## JEG3 PFAS Screen Data Processing Pipeline
## BEB 12072018
## Construction of data frame shells for Plate Maps 1-4

library(readxl)
library(rlist)
library(dplyr)

## Function to clean up data frames for chem ID, chem concentration, and well site ID
cleanup.cheminfo<-function(x){
                  x<-x[-c(1)]
                  colnames(x)<-c(1:24)
                  rownames(x)<-c(LETTERS[1:16])
                  x<-as.matrix(x)
                  x<-t(x)
                  x<-array(x)}

## Build data frame shells for all 4 plate maps
## Set working directory to folder containing plate maps with chemical CAS #s, concentrations, and full chemical names
setwd("C:/Users/Public/Documents/My Documents/JEG3/Experiments/24h 384well PFAS screen 500uM-50uM/R Data Pipeline/Plate Maps")

site.id.384<-as.data.frame(read_excel("Site ID 384 well plate map.xlsx"))

pm1cas<-as.data.frame(read_excel("Platemap-01 chemical CAS.xlsx"))
pm1id<-as.data.frame(read_excel("Platemap-01 chemical ID.xlsx"))
pm1conc<-as.data.frame(read_excel("Platemap-01 dose concentrations.xlsx"))

pm2cas<-as.data.frame(read_excel("Platemap-02 chemical CAS.xlsx"))
pm2id<-as.data.frame(read_excel("Platemap-02 chemical ID.xlsx"))
pm2conc<-as.data.frame(read_excel("Platemap-02 dose concentrations.xlsx"))

pm3cas<-as.data.frame(read_excel("Platemap-03 chemical CAS.xlsx"))
pm3id<-as.data.frame(read_excel("Platemap-03 chemical ID.xlsx"))
pm3conc<-as.data.frame(read_excel("Platemap-03 dose concentrations.xlsx"))

pm4cas<-as.data.frame(read_excel("Platemap-04 chemical CAS.xlsx"))
pm4id<-as.data.frame(read_excel("Platemap-04 chemical ID.xlsx"))
pm4conc<-as.data.frame(read_excel("Platemap-04 dose concentrations.xlsx"))

## Run series of code that will bind matrices into list that correspond to well site id,
## chemical CAS, chemical name, and chemical concentration
## Then run cleanup.cheminfo function across all matrices within list
## Then cbind list items together and make into dataframe and rename column headers

pm1.list<-list(site.id.384,pm1cas,pm1id,pm1conc)
pm1.list.clean<-lapply(pm1.list, cleanup.cheminfo)
pm1.shell<-as.data.frame(list.cbind(pm1.list.clean))
names(pm1.shell)<-c("site.id","CAS","chem.id","conc")

pm2.list<-list(site.id.384,pm2cas,pm2id,pm2conc)
pm2.list.clean<-lapply(pm2.list, cleanup.cheminfo)
pm2.shell<-as.data.frame(list.cbind(pm2.list.clean))
names(pm2.shell)<-c("site.id","CAS","chem.id","conc")

pm3.list<-list(site.id.384,pm3cas,pm3id,pm3conc)
pm3.list.clean<-lapply(pm3.list, cleanup.cheminfo)
pm3.shell<-as.data.frame(list.cbind(pm3.list.clean))
names(pm3.shell)<-c("site.id","CAS","chem.id","conc")

pm4.list<-list(site.id.384,pm4cas,pm4id,pm4conc)
pm4.list.clean<-lapply(pm4.list, cleanup.cheminfo)
pm4.shell<-as.data.frame(list.cbind(pm4.list.clean))
names(pm4.shell)<-c("site.id","CAS","chem.id","conc")

## Write .csv files for data frame shells

write.csv(pm1.shell, "Platemap-01 shell.csv", row.names = F)
write.csv(pm2.shell, "Platemap-02 shell.csv", row.names = F)
write.csv(pm3.shell, "Platemap-03 shell.csv", row.names = F)
write.csv(pm4.shell, "Platemap-04 shell.csv", row.names = F)