如何解决while(time <= time_end)中的「需TRUE/FALSE却得缺失值」错误？

• 已在逻辑语句中处理NULL值，但仍触发报错，数据中存在无法删除的NULL值
• Visual是包含整数格式时间（例如8:00对应800）及其他字符型数据的DataFrame
• final_1和final_2是Visual的空版本
• 还需编写时间逻辑：当时间的分钟部分达到60时，小时部分增加100（例如850+20需得到910，而非870）

运行代码 #

for (i in 1:nrow(Visual)){
  time <- Visual[i,]$Start_Time
  time_end <- Visual[i,]$End_Time
  if(is.null(time)){
    time <- min_time
    if(is.null(time_end)){
      while(time <= max_time){
        final_2[i,]$Time <- time
        final_2[i,]$Appointment_AIM <- Visual[i,]$Appointment
        final_2[i,]$AIM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Standard_Duration <- Visual[i,]$Standard_Duration
        final_2[i,]$Booking_Factor <- Visual[i,]$Booking_Factor
        final_2[i,]$Appointment_Categories_ACM <- Visual[i,]$Appointment_Categories
        final_2[i,]$ACM_Abbreviation <- Visual[i,]$ACM_Abbreviation
        final_2[i,]$Color_Code <- Visual[i,]$Color_Code
        final_1 <- rbind(final_1, final_2)
        time <- time + Visual[i,]$Standard_Duration
      }
    }else{
      while(time <= time_end){
        final_2[i,]$Time <- time
        final_2[i,]$Appointment_AIM <- Visual[i,]$Appointment
        final_2[i,]$AIM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Standard_Duration <- Visual[i,]$Standard_Duration
        final_2[i,]$Booking_Factor <- Visual[i,]$Booking_Factor
        final_2[i,]$Appointment_Categories_ACM <- Visual[i,]$Appointment_Categories
        final_2[i,]$ACM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Color_Code <- Visual[i,]$Color_Code
        final_1 <- rbind(final_1, final_2)
        time <- time + Visual[i,]$Standard_Duration
      }
    }
  }else{
    if(is.null(time_end)){
      while(time <= max_time){
        final_2[i,]$Time <- time
        final_2[i,]$Appointment_AIM <- Visual[i,]$Appointment
        final_2[i,]$AIM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Standard_Duration <- Visual[i,]$Standard_Duration
        final_2[i,]$Booking_Factor <- Visual[i,]$Booking_Factor
        final_2[i,]$Appointment_Categories_ACM <- Visual[i,]$Appointment_Categories
        final_2[i,]$ACM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Color_Code <- Visual[i,]$Color_Code
        final_1 <- rbind(final_1, final_2)
        time <- time + Visual[i,]$Standard_Duration
      }
    }else{
      while(time <= time_end){
        final_2[i,]$Time <- time
        final_2[i,]$Appointment_AIM <- Visual[i,]$Appointment
        final_2[i,]$AIM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Standard_Duration <- Visual[i,]$Standard_Duration
        final_2[i,]$Booking_Factor <- Visual[i,]$Booking_Factor
        final_2[i,]$Appointment_Categories_ACM <- Visual[i,]$Appointment_Categories
        final_2[i,]$ACM_Abbreviation <- Visual[i,]$Abbreviation
        final_2[i,]$Color_Code <- Visual[i,]$Color_Code
        final_1 <- rbind(final_1, final_2)
        time <- time + Visual[i,]$Standard_Duration
      }
    }
  }
}

报错信息 #

Error in while (NA) { : missing value where TRUE/FALSE needed

错误原因 #

1. NULL与NA混淆：在R的DataFrame中，缺失值默认存储为NA而非NULL，原代码用is.null()无法检测到NA，导致循环条件中出现NA，触发报错。
2. 时间进位逻辑缺失：原代码直接对时间整数做加法，未处理分钟满60进1小时的需求。
3. 循环效率低下：在循环中反复使用rbind()拼接数据框，会大幅降低运行效率，尤其当数据量较大时。

解决步骤 #

1. 替换缺失值检测逻辑 #

将is.null()替换为is.na()，或同时检测NULL和NA，确保覆盖所有缺失值场景：

# 处理Start_Time的缺失值
if (is.null(time) || is.na(time)) {
  time <- min_time
}
# 处理End_Time的缺失值
if (is.null(time_end) || is.na(time_end)) {
  time_end <- max_time
}

2. 实现时间进位函数 #

编写函数处理时间加法后的进位逻辑：

add_time <- function(current_time, duration) {
  # 拆分小时和分钟
  hour <- floor(current_time / 100)
  minute <- current_time %% 100
  # 计算新的分钟和小时
  total_minute <- minute + duration
  new_hour <- hour + floor(total_minute / 60)
  new_minute <- total_minute %% 60
  # 组合成整数格式时间
  return(new_hour * 100 + new_minute)
}

3. 重构循环逻辑 #

简化重复代码，优化数据拼接方式：

# 预分配结果数据框，避免循环内频繁rbind
final_1 <- data.frame(matrix(nrow = 0, ncol = 8))
colnames(final_1) <- c("Time", "Appointment_AIM", "AIM_Abbreviation", 
                       "Standard_Duration", "Booking_Factor", 
                       "Appointment_Categories_ACM", "ACM_Abbreviation", 
                       "Color_Code")

for (i in 1:nrow(Visual)) {
  time <- Visual[i,]$Start_Time
  time_end <- Visual[i,]$End_Time
  duration <- Visual[i,]$Standard_Duration
  
  # 统一处理缺失值
  if (is.null(time) || is.na(time)) time <- min_time
  if (is.null(time_end) || is.na(time_end)) time_end <- max_time
  
  # 循环生成时间序列
  while (time <= time_end) {
    # 构造当前行数据
    new_row <- data.frame(
      Time = time,
      Appointment_AIM = Visual[i,]$Appointment,
      AIM_Abbreviation = Visual[i,]$Abbreviation,
      Standard_Duration = duration,
      Booking_Factor = Visual[i,]$Booking_Factor,
      Appointment_Categories_ACM = Visual[i,]$Appointment_Categories,
      ACM_Abbreviation = Visual[i,]$ACM_Abbreviation,
      Color_Code = Visual[i,]$Color_Code
    )
    # 合并到结果
    final_1 <- rbind(final_1, new_row)
    # 计算下一个时间（带进位）
    time <- add_time(time, duration)
  }
}

内容的提问来源于stack exchange，提问作者Michael Schulte