我有多个大的csv文件，它们都有相同的列。我将它们读入R语言中一个名为www.example.com的列表中listFinal.data。我需要计算在某些列中出现的不同项的出现次数。例如，有一个名为“Biological.Stage”的列。该列中的值可以是“无”、“孵育”、“有症状”或“恢复期”。我需要计算出这些不同阶段发生的次数。这个很好用。但是，我有另一个名为“Incapacitation.Status”的列，其中的值可以是“Not Incapacitated”或“Incapacitated”。我还需要计算每种情况发生的次数。我使用相同的方法，但我得到一个错误，它说：

Error in incapacitated_count[[l]] <- nrow(listFinal.data[[l]][listFinal.data[[l]]$Incapacitation.Status ==  : object 'incapacitated_count' not found

这看起来并不难，但我已经绕了一整天，不能使这工作。有人能在我发疯之前指出我的错误吗？
我编辑了我的帖子，以更完整地展示我的代码，而不仅仅是一个好心的人建议的问题循环，他们试图帮助我。我试图避免发布太多的代码，因为我知道它可能不是很好。请记住，我是自学成才，不做很多编码。

library(tidyverse)
library(ggplot2)
library(rlist)
library(magrittr)
library(DT)
library(readr)
library(dplyr)
 

options(shiny.maxRequestSize = 200*1024^2)

wd <<- choose.dir(caption = "Select top level folder where your data is located")
setwd(wd)

#List the full path and filename of all files in the working directory and sub-directories that starts
#with "BioModel_" and ends with ".csv"
out_files <- list.files(pattern = "^BioModel(.*)csv$", recursive = TRUE)

# create an empty list that will serve as a container to receive the incoming files
list.data<-list()
listForceFinal.data<-list()
listForceInitial.data<-list()
outputInitial.data<-list()
listOcularFinal.data<-list()
listInhalationFinal.data<-list()
listSurfaceFinal.data<-list()
listChemFinal.data<-list()
listBioFinal<-list()
listFatalities.data<-list()
listFinal.data<-list()
listContaminatedVeh.data<-list()
none_count<-list()
minor_count<-list()
major_count<-list()
lethal_count<-list()
incubation_count<-list()
symptomatic_count<-list()
convalescence_count<-list()
incubation_count<-list()
vehicle_count<-list()
incapacitation_count<-list()
# 
 num_reps <- length(out_files)

# create a loop to read in your data

for (i in 1:length(out_files))
{
  list.data[[i]]<-read.csv(out_files[i], check.names = TRUE)
}

# # create a loop to get the final contamination level and remove all others
for (h in 1:length(list.data))
  listFinal.data[[h]] <- list.data[[h]] %>%
  group_by(Actor.ID, Actor.Name) %>%
  slice(n())

output <- plyr::ldply(listFinal.data, function(x) x %>%
  group_by(Entity.Type) %>%
  summarise(n=n()))

listContaminatedVeh.data <- output %>%
  filter(Entity.Type != "Lifeform") %>%
  group_by(Entity.Type) %>%
  summarise(Mean.Final = mean(n)) %>%
  rename("Platform Type" = Entity.Type, Mean = Mean.Final)

#Contaminated Entities

#create a loop to sum up the total number of contaminated and incapacitated entities in each rep
for (l in 1:length(listFinal.data)){
  incubation_count[[l]] <- nrow(listFinal.data[[l]][listFinal.data[[l]]$Biological.Stage == "incubation",])
  symptomatic_count[[l]] <- nrow(listFinal.data[[l]][listFinal.data[[l]]$Biological.Stage == "symptomatic",])
  convalescence_count[[l]] <- nrow(listFinal.data[[l]][listFinal.data[[l]]$Biological.Stage == "convalescence",])
  incapacitated_count[[l]] <- nrow(listFinal.data[[l]][listFinal.data[[l]]$Incapacitation.Status == "Incapacitated",])
}

# calculate the average number of entities with biological contamination / incapacitation across all reps
result.incubationAvg <- mean(as.numeric(incubation_count))
result.incubationAvg <- round(result.incubationAvg)

result.symptomaticAvg <- mean(as.numeric(symptomatic_count))
result.symptomaticAvg <- round(result.symptomaticAvg)

result.convalescenceAvg <- mean(as.numeric(convalescence_count))
result.convalescenceAvg <- round(result.convalescenceAvg)

result.incapacitationAvg <- mean(as.numeric(incapacitation_count))
result.incapacitationAvg <- round(result.incapacitationAvg)

这是我的数据文件的压缩版本。这是一个很大的文件，所以我删除了与问题无关的列，只包含了一小部分行。

df <- read.table(text = "
Time Stamp,Biological Stage,Contaminant Type,Incapacitation Status,Concentration of Agent  
13206478,symptomatic,biological,Incapacitated,8.38E-05  
13087148,none,biological,Not Incapacitated,0  
12966365,none,biological,Not Incapacitated,0  
13207078,none,biological,Not Incapacitated,8.38E-05  
", header = TRUE, sep = ",")