packages

library(knitr)
library(readxl)
#detach(package:plyr)
library(dplyr)
library(ggplot2)
library(reshape2)
library(stringr)
library(kableExtra)
set.seed(100)

upload files

path<-"/Users/kylielanglois/OneDrive - SCCWRP/eDNA citrus"

dat<-read.csv(file.path(path, "2026-03-03 gPie.csv"))

check negative and positive controls

## [1] "NTC positive droplets: 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0"
Positive controls, golden mussel 3-3-26
Sample Target dilution Positives Negatives Total
GBLOCK gPie 1e+04 38129 3917 42046
GBLOCK gPie 1e+06 9615 33678 43293
TISSUE gPie 1e+00 36961 0 36961
TISSUE gPie 1e+02 33612 4543 38155
TISSUE gPie 1e+06 2 35996 35998 
#convert to per microliter-------
dat1.m$Conc.copies.µL.<-as.numeric(as.character(dat1.m$Conc.copies.µL.))
dat1.m$Copies.ul<-((dat1.m$Conc.copies.µL.*20)/
                     dat1.m$Template.uL)*dat1.m$dilution
dat1.m$PoissConfMaxcopiesperµL<-((dat1.m$PoissonConfMax*20)/
                                   dat1.m$Template.uL)*dat1.m$dilution
dat1.m$PoissConfMincopiesperµL<-((dat1.m$PoissonConfMin*20)/
                                   dat1.m$Template.uL)*dat1.m$dilution

#convert to different metrics--------
dat1.m$LOQ.Conc.<--log(1-(3/dat1.m$Accepted.Droplets))/drop.size
dat1.m$LOQ.ul<-(dat1.m$LOQ.Conc.*20)/dat1.m$Template.uL

#flag if under LOQ-----------
dat1.m$underLOQ<-ifelse(dat1.m$Copies.ul<dat1.m$LOQ.ul, "under LOQ", "")

#confidence68--------
dat1.m$TotalConfidenceMin68perµL<-
  ((dat1.m$TotalConfidenceMin68*20)/dat1.m$Template.uL)*dat1.m$dilution
dat1.m$TotalConfidenceMax68perµL<-
  ((dat1.m$TotalConfidenceMax68*20)/dat1.m$Template.uL)*dat1.m$dilution