Package 'aelab' reference manual

Title:	Data Processing for Aquatic Ecology
Description:	Facilitate the analysis of data related to aquatic ecology, specifically the establishment of carbon budget. Currently, the package allows the below analysis. (i) the calculation of greenhouse gas flux based on data obtained from trace gas analyzer using the method described in Lin et al. (2024). (ii) the calculation of Dissolved Oxygen (DO) metabolism based on data obtained from dissolved oxygen data logger using the method described in Staehr et al. (2010). Yong et al. (2024) <doi:10.5194/bg-21-5247-2024>. Staehr et al. (2010) <doi:10.4319/lom.2010.8.0628>.
Authors:	Zhao-Jun Yong [cre, aut]
Maintainer:	Zhao-Jun Yong <[email protected]>
License:	GPL (>= 3)
Version:	1.1.5
Built:	2026-06-05 07:20:44 UTC
Source:	https://github.com/zhao-jun-yong/aelab

add_sun_times

Description

Attach per-day sunrise and sunset times to a data frame of readings, computed from a site's coordinates with the suncalc package. Replaces the manual lookup of sunrise/sunset for each deployment date; the resulting sunrise/sunset columns are consumed by calculate_do.

Usage

add_sun_times(df, lat, lon, time_col = "date_time", tz = "Asia/Taipei")
add_sun_times(df, lat, lon, time_col = "date_time", tz = "Asia/Taipei")

Arguments

df

A data frame containing a POSIXct time column.

lat, lon

Site latitude and longitude in decimal degrees (length 1).

time_col

Name of the POSIXct time column. Default "date_time".

tz

Time zone for the returned sun times. Default "Asia/Taipei".

Value

df with two added POSIXct columns, sunrise and sunset, matched to each row's calendar date.

Examples

## Not run: 
df <- add_sun_times(df, lat = 22.3900, lon = 120.5777)

## End(Not run)
## Not run: 
df <- add_sun_times(df, lat = 22.3900, lon = 120.5777)

## End(Not run)

aelab_palettes

Description

Retrieve a named aelab colour palette as a character vector.

Usage

aelab_palettes(name, n, type = c("discrete", "continuous"))
aelab_palettes(name, n, type = c("discrete", "continuous"))

Arguments

name

Name of the palette (string).

n

Number of colours to return. Defaults to the full palette length.

type

"discrete" (default) or "continuous". For "continuous", colours are interpolated to length n.

Details

Available palette names: "rainbow", "two", "control", "control2", "control3", "period", "ghg".

Value

A character vector of hex colour codes with class "palette".

Examples

aelab_palettes("rainbow", 5)
aelab_palettes("ghg", type = "continuous", n = 20)
aelab_palettes("rainbow", 5)
aelab_palettes("ghg", type = "continuous", n = 20)

Instrument precision for supported trace gas analyzers

Description

A lookup table of 1-second (1σ) precision values used to compute kappamax in calculate_regression when fit_type = "auto". Add rows for additional analyzer models as needed.

Format

A data frame with 5 rows and 3 variables:

model: Analyzer model name (character).
gas: Gas measured: "CH4", "CO2", or "N2O" (character).
precision_ppm: Instrument precision in ppm (1σ, 1 s) (numeric).

Source

LI-COR Biosciences product specifications; LGR (Los Gatos Research) UGGA product specifications.

Examples

analyzer_precision
analyzer_precision[analyzer_precision$gas == "N2O", "precision_ppm"]
analyzer_precision
analyzer_precision[analyzer_precision$gas == "N2O", "precision_ppm"]

aov_test

Description

Perform one-way ANOVA followed by Tukey HSD post-hoc test with compact letter display.

Usage

aov_test(df, variable_name, group)
aov_test(df, variable_name, group)

Arguments

df

A data frame.

variable_name

Name of the response variable column (string).

group

Name of the grouping column (string).

Value

A named list with elements anova_summary, tukey_results, and compact_letters.

Examples

df <- data.frame(
  grp = rep(c("A","B","C"), each = 5),
  val = c(1,2,1,2,1, 3,4,3,4,3, 5,6,5,6,5)
)
aov_test(df, "val", "grp")
df <- data.frame(
  grp = rep(c("A","B","C"), each = 5),
  val = c(1,2,1,2,1, 3,4,3,4,3, 5,6,5,6,5)
)
aov_test(df, "val", "grp")

calc_chla_trichromatic

Description

Calculate chlorophyll-a concentration from trichromatic spectrophotometric absorbance readings using the Jeffrey & Humphrey (1975) equations.

Usage

calc_chla_trichromatic(wl_630, wl_647, wl_664, wl_750)
calc_chla_trichromatic(wl_630, wl_647, wl_664, wl_750)

Arguments

wl_630

Absorbance at 630 nm.

wl_647

Absorbance at 647 nm.

wl_664

Absorbance at 664 nm.

wl_750

Absorbance at 750 nm (turbidity blank).

Details

Absorbance values should be measured in a 1 cm path-length cuvette. The 750 nm reading is used as a turbidity blank correction. Formula: $11.85 \times E_{664} - 1.54 \times E_{647} - 0.08 \times E_{630}$ where $E_{\lambda} = A_{\lambda} - A_{750}$ .

Value

Chlorophyll-a concentration in µg L $^{-1}$ (assuming a 1 cm path length and standard extraction volume).

Examples

calc_chla_trichromatic(wl_630 = 0.05, wl_647 = 0.08, wl_664 = 0.20, wl_750 = 0.01)
calc_chla_trichromatic(wl_630 = 0.05, wl_647 = 0.08, wl_664 = 0.20, wl_750 = 0.01)

calculate_do

Description

Calculate the Net Ecosystem Production, Gross Primary Production and Ecosystem respiration based on the change in dissolved oxygen concentration.

Usage

calculate_do(df)
calculate_do(df)

Arguments

df

Merged dataframe produced by process_hobo(), process_weather() and process_info() functions.

Value

A dataframe.

Examples

data(hobo)
calculate_do(hobo)
data(hobo)
calculate_do(hobo)

calculate_ghg_flux

Description

Calculate the greenhouse gas (GHG) flux based on input parameters from a data frame.

Usage

calculate_ghg_flux(
  data,
  slope = "slope",
  area = "area",
  volume = "volume",
  temp = "temp"
)
calculate_ghg_flux(
  data,
  slope = "slope",
  area = "area",
  volume = "volume",
  temp = "temp"
)

Arguments

data

A data frame containing relevant data with columns for slope, area, volume, and temperature.

slope

Name of the column in 'data' that contains the slope values of the GHG concentration change (in ppm/s).

area

Name of the column in 'data' that contains the values of the area of the chamber (in square meter).

volume

Name of the column in 'data' that contains values of the volume of the chamber (in litre).

temp

Name of the column in 'data' that contains values of the temperature of the gas (in Celsius).

