README.md
In Centre-for-Marine-Applied-Research/strings: Compile, Format, and Visualize Sensor String Data
strings
This package will soon be deprecated in favor of the sensorstrings
package located here.
Compile, format, and visualize Water Quality (temperature, dissolved
oxygen, salinity) data measured by different sensors.
Installation
You can install the development version of strings from
GitHub with:
# install.packages("devtools")
devtools::install_github("Centre-for-Marine-Applied-Research/strings")
Background
The Centre for Marine Applied Research (CMAR)
coordinates an extensive Coastal Monitoring
Program to measure
Essential Ocean
Variables
from around the coast of Nova Scotia, Canada. There are three main
branches of the program: Water Quality, Currents, and Waves.
Processed data for each branch can be viewed and downloaded from several
sources, as outlined in the CMAR Report & Data Access Cheat
Sheet
(download for clickable links).
The strings package is used to compile, format, and visualize data
from the Water Quality branch of the Coastal Monitoring Program.
Water Quality data (temperature, dissolved oxygen, and salinity) is
collected using “sensor strings”. Each sensor string is attached to the
seafloor by an anchor and suspended by a sub-surface buoy, with
autonomous sensors attached at various depths (Figure 1). A string
typically includes three sensor models: Hobo, aquaMeasure, and VR2AR
(Table 1). Strings are deployed at a station for several months and data
are measured every 1 minute to 1 hour, depending on the sensor.
Figure 1: Typical sensor string configuration (not to scale).
After retrieval, data from each sensor is exported to a separate csv
file using manufacturer-specific software. Each type of sensor generates
a data file with unique columns and header fields, which poses a
significant challenge for compiling all data from a deployment into a
single format for analysis.
The strings package was originally built to address this challenge,
and now offers functions to compile, format, convert units, and
visualize sensor string data.
strings was developed specifically to streamline CMAR’s workflow, but
is flexible enough that other users can apply it to process data from
the accepted sensors (Table 1). Refer to the vignettes for more detail.
| Sensor (link to spec sheet) | Variable(s) Measured |
|:---------------------------------------------------------------------------------------------------------------------------------|:------------------------------|
| HOBO Pro V2 | Temperature |
| HOBO DO | Temperature, Dissolved Oxygen |
| aquaMeasure DOT | Temperature, Dissolved Oxygen |
| aquaMeasure SAL | Temperature, Salinity |
| VR2AR | Temperature |
For more information on Water Quality data collection and processing,
visit the CMAR Water Quality Data Collection & Processing Cheat
Sheet
(download for clickable links).
Example
library(strings)
library(readr)
Consider a string deployed from May 31, 2019 to October 19, 2019 with
three sensors:
Sensor
Serial \#
Depth
HOBO Pro V2
10755220
2
aquaMeasure DOT
670364
5
VR2AR
547109
15
Raw data files
The data from each sensor is exported to a separate csv file, each with
manufacturer-specific columns.
Import raw data files:
path <- system.file("extdata", package = "strings")
hobo_raw <- read_csv(paste0(path, "/HOBO/10755220.csv"))
#> New names:
#> Rows: 3408 Columns: 8
#> ── Column specification
#> ──────────────────────────────────────────────────────── Delimiter: "," chr
#> (3): Date Time, GMT+00:00, Stopped (LGR S/N: 10755220), End Of File (LGR... dbl
#> (2): #, Temp, °C (LGR S/N: 10755220, SEN S/N: 10755220) lgl (3): ...4, Coupler
#> Attached (LGR S/N: 10755220), Host Connected (LGR S/N...
#> ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
#> Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> • `` -> `...4`
aquaMeasure_raw <- read_csv(paste0(path, "/aquaMeasure/aquaMeasure-670364_2019-10-19_UTC.csv"))
#> Rows: 8482 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): Timestamp(UTC), Sensor, Record Type
#> dbl (2): Dissolved Oxygen, Temperature
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
vemco_raw <- read_csv(paste0(path, "/Vemco/Vemco_Borgles_Island_2019_05_30.csv"))
#> Rows: 2322 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): Date and Time (UTC), Receiver, Description
#> dbl (1): Data
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Examine the first rows of each raw data file:
Raw Hobo data
head(hobo_raw)
#> # A tibble: 6 × 8
#> `#` `Date Time, GMT+00:00` Temp, °C …¹ ...4 Coupl…² Host …³ Stopp…⁴ End O…⁵
#> <dbl> <chr> <dbl> <lgl> <lgl> <lgl> <chr> <chr>
#> 1 1 2019-05-30 18:00 12.2 NA NA NA <NA> <NA>
#> 2 2 2019-05-30 19:00 7.87 NA NA NA <NA> <NA>
#> 3 3 2019-05-30 20:00 6.58 NA NA NA <NA> <NA>
#> 4 4 2019-05-30 21:00 6.66 NA NA NA <NA> <NA>
#> 5 5 2019-05-30 22:00 6.66 NA NA NA <NA> <NA>
#> 6 6 2019-05-30 23:00 7.29 NA NA NA <NA> <NA>
#> # … with abbreviated variable names
#> # ¹`Temp, °C (LGR S/N: 10755220, SEN S/N: 10755220)`,
#> # ²`Coupler Attached (LGR S/N: 10755220)`,
#> # ³`Host Connected (LGR S/N: 10755220)`, ⁴`Stopped (LGR S/N: 10755220)`,
#> # ⁵`End Of File (LGR S/N: 10755220)`
Raw aquaMeasure data
head(aquaMeasure_raw)
#> # A tibble: 6 × 5
#> `Timestamp(UTC)` Sensor Record…¹ Disso…² Tempe…³
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 352s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> 2 1691s after startup (time not set) aquaMeasure-670364 Dissolv… 100. NA
#> 3 3015s after startup (time not set) aquaMeasure-670364 Dissolv… 100. NA
#> 4 4346s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> 5 5690s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> 6 364s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> # … with abbreviated variable names ¹`Record Type`, ²`Dissolved Oxygen`,
#> # ³Temperature
Raw Vemco data
head(vemco_raw)
#> # A tibble: 6 × 4
#> `Date and Time (UTC)` Receiver Description Data
#> <chr> <chr> <chr> <dbl>
#> 1 2019-05-30 20:00 VR2AR-547109 Temperature 4.2
#> 2 2019-05-31 0:02 VR2AR-547109 Temperature 4.6
#> 3 2019-05-31 0:07 VR2AR-547109 Temperature 4.6
#> 4 2019-05-31 0:12 VR2AR-547109 Temperature 4.6
#> 5 2019-05-31 0:17 VR2AR-547109 Temperature 4.7
#> 6 2019-05-31 0:22 VR2AR-547109 Temperature 4.6
Data from each sensor is exported in a slightly different layout, making
it difficult to work with and analyze all of the data from a single
deployment.
Compile and format with strings
Compile data from the 3 sensors using strings::compile_all_data():
deployment <- data.frame(START = "2019-05-30", END = "2019-10-19")
serial.table.HOBO <- data.frame(SENSOR = "HOBO-10755220", DEPTH = "2m")
serial.table.aM <- data.frame(SENSOR = "aquaMeasure-670364", DEPTH = "5m")
depth.vemco <- "15m"
#Compile data from a single deployment
ALL_data <- compile_all_data(
path = path,
deployment.range = deployment,
area.name = area,
# hobo
serial.table.HOBO = serial.table.HOBO,
# aquaMeasure
serial.table.aM = serial.table.aM,
# vemco
depth.vemco = depth.vemco
)
#> New names:
#> New names:
#> • `` -> `...4`
#> • `` -> `...5`
#> • `` -> `...6`
#> • `` -> `...7`
#> • `` -> `...8`
#> [1] "HOBO data compiled"
#> [1] "found Temperature in file aquaMeasure-670364_2019-10-19_UTC.csv"
#> [2] "found Dissolved Oxygen in file aquaMeasure-670364_2019-10-19_UTC.csv"
#> [1] "aquaMeasure data compiled"
#> [1] "Vemco data compiled: Temperature"
