)
x <- dplyr::bind_rows(
Portland$data %>% dplyr::mutate(step = "hour"),
Portland10$data %>% dplyr::mutate(step = "10-minute")) %>%
dplyr::group_by(step)
ggplot(x, aes(x=datetime, y = height, colour = step, size = step)) +
geom_line() +
labs(title = "Tides at Portland Head Light", x = "Date", y = "Height (ft)") +
scale_colour_manual(breaks = c("10-minute", "hour"), values = c("orange", "black")) +
scale_size_manual(breaks = c("10-minute", "hour"), values = c(2,1))
They are quite close, so keep in mind that an hourly estimate is often sufficient for modeling purposes.
Fetch tide-centric events for a single location
Events such as sun rise/set, moon rise/set and hi/lo tide stages can be extracted. By default they are exported from xtide in one table [timestamp, event], but we find it
useful to separate the events into sun. moon and tide.
Portland_events <- get_plain_mode(location = "Portland",
time = as.POSIXct(c("1995-12-16 00:00:00", "1995-12-20 23:59:59"), tz = "UTC"),
ofile = "~/1995-12-16-plaintide.csv",
compress = TRUE)
# $location
# [1] "Portland Head Light, Casco Bay, Maine"
#
# $lonlat
# [1] -70.2067 43.6233
#
# $file
# [1] "~/1995-12-16-plaintide.csv.gz"
#
# $moon
# # A tibble: 9 x 2
# datetime event
# <dttm> <chr>
# 1 1995-12-16 12:21:00 Moonset
# 2 1995-12-17 01:42:00 Moonrise
# 3 1995-12-17 12:54:00 Moonset
# 4 1995-12-18 02:50:00 Moonrise
# 5 1995-12-18 01:31:00 Moonset
# 6 1995-12-19 04:00:00 Moonrise
# 7 1995-12-19 02:15:00 Moonset
# 8 1995-12-20 05:11:00 Moonrise
# 9 1995-12-20 03:07:00 Moonset
#
# $sun
# # A tibble: 10 x 2
# datetime event
# <dttm> <chr>
# 1 1995-12-16 07:07:00 Sunrise
# 2 1995-12-16 04:04:00 Sunset
# 3 1995-12-17 07:08:00 Sunrise
# 4 1995-12-17 04:04:00 Sunset
# 5 1995-12-18 07:09:00 Sunrise
# 6 1995-12-18 04:05:00 Sunset
# 7 1995-12-19 07:09:00 Sunrise
# 8 1995-12-19 04:05:00 Sunset
# 9 1995-12-20 07:10:00 Sunrise
# 10 1995-12-20 04:06:00 Sunset
#
# $tide
# # A tibble: 19 x 3
# datetime height stage
# <dttm> <dbl> <chr>
# 1 1995-12-16 05:09:00 8.73 high
# 2 1995-12-16 11:22:00 1.04 low
# 3 1995-12-16 05:32:00 8.27 high
# 4 1995-12-16 11:40:00 0.75 low
# 5 1995-12-17 06:01:00 9.09 high
# 6 1995-12-17 12:21:00 0.64 low
# 7 1995-12-17 06:32:00 8.39 high
# 8 1995-12-18 12:36:00 0.580 low
# 9 1995-12-18 06:56:00 9.56 high
# 10 1995-12-18 01:20:00 0.09 low
# 11 1995-12-18 07:31:00 8.66 high
# 12 1995-12-19 01:32:00 0.28 low
# 13 1995-12-19 07:50:00 0.11 high
# 14 1995-12-19 02:16:00 0.54 low
# 15 1995-12-19 08:28:00 9.03 high
# 16 1995-12-20 02:27:00 0.11 low
# 17 1995-12-20 08:43:00 0.65 high
# 18 1995-12-20 03:10:00 1.15 low
# 19 1995-12-20 09:22:00 9.42 high
BigelowLab/maree documentation built on April 23, 2023, 4:19 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
maree
maree is a small R package that wraps around a local installation of xtide. maree
provides fuctionality to generate and read both raw and plain output modes.
The maree package doesn't add any new functionality to xtide.
It is important to note there already exists nice alternatives including
rtide. We have found it handy to
have programmatic access to a local installation of the xtide application.