Value

A list containing the calculated flux and its unit.

Examples

data <- data.frame(
  slope = c(1.2, 1.5, 1.1),
  area = c(100, 150, 120),
  volume = c(10, 15, 12),
  temp = c(25, 30, 22)
)
results <- calculate_ghg_flux(data)
print(results)
data <- data.frame(
  slope = c(1.2, 1.5, 1.1),
  area = c(100, 150, 120),
  volume = c(10, 15, 12),
  temp = c(25, 30, 22)
)
results <- calculate_ghg_flux(data)
print(results)

calculate_MDF

Description

Calculate the Minimum Detectable Flux (MDF) for a static chamber GHG measurement system.

Usage

calculate_MDF(
  precision_ppm,
  closure_time_s,
  data_point_n,
  chamber_volume_m3,
  temperature_C,
  chamber_area_m2,
  pressure_pa = 101325,
  ideal_constant = 8.314,
  ghg = "co2"
)
calculate_MDF(
  precision_ppm,
  closure_time_s,
  data_point_n,
  chamber_volume_m3,
  temperature_C,
  chamber_area_m2,
  pressure_pa = 101325,
  ideal_constant = 8.314,
  ghg = "co2"
)

Arguments

precision_ppm

Precision of the gas analyser (ppm).

closure_time_s

Closure time of the measurement (seconds).

data_point_n

Number of data points recorded during the closure period.

chamber_volume_m3

Internal volume of the chamber (m $^3$ ).

temperature_C

Air temperature at the measurement location ( $^\circ$ C).

chamber_area_m2

Base area of the chamber (m $^2$ ).

pressure_pa

Atmospheric pressure (Pa). Default 101325.

ideal_constant

Ideal gas constant (J mol $^{-1}$ K $^{-1}$ ). Default 8.314.

ghg

Greenhouse gas type: "co2", "ch4", or "n2o". Default "co2".

Value

A named list with MDF (numeric, $\mu$ g m $^{-2}$ h $^{-1}$ ) and unit (string).

Examples

calculate_MDF(
  precision_ppm     = 1,
  closure_time_s    = 300,
  data_point_n      = 300,
  chamber_volume_m3 = 0.0064,
  temperature_C     = 25,
  chamber_area_m2   = 0.07
)
calculate_MDF(
  precision_ppm     = 1,
  closure_time_s    = 300,
  data_point_n      = 300,
  chamber_volume_m3 = 0.0064,
  temperature_C     = 25,
  chamber_area_m2   = 0.07
)

calculate_regression

Description

Calculate the slope of GHG concentration change over time using linear or exponential regression.

Usage

calculate_regression(
  data,
  reference_df,
  ghg,
  reference_time,
  site,
  analyzer_code,
  duration_minutes = 7,
  num_rows = 300,
  fit_type = "linear",
  t_zero = 0,
  window_type = "sliding",
  start_offset_s = 0,
  r2_threshold = 0.8,
  cor_threshold = 0.5,
  instrument_precision = NULL,
  kappamax = NULL,
  g_factor_threshold = NULL
)
calculate_regression(
  data,
  reference_df,
  ghg,
  reference_time,
  site,
  analyzer_code,
  duration_minutes = 7,
  num_rows = 300,
  fit_type = "linear",
  t_zero = 0,
  window_type = "sliding",
  start_offset_s = 0,
  r2_threshold = 0.8,
  cor_threshold = 0.5,
  instrument_precision = NULL,
  kappamax = NULL,
  g_factor_threshold = NULL
)

Arguments

data

Data from the GHG analyzer that has been processed and time-converted.

reference_df

A data frame containing measurement reference times, site names, and an analyzer column.

ghg

Column name in data containing GHG concentration values (e.g., "CH4", "N2O").

reference_time

Column name in reference_df containing the measurement start times (POSIXct).

site

Column name in reference_df containing site identifiers.

analyzer_code

String pattern used to filter results by the analyzer column of reference_df.

duration_minutes

The duration of the measurement window in minutes. Default 7. When window_type = "fixed" and reference_df contains a duration_minutes column, the per-row value (if not NA) overrides this global parameter for that specific measurement. This allows different window lengths for individual chamber closures.

num_rows

The number of rows used per sliding regression window. Only used when window_type = "sliding". Default 300.

fit_type

Model used to fit the concentration curve. One of "linear" (default), "quadratic" (second-order polynomial; slope is the initial rate at t=0), "exp_tz" (exponential with linear drift and user-specified t_zero), "exp_zhao18" (exponential with linear drift and estimated t0), or "auto" (automatic model selection: tries linear → quadratic → exp_tz in order, accepting a more complex model only when R² improves by > 0.01 and, for exp_tz, the curvature parameter κ does not exceed kappamax). When fit_type = "auto" and instrument_precision = NULL, precision is looked up from analyzer_precision by gas name. The model chosen for each measurement is recorded in the fit_used output column.

t_zero

Reference time in seconds from window start for the "exp_tz" slope calculation. Default 0.

window_type

"sliding" (default) selects the num_rows-length sub-window with the best R² within the measurement period. "fixed" fits a single window starting at start_offset_s seconds after the reference time and running for duration_minutes. Use "fixed" to ensure all gases are regressed over the same time window.

start_offset_s

Seconds to skip from the reference time before the regression window starts. Only used when window_type = "fixed". Default 0. When reference_df contains a start_offset_s column, the per-row value (if not NA) overrides this global parameter for that specific measurement.

r2_threshold

Minimum R-squared for a measurement to pass quality check. Default 0.8.

cor_threshold

Minimum absolute Pearson correlation between concentration and elapsed time. Measurements below this have no detectable trend and are flagged "zero". Default 0.5.

instrument_precision

Optional. Analyser precision in ppm. Used in two ways: (1) minimum-detectable-slope check — measurements whose absolute slope falls below instrument_precision / window_duration_s are flagged "zero" even if R² and correlation pass; (2) kappamax for fit_type = "auto" — following Hüppi et al. (2018), $\kappa_{max} = |\text{slope}_{lm}| / \text{precision}$ , so that stronger fluxes permit more curvature and near-detection-limit measurements are kept linear.

kappamax

Optional. Maximum acceptable exponential curvature parameter $b$ (s $^{-1}$ ). Only applies when fit_type is "exp_tz" or "exp_zhao18". When $b > \kappa_{max}$ , the fit implies an implausibly fast equilibration and the slope is replaced by the linear equivalent. Corresponds to the kappamax quality criterion in Hüppi et al. (2018).

g_factor_threshold

Optional. Maximum acceptable ratio of the chosen model slope to the linear baseline slope (g-factor; Gaudard et al. 2025). Only computed when fit_type = "auto" and the selected model is not linear. Values far from 1 indicate that the nonlinear model diverges strongly from the linear fit, which may signal noise artefacts or poor convergence. When this threshold is exceeded, the flag is overridden to "discard". Default NULL (disabled). A value of 2 flags measurements where the nonlinear slope is more than twice the linear slope.

