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Generate Statistics Table

Source: R/Tables.R
TADA_Stats.Rd

This function creates a summary table of the dataset based on grouping columns. The 'TADA.ComparableDataIdentifier' column is the required and default grouping column, but the user may include additional columns if desired. The summary table includes the measurement count, location count, censored data stats, min, max, and percentile stats, and a suggested non-detect estimation method. The estimation method is based on the following article: Baseline Assessment of Left-Censored Environmental Data Using R Tech Note. More info can be found here: https://www.epa.gov/sites/default/files/2016-05/documents/tech_notes_10_jun2014_r.pdf Suggested methods are based on the measurement count, the number of non-detects in the dataset, and the number of censoring levels (detection limit types) and methods include Maximum Likelihood Estimation, Robust ROS and Kaplan Meier.

Usage

TADA_Stats(
  .data,
  group_cols = c("TADA.ComparableDataIdentifier"),
  sig_figs = 3,
  pct_digits = 1
)

Arguments

.data

TADA dataframe containing the data downloaded from the WQP, where each row represents a unique data record. Dataframe must include the columns 'TADA.ResultMeasureValue', 'TADA.ResultMeasure.MeasureUnitCode', 'TADA.ResultSampleFractionText', 'TADA.MethodSpeciationName', 'TADA.ComparableDataIdentifier', 'TADA.CensoredData.Flag', 'DetectionQuantitationLimitTypeName', and 'TADA.MonitoringLocationIdentifier' to run this function. The 'TADA.ComparableDataIdentifier' can be added to the dataframe by running the function TADA_CreateComparableID().

group_cols

Character vector of additional grouping columns to include along with 'TADA.ComparableDataIdentifier'. For example: group_cols = c("TADA.MonitoringLocationIdentifier").

sig_figs

Integer. Number of significant figures to display for continuous statistics (UpperFence, LowerFence, Min, Mean, Max, and percentiles). Default is 3. Uses base::signif().

pct_digits

Integer. Number of decimal places to display for percentage fields (Non_Detect_Pct, Over_Detect_Pct). Default is 1. Uses base::round().

Value

A dataframe (stats table) with one row per group and the following columns: Location_Count, Measurement_Count, Non_Detect_Count, Non_Detect_Pct, Non_Detect_Lvls, Over_Detect_Count, Over_Detect_Pct, UpperFence, LowerFence, Min, Mean, Max, Percentile_5th, Percentile_10th, Percentile_15th, Percentile_25th, Percentile_50th_Median, Percentile_75th, Percentile_85th, Percentile_95th, Percentile_98th, and ND_Estimation_Method.

Details

The output is formatted for readability:

  • Continuous statistics (fences, min, mean, max, and percentiles) are rounded to a user-specified number of significant figures via base::signif().

  • Percentage fields (Non_Detect_Pct, Over_Detect_Pct) are rounded to a user-specified number of decimal places via base::round().

  • Missing values in TADA.ResultMeasureValue are excluded (na.rm = TRUE) when computing summary statistics.

  • If TADA.CensoredData.Flag is not present, the function calls TADA_IDCensoredData() to create it.

  • If TADA.NutrientSummation.Flag is present, an informational note is printed.

  • This function also suggests a ND_Estimation_Method following general guidance (Kaplan-Meier, ROS, MLE) based on censored percentage, censoring levels, and measurement count.

Examples

# Load example dataset:
utils::data(Data_6Tribes_5y_Harmonized)

# Default rounding: 3 significant figures for continuous stats, 1 decimal for percentages
Data_6Tribes_5y_Harmonized_stats <- TADA_Stats(Data_6Tribes_5y_Harmonized)

# Custom rounding: 4 significant figures and whole-number percentages
Data_6Tribes_5y_Harmonized_stats_rounded <- TADA_Stats(
  Data_6Tribes_5y_Harmonized,
  sig_figs = 4,
  pct_digits = 0
)