head(tibble(ALL_data), n = 10)
#> # A tibble: 10 × 8
#> TIMESTAMP.x PLACE…¹ TIMES…² PLACE…³ TIMES…⁴ PLACE…⁵ TIMES…⁶ PLACE…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 2019-May-30 to 2019-… 2019-M… 2019-M… 2019-M… 2019-M… 2019-M… 2019-M… 2019-M…
#> 2 HOBO-10755220 HOBO-1… aquaMe… aquaMe… aquaMe… aquaMe… VR2AR-… VR2AR-…
#> 3 Temperature-2m Temper… Temper… Temper… Dissol… Dissol… Temper… Temper…
#> 4 Date Time, GMT+00:00 Temp, … Timest… Temper… Timest… Dissol… Date a… Temper…
#> 5 2019-05-30 18:00:00 12.243 2019-0… 5.5 2019-0… 103 2019-0… 4.2
#> 6 2019-05-30 19:00:00 7.87 2019-0… 5.6 2019-0… 103.4 2019-0… 4.6
#> 7 2019-05-30 20:00:00 6.585 2019-0… 5.92 2019-0… 99.3 2019-0… 4.6
#> 8 2019-05-30 21:00:00 6.661 2019-0… 5.88 2019-0… 102 2019-0… 4.6
#> 9 2019-05-30 22:00:00 6.661 2019-0… 6.26 2019-0… 104.3 2019-0… 4.7
#> 10 2019-05-30 23:00:00 7.293 2019-0… 12.62 2019-0… 109.7 2019-0… 4.6
#> # … with abbreviated variable names ¹PLACEHOLDER.x, ²TIMESTAMP.x.x,
#> # ³PLACEHOLDER.x.x, ⁴TIMESTAMP.y, ⁵PLACEHOLDER.y, ⁶TIMESTAMP.y.y,
#> # ⁷PLACEHOLDER.y.y
The data is compiled in a “wide” format, with metadata in the first four
rows indicating the deployment period, the sensor serial number, the
variable and depth of the sensor, and the timezone of the timestamps.
The remaining columns alternate between timestamp (in the format “Y-m-d
H:M:S”) and variable value (rounded to three decimal places). Sensors
can be initialized at different times and record on different intervals,
so values in a single row do not necessarily correspond to the same
timestamp.
This format is an artifact of the former compiling process and will be
removed for future versions of the package. The format is convenient for
human readers, who can quickly scan the metadata to determine the number
of sensors deployed, the depths of deployment, etc. However, this format
is less convenient for analysis. The data frame should be converted to a
“tidy” format using strings::convert_to_tidydata() prior to analysis.
ALL_tidy <- convert_to_tidydata(ALL_data)
head(tibble(ALL_tidy))
#> # A tibble: 6 × 6
#> DEPLOYMENT_PERIOD SENSOR TIMESTAMP VARIA…¹ DEPTH VALUE
#> <chr> <chr> <dttm> <chr> <ord> <dbl>
#> 1 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 18:00:00 Temper… 2 12.2
#> 2 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 19:00:00 Temper… 2 7.87
#> 3 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 20:00:00 Temper… 2 6.58
#> 4 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 21:00:00 Temper… 2 6.66
#> 5 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 22:00:00 Temper… 2 6.66
#> 6 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 23:00:00 Temper… 2 7.29
#> # … with abbreviated variable name ¹VARIABLE
ALL_tidy as 6 columns:
DEPLOYMENT_RANGE: The deployment and retrieval dates (character)SENSOR: The sensor that recorded the measurement (character)TIMESTAMP: The timestamp of the measurement (POSIXct)VARIABLE: The parameter measured (Temperature, Dissolved Oxygen, or
Salinity) (character)DEPTH: The depth of the sensor (ordered factor)VALUE: The value of the measurement (numeric)
ALL_tidy can be plotted with plot_variables_at_depth():
plot_variables_at_depth(ALL_tidy)
#> Warning: Removed 7 rows containing missing values (`geom_point()`).
If the figure needs to be modified, it may be more convenient to plot
using ggplot_variables_at_depth:
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.2.3
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
ggplot_variables_at_depth(ALL_tidy) +
geom_vline(xintercept = as_datetime("2019-07-01"), colour = "red")
Centre-for-Marine-Applied-Research/strings documentation built on Aug. 21, 2023, 8 a.m.