Value

A tibble with columns: start_time, end_time, slope (ppm s $^{-1}$ ), r_square, correlation, b_param (exponential curvature; NA for linear/quadratic), fit_used (model actually used: "linear", "quadratic", or "exp_tz"; always matches fit_type unless fit_type = "auto"), g_factor (ratio of chosen slope to linear baseline slope; NA when fit_used = "linear" or for explicit non-auto fits), flag ("ok", "discard", "zero", or "no_data"), reference_time, site, analyzer.

Examples

data(n2o)
n2o_converted <- convert_time(n2o)
ref <- data.frame(
  date_time = n2o_converted$real_datetime[1],
  site = "S1",
  analyzer = "1074"
)
calculate_regression(n2o_converted, ref, "N2O",
                     reference_time = "date_time", site = "site",
                     analyzer_code = "1074")
data(n2o)
n2o_converted <- convert_time(n2o)
ref <- data.frame(
  date_time = n2o_converted$real_datetime[1],
  site = "S1",
  analyzer = "1074"
)
calculate_regression(n2o_converted, ref, "N2O",
                     reference_time = "date_time", site = "site",
                     analyzer_code = "1074")

calculate_total_co2e

Description

Convert individual GHG fluxes (mg m $^{-2}$ h $^{-1}$ ) to a total CO $_2$ -equivalent flux (g m $^{-2}$ d $^{-1}$ ) using IPCC AR6 100-year GWPs (CO $_2$ = 1, CH $_4$ = 27, N $_2$ O = 273).

Usage

calculate_total_co2e(co2 = 0, ch4 = 0, n2o = 0)
calculate_total_co2e(co2 = 0, ch4 = 0, n2o = 0)

Arguments

co2

CO $_2$ flux in mg m $^{-2}$ h $^{-1}$ . Default 0.

ch4

CH $_4$ flux in mg m $^{-2}$ h $^{-1}$ . Default 0.

n2o

N $_2$ O flux in mg m $^{-2}$ h $^{-1}$ . Default 0.

Value

Total CO $_2$ e flux as a numeric scalar (g m $^{-2}$ d $^{-1}$ ), printed with a diagnostic message.

Examples

calculate_total_co2e(co2 = 4.02, ch4 = 0.001, n2o = 0.003)
calculate_total_co2e(co2 = 4.02, ch4 = 0.001, n2o = 0.003)

combine_hobo

Description

Tidy multiple data retrieved from HOBO U26 Dissolved Oxygen Data Logger.

Usage

combine_hobo(file_path, file_prefix = "no.")
combine_hobo(file_path, file_prefix = "no.")

Arguments

file_path

Directory of the folder containing the files.

file_prefix

The prefix before the code for the data logger, defaults to "no."

Value

A dataframe.

Examples

hobo_data_path <- system.file("extdata", package = "aelab")
df <- combine_hobo(hobo_data_path, file_prefix = "ex_ho")
hobo_data_path <- system.file("extdata", package = "aelab")
df <- combine_hobo(hobo_data_path, file_prefix = "ex_ho")

combine_weather

Description

Tidy multiple daily weather data downloaded from weather station in Taiwan.

Usage

combine_weather(file_path, start_date, end_date, zone)
combine_weather(file_path, start_date, end_date, zone)

Arguments

file_path

Directory of folder containing the files (including the character in the file name that precedes the date).

start_date

Date of the daily weather data in yyyy-mm-dd format.

end_date

Date of the daily weather data in yyyy-mm-dd format.

zone

Code for the region of the weather station.

Value

A dataframe.

Examples

weather_data_path <- system.file("extdata", package = "aelab")
modified_data_path <- paste0(weather_data_path, "/ex_")
df <- combine_weather(modified_data_path,
  start_date = "2024-01-01",
  end_date = "2024-01-02", "site_A"
)
weather_data_path <- system.file("extdata", package = "aelab")
modified_data_path <- paste0(weather_data_path, "/ex_")
df <- combine_weather(modified_data_path,
  start_date = "2024-01-01",
  end_date = "2024-01-02", "site_A"
)

combine_weather_mh

Description

Batch-import and combine multiple CWA "MH" (Multifield Hourly) weather reports. Hourly CODiS downloads are capped at a one-month range, so a date span typically yields several LotsDataReports.txt files; this reads them all and row-binds the result.

Usage

combine_weather_mh(
  file_paths,
  station_id = NULL,
  expand_30min = TRUE,
  tz = "Asia/Taipei"
)
combine_weather_mh(
  file_paths,
  station_id = NULL,
  expand_30min = TRUE,
  tz = "Asia/Taipei"
)

Arguments

file_paths

Either a character vector of MH file paths, or a single directory path (all .txt files within are read, recursively).

station_id

Optional character vector of station codes to keep. Passed to process_weather_mh.

expand_30min

Passed to process_weather_mh. Default TRUE.

tz

Time zone. Default "Asia/Taipei".

Value

A combined data frame, de-duplicated and ordered by station and time.

Examples

## Not run: 
df <- combine_weather_mh("data/raw/sw_mono_do/weather_mh",
  station_id = c("C0R660", "C0R850")
)

## End(Not run)
## Not run: 
df <- combine_weather_mh("data/raw/sw_mono_do/weather_mh",
  station_id = c("C0R660", "C0R850")
)

## End(Not run)

combine_weather_month

Description

Batch-import monthly weather CSV files from a Taiwan Central Weather Administration station for a consecutive range of months.

Usage

combine_weather_month(file_path, start_month, end_month, year = 2024, zone)
combine_weather_month(file_path, start_month, end_month, year = 2024, zone)

Arguments

file_path

Path prefix (directory + filename prefix before the date portion, e.g. "data/weather/").

start_month

First month to import (1–9; two-digit months not yet supported).

end_month

Last month to import.

year

Four-digit year. Default 2024.

zone

Character label for the weather station / region.

Details

File names are expected to follow the pattern <file_path><year>-0<month>.csv (e.g. 2024-01.csv).

Value

A combined data frame produced by process_weather_month.

Examples

## Not run: 
df <- combine_weather_month("data/weather/",
  start_month = 1,
  end_month = 6, year = 2024, zone = "site_A"
)

## End(Not run)
## Not run: 
df <- combine_weather_month("data/weather/",
  start_month = 1,
  end_month = 6, year = 2024, zone = "site_A"
)

## End(Not run)

convert_ghg_unit

Description

Convert a greenhouse gas (GHG) flux value (or a character string containing one or more numeric values, e.g. "0.002 +/- 0.003") to micrograms per square meter per hour.

