Summarize a results data frame by a grouping variable
Arguments
- dat
input data frame
- accdat
data.framefor data quality objectives file for accuracy as returned byreadMWRacc- param
character string for the parameter to evaluate as provided in the
"Parameter"column of"accdat"- sumfun
character indicating one of
"auto"(default),"mean","geomean","median","min", or"max", see details- confint
logical if user expects a confidence interval to be returned with the summary
Value
A summarized data frame, a warning will be returned if the confidence interval cannot be estimated and confint = TRUE
Details
This function summarizes a results data frame by an existing grouping variable using the function supplied to sumfun. The mean or geometric mean is used for sumfun = "auto" based on information in the data quality objective file for accuracy, i.e., parameters with "log" in any of the columns are summarized with the geometric mean, otherwise arithmetic. Using "mean" or "geomean" for sumfun will apply the appropriate function regardless of information in the data quality objective file for accuracy.
Examples
library(dplyr)
# results data path
respth <- system.file('extdata/ExampleResults.xlsx', package = 'MassWateR')
# results data
resdat <- readMWRresults(respth)
#> Running checks on results data...
#> Checking column names... OK
#> Checking all required columns are present... OK
#> Checking valid Activity Types... OK
#> Checking Activity Start Date formats... OK
#> Checking depth data present... OK
#> Checking for non-numeric values in Activity Depth/Height Measure... OK
#> Checking Activity Depth/Height Unit... OK
#> Checking Activity Relative Depth Name formats... OK
#> Checking values in Activity Depth/Height Measure > 1 m / 3.3 ft... OK
#> Checking Characteristic Name formats... OK
#> Checking Result Values... OK
#> Checking for non-numeric values in Quantitation Limit... OK
#> Checking QC Reference Values... OK
#> Checking for missing entries for Result Unit... OK
#> Checking if more than one unit per Characteristic Name... OK
#> Checking acceptable units for each entry in Characteristic Name... OK
#>
#> All checks passed!
# accuracy path
accpth <- system.file('extdata/ExampleDQOAccuracy.xlsx',
package = 'MassWateR')
# accuracy data
accdat <- readMWRacc(accpth)
#> Running checks on data quality objectives for accuracy...
#> Checking column names... OK
#> Checking all required columns are present... OK
#> Checking column types... OK
#> Checking no "na" in Value Range... OK
#> Checking for text other than <=, ≤, <, >=, ≥, >, ±, %, AQL, BQL, log, or all... OK
#> Checking overlaps in Value Range... OK
#> Checking gaps in Value Range... OK
#> Checking Parameter formats... OK
#> Checking for missing entries for unit (uom)... OK
#> Checking if more than one unit (uom) per Parameter... OK
#> Checking acceptable units (uom) for each entry in Parameter... OK
#> Checking empty columns... OK
#>
#> All checks passed!
# fill BDL, AQL
resdat <- utilMWRlimits(resdat = resdat, accdat = accdat, param = "DO")
dat <- resdat %>%
group_by(`Monitoring Location ID`)
# summarize sites by mean
utilMWRsummary(dat, accdat, param = 'DO', sumfun = 'auto', confint = TRUE)
#> # A tibble: 11 × 4
#> `Monitoring Location ID` lov hiv `Result Value`
#> <chr> <dbl> <dbl> <dbl>
#> 1 ABT-026 6.62 7.99 7.31
#> 2 ABT-062 6.00 9.54 7.77
#> 3 ABT-077 7.88 9.36 8.62
#> 4 ABT-144 7.22 8.86 8.04
#> 5 ABT-237 3.81 9.27 6.54
#> 6 ABT-301 6.00 8.08 7.04
#> 7 ABT-312 6.36 8.10 7.23
#> 8 DAN-013 2.89 9.83 6.36
#> 9 ELZ-004 3.48 6.56 5.02
#> 10 HOP-011 4.43 9.83 7.13
#> 11 NSH-002 4.00 8.77 6.38
# summarize sites by minimum
utilMWRsummary(dat, accdat, param = 'DO', sumfun = 'min', confint = FALSE)
#> # A tibble: 11 × 4
#> `Monitoring Location ID` `Result Value` lov hiv
#> <chr> <dbl> <lgl> <lgl>
#> 1 ABT-026 6.39 NA NA
#> 2 ABT-062 7.17 NA NA
#> 3 ABT-077 7.96 NA NA
#> 4 ABT-144 7.85 NA NA
#> 5 ABT-237 5.89 NA NA
#> 6 ABT-301 5.78 NA NA
#> 7 ABT-312 6.05 NA NA
#> 8 DAN-013 2.13 NA NA
#> 9 ELZ-004 3.83 NA NA
#> 10 HOP-011 4.1 NA NA
#> 11 NSH-002 3.93 NA NA