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strings
This package will soon be deprecated in favor of the sensorstrings
package located here.
Compile, format, and visualize Water Quality (temperature, dissolved oxygen, salinity) data measured by different sensors.
Installation
You can install the development version of strings from
GitHub with:
# install.packages("devtools")
devtools::install_github("Centre-for-Marine-Applied-Research/strings")
Background
The Centre for Marine Applied Research (CMAR) coordinates an extensive Coastal Monitoring Program to measure Essential Ocean Variables from around the coast of Nova Scotia, Canada. There are three main branches of the program: Water Quality, Currents, and Waves. Processed data for each branch can be viewed and downloaded from several sources, as outlined in the CMAR Report & Data Access Cheat Sheet (download for clickable links).
The strings package is used to compile, format, and visualize data
from the Water Quality branch of the Coastal Monitoring Program.
Water Quality data (temperature, dissolved oxygen, and salinity) is collected using “sensor strings”. Each sensor string is attached to the seafloor by an anchor and suspended by a sub-surface buoy, with autonomous sensors attached at various depths (Figure 1). A string typically includes three sensor models: Hobo, aquaMeasure, and VR2AR (Table 1). Strings are deployed at a station for several months and data are measured every 1 minute to 1 hour, depending on the sensor.
Figure 1: Typical sensor string configuration (not to scale).
After retrieval, data from each sensor is exported to a separate csv file using manufacturer-specific software. Each type of sensor generates a data file with unique columns and header fields, which poses a significant challenge for compiling all data from a deployment into a single format for analysis.
The strings package was originally built to address this challenge,
and now offers functions to compile, format, convert units, and
visualize sensor string data.
strings was developed specifically to streamline CMAR’s workflow, but
is flexible enough that other users can apply it to process data from
the accepted sensors (Table 1). Refer to the vignettes for more detail.
| Sensor (link to spec sheet) | Variable(s) Measured | |:---------------------------------------------------------------------------------------------------------------------------------|:------------------------------| | HOBO Pro V2 | Temperature | | HOBO DO | Temperature, Dissolved Oxygen | | aquaMeasure DOT | Temperature, Dissolved Oxygen | | aquaMeasure SAL | Temperature, Salinity | | VR2AR | Temperature |
For more information on Water Quality data collection and processing, visit the CMAR Water Quality Data Collection & Processing Cheat Sheet (download for clickable links).
Example
library(strings)
library(readr)
Consider a string deployed from May 31, 2019 to October 19, 2019 with three sensors:
Sensor Serial \# Depth HOBO Pro V2 10755220 2 aquaMeasure DOT 670364 5 VR2AR 547109 15Raw data files
The data from each sensor is exported to a separate csv file, each with manufacturer-specific columns.
Import raw data files:
path <- system.file("extdata", package = "strings")
hobo_raw <- read_csv(paste0(path, "/HOBO/10755220.csv"))
#> New names:
#> Rows: 3408 Columns: 8
#> ── Column specification
#> ──────────────────────────────────────────────────────── Delimiter: "," chr
#> (3): Date Time, GMT+00:00, Stopped (LGR S/N: 10755220), End Of File (LGR... dbl
#> (2): #, Temp, °C (LGR S/N: 10755220, SEN S/N: 10755220) lgl (3): ...4, Coupler
#> Attached (LGR S/N: 10755220), Host Connected (LGR S/N...
#> ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
#> Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> • `` -> `...4`
aquaMeasure_raw <- read_csv(paste0(path, "/aquaMeasure/aquaMeasure-670364_2019-10-19_UTC.csv"))
#> Rows: 8482 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): Timestamp(UTC), Sensor, Record Type
#> dbl (2): Dissolved Oxygen, Temperature
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
vemco_raw <- read_csv(paste0(path, "/Vemco/Vemco_Borgles_Island_2019_05_30.csv"))
#> Rows: 2322 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): Date and Time (UTC), Receiver, Description
#> dbl (1): Data
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Examine the first rows of each raw data file:
Raw Hobo data
head(hobo_raw)
#> # A tibble: 6 × 8
#> `#` `Date Time, GMT+00:00` Temp, °C …¹ ...4 Coupl…² Host …³ Stopp…⁴ End O…⁵
#> <dbl> <chr> <dbl> <lgl> <lgl> <lgl> <chr> <chr>
#> 1 1 2019-05-30 18:00 12.2 NA NA NA <NA> <NA>
#> 2 2 2019-05-30 19:00 7.87 NA NA NA <NA> <NA>
#> 3 3 2019-05-30 20:00 6.58 NA NA NA <NA> <NA>
#> 4 4 2019-05-30 21:00 6.66 NA NA NA <NA> <NA>
#> 5 5 2019-05-30 22:00 6.66 NA NA NA <NA> <NA>
#> 6 6 2019-05-30 23:00 7.29 NA NA NA <NA> <NA>
#> # … with abbreviated variable names
#> # ¹`Temp, °C (LGR S/N: 10755220, SEN S/N: 10755220)`,
#> # ²`Coupler Attached (LGR S/N: 10755220)`,
#> # ³`Host Connected (LGR S/N: 10755220)`, ⁴`Stopped (LGR S/N: 10755220)`,
#> # ⁵`End Of File (LGR S/N: 10755220)`
Raw aquaMeasure data
head(aquaMeasure_raw)
#> # A tibble: 6 × 5
#> `Timestamp(UTC)` Sensor Record…¹ Disso…² Tempe…³
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 352s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> 2 1691s after startup (time not set) aquaMeasure-670364 Dissolv… 100. NA
#> 3 3015s after startup (time not set) aquaMeasure-670364 Dissolv… 100. NA
#> 4 4346s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> 5 5690s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> 6 364s after startup (time not set) aquaMeasure-670364 Dissolv… 101. NA
#> # … with abbreviated variable names ¹`Record Type`, ²`Dissolved Oxygen`,
#> # ³Temperature
Raw Vemco data
head(vemco_raw)
#> # A tibble: 6 × 4
#> `Date and Time (UTC)` Receiver Description Data
#> <chr> <chr> <chr> <dbl>
#> 1 2019-05-30 20:00 VR2AR-547109 Temperature 4.2
#> 2 2019-05-31 0:02 VR2AR-547109 Temperature 4.6
#> 3 2019-05-31 0:07 VR2AR-547109 Temperature 4.6
#> 4 2019-05-31 0:12 VR2AR-547109 Temperature 4.6
#> 5 2019-05-31 0:17 VR2AR-547109 Temperature 4.7
#> 6 2019-05-31 0:22 VR2AR-547109 Temperature 4.6
Data from each sensor is exported in a slightly different layout, making it difficult to work with and analyze all of the data from a single deployment.
Compile and format with strings
Compile data from the 3 sensors using strings::compile_all_data():
deployment <- data.frame(START = "2019-05-30", END = "2019-10-19")
serial.table.HOBO <- data.frame(SENSOR = "HOBO-10755220", DEPTH = "2m")
serial.table.aM <- data.frame(SENSOR = "aquaMeasure-670364", DEPTH = "5m")
depth.vemco <- "15m"
#Compile data from a single deployment
ALL_data <- compile_all_data(
path = path,
deployment.range = deployment,
area.name = area,
# hobo
serial.table.HOBO = serial.table.HOBO,
# aquaMeasure
serial.table.aM = serial.table.aM,
# vemco
depth.vemco = depth.vemco
)
#> New names:
#> New names:
#> • `` -> `...4`
#> • `` -> `...5`
#> • `` -> `...6`
#> • `` -> `...7`
#> • `` -> `...8`
#> [1] "HOBO data compiled"
#> [1] "found Temperature in file aquaMeasure-670364_2019-10-19_UTC.csv"
#> [2] "found Dissolved Oxygen in file aquaMeasure-670364_2019-10-19_UTC.csv"
#> [1] "aquaMeasure data compiled"