Usage

convert_ghg_unit(
  input,
  ghg,
  mass = "µg",
  area = "m2",
  time = "hr",
  digits = 2,
  ratio = FALSE
)
convert_ghg_unit(
  input,
  ghg,
  mass = "µg",
  area = "m2",
  time = "hr",
  digits = 2,
  ratio = FALSE
)

Arguments

input

A single numeric value or a character string containing one or more numbers.

ghg

The molecular formula of the greenhouse gas: "co2", "ch4", or "n2o".

mass

Mass unit of the input flux. One of "mmol", "mg", "g", "ug" (micrograms), "nmol", "Mg", "umol" (micromoles), "mol". Default "ug".

area

Area unit of the input flux. One of "ha", "m2". Default "m2".

time

Time unit of the input flux. One of "yr", "day", "hr", "sec", "min". Default "hr".

digits

Number of decimal places to round to. Default 2.

ratio

Logical. If TRUE, apply an elemental-ratio correction (C-basis for CH4, N-basis for N2O). Default FALSE.

Details

Numeric values embedded in a string (e.g. mean +/- SD notation) are each converted individually and the surrounding text is preserved. Commas are treated as decimal separators.

Value

A named list with value (converted string) and unit, or "EMPTY" for missing/non-numeric input.

Examples

convert_ghg_unit(97, ghg = "ch4", mass = "mg", area = "m2", time = "hr")
convert_ghg_unit(97, ghg = "ch4", mass = "mg", area = "m2", time = "hr")

convert_time

Description

Convert the time of the LI-COR Trace Gas Analyzer to match the time in real life.

Usage

convert_time(data, day = 0, hr = 0, min = 0, sec = 0)
convert_time(data, day = 0, hr = 0, min = 0, sec = 0)

Arguments

data

Data from the LI-COR Trace Gas Analyzer that had been processed by tidy_licor().

day

Day(s) to add or subtract.

hr

Hour(s) to add or subtract.

min

Minute(s) to add or subtract.

sec

Second(s) to add or subtract.

Value

The input data with a new column in POSIXct format converted based on the input value.

Examples

data(n2o)
converted_n2o <- convert_time(n2o, min = -10, sec = 5)
data(n2o)
converted_n2o <- convert_time(n2o, min = -10, sec = 5)

descriptive_statistic

Description

Compute grouped mean ± SD and min–max summary statistics for one or more numeric variables.

Usage

descriptive_statistic(data, vars, groups, digits = 2)
descriptive_statistic(data, vars, groups, digits = 2)

Arguments

data

A data frame.

vars

<['tidy-select'][dplyr::dplyr_tidy_select]> Columns to summarise.

groups

<['tidy-select'][dplyr::dplyr_tidy_select]> Grouping columns.

digits

Number of decimal places to round to. Default is 2.

Value

A tibble with one row per group and two summary columns per variable ('<var>_mean_sd' and '<var>_min_max').

Examples

df <- data.frame(group = c("A","A","B","B"), value = c(1.1, 2.3, 3.5, 4.7))
descriptive_statistic(df, vars = value, groups = group)
df <- data.frame(group = c("A","A","B","B"), value = c(1.1, 2.3, 3.5, 4.7))
descriptive_statistic(df, vars = value, groups = group)

df_trans

Description

Apply a reverse square-root or reverse log transformation to a numeric column and append the result as a new column.

Usage

df_trans(df, variable_name, transformation)
df_trans(df, variable_name, transformation)

Arguments

df

A data frame.

variable_name

Name of the column to transform (string).

transformation

Transformation type: "sqrt" or "log".

Value

The input data frame with an additional column named <variable_name>_sqrt or <variable_name>_log.

Examples

df <- data.frame(val = c(1, 4, 9, 16))
df_trans(df, "val", "sqrt")
df <- data.frame(val = c(1, 4, 9, 16))
df_trans(df, "val", "sqrt")

filter_complete_days

Description

Keep only the calendar days that have (near-)complete diel coverage, per logger. A day qualifies when the number of distinct time-of-day slots present reaches min_slots. This replaces the manual inspection of which deployment days run a full 00:00-24:00, so daily DO metabolism is computed only from complete days.

Usage

filter_complete_days(
  df,
  group_cols = "no_hobo",
  time_col = "date_time",
  min_slots = 44L,
  tz = "Asia/Taipei",
  summary_only = FALSE
)
filter_complete_days(
  df,
  group_cols = "no_hobo",
  time_col = "date_time",
  min_slots = 44L,
  tz = "Asia/Taipei",
  summary_only = FALSE
)

Arguments

df

A data frame of HOBO readings, e.g. from process_hobo or combine_hobo, containing a POSIXct time column.

group_cols

Character vector of grouping columns identifying one logger deployment, default "no_hobo".

time_col

Name of the POSIXct time column. Default "date_time".

min_slots

Minimum number of distinct 30-minute slots a day must have to count as complete. Default 44 (of 48; allows minor gaps).

tz

Time zone used to derive the calendar date. Default "Asia/Taipei".

summary_only

If TRUE, return a per-day summary (group_cols, date, n_slots, complete) instead of the filtered readings. Useful for inspecting coverage. Default FALSE.

Value

Either the input rows restricted to complete days (default), or a coverage summary when summary_only = TRUE.

Examples

## Not run: 
hobo <- combine_hobo("data/raw/sw_mono_do/csv/FL_T1")
filter_complete_days(hobo, summary_only = TRUE)   # inspect coverage
clean <- filter_complete_days(hobo)               # keep complete days

## End(Not run)
## Not run: 
hobo <- combine_hobo("data/raw/sw_mono_do/csv/FL_T1")
filter_complete_days(hobo, summary_only = TRUE)   # inspect coverage
clean <- filter_complete_days(hobo)               # keep complete days

## End(Not run)

find_outlier

Description

Identify outliers in a numeric column using the IQR method (values outside 1.5 $\times$ IQR from Q1/Q3).

Usage

find_outlier(df, var, other_var = NULL)
find_outlier(df, var, other_var = NULL)

Arguments

df

A data frame.

var

Name of the column to check for outliers (string).

other_var

Character vector of additional column names to return alongside the outlier values, or NULL.

Value

A tibble with columns row_index, outlier_value, and any requested other_var columns.

Examples

df <- data.frame(val = c(1, 2, 2, 3, 100), id = 1:5)
find_outlier(df, "val", "id")
df <- data.frame(val = c(1, 2, 2, 3, 100), id = 1:5)
find_outlier(df, "val", "id")

Processed data from Onset HOBO Dissolved Oxygen Data Logger. A dataset containing 336 dissolved oxygen concentrations changed over time.

Description

Processed data from Onset HOBO Dissolved Oxygen Data Logger. A dataset containing 336 dissolved oxygen concentrations changed over time.

