aelab_palettes

Description

Retrieve a named aelab colour palette as a character vector.

Usage

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

Arguments

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)

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)

Arguments

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")

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)

Arguments

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)

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"
)

Arguments

Value

A named list with MDF (numeric, \mug 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_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)

Arguments

Value

A dataframe.

Examples

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"
)

Arguments

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)

calculate_regression

Description

Calculate the slope of greenhouse gas (GHG) concentration change over time using simple linear regression.

Usage

calculate_regression(
  data,
  ghg,
  reference_time,
  duration_minutes = 7,
  num_rows = 300
)

Arguments

Value

A tibble containing the time range (POSIXct format) of the slope and R2 (both numeric) from the simple linear regression.

Examples

data(n2o)
calculate_regression(n2o, "N2O", as.POSIXct("2023-05-04 09:16:15", tz = "UTC"))

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_2O = 273).

Usage

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

Arguments

Value

Total CO_2e 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)

combine_hobo

Description

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

Usage

combine_hobo(file_path, file_prefix = "no.")

Arguments

Value

A dataframe.

Examples

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)

Arguments

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")

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)

Arguments

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)

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
)

Arguments

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_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)

Arguments

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)

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)

Arguments

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_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)

Arguments

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")

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)

Arguments

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")

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)

Arguments

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")

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)

Arguments

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")

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)

Arguments

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")

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"
)

Arguments

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)

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"
)

Arguments

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)

plot_hobo

Description

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

Usage

plot_hobo(df)

Arguments

Value

A plot generated by ggplot2.

Examples

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"
)

Arguments

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)

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
)

Arguments

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)

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"
)

Arguments

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)

process_hobo

Description

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

Usage

process_hobo(file_path, no_hobo)

Arguments

Value

A dataframe.

Examples

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)

Arguments

Value

A dataframe.

Examples

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)

Arguments

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")

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)

Arguments

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)

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)

Arguments

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)

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)

Arguments

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)

scale_fill_aelab_c

Description

Continuous ggplot2 fill scale using an aelab palette.

Usage

scale_fill_aelab_c(name, direction = 1)

Arguments

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)

scale_fill_aelab_d

Description

Discrete ggplot2 fill scale using an aelab palette.

Usage

scale_fill_aelab_d(name, direction = 1)

Arguments

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)

tidy_ghg_analyzer

Description

Tidy the data downloaded from GHG Analyzer.

Usage

tidy_ghg_analyzer(file_path, gas, analyzer = "licor")

Arguments

Value

Return the loaded XLSX file after tidying for further analysis.

Examples

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