Requirements
Installation
Install from github.
devtools::install_github("BigelowLab\maree")
Usage
Listing available stations
Station (locations) are either reference (harmonic) or substations. Each has a identifier and name.
library(maree)
# list all of the available stations
locs <- fetch_locations()
# # A tibble: 5,382 x 5
# `Station ID` Name Type Latitude Longitude
# <chr> <chr> <chr> <dbl> <dbl>
# 1 PCT3896_1 0.2 mile off Flat Point, Taku Inlet, Stephe… Sub 58.3 -134.
# 2 PCT3901_1 0.2 mile off Taku Point, Taku Inlet, Stephe… Sub 58.4 -134.
# 3 ACT8491_1 0.5 mile southeast of, Pinellas Point, Tamp… Sub 27.7 -82.6
# 4 8537731 0.8 n.mi. above entrance, Alloway Creek, Ne… Sub 39.5 -75.5
# 5 8537535 1 n.mi. above entrance, Mad Horse Creek, Ne… Sub 39.4 -75.4
# 6 ACT8496_1 1.9 miles SE of, Pinellas Point, Tampa Bay,… Sub 27.7 -82.6
# 7 8677833 2.5 miles above mouth, Little Satilla River… Sub 31.1 -81.5
# 8 8537753 2.5 n.mi. above entrance, Alloway Creek, Ne… Sub 39.5 -75.5
# 9 ACT8486_1 2.6 miles south of, Pinellas Point, Tampa B… Sub 27.7 -82.6
# 10 PCT0101_1 28th St. Pier (San Diego), 0.35 nmi. SW (De… Sub 32.7 -117.
# # … with 5,372 more rows
# or fetch a subset
locs <- fetch_locations(pattern = glob2rx("*Maine*"))
# # A tibble: 126 x 5
# `Station ID` Name Type Latitude Longitude
# <chr> <chr> <chr> <dbl> <dbl>
# 1 8417377 Androscoggin River entrance, Kennebec River, Mai… Sub 44.0 -69.9
# 2 8417144 Augusta, MCRR Bridge, Kennebec River, Maine Ref 44.3 -69.8
# 3 8418175 Back Cove, Casco Bay, Maine Sub 43.7 -70.3
# 4 8416961 Back River, Sheepscot River, Maine Sub 44.0 -69.7
# 5 8414612 Bangor, Penobscot River, Maine Ref 44.8 -68.8
# 6 8414612 Bangor, Penobscot River, Maine (sub) Sub 44.8 -68.8
# 7 8413320 Bar Harbor, Frenchman Bay, Maine Ref 44.4 -68.2
# 8 8413651 Bass Harbor, Mount Desert Island, Maine Sub 44.2 -68.4
# 9 8417227 Bath, Kennebec River, Maine Ref 43.9 -69.8
# 10 8417227 Bath, Kennebec River, Maine (sub) Sub 43.9 -69.8
# # … with 116 more rows
Fetch the tide heights at a single location
You can specify the time interval and the step increment. Note that locations are made by simple pattern matching. Where multiple matches are found the first encountered is returned. Use increasing specificity to match a particular location.
Portland <- get_raw_mode(location = "Portland",
time = as.POSIXct(c("1995-12-16 00:00:00", "1995-12-20 23:59:59"), tz = "UTC"),
step = '01:00',
ofile = "~/1995-12-16-rawtide.csv",
compress = TRUE)
# $location
# [1] "Portland"
#
# $file
# [1] "/home/btupper/tide.csv.gz"
#
# $data
# # A tibble: 120 x 2
# datetime height
# <dttm> <dbl>
# 1 1995-12-16 05:00:00 1.47
# 2 1995-12-16 06:00:00 2.84
# 3 1995-12-16 07:00:00 4.70
# 4 1995-12-16 08:00:00 6.67
# 5 1995-12-16 09:00:00 8.14
# 6 1995-12-16 10:00:00 8.72
# 7 1995-12-16 11:00:00 8.43
# 8 1995-12-16 12:00:00 7.34
# 9 1995-12-16 13:00:00 5.61
# 10 1995-12-16 14:00:00 3.64
# # … with 110 more rows
The above provides the tide height every hour. It is interesting to compare with a higher sampling rate, say 10 minutes.