Format

A data.frame with 336 rows and 13 variables:

date_time: Date and time in POSIXct format.
pressure_hpa: Atmospheric pressure (hpa).
wind_ms: Wind speed (m/s).
do: Dissolved oxygen concentrations (mg/L)
temp: Water temperature (Celsius)
depth: Water depth (m).
salinity: Salinity (ppt).
start_date_time: Start date and time of the deployment.
end_date_time: End date and time of the deployment.
sunrise: Sunrise time during that day.
sunset: Sunset time during that day.
no_hobo: Name for the data logger .
site: Name for the site.

Source

own data.

ks_test

Description

Perform Kruskal-Wallis test followed by Dunn post-hoc test (Bonferroni correction) with compact letter display.

Usage

ks_test(df, variable_name, group)
ks_test(df, variable_name, group)

Arguments

df

A data frame.

variable_name

Name of the response variable column (string).

group

Name of the grouping column (string).

Value

A named list with elements ks_results, dunn_results, mean_summary, and compact_letters.

Examples

df <- data.frame(
  grp = rep(c("A","B","C"), each = 5),
  val = c(1,2,1,2,1, 3,4,3,4,3, 5,6,5,6,5)
)
ks_test(df, "val", "grp")
df <- data.frame(
  grp = rep(c("A","B","C"), each = 5),
  val = c(1,2,1,2,1, 3,4,3,4,3, 5,6,5,6,5)
)
ks_test(df, "val", "grp")

make_reference

Description

Build a reference_df for calculate_regression from a compact inline string of field notes. Each line is one closure measurement with whitespace-separated columns (any number of spaces or tabs): time site analyzer [dur=N] [off=N].

The first three columns are positional (required). Optional per-row window overrides use key=value syntax in any order: dur= sets duration_minutes, off= sets start_offset_s. Rows without an override simply omit the token — no NA placeholder needed.

Usage

make_reference(date, measurements, output = NULL, tz = "Asia/Taipei")
make_reference(date, measurements, output = NULL, tz = "Asia/Taipei")

Arguments

date

Sampling date applied to all rows. A single "YYYY-MM-DD" string or Date. For multi-day campaigns, supply a character/Date vector with one value per row.

measurements

A multiline character string, one row per closure: time site analyzer [dur=N] [off=N]. time accepts "HH:MM" or "HH:MM:SS".

output

Optional path for the output .xlsx file. If NULL (default), the tibble is returned.

tz

Timezone for date_time. Default "Asia/Taipei".

Value

A tibble with columns site, date_time, analyzer, and optionally duration_minutes and/or start_offset_s (only present when at least one row specifies the override). When output is given, the tibble is written to Excel and returned invisibly.

Examples

make_reference(
  date = "2026-05-22",
  measurements = "
    10:30:15  JN1-1  1337_1074
    10:42:00  JN1-2  1337_1074
    10:54:30  JN2-1  1337_1074
  "
)

# per-row window overrides — no NA placeholders needed
make_reference(
  date = "2026-05-22",
  measurements = "
    10:30:15  TT3-1  1450  dur=3.5
    10:42:00  TT3-2  1450  dur=2.0
    10:54:30  TT3-3  1450  off=45
    11:10:00  TT1-1  1450
  "
)
make_reference(
  date = "2026-05-22",
  measurements = "
    10:30:15  JN1-1  1337_1074
    10:42:00  JN1-2  1337_1074
    10:54:30  JN2-1  1337_1074
  "
)

# per-row window overrides — no NA placeholders needed
make_reference(
  date = "2026-05-22",
  measurements = "
    10:30:15  TT3-1  1450  dur=3.5
    10:42:00  TT3-2  1450  dur=2.0
    10:54:30  TT3-3  1450  off=45
    11:10:00  TT1-1  1450
  "
)

Processed data from N2O LI-COR Trace Gas Analyzer. A dataset containing 567 N2O concentrations changed over time.

Description

Processed data from N2O LI-COR Trace Gas Analyzer. A dataset containing 567 N2O concentrations changed over time.

Format

A data.frame with 567 rows and 4 variables:

DATE: Date in character format.
TIME: Time in character format.
N2O: Concentrations of nitrous oxide (N2O), in ppb.
date_time: Date and time in POSIXct format.

Source

own data.

normality_test_aov

Description

Test normality of ANOVA model residuals using Shapiro-Wilk on raw, square-root, and log10 transforms (one-way or two-way).

Usage

normality_test_aov(df, variable_name, group_1, group_2 = NULL)
normality_test_aov(df, variable_name, group_1, group_2 = NULL)

Arguments

df

A data frame.

variable_name

Name of the response variable column (string).

group_1

Name of the first grouping column (string).

group_2

Name of the second grouping column (string), or NULL for a one-way model.

Value

A tibble with Shapiro-Wilk p-values for each transformation.

Examples

df <- data.frame(
  grp = c("A","A","B","B"),
  val = c(1.1, 1.4, 3.2, 3.8)
)
normality_test_aov(df, "val", "grp")
df <- data.frame(
  grp = c("A","A","B","B"),
  val = c(1.1, 1.4, 3.2, 3.8)
)
normality_test_aov(df, "val", "grp")

normality_test_t

Description

Test normality of a variable within two groups using Shapiro-Wilk on raw, square-root, and log10 transforms (for t-test context).

Usage

normality_test_t(df, variable_name, group, group_1, group_2)
normality_test_t(df, variable_name, group, group_1, group_2)

Arguments

df

A data frame.

variable_name

Name of the numeric variable column (string).

group

<['data-masking'][dplyr::dplyr_data_masking]> The grouping column.

group_1

Value identifying the first group.

group_2

Value identifying the second group.

Value

A tibble with Shapiro-Wilk p-values for each group × transformation combination.

Examples

df <- data.frame(
  grp = c("A","A","A","B","B","B"),
  val = c(1.1, 2.0, 1.5, 4.2, 3.8, 4.5)
)
normality_test_t(df, "val", grp, "A", "B")
df <- data.frame(
  grp = c("A","A","A","B","B","B"),
  val = c(1.1, 2.0, 1.5, 4.2, 3.8, 4.5)
)
normality_test_t(df, "val", grp, "A", "B")

plot_bar

Description

Create a bar plot using the aelab theme.

Usage

plot_bar(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  line_width = 1,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw",
  position = "dodge",
  stat = "identity"
)
plot_bar(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  line_width = 1,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw",
  position = "dodge",
  stat = "identity"
)

Arguments

df

A data frame.

x

<['data-masking'][ggplot2::aes]> Column mapped to the x-axis.

y

<['data-masking'][ggplot2::aes]> Column mapped to the y-axis.

z

<['data-masking'][ggplot2::aes]> Optional column mapped to fill colour.

base_size

Base font size. Default 25.

line_width

Bar outline width. Default 1.

text_color

Text colour. Default "black".

Logical; add facet grid? Default FALSE.

Column name (string) for the horizontal facet dimension.

Column name (string) for the vertical facet dimension.

style

Theme style. Default "bw".

position

Bar position: "dodge" or "stack". Default "dodge".

stat

Stat type: "identity" or "count". Default "identity".

