Why is the summarySE function not recognizing my column?

Question

I am attempting to run a for loop that creates a bar plot for each genotype in my dataset, and I am using the summarySE function (http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/) to generate summary stats for my plots.

setwd("C:/Users/aundr/Documents/Salk/Asahina Lab/2022_GSAposter")

#Load in dataset
allData <- read.csv("SimpleNewASMantiTdc2CellCount.csv")

#Convert Genotype column from chr to factor format
allData <- data.frame(Expt = allData$Expt,
                      Genotype = as.factor(allData$Genotype),
                      Slide.position = allData$Slide.position,
                      Region = allData$Region,
                      Labeling = allData$Labeling,
                      Total = allData$Total)

str(allData)

library("plyr")
library("ggplot2")
library("EnvStats")
library("tidyverse")

#Make list of genotype factor levels
Genotype_list <- list(levels(allData$Genotype))

## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summarized
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)

##   My notes: see http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/
##   I also added max

summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
  #library(plyr)
  
  # New version of length which can handle NA's: if na.rm==T, don't count them
  length2 <- function (x, na.rm=FALSE) {
    if (na.rm) sum(!is.na(x))
    else       length(x)
  }
  
  # This does the summary. For each group's data frame, return a vector with
  # N, mean, and sd
  datac <- ddply(data, groupvars, .drop=.drop,
                 .fun = function(xx, col) {
                   c(N    = length2(xx[[col]], na.rm=na.rm),
                     mean = mean   (xx[[col]], na.rm=na.rm),
                     sd   = sd     (xx[[col]], na.rm=na.rm),
                     maximum=max   (xx[[col]], na.rm=na.rm)
                   )
                 },
                 measurevar
  )
  
  # Rename the "mean" column    
  datac <- rename(datac, c("mean" = measurevar))
  
  datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
  
  # Confidence interval multiplier for standard error
  # Calculate t-statistic for confidence interval: 
  # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
  ciMult <- qt(conf.interval/2 + .5, datac$N-1)
  datac$ci <- datac$se * ciMult
  
  return(datac)
}

#duplicating and factoring regions into order of importance/location in brain
allData1 <- allData
allData1$Region <-factor(allData1$Region,
                   levels = c("ASM","AL1","AL2","VL","VM","Other"))

#for loop generating plots for each genotype 
for(i in Genotype_list) {
  
#calculating mean, se, max
allDataStat <- summarySE(allData1, measurevar="Total", groupvars=c("Labeling","Region"))
allDataStat

#calculating tick marks and max limit

halfMax <- max(allDataStat$maximum)/2
halfMax

evenMaxTick <- 2*ceiling(halfMax)
evenMaxTick

halfMaxTick <- evenMaxTick/2
halfMaxTick

tickMarks <- c(0,halfMaxTick, evenMaxTick)
tickMarks

maxLimit <- evenMaxTick + 0.5
maxLimit

#bar plot
print(ggplot(NULL, aes(Region, Total, fill= Labeling, color = Labeling)) +
  geom_bar(allDataStat, mapping = aes(), position = "dodge", stat = "summary") +  
    scale_fill_manual(values = c("#248491","#C8503B")) + scale_colour_manual(values = c("#248491","#C8503B")) +
      geom_errorbar(allDataStat, mapping = aes(ymin= Total, ymax= Total+se), width=.3, size = .6) +
        theme(axis.ticks.x = element_blank(),panel.background = element_blank(),
          panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
            panel.border = element_blank(), axis.title = element_text(size = 12),
              axis.text = element_text(size = 9)) + labs(x = "Region", y = "Cell number per brain") +
                scale_y_continuous(breaks = c(tickMarks), limits = c(0,maxLimit)) +
                  annotate(geom = "segment",x = 0, xend = 0, y = 0, yend = evenMaxTick, size = .75))

#end of for loop
}

I have used the summarySE function previously to generate a similar plot and did not run into any issues, but am now getting the following error:

Error in stop_subscript(): ! Can't rename columns that don't exist. x Column Total doesn't exist.

It looks like the function is getting caught up on measurevar = "Total" in allDataStat <- summarySE(allData1, measurevar="Total", groupvars=c("Labeling","Region")) allDataStat and not recognizing my 'Total' column, even though it is there:

str(allData)
'data.frame':   300 obs. of  6 variables:
 $ Expt          : chr  "anti Tdc2 SG4 (sim ASM) expt 3" "anti Tdc2 SG4 (sim ASM) expt 3" "anti Tdc2 SG4 (sim ASM) expt 3" "anti Tdc2 SG4 (sim ASM) expt 3" ...
 $ Genotype      : Factor w/ 11 levels "ALK 2-07 (m2p1)",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ Slide.position: chr  "L" "L" "L" "L" ...
 $ Region        : chr  "ASM" "VL" "AL1" "AL2" ...
 $ Labeling      : chr  "Tdc2+ SG4+" "Tdc2+ SG4+" "Tdc2+ SG4+" "Tdc2+ SG4+" ...
 $ Total         : int  9 0 1 0 0 8 8 0 2 0 ...

After diving into the error tracing, it looks like the problem is coming from datac <- rename(datac, c("mean" = measurevar)). I don't know why this was not an issue previously and now is, so any help would be much appreciated!