R/coef_server_app_old.R

In JARS3N/shinyStar: shinyStar

coef_server_app_old<-function(){
  require(shiny)
  require(seastar)
  require(asyr)
  require(dplyr)
  require(ggplot2)
  require(ggthemes)

  shinyServer(function(input, output) {

    observeEvent(input$dirsel,{
      seldir<- choose.dir()

      A<- list.files(path=seldir,pattern='asyr',full.names=T) %>%
        lapply(.,XML::xmlTreeParse,useInternalNodes=T) %>%
        lapply(.,asyr::process) %>%
        lapply(.,asyr::extract_wetQC) %>%
        dplyr::bind_rows()

      lot_used<-unique(A$Lot)

      #########
      if(substr(lot_used,1,1)=="C"){

        xls<-list.files(path=seldir,full.names=T,pattern='xlsx')
        xl<-setNames(lapply(xls,readxl::read_excel,sheet='Resume'),
                     xls) %>%
        bind_rows(.,.id='files')
        
        
        pH_led_table<-
          filter(xl,Tick=='Calibration') %>%
          group_by(.,Emission) %>%
          summarise(.,pH_LED_AVG=mean(Counts)) %>%
          rename(.,Target=Emission)
        O2cal<-
          group_by(A,Target) %>%
          summarize(O2_LED_AVG=mean(O2.LED))


        gaindat<<-filter(xl,Tick!='Calibration') %>%
          mutate(m=paste0('m',as.numeric(factor(Tick>3,levels = c(F,T))))) %>%
          group_by(files,Well,Emission,m) %>%
          summarise(.,counts=mean(Counts)) %>%
          ungroup() %>%
          tidyr::spread(.,m,counts) %>%
          mutate(.,Gain=(m1-m2)*(800^-1)*(Emission/m1)) %>%
          select(.,-m1,-m2,-Well) %>%
          rename(.,Target=Emission)

        gain_table<-filter(gaindat,!is.na(Target)) %>%
          group_by(.,Target) %>%
          summarise(.,
                    Gain_AVG=mean(Gain,na.rm=T),
                    Gain_SD=sd(Gain),
                    Gain_minus3SD=Gain_AVG-(3*Gain_SD),
                    Gain_plus3SD=Gain_AVG+(3*Gain_SD)
          )
        gain_and_led_table<<-left_join(left_join(pH_led_table,O2cal),gain_table)
        
        median_target<<-median(gain_and_led_table$Target,na.rm = T)
      
      }else{
      gain_and_led_table<<-
        filter(A,!is.na(Target)) %>%
        group_by(.,Target) %>%
        summarise(.,pH_LED_AVG=mean(pH.LED,),
                  O2_LED_AVG=mean(O2.LED),
                  Gain_AVG=mean(Gain,na.rm=T),
                  Gain_SD=sd(Gain),
                  Gain_minus3SD=Gain_AVG-(3*Gain_SD),
                  Gain_plus3SD=Gain_AVG+(3*Gain_SD)
        )
      gaindat<<-filter(A,!is.na(Target))
        median_target<<-median(A$Target,na.rm = T)
      }
      #########
      ksv_f0<-filter(A,!is.na(KSV)) %>%
        summarize(.,AVG_KSV=mean(KSV),AVG_F0=mean(F0),
                  Median_ksv=median(KSV),Median_F0=median(F0)
        )

      #############


      gain_lm<-lm(Gain~Target,data=gaindat)
      SUM_LM<-summary(gain_lm)
      coeffs<-data.frame(
        vars=c("rsquared",
               "slope",
               "intercep",
               "Gain"),
        val=c(round(SUM_LM$r.squared,6),
              round(coef(gain_lm)[2],6),
              round(coef(gain_lm)[1],6),
              (median_target *coef(gain_lm)[2])+coef(gain_lm)[1])
      )


      coeffplot<-ggplot(gaindat,aes(Target,Gain))+
        geom_point(alpha=.2)+
        geom_smooth(method='lm')+
        theme_bw()+
        ggtitle(label=unique(lot_used),subtitle=
                  paste0("Rsquared: ",round(SUM_LM$r.squared,6),"\n",
                         "eq: Gain = (Target * ",round(coef(gain_lm)[2],6),") +",round(coef(gain_lm)[1],6)
                  )
        )


      write.csv(A,file.path(seldir,"data.csv"),row.names = F)
      write.csv(gain_and_led_table,file.path(seldir,"gain_and_led_table.csv"),row.names = F)
      write.csv(ksv_f0,file.path(seldir,"ksv_f0.csv"),row.names = F)
      write.csv(coeffs,file.path(seldir,"coeffs.csv"),row.names=F)
      ggsave(file.path(seldir,"coeffplot.png"),coeffplot,width=10,height=6)

    })

  })
}