#> [1] "Vemco data compiled: Temperature"
head(tibble(ALL_data), n = 10)
#> # A tibble: 10 × 8
#> TIMESTAMP.x PLACE…¹ TIMES…² PLACE…³ TIMES…⁴ PLACE…⁵ TIMES…⁶ PLACE…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 2019-May-30 to 2019-… 2019-M… 2019-M… 2019-M… 2019-M… 2019-M… 2019-M… 2019-M…
#> 2 HOBO-10755220 HOBO-1… aquaMe… aquaMe… aquaMe… aquaMe… VR2AR-… VR2AR-…
#> 3 Temperature-2m Temper… Temper… Temper… Dissol… Dissol… Temper… Temper…
#> 4 Date Time, GMT+00:00 Temp, … Timest… Temper… Timest… Dissol… Date a… Temper…
#> 5 2019-05-30 18:00:00 12.243 2019-0… 5.5 2019-0… 103 2019-0… 4.2
#> 6 2019-05-30 19:00:00 7.87 2019-0… 5.6 2019-0… 103.4 2019-0… 4.6
#> 7 2019-05-30 20:00:00 6.585 2019-0… 5.92 2019-0… 99.3 2019-0… 4.6
#> 8 2019-05-30 21:00:00 6.661 2019-0… 5.88 2019-0… 102 2019-0… 4.6
#> 9 2019-05-30 22:00:00 6.661 2019-0… 6.26 2019-0… 104.3 2019-0… 4.7
#> 10 2019-05-30 23:00:00 7.293 2019-0… 12.62 2019-0… 109.7 2019-0… 4.6
#> # … with abbreviated variable names ¹PLACEHOLDER.x, ²TIMESTAMP.x.x,
#> # ³PLACEHOLDER.x.x, ⁴TIMESTAMP.y, ⁵PLACEHOLDER.y, ⁶TIMESTAMP.y.y,
#> # ⁷PLACEHOLDER.y.y
The data is compiled in a “wide” format, with metadata in the first four rows indicating the deployment period, the sensor serial number, the variable and depth of the sensor, and the timezone of the timestamps.
The remaining columns alternate between timestamp (in the format “Y-m-d H:M:S”) and variable value (rounded to three decimal places). Sensors can be initialized at different times and record on different intervals, so values in a single row do not necessarily correspond to the same timestamp.
This format is an artifact of the former compiling process and will be
removed for future versions of the package. The format is convenient for
human readers, who can quickly scan the metadata to determine the number
of sensors deployed, the depths of deployment, etc. However, this format
is less convenient for analysis. The data frame should be converted to a
“tidy” format using strings::convert_to_tidydata() prior to analysis.
ALL_tidy <- convert_to_tidydata(ALL_data)
head(tibble(ALL_tidy))
#> # A tibble: 6 × 6
#> DEPLOYMENT_PERIOD SENSOR TIMESTAMP VARIA…¹ DEPTH VALUE
#> <chr> <chr> <dttm> <chr> <ord> <dbl>
#> 1 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 18:00:00 Temper… 2 12.2
#> 2 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 19:00:00 Temper… 2 7.87
#> 3 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 20:00:00 Temper… 2 6.58
#> 4 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 21:00:00 Temper… 2 6.66
#> 5 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 22:00:00 Temper… 2 6.66
#> 6 2019-May-30 to 2019-Oct-19 HOBO-10755… 2019-05-30 23:00:00 Temper… 2 7.29
#> # … with abbreviated variable name ¹VARIABLE
ALL_tidy as 6 columns:
DEPLOYMENT_RANGE: The deployment and retrieval dates (character)SENSOR: The sensor that recorded the measurement (character)TIMESTAMP: The timestamp of the measurement (POSIXct)VARIABLE: The parameter measured (Temperature, Dissolved Oxygen, or Salinity) (character)DEPTH: The depth of the sensor (ordered factor)VALUE:The value of the measurement (numeric)
ALL_tidy can be plotted with plot_variables_at_depth():
plot_variables_at_depth(ALL_tidy)
#> Warning: Removed 7 rows containing missing values (`geom_point()`).
If the figure needs to be modified, it may be more convenient to plot
using ggplot_variables_at_depth:
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.2.3
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
ggplot_variables_at_depth(ALL_tidy) +
geom_vline(xintercept = as_datetime("2019-07-01"), colour = "red")
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