library(ggplot2)
Portland10 <- get_raw_mode(location = "Portland",
time = as.POSIXct(c("1995-12-16 00:00:00", "1995-12-20 23:59:59"), tz = "UTC"),
step = '00:10',
ofile = "~/1995-12-16-10minute-rawtide.csv",
compress = TRUE)
x <- dplyr::bind_rows(
Portland$data %>% dplyr::mutate(step = "hour"),
Portland10$data %>% dplyr::mutate(step = "10-minute")) %>%
dplyr::group_by(step)
ggplot(x, aes(x=datetime, y = height, colour = step, size = step)) +
geom_line() +
labs(title = "Tides at Portland Head Light", x = "Date", y = "Height (ft)") +
scale_colour_manual(breaks = c("10-minute", "hour"), values = c("orange", "black")) +
scale_size_manual(breaks = c("10-minute", "hour"), values = c(2,1))
They are quite close, so keep in mind that an hourly estimate is often sufficient for modeling purposes.
Fetch tide-centric events for a single location
Events such as sun rise/set, moon rise/set and hi/lo tide stages can be extracted. By default they are exported from xtide in one table [timestamp, event], but we find it
useful to separate the events into sun. moon and tide.
Portland_events <- get_plain_mode(location = "Portland",
time = as.POSIXct(c("1995-12-16 00:00:00", "1995-12-20 23:59:59"), tz = "UTC"),
ofile = "~/1995-12-16-plaintide.csv",
compress = TRUE)
# $location
# [1] "Portland Head Light, Casco Bay, Maine"
#
# $lonlat
# [1] -70.2067 43.6233
#
# $file
# [1] "~/1995-12-16-plaintide.csv.gz"
#
# $moon
# # A tibble: 9 x 2
# datetime event
# <dttm> <chr>
# 1 1995-12-16 12:21:00 Moonset
# 2 1995-12-17 01:42:00 Moonrise
# 3 1995-12-17 12:54:00 Moonset
# 4 1995-12-18 02:50:00 Moonrise
# 5 1995-12-18 01:31:00 Moonset
# 6 1995-12-19 04:00:00 Moonrise
# 7 1995-12-19 02:15:00 Moonset
# 8 1995-12-20 05:11:00 Moonrise
# 9 1995-12-20 03:07:00 Moonset
#
# $sun
# # A tibble: 10 x 2
# datetime event
# <dttm> <chr>
# 1 1995-12-16 07:07:00 Sunrise
# 2 1995-12-16 04:04:00 Sunset
# 3 1995-12-17 07:08:00 Sunrise
# 4 1995-12-17 04:04:00 Sunset
# 5 1995-12-18 07:09:00 Sunrise
# 6 1995-12-18 04:05:00 Sunset
# 7 1995-12-19 07:09:00 Sunrise
# 8 1995-12-19 04:05:00 Sunset
# 9 1995-12-20 07:10:00 Sunrise
# 10 1995-12-20 04:06:00 Sunset
#
# $tide
# # A tibble: 19 x 3
# datetime height stage
# <dttm> <dbl> <chr>
# 1 1995-12-16 05:09:00 8.73 high
# 2 1995-12-16 11:22:00 1.04 low
# 3 1995-12-16 05:32:00 8.27 high
# 4 1995-12-16 11:40:00 0.75 low
# 5 1995-12-17 06:01:00 9.09 high
# 6 1995-12-17 12:21:00 0.64 low
# 7 1995-12-17 06:32:00 8.39 high
# 8 1995-12-18 12:36:00 0.580 low
# 9 1995-12-18 06:56:00 9.56 high
# 10 1995-12-18 01:20:00 0.09 low
# 11 1995-12-18 07:31:00 8.66 high
# 12 1995-12-19 01:32:00 0.28 low
# 13 1995-12-19 07:50:00 0.11 high
# 14 1995-12-19 02:16:00 0.54 low
# 15 1995-12-19 08:28:00 9.03 high
# 16 1995-12-20 02:27:00 0.11 low
# 17 1995-12-20 08:43:00 0.65 high
# 18 1995-12-20 03:10:00 1.15 low
# 19 1995-12-20 09:22:00 9.42 high
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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