Value

A ggplot object.

Examples

## Not run: 
df <- data.frame(x = c("A","B","A","B"), g = c("X","X","Y","Y"), y = c(1,2,3,4))
plot_bar(df, x, y, g)

## End(Not run)
## Not run: 
df <- data.frame(x = c("A","B","A","B"), g = c("X","X","Y","Y"), y = c(1,2,3,4))
plot_bar(df, x, y, g)

## End(Not run)

plot_box

Description

Create a box plot with mean overlay using the aelab theme.

Usage

plot_box(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  line_width = 0.5,
  outlier_size = 1.5,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw"
)
plot_box(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  line_width = 0.5,
  outlier_size = 1.5,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw"
)

Arguments

df

A data frame.

x

<['data-masking'][ggplot2::aes]> Column mapped to the x-axis.

y

<['data-masking'][ggplot2::aes]> Column mapped to the y-axis.

z

<['data-masking'][ggplot2::aes]> Optional column mapped to fill colour.

base_size

Base font size. Default 25.

line_width

Box outline width. Default 0.5.

outlier_size

Outlier point size. Default 1.5.

text_color

Text colour. Default "black".

Logical; add facet grid? Default FALSE.

Column name (string) for the horizontal facet dimension.

Column name (string) for the vertical facet dimension.

style

Theme style. Default "bw".

Value

A ggplot object.

Examples

## Not run: 
df <- data.frame(x = rep(c("A","B"), each = 5), y = c(1:5, 3:7))
plot_box(df, x, y)

## End(Not run)
## Not run: 
df <- data.frame(x = rep(c("A","B"), each = 5), y = c(1:5, 3:7))
plot_box(df, x, y)

## End(Not run)

plot_ghg_flux

Description

Plot GHG concentration over time for each measurement window, overlaid with the fitted regression line and colored by quality flag. Use this for visual QC of calculate_regression output.

Usage

plot_ghg_flux(
  data,
  regression_result,
  ghg,
  fit_type = "linear",
  t_zero = 0,
  flag_colors = c(ok = "#2ca25f", discard = "#fc8d59", zero = "#74c7ef", no_data =
    "#aaaaaa")
)
plot_ghg_flux(
  data,
  regression_result,
  ghg,
  fit_type = "linear",
  t_zero = 0,
  flag_colors = c(ok = "#2ca25f", discard = "#fc8d59", zero = "#74c7ef", no_data =
    "#aaaaaa")
)

Arguments

data

Analyzer data processed by tidy_ghg_analyzer() and convert_time().

regression_result

Output tibble from calculate_regression().

ghg

Column name in data for the gas concentration (e.g. "CH4", "N2O").

fit_type

Model used to draw the fitted line. Should match the fit_type used in calculate_regression(). One of "linear" (default), "exp_tz", "exp_zhao18".

t_zero

Reference time in seconds for "exp_tz" slope. Default 0.

flag_colors

Named character vector mapping flag values to colors.

Value

A ggplot object with one facet per site. Each panel shows raw concentration points and the fitted line, with strip text showing slope, R², and flag.

Examples

data(n2o)
n2o_converted <- convert_time(n2o)
ref <- data.frame(
  date_time = n2o_converted$real_datetime[1],
  site = "S1", analyzer = "1074"
)
result <- calculate_regression(n2o_converted, ref, "N2O",
                               reference_time = "date_time", site = "site",
                               analyzer_code = "1074")
plot_ghg_flux(n2o_converted, result, "N2O")
data(n2o)
n2o_converted <- convert_time(n2o)
ref <- data.frame(
  date_time = n2o_converted$real_datetime[1],
  site = "S1", analyzer = "1074"
)
result <- calculate_regression(n2o_converted, ref, "N2O",
                               reference_time = "date_time", site = "site",
                               analyzer_code = "1074")
plot_ghg_flux(n2o_converted, result, "N2O")

plot_hobo

Description

Plot the dissolved oxygen concentration over time series grouped by different data loggers to observe the variations.

Usage

plot_hobo(df)
plot_hobo(df)

Arguments

df

Dataframe produced by process_hobo() function.

Value

A plot generated by ggplot2.

Examples

data(hobo)
plot_hobo(hobo)
data(hobo)
plot_hobo(hobo)

plot_line

Description

Create a line plot using the aelab theme.

Usage

plot_line(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  line_width = 3,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw"
)
plot_line(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  line_width = 3,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw"
)

Arguments

df

A data frame.

x

<['data-masking'][ggplot2::aes]> Column mapped to the x-axis.

y

<['data-masking'][ggplot2::aes]> Column mapped to the y-axis.

z

<['data-masking'][ggplot2::aes]> Optional column mapped to colour and group.

base_size

Base font size. Default 25.

line_width

Line width. Default 3.

text_color

Text colour. Default "black".

Logical; add facet grid? Default FALSE.

Column name (string) for the horizontal facet dimension.

Column name (string) for the vertical facet dimension.

style

Theme style. Default "bw".

Value

A ggplot object.

Examples

## Not run: 
df <- data.frame(x = 1:6, y = c(1,3,2,5,4,6), g = rep(c("A","B"), 3))
plot_line(df, x, y, g)

## End(Not run)
## Not run: 
df <- data.frame(x = 1:6, y = c(1,3,2,5,4,6), g = rep(c("A","B"), 3))
plot_line(df, x, y, g)

## End(Not run)

plot_map_taiwan

Description

Plot sampling sites on a map of Taiwan with a north arrow and scale bar.

Usage

plot_map_taiwan(
  long,
  lat,
  names,
  color = "darkgrey",
  textsize = 5,
  basesize = 16,
  shape_type = 22
)
plot_map_taiwan(
  long,
  lat,
  names,
  color = "darkgrey",
  textsize = 5,
  basesize = 16,
  shape_type = 22
)

Arguments

long

Numeric vector of longitudes.

lat

Numeric vector of latitudes.

names

Character vector of site labels (same length as long).

color

Fill colour for site markers. Default "darkgrey".

textsize

Size for annotation and point labels. Default 5.

basesize

Base font size for the map theme. Default 16.

shape_type

ggplot2 point shape number. Default 22 (filled square).

Value

A ggplot object (also printed to the active device).

Examples

## Not run: 
plot_map_taiwan(
  long = c(120.2, 121.5),
  lat  = c(22.9, 24.1),
  names = c("Site A", "Site B")
)

## End(Not run)
## Not run: 
plot_map_taiwan(
  long = c(120.2, 121.5),
  lat  = c(22.9, 24.1),
  names = c("Site A", "Site B")
)

## End(Not run)

plot_point

Description

Create a scatter plot using the aelab theme.

