This package provides the user with convenience functions for cleaning and summarizing data for use in Reasonable Potential Analyses (RPAs) and Water Quality-based Effluent Limitation (WQBEL) development. Examples of tasks one can do with reasonabletools include constructing composite parameter measurement (e.g., total PCBs) from congener water samples, finding maximum observed concentrations from partially censored datasets, and projecting maximum effluent concentrations (MECs). Methods for projecting MECs and computing effluent limitations are based on EPA’s Technical Support Document for Water Quality-based Toxics Control (TSD).
You can install the current version of reasonabletools from github using devtools.
# If devetools is not installed, install it.
install.packages("devtools")
# Install reasonabletools from the github repository.
devtools::install_github("mattreusswig/reasonabletools")Consistent with the TSD, reasonabletools requires a minimum level of information content in the dataset in order to estimate the Coefficient of Variation (CV) for a pollutant. There must be at least 10 observations and fewer than 80 percent of the dataset must be non-detect to calculate a CV value. If either condition is not met, a CV value of 0.6 is assumed and output.
Consistent with the TSD, reasonabletools uses substitution methods when calculating mean and standard deviation values when non-detects are present. When non-detects are present, result values corresponding to non-detect qual values are multiplied by 0.5 prior to computing the mean and standard deviation.
Consistent with the TSD, reasonabletools assumes a lognormal distribution is applicable to the data when performing effluent limitation computations.
Functions in reasonabletools accept vectors as arguments–typically, a vector of numeric data representing concentration point measurements or detection limits, and a vector of character data indicating the detected/non-detect/detected-but-not-quantified status of each numeric value.
Combining the input vectors in a dataframe or a tibble is not necessary, but can be useful when developing summaries for multiple pollutants at once (see discussion below).
library(reasonabletools)
## Create an example dataset
zinc <- data.frame(qual = c(rep("", 10), rep("<", 10)),
result = 1:20,
stringsAsFactors = FALSE)
zinc
#> qual result
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> 6 6
#> 7 7
#> 8 8
#> 9 9
#> 10 10
#> 11 < 11
#> 12 < 12
#> 13 < 13
#> 14 < 14
#> 15 < 15
#> 16 < 16
#> 17 < 17
#> 18 < 18
#> 19 < 19
#> 20 < 20
## Find observed MEC
find_mec(qual = zinc$qual, result = zinc$result)
#> qual result
#> 1 10Functions in reasonabletools can distinguish between detected and non-detect values. Non-detect values are indicated in the qualifier vector with pre-specified character string. By default, this is a “<”, “nd”, or “ND” character string. Any string in the non-detect qualifier vector which does not match these indicators is treated as a detected value. However, the non-detect flags can be changed using the “nd” argument (e.g., you could change it to “My ND Indicator”–see example below). In function output, “<” is always used to indicate a censored value and an empty string "" is used to indicate a detected value.
## Create an example dataset with new custom non-detect indicator
zinc <- data.frame(qual = c(rep("My ND Indicator", 20)),
result = 1:20,
stringsAsFactors = FALSE)
zinc
#> qual result
#> 1 My ND Indicator 1
#> 2 My ND Indicator 2
#> 3 My ND Indicator 3
#> 4 My ND Indicator 4
#> 5 My ND Indicator 5
#> 6 My ND Indicator 6
#> 7 My ND Indicator 7
#> 8 My ND Indicator 8
#> 9 My ND Indicator 9
#> 10 My ND Indicator 10
#> 11 My ND Indicator 11
#> 12 My ND Indicator 12
#> 13 My ND Indicator 13
#> 14 My ND Indicator 14
#> 15 My ND Indicator 15
#> 16 My ND Indicator 16
#> 17 My ND Indicator 17
#> 18 My ND Indicator 18
#> 19 My ND Indicator 19
#> 20 My ND Indicator 20
## Find observed MEC
find_mec(qual = zinc$qual, result = zinc$result,
nd = c("My ND Indicator"))
#> qual result
#> 1 < 1When cleaning data for summary, recall that R is a case-sensitive language and capitalization matters in non-detect flags. The default censored flags in reasonabletools are “<”, “nd”, and “ND”. This means an observation in your data marked “nd” will be treated as a non-detect, but an observation marked “nD” (notice the change in capitalization on the second letter) will be treated as a detected value.
All elements in the results vector should have a numeric value–do not include NA values. For non-detect values, the method detection limit should be entered in the results column. NA values in the results column are ignored and will result in incorrect results.
Functions in reasonabletools do not have specialized behavior for an observation which is detected-but-not-quantified (DNQ or j-flag). The user must decide when preprocessing the data whether these values should be treated as non-detect values or as detected values. The observation should be marked as non-detect or detected in the qualifier vector, and the reporting limit or estimated concentration placed in result vector.
Real-life datasets used in NPDES permit development will typically include many pollutants of concern which need to be summarized. This is a split-apply-combine task which is cumbersome to address in Excel and other spreadsheet-based programs for anything but simple summary functions (e.g., computing averages or maximums without conditionality).
In base R (or using dplyr/tidyverse methods) this task becomes straightforward. Here are some strategies from base R and dplyr for summarizing effluent data with reasonabletools.
