Skip to content

Latest commit

 

History

History
152 lines (122 loc) · 5.61 KB

README.md

File metadata and controls

152 lines (122 loc) · 5.61 KB
Raw

emolt

eMOLT data served via ERRDAP.

Requirements

Installation

remotes::install_github("BigelowLab/emolt")

Initial Use

The premise of this package is that data may be stored in a single location but accessed by many users. To achieve this resource-friendly goal and still simplified access for each user, we need to inform the package where the data resides. We do this by storing the path to the data location in each user’s home directory in a hidden text file, “~/.emolt”. That text file has just one line in it which contains the full path to the shared dataset. For example, the author’s contains mnt/ecocast/coredata/emolt which points to a shared network drive mounted on our linux platform.

When the package is first loaded (ala library(emolt)) the existence of the file is checked, and if missing a warning is issued.

You can create and populate that ~/.emolt using a text editors, or you can create using the provided function set_data_path(). Here is how the author created his own…

library(emolt)
emolt::set_data_path("/mnt/ecocast/coredata/emolt)

That’s it. If you ever move the data you’ll have to modify the contents of this hidden text file.

Fetching Data

Once you have the hiddent file set up. It is easy to fetch the entire dataset. It includes data going back a number of years with a file for dissolved oxygen (do) and a file for temperature (temp)

suppressPackageStartupMessages({
  library(rnaturalearth)
  library(emolt)
  library(sf)
  library(ggplot2)
  library(dplyr)
})

A single function will fetch the entire dataset for DO and for temperature. Accepting the default arguments places a gzipped CSV file into your data directory under the raw subdirectory.

fetch_do()
fetch_temp()

We don’t know the update schedule, but for now assume monthly or a longer interval. Updating your data may be best done by simply reruning the fetch_* functions.

Read the data

do <- read_emolt(what = "do", form = "raw")
do
## # A tibble: 579,642 × 9
##    tow_id time                latitude longitude temperature    DO DO_percentage
##    <chr>  <dttm>                 <dbl>     <dbl>       <dbl> <dbl>         <dbl>
##  1 136    2023-08-10 21:35:59     44.1     -68.7        26.6  7.76          96.6
##  2 136    2023-08-10 21:50:59     44.1     -68.7        25.6  7.98          97.6
##  3 136    2023-08-10 22:05:59     44.1     -68.7        27.2  7.77          98.0
##  4 136    2023-08-10 22:20:59     44.1     -68.7        26.8  7.82          98.0
##  5 136    2023-08-10 22:35:59     44.1     -68.7        26.2  7.92          98.0
##  6 136    2023-08-10 22:50:59     44.1     -68.7        25.6  8.01          98.1
##  7 136    2023-08-10 23:05:59     44.1     -68.7        25.0  8.1           98.0
##  8 136    2023-08-10 23:20:59     44.1     -68.7        24.4  8.2           98.2
##  9 136    2023-08-10 23:35:59     44.1     -68.7        23.8  8.26          97.8
## 10 136    2023-08-10 23:50:59     44.1     -68.7        23.2  8.36          97.8
## # ℹ 579,632 more rows
## # ℹ 2 more variables: water_detect_perc <dbl>, sensor_type <chr>

tow_id1 = dplyr::count(do, tow_id) |>
  dplyr::arrange(desc(n)) |>
  dplyr::slice(1) |>
  dplyr::pull(tow_id)
ggplot(data = filter(do, tow_id == tow_id1), 
       mapping = aes(x = time, y = DO, color = tow_id)) +
  geom_line()

temp = read_emolt(what = "temp", form = "raw")
temp
## # A tibble: 12,240,640 × 8
##    tow_id segment_type  time                latitude longitude depth temperature
##    <chr>  <chr>         <dttm>                 <dbl>     <dbl> <dbl>       <dbl>
##  1 5      Profiling Do… 2023-08-01 15:59:50     43.9     -69.6   2.4        23.9
##  2 5      Profiling Do… 2023-08-01 15:59:51     43.9     -69.6   4.6        24.0
##  3 5      Profiling Do… 2023-08-01 15:59:52     43.9     -69.6   7.3        24.2
##  4 5      Profiling Do… 2023-08-01 15:59:53     43.9     -69.6  10.4        24.4
##  5 5      Profiling Do… 2023-08-01 15:59:54     43.9     -69.6  12.6        24.6
##  6 5      Profiling Do… 2023-08-01 15:59:55     43.9     -69.6  13.8        24.8
##  7 5      Profiling Do… 2023-08-01 15:59:56     43.9     -69.6  15.2        24.9
##  8 5      Profiling Do… 2023-08-01 15:59:58     43.9     -69.6  17.2        25.1
##  9 5      Profiling Do… 2023-08-01 16:00:03     43.9     -69.6  23.9        25.5
## 10 5      Profiling Do… 2023-08-01 16:00:04     43.9     -69.6  26.2        25.6
## # ℹ 12,240,630 more rows
## # ℹ 1 more variable: sensor_type <chr>

ggplot(data = filter(temp, tow_id == "5") ,
       mapping = aes(x = time, y = depth, color = temperature, shape = segment_type)) +
  geom_point() + 
  scale_y_reverse()

Cast as spatial data

dos = do |>
  filter(between(latitude,30, 50)) |>
  raw_as_sf() |>
  dplyr::group_by(tow_id) |>
  dplyr::slice_head(n=1)
coast = ne_coastline(scale = "medium", returnclass = "sf")
plot(dos['sensor_type'], axes = TRUE, pch = 1, reset = FALSE)
plot(st_geometry(coast), add = TRUE)