eMOLT data served via ERRDAP.
remotes::install_github("BigelowLab/emolt")
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.
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 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.
do <- read_emolt(what = "do", form = "raw")
do## # A tibble: 5,367,225 × 9
## tow_id time latitude longitude temperature DO DO_percentage
## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 11455 2022-01-04 04:01:00 41.8 -70.5 -5.43 NaN NaN
## 2 11455 2022-01-04 04:02:00 41.8 -70.5 -5.45 NaN NaN
## 3 11455 2022-01-04 04:03:00 41.8 -70.5 -5.48 NaN NaN
## 4 11455 2022-01-04 04:04:00 41.8 -70.5 -5.51 NaN NaN
## 5 11455 2022-01-04 04:05:00 41.8 -70.5 -5.53 NaN NaN
## 6 11455 2022-01-04 04:06:00 41.8 -70.5 -5.56 NaN NaN
## 7 11455 2022-01-04 04:07:00 41.8 -70.5 -5.58 NaN NaN
## 8 11455 2022-01-04 04:08:00 41.8 -70.5 -5.61 NaN NaN
## 9 11455 2022-01-04 04:09:00 41.8 -70.5 -5.63 NaN NaN
## 10 11455 2022-01-04 04:10:00 41.8 -70.5 -5.66 NaN NaN
## # ℹ 5,367,215 more rows
## # ℹ 2 more variables: water_detect_perc <dbl>, sensor_type <chr>
ggplot(data = filter(do, tow_id == "10970"),
mapping = aes(x = time, y = DO, color = tow_id)) +
geom_line()
temp = read_emolt(what = "temp", form = "raw")
temp## # A tibble: 9,166,234 × 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 9,166,224 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()
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 = ".", reset = FALSE)
plot(st_geometry(coast), add = TRUE)