We will use the following toy dataset in these examples:
set.seed(10010)
data <- data.frame(pollutant = sort(rep(c("zinc", "arsenic", "copper",
"lead", "nickel", "pcbs"), 5)),
qualifier = sample(c("<", ""), size = 30,
replace = TRUE, prob = c(0.8, 0.2)),
result = sample(seq(0.1, 0.5, 0.1), 30, replace = TRUE),
sampling.date = rep(seq.Date(from = as.Date("2012-03-10"),
to = as.Date("2012-03-10") + 365 * 5,
by = 365), 5),
stringsAsFactors = FALSE)
data
#> pollutant qualifier result sampling.date
#> 1 arsenic < 0.1 2012-03-10
#> 2 arsenic 0.3 2013-03-10
#> 3 arsenic < 0.4 2014-03-10
#> 4 arsenic < 0.1 2015-03-10
#> 5 arsenic < 0.3 2016-03-09
#> 6 copper < 0.5 2017-03-09
#> 7 copper < 0.4 2012-03-10
#> 8 copper < 0.5 2013-03-10
#> 9 copper < 0.5 2014-03-10
#> 10 copper < 0.2 2015-03-10
#> 11 lead < 0.1 2016-03-09
#> 12 lead < 0.1 2017-03-09
#> 13 lead < 0.2 2012-03-10
#> 14 lead < 0.4 2013-03-10
#> 15 lead < 0.4 2014-03-10
#> 16 nickel 0.5 2015-03-10
#> 17 nickel 0.2 2016-03-09
#> 18 nickel < 0.4 2017-03-09
#> 19 nickel < 0.1 2012-03-10
#> 20 nickel < 0.3 2013-03-10
#> 21 pcbs < 0.2 2014-03-10
#> 22 pcbs 0.4 2015-03-10
#> 23 pcbs < 0.5 2016-03-09
#> 24 pcbs < 0.2 2017-03-09
#> 25 pcbs < 0.4 2012-03-10
#> 26 zinc 0.4 2013-03-10
#> 27 zinc < 0.3 2014-03-10
#> 28 zinc < 0.5 2015-03-10
#> 29 zinc 0.2 2016-03-09
#> 30 zinc < 0.3 2017-03-09Use the split family of functions to create a list of dataframes grouped by pollutant name, use lapply to apply the function to each element of the list, and do.call + rbind to recombine the output.
data_split <- split.data.frame(x = data, f = data$pollutant)
## Note we need to use an anonymous function in lapply for functions with
## multiple non-default input arguments, like find_mec.
data_apply <- lapply(X = data_split,
FUN = function(x) find_mec(x$qualifier, x$result))
combined_summary <- do.call(rbind, data_apply)
combined_summary
#> qual result
#> arsenic 0.3
#> copper < 0.2
#> lead < 0.1
#> nickel 0.5
#> pcbs 0.4
#> zinc 0.4Or, we can combine the apply and combine steps into a single line by nesting the functions. Unfortunately, we can’t do this in fewer than two steps.
data_split <- split.data.frame(x = data, f = data$pollutant)
combined_summary <- do.call(
rbind,
lapply(X = data_split,
FUN = function(x) find_mec(x$qualifier, x$result))
)
combined_summary
#> qual result
#> arsenic 0.3
#> copper < 0.2
#> lead < 0.1
#> nickel 0.5
#> pcbs 0.4
#> zinc 0.4Using dplyr is often a more intuitive way to implement split-apply-combine for many people. Using dplyr, we will use dplyr::group_by and wrap the reasonabletools summary function in dplyr::summarise and it will automatically output all columns grouped by pollutant. (Note that old versions of dplyr::summarise (pre-1.0) are not set up to output two columns from a single summary function–see the next section for pre-1.0 dplyr strategies.)
library(dplyr)
data %>%
group_by(pollutant) %>%
summarise(find_mec(qualifier, result))
#> # A tibble: 6 x 3
#> pollutant qual result
#> <chr> <chr> <dbl>
#> 1 arsenic "" 0.3
#> 2 copper "<" 0.2
#> 3 lead "<" 0.1
#> 4 nickel "" 0.5
#> 5 pcbs "" 0.4
#> 6 zinc "" 0.4Older versions of dplyr were not set up to handle multiple column output in the summarise functions. There were two main workarounds for this–one built into reasonabletools and one using dplyr itself.
A number of data summarising functions in reasonabletools that output two column values, like find_mec or project_mec, include an option for automatically concatenating the two output vectors into a single character vector. The “simple_output” argument will produce a single character vector when set to TRUE, and will produce two vectors when left at its default FALSE value.
library(dplyr)
## simple_output = TRUE turns output into single character vector.
data %>%
group_by(pollutant) %>%
summarise(MEC = find_mec(qualifier, result, simple_output = TRUE))
#> # A tibble: 6 x 2
#> pollutant MEC
#> <chr> <chr>
#> 1 arsenic 0.3
#> 2 copper <0.2
#> 3 lead <0.1
#> 4 nickel 0.5
#> 5 pcbs 0.4
#> 6 zinc 0.4Note this setting is also useful when constructing summary tables for inclusion in permit documents where one wants to present the qualifier and value in the same table cell.
If one wants both output vectors retained in the output, use the dplyr::do function as a wrapper around the desired reasonabletools function. Use a dot prior to the $ operator and column name (e.g., .$qualifier) to indicate to dplyr::do it should look for the inputs in the piped dataframe.