创建R markdown PDF报告时出错

Question

从R markdown文件创建PDF报告时出现错误。以下是错误的代码片段：

   Error in --dayBikeData <- read.csv("D:\\Madhav\\Study\\MSIS\\PredictiveLearning\\Week-1\\Homework\\Bike-Sharing-Dataset\\day.csv") : 
  object 'dayBikeData' not found
Calls: <Anonymous> ... handle -> withCallingHandlers -> withVisible -> eval -> eval
Execution halted

我在会话中有这个对象-dayBikeData，但它仍然给出错误，不知道如何继续。

从csv文件中获取数据的代码：

```{r}

read.csv("D:\Madhav\Study\MSIS\PredictiveLearning <- dayBikeData

                    \\Week-1\\Homework\\Bike-Sharing-Dataset\\day.csv")

执行问题中询问的每个操作

函数<- basicOperations (InputData){

长度小于- lenData (InputData)

avg <- round( mean (inputData，na.rm = TRUE)，digits = 2) #平均计算

standardDeviation <- round(sd(inputData)，位数= 2) #标准差

扫描电镜<-轮(标准偏差/平方根(LenData)，位数= 2)

CI的公式是均值误差，其中误差是

错误= round(qnorm(0.975)*standardDeviation/sqrt(lenData)，数字= 2)

lower_ci <-平均错误

upper_ci <- avg +错误

均值<- resultList (obs= lenData，mean = avg，standarDeviation = sd，

standardMeanError= sem，lowerCI = lower_ci，upperCI = upper_ci

resultList <- c(lenData，avg，standardDeviation，sem，lower_ci，upper_ci)

打印(ResultList)

}

# Year Wise数据的计算

dData2011 <- dayBikeDatadayBikeData$yr==0，

dData2012 <- dayBikeDatadayBikeData$yr==1，

dData2011ResultSet <- basicOperations(dayBikeDatadayBikeData$yr==0，$cnt)

dData2012ResultSet <- basicOperations(dayBikeDatadayBikeData$yr==1，$cnt)

# Holiday Wise数据的计算

dDataHoliady_0 <- dayBikeDatadayBikeData$holiday ==0，

dDataHoliady_1 <- dayBikeDatadayBikeData$holiday ==1，

dDataHoliady0ResultSet <- basicOperations(dayBikeDatadayBikeData$holiday ==0，$cnt)

dDataHoliady1ResultSet <- basicOperations(dayBikeDatadayBikeData$holiday ==1，$cnt)

# WorkingDay Wise数据的计算

dDataWorkingDay_0 <- dayBikeDatadayBikeData$workingday ==0，

dDataWorkingDay_1 <- dayBikeDatadayBikeData$workingday ==1，

dDataWorkingDay0ResultSet <- basicOperations(dayBikeDatadayBikeData$workingday ==0，$cnt)

dDataWorkingDay1ResultSet <- basicOperations(dayBikeDatadayBikeData$workingday ==1，$cnt)

#温度智能数据的计算

avgTemp <- mean(dayBikeData$temp，na.rm = TRUE)

dDataTempGreaterEq <- dayBikeDatadayBikeData$temp >= avgTemp，

dDataTempLess <- dayBikeDatadayBikeData$temp < avgTemp，

dDataTempGreaterEqResultSet <- basicOperations(dDataTempGreaterEq$cnt)

dDataTempLessResultSet <- basicOperations(dDataTempLess$cnt)

# Weather wise数据的计算

dDataWeather_1 <- dayBikeDatadayBikeData$weathersit ==1，

dDataWeather_2 <- dayBikeDatadayBikeData$weathersit ==2，

dDataWeather_3 <- dayBikeDatadayBikeData$weathersit ==3，

dDataWeather1ResultSet <- basicOperations(dayBikeDatadayBikeData$weathersit ==1，$cnt)

dDataWeather2ResultSet <- basicOperations(dayBikeDatadayBikeData$weathersit ==2，$cnt)

dDataWeather3ResultSet <- basicOperations(dayBikeDatadayBikeData$weathersit ==3，$cnt)

#季节数据的计算

dDataSeason_1 <- dayBikeDatadayBikeData$season ==1，

dDataSeason_2 <- dayBikeDatadayBikeData$season ==2，

dDataSeason_3 <- dayBikeDatadayBikeData$season ==3，

dDataSeason_4 <- dayBikeDatadayBikeData$season ==4，

dDataSeason1ResultSet <- basicOperations(dayBikeDatadayBikeData$season ==1，$cnt)

dDataSeason2ResultSet <- basicOperations(dayBikeDatadayBikeData$season ==2，$cnt)

dDataSeason3ResultSet <- basicOperations(dayBikeDatadayBikeData$season ==3，$cnt)

dDataSeason4ResultSet <- basicOperations(dayBikeDatadayBikeData$season ==4，$cnt)

#按行构造数据

resultData <- rbind(dData2011ResultSet，dData2012ResultSet，dDataHoliady0ResultSet，

                dDataHoliady1ResultSet,dDataWorkingDay0ResultSet, 

                dDataWorkingDay1ResultSet,dDataTempGreaterEqResultSet,

                dDataTempLessResultSet, dDataWeather1ResultSet, 

                dDataWeather2ResultSet, dDataWeather3ResultSet,dDataSeason1ResultSet, 

                dDataSeason2ResultSet, dDataSeason3ResultSet,dDataSeason4ResultSet)

列名称(ResultData) <- c("N"，"Mean"，"SD“，"SEM"，"Lower_CI"，"UPPER_CI")

行名(ResultData) <- c(“年份-0”，“年份-1”，“假日-0”，“假日-1”，“工作日-0”，

                      "WorkingDay-1","Temperature >=","Temperature <", "Weather-1",

                      "Weather-2","Weather-3","Season-1","Season-2", "Season-3", 

                      "Season-4")

df.resultData <- as.data.frame(resultData)

Df.resultData“值”<- NA

df.resultData$Value <- c(2011,2012，0,1，0,1,1，0，1,2,3,1,2,3,4)

df.resultData = df.resultData，c(7,1,2,3,4,5,6)

库(Knitr)

打印(xtable(df.resultData)，type = "latex")

kable(df.resultData，format = "markdown")

文件(文件，df.resultData= "D:\X\Study\MSIS\PredictiveLearning\OutputResult.csv")

Answer 1

您的文件路径错误...有一行新行，中间有很多空格。

> "D:\\Madhav\\Study\\MSIS\\PredictiveLearning
+                         \\Week-1\\Homework\\Bike-Sharing-Dataset\\day.csv"
[1] "D:\\Madhav\\Study\\MSIS\\PredictiveLearning\n                        \\Week-1\\Homework\\Bike-Sharing-Dataset\\day.csv"

因此，无法正确读取该文件，因此该对象在object会话中不可用。

Answer 2

我从UCI Machine Learning Repository下载了您的数据集，将您的标记保存在一个新文件夹中，通过删除路径调整了文件名，并运行了它，它工作得很好。

所以我可能你的会话被破坏了，或者路径错误，或者别的什么。尝试我所做的，它应该会起作用。

证明：

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