Usage

plot_point(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  point_size = 3,
  stroke_size = 1,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw"
)
plot_point(
  df,
  x,
  y,
  z = NULL,
  base_size = 25,
  point_size = 3,
  stroke_size = 1,
  text_color = "black",
  facet = FALSE,
  facet_x = NULL,
  facet_y = NULL,
  style = "bw"
)

Arguments

df

A data frame.

x

<['data-masking'][ggplot2::aes]> Column mapped to the x-axis.

y

<['data-masking'][ggplot2::aes]> Column mapped to the y-axis.

z

<['data-masking'][ggplot2::aes]> Optional column mapped to fill colour.

base_size

Base font size passed to the ggplot2 theme. Default 25.

point_size

Point size. Default 3.

stroke_size

Point stroke width. Default 1.

text_color

Text colour. Default "black".

Logical; add facet grid? Default FALSE.

Column name (string) for the horizontal facet dimension.

Column name (string) for the vertical facet dimension.

style

Theme style. One of "bw", "minimal", "classic", "graycolor", "light". Default "bw".

Value

A ggplot object.

Examples

## Not run: 
df <- data.frame(x = 1:5, y = c(2,4,1,5,3), g = c("A","A","B","B","A"))
plot_point(df, x, y, g)

## End(Not run)
## Not run: 
df <- data.frame(x = 1:5, y = c(2,4,1,5,3), g = c("A","A","B","B","A"))
plot_point(df, x, y, g)

## End(Not run)

process_hobo

Description

Tidy data exported from a HOBO data logger. Supports the HOBO U26 Dissolved Oxygen Data Logger (type = "do") and the HOBO Pro v2 Temperature/RH Logger (type = "temp"). Handles both Chinese (上午/下午) and English (AM/PM or 24-hour) HOBOware locale exports, with 2- or 4-digit years. For type = "do", readings with a negative (sensor-error) dissolved-oxygen or temperature value are dropped before aggregation.

Usage

process_hobo(file_path, no_hobo, type = c("do", "temp"))
process_hobo(file_path, no_hobo, type = c("do", "temp"))

Arguments

file_path

Path to the CSV file exported from HOBOware.

no_hobo

The code for the data logger.

type

Logger type: "do" (default) for HOBO U26 DO logger, or "temp" for HOBO Pro v2 temperature/RH logger.

Value

A dataframe. For type = "do": columns date_time, do, temp, no_hobo, aggregated to 30-minute intervals. For type = "temp": columns date_time, air_temp, rh, no_hobo, one row per logger reading.

Examples

hobo_data_path <- system.file("extdata", "ex_hobo.csv", package = "aelab")
df <- process_hobo(hobo_data_path, "code_for_logger")
hobo_data_path <- system.file("extdata", "ex_hobo.csv", package = "aelab")
df <- process_hobo(hobo_data_path, "code_for_logger")

process_info

Description

Import and process the necessary information, including the sunrise and sunset times of the day, the date and time range of the deployment, and the code for the data logger.

Usage

process_info(file_path)
process_info(file_path)

Arguments

file_path

Directory of file.

Value

A dataframe.

Examples

info_data_path <- system.file("extdata", "info.xlsx", package = "aelab")
df <- process_info(info_data_path)
info_data_path <- system.file("extdata", "info.xlsx", package = "aelab")
df <- process_info(info_data_path)

convert_time

Description

Tidy the daily weather data downloaded from weather station in Taiwan.

Usage

process_weather(file_path, date, zone)
process_weather(file_path, date, zone)

Arguments

file_path

Directory of file.

date

Date of the daily weather data in yyyy-mm-dd format.

zone

Code for the region of the weather station.

Value

A dataframe.

Examples

weather_data_path <- system.file("extdata", "ex_weather.csv", package = "aelab")
df <- process_weather(weather_data_path, "2024-01-01", "site_A")
weather_data_path <- system.file("extdata", "ex_weather.csv", package = "aelab")
df <- process_weather(weather_data_path, "2024-01-01", "site_A")

process_weather_mh

Description

Import and tidy an hourly weather report in the CWA "MH" (Multifield Hourly) text format, as bulk-downloaded from the CODiS portal (LotsDataReports.txt). The file is fixed-/space-delimited with a '#' column-title row listing element codes (e.g. PS01, WD01) and '*' comment rows. One file may contain multiple stations and a full month of hourly data.

Usage

process_weather_mh(
  file_path,
  station_id = NULL,
  expand_30min = TRUE,
  tz = "Asia/Taipei"
)
process_weather_mh(
  file_path,
  station_id = NULL,
  expand_30min = TRUE,
  tz = "Asia/Taipei"
)

Arguments

file_path

Path to the MH-format .txt file.

station_id

Optional character vector of station codes (e.g. "C0R660") to keep. Default NULL keeps all stations.

expand_30min

If TRUE (default), each hourly value is expanded to the two 30-minute marks (H-1):30 and H:00, matching process_weather so it merges with 30-minute HOBO data. If FALSE, hourly rows are returned unchanged.

tz

Time zone of the observations. Default "Asia/Taipei".

Details

Element codes are renamed as: PS01 -> pressure_hpa, WD01 -> wind_ms, TX01 -> air_temp, RH01 -> humidity, WD02 -> wind_dir, PP01 -> rain_mm. Only the codes present in the file are returned. CWA missing-value codes (large negatives, e.g. -9991) are set to NA. The timestamp yyyymmddhh uses hours 01-24, where hour 24 denotes 00:00 of the following day.

Value

A data frame with columns stno, date_time, and the renamed weather elements present in the file.

Examples

## Not run: 
df <- process_weather_mh("LotsDataReports.txt", station_id = "C0R660")

## End(Not run)
## Not run: 
df <- process_weather_mh("LotsDataReports.txt", station_id = "C0R660")

## End(Not run)

process_weather_month

Description

Import and tidy a monthly weather CSV file downloaded from a Taiwan Central Weather Administration station. Column selection is done via regex so minor header changes are handled gracefully.

Usage

process_weather_month(file_path, month, year = 2024, zone)
process_weather_month(file_path, month, year = 2024, zone)

Arguments

file_path

Path to the monthly CSV file.

month

Month number (1–12) covered by the file.

year

Four-digit year. Default 2024.

zone

Character label for the weather station / region.

Value

A data frame with columns day, pressure_hpa, temp, humidity_percent, wind_ms, rain_mm, daylight_hr, radiation, date, and zone.

Examples

## Not run: 
df <- process_weather_month("path/to/2024-01.csv",
  month = 1, year = 2024,
  zone = "site_A"
)

## End(Not run)
## Not run: 
df <- process_weather_month("path/to/2024-01.csv",
  month = 1, year = 2024,
  zone = "site_A"
)

## End(Not run)

scale_colour_aelab_c

Description

Continuous ggplot2 colour scale using an aelab palette.

