Using blindspiker

The R package, blindspiker, was initially developed for the Internal Dosimetry program at the Savannah River Site, based on an R script, “SRS Blind Spikes-4.R”, written by Tom Labone.

The first step in using the package is to use the bs_prep_and_analysis function. The package anticipates that your organization runs a blind spike program, providing test samples to a laboratory with known test values. Here, you need to supply two sets of data, the test sample data and the laboratory’s analysis results.

The bs_prep_and_analysis function requires two data sets. You can either load your data into R separately or use this function to load your comma separated variable (csv) file.

The bs_prep_and_analysis function runs a check of the data names, checks data types, merges the data sets, and identifies false positive and false negative results.

Sample test data

Test data must haves:

sample_ID
analyte
spike_value
spike_unit

Test data optional extras:

provider_lab
submission_date
sv_unc
sv_k

Laboratory results data

Laboratory results must haves:

sample_ID
analyte
result_date
result
unc (analysis uncertainty)
units
det_lvl (laboratory’s detection level)

Laboratory results optional extras:

k (coverage factor)

Details

Sample test data details

sample_ID = unique identifier for the sample
analyte = name of analyte in the sample
spike_value = quantity of the analyte
spike_unit = unit of measurement for the spike value
provider_lab = for the spiked samples - Optional, not currently used for any function
submission_date = date the sample was delivered to the laboratory in format yyyy-mm-dd (optional, not currently used, but user could consider it for later analysis of results in the combined data)
sv_unc = uncertainty in the spike value - Optional, default = 0
sv_k = coverage factor for the spike value uncertainty - Optional, default = 2

Laboratory results data details

sample_ID = unique identifier for the sample - must match spike data
analyte = name of analyte in the sample - must match spike data
result_date = date the sample was reported in format yyyy-mm-dd
k = coverage factor for the laboratory’s uncertainty of the result - Optional, default = 2
result = analysis result
unc = analysis uncertainty in same units as result
units = laboratory units - must match spike value units
det_lvl = laboratory’s detection level

Save the results of the bs_prep_and_analysis function. In examples, we use bs_df as the name of the saved blind spike data frame. You can then evaluate the saved data. The following functions are available:

table_spike summarizes all individual spike values and

spike_combos provides any combinations of spike values in individual samples that the user chooses to have tallied.

plot_run provides run charts with the option of plotting in analysis units or by a ratio of the laboratory results to the blind spike values. Both versions show uncertainty ranges of the results. Uncertainty bars are shown on the laboratory results and the ratios. When there is no overlap with the spike values, the laboratory result is either a false positive or a false negative. False positives are excluded from the ratio version when there is no spike value to avoid division by zero.

table_false provides confidence intervals on error rates for all laboratory results.

plot_tat plots the laboratory turnaround time for each spike sample.

plot_qq provides quantile-quantile plots of the results. Random errors are expected to fluctuate in a normally distributed pattern. When the QQ plot shows a deviation from normal, further investigation in the underlying process may be needed.