Usage

scale_colour_aelab_c(name, direction = 1)

scale_color_aelab_c(name, direction = 1)
scale_colour_aelab_c(name, direction = 1)

scale_color_aelab_c(name, direction = 1)

Arguments

name

Palette name passed to aelab_palettes.

direction

1 (default) for normal order; -1 to reverse.

Value

A ggplot2 scale.

Examples

## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg, colour = mpg)) +
  ggplot2::geom_point() + scale_colour_aelab_c("rainbow")

## End(Not run)
## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg, colour = mpg)) +
  ggplot2::geom_point() + scale_colour_aelab_c("rainbow")

## End(Not run)

scale_colour_aelab_d

Description

Discrete ggplot2 colour scale using an aelab palette.

Usage

scale_colour_aelab_d(name, direction = 1)

scale_color_aelab_d(name, direction = 1)
scale_colour_aelab_d(name, direction = 1)

scale_color_aelab_d(name, direction = 1)

Arguments

name

Palette name passed to aelab_palettes.

direction

1 (default) for normal order; -1 to reverse.

Value

A ggplot2 scale.

Examples

## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg, colour = factor(cyl))) +
  ggplot2::geom_point() + scale_colour_aelab_d("rainbow")

## End(Not run)
## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg, colour = factor(cyl))) +
  ggplot2::geom_point() + scale_colour_aelab_d("rainbow")

## End(Not run)

scale_fill_aelab_c

Description

Continuous ggplot2 fill scale using an aelab palette.

Usage

scale_fill_aelab_c(name, direction = 1)
scale_fill_aelab_c(name, direction = 1)

Arguments

name

Palette name passed to aelab_palettes.

direction

1 (default) for normal order; -1 to reverse.

Value

A ggplot2 scale.

Examples

## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(factor(cyl), fill = mpg)) +
  ggplot2::geom_col() + scale_fill_aelab_c("ghg")

## End(Not run)
## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(factor(cyl), fill = mpg)) +
  ggplot2::geom_col() + scale_fill_aelab_c("ghg")

## End(Not run)

scale_fill_aelab_d

Description

Discrete ggplot2 fill scale using an aelab palette.

Usage

scale_fill_aelab_d(name, direction = 1)
scale_fill_aelab_d(name, direction = 1)

Arguments

name

Palette name passed to aelab_palettes.

direction

1 (default) for normal order; -1 to reverse.

Value

A ggplot2 scale.

Examples

## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(factor(cyl), fill = factor(cyl))) +
  ggplot2::geom_bar() + scale_fill_aelab_d("control")

## End(Not run)
## Not run: 
ggplot2::ggplot(mtcars, ggplot2::aes(factor(cyl), fill = factor(cyl))) +
  ggplot2::geom_bar() + scale_fill_aelab_d("control")

## End(Not run)

sig_labels

Description

Run ks_test separately for each level of a faceting variable and return compact letter display (CLD) annotations ready for geom_text. Only facets where the Kruskal-Wallis p-value is below alpha are included in the output.

Usage

sig_labels(
  stat_data,
  variable,
  group,
  by = "year",
  plot_data = NULL,
  alpha = 0.05
)
sig_labels(
  stat_data,
  variable,
  group,
  by = "year",
  plot_data = NULL,
  alpha = 0.05
)

Arguments

stat_data

Data frame of raw observations used for the statistical test. Should already be filtered to the relevant subset (e.g. a single water type).

variable

Name of the response variable column (string).

group

Name of the grouping column (string).

by

Name of the faceting column whose levels are iterated over (string, default "year").

plot_data

Data frame used to compute label y-positions via max(variable) per group. Defaults to stat_data. Pass the aggregated/boxplot data here when it differs from the raw test data.

alpha

Significance threshold; facets with KW p-value >= alpha are dropped (default 0.05).

Value

A data frame with columns <group>, Letter, MonoLetter, y_pos, and <by>. Returns an empty data frame when no facet reaches significance.

Examples

set.seed(1)
df <- data.frame(
  year = rep(c("2023", "2024"), each = 20),
  grp  = rep(c("A","B","C","D"), 10),
  val  = c(rnorm(20, mean = rep(c(1,3,2,6), 5)), rnorm(20))
)
sig_labels(df, "val", "grp", by = "year")
set.seed(1)
df <- data.frame(
  year = rep(c("2023", "2024"), each = 20),
  grp  = rep(c("A","B","C","D"), 10),
  val  = c(rnorm(20, mean = rep(c(1,3,2,6), 5)), rnorm(20))
)
sig_labels(df, "val", "grp", by = "year")

tidy_ghg_analyzer

Description

Tidy the data downloaded from GHG Analyzer, with optional timestamp correction.

Usage

tidy_ghg_analyzer(
  file_path,
  gas,
  analyzer = "licor",
  day = 0,
  hr = 0,
  min = 0,
  sec = 0
)
tidy_ghg_analyzer(
  file_path,
  gas,
  analyzer = "licor",
  day = 0,
  hr = 0,
  min = 0,
  sec = 0
)

Arguments

file_path

Directory of file.

gas

Choose between CO2/CH4 or N2O LI-COR Trace Gas Analyzer, which is "ch4" and "n2o", respectively.

analyzer

The brand of the analyzer which the data was downloaded from.

day

Day offset to correct the analyzer timestamp.

hr

Hour offset to correct the analyzer timestamp.

min

Minute offset to correct the analyzer timestamp.

sec

Second offset to correct the analyzer timestamp.

Value

Return the loaded XLSX file after tidying. If any time offset is non-zero, a real_datetime column is added via convert_time.

Examples

ghg_data_path <- system.file("extdata", "ch4.xlsx", package = "aelab")
tidy_ghg_analyzer(ghg_data_path, "ch4")
tidy_ghg_analyzer(ghg_data_path, "ch4", sec = -30)
ghg_data_path <- system.file("extdata", "ch4.xlsx", package = "aelab")
tidy_ghg_analyzer(ghg_data_path, "ch4")
tidy_ghg_analyzer(ghg_data_path, "ch4", sec = -30)

Package 'aelab'

Help Index

add_sun_times

Description

Usage

Arguments

Value

Examples

aelab_palettes

Description

Usage

Arguments

Details

Value

Examples

Instrument precision for supported trace gas analyzers

Description

Format

Source

Examples

aov_test

Description

Usage

Arguments

Value

Examples

calc_chla_trichromatic

Description

Usage

Arguments

Details

Value

Examples

calculate_do

Description

Usage

Arguments

Value

Examples

calculate_ghg_flux

Description

Usage

Arguments

Value

Examples

calculate_MDF

Description

Usage

Arguments

Value

Examples

calculate_regression

Description

Usage

Arguments

Value

Examples

calculate_total_co2e

Description

Usage

Arguments

Value

Examples

combine_hobo

Description

Usage

Arguments

Value

Examples

combine_weather

Description

Usage

Arguments

Value

Examples

combine_weather_mh

Description

Usage

Arguments

Value