Getting error while creating R markdown PDF report - r

Question

I am getting an error while creating a PDF report out of R markdown file. Below is the snippet of the error:
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
I have this object -dayBikeData in the session but still it is giving the error don't know how to proceed on this.
Code for fetching the data from the csv file:
```{r}
dayBikeData <- read.csv("D:\\Madhav\\Study\\MSIS\\PredictiveLearning
\\Week-1\\Homework\\Bike-Sharing-Dataset\\day.csv")
# Performs each of the operation asked in the question
basicOperations <- function(inputData){
lenData <- length(inputData)
avg <- round(mean(inputData, na.rm = TRUE), digits = 2) # mean calculation
standardDeviation <- round(sd(inputData), digits = 2) # Standard deviation
sem <- round(standardDeviation/sqrt(lenData), digits = 2)
# Formula for CI is mean - error where error is
error = round(qnorm(0.975)*standardDeviation/sqrt(lenData), digits = 2)
lower_ci <- avg - error
upper_ci <- avg + error
# resultList <- list(obs = lenData, mean = avg, standarDeviation = sd,
# standardMeanError= sem, lowerCI = lower_ci, upperCI = upper_ci
resultList <- c(lenData, avg, standardDeviation, sem,lower_ci,upper_ci)
print(resultList)
}
#Calculations for the Year Wise Data
# dData2011 <- dayBikeData[dayBikeData$yr==0,]
# dData2012 <- dayBikeData[dayBikeData$yr==1,]
dData2011ResultSet <- basicOperations(dayBikeData[dayBikeData$yr==0,]$cnt)
dData2012ResultSet <- basicOperations(dayBikeData[dayBikeData$yr==1,]$cnt)
#Calculations for the Holiday Wise Data
# dDataHoliady_0 <- dayBikeData[dayBikeData$holiday ==0,]
# dDataHoliady_1 <- dayBikeData[dayBikeData$holiday ==1,]
dDataHoliady0ResultSet <- basicOperations(dayBikeData[dayBikeData$holiday ==0,]$cnt)
dDataHoliady1ResultSet <- basicOperations(dayBikeData[dayBikeData$holiday ==1,]$cnt)
#Calculations for the WorkingDay Wise Data
# dDataWorkingDay_0 <- dayBikeData[dayBikeData$workingday ==0,]
# dDataWorkingDay_1 <- dayBikeData[dayBikeData$workingday ==1,]
dDataWorkingDay0ResultSet <- basicOperations(dayBikeData[dayBikeData$workingday ==0,]$cnt)
dDataWorkingDay1ResultSet <- basicOperations(dayBikeData[dayBikeData$workingday ==1,]$cnt)
#Calculations for the Temperature wise data
avgTemp <- mean(dayBikeData$temp, na.rm = TRUE)
dDataTempGreaterEq <- dayBikeData[dayBikeData$temp >= avgTemp,]
dDataTempLess <- dayBikeData[dayBikeData$temp < avgTemp,]
dDataTempGreaterEqResultSet <- basicOperations(dDataTempGreaterEq$cnt)
dDataTempLessResultSet <- basicOperations(dDataTempLess$cnt)
#Calculations for the Weather wise data
# dDataWeather_1 <- dayBikeData[dayBikeData$weathersit ==1,]
# dDataWeather_2 <- dayBikeData[dayBikeData$weathersit ==2,]
# dDataWeather_3 <- dayBikeData[dayBikeData$weathersit ==3,]
dDataWeather1ResultSet <- basicOperations(dayBikeData[dayBikeData$weathersit ==1,]$cnt)
dDataWeather2ResultSet <- basicOperations(dayBikeData[dayBikeData$weathersit ==2,]$cnt)
dDataWeather3ResultSet <- basicOperations(dayBikeData[dayBikeData$weathersit ==3,]$cnt)
#Calculations for the Season wise data
# dDataSeason_1 <- dayBikeData[dayBikeData$season ==1,]
# dDataSeason_2 <- dayBikeData[dayBikeData$season ==2,]
# dDataSeason_3 <- dayBikeData[dayBikeData$season ==3,]
# dDataSeason_4 <- dayBikeData[dayBikeData$season ==4,]
dDataSeason1ResultSet <- basicOperations(dayBikeData[dayBikeData$season ==1,]$cnt)
dDataSeason2ResultSet <- basicOperations(dayBikeData[dayBikeData$season ==2,]$cnt)
dDataSeason3ResultSet <- basicOperations(dayBikeData[dayBikeData$season ==3,]$cnt)
dDataSeason4ResultSet <- basicOperations(dayBikeData[dayBikeData$season ==4,]$cnt)
#Constrcut a row wise data
resultData <- rbind(dData2011ResultSet, dData2012ResultSet, dDataHoliady0ResultSet,
dDataHoliady1ResultSet,dDataWorkingDay0ResultSet,
dDataWorkingDay1ResultSet,dDataTempGreaterEqResultSet,
dDataTempLessResultSet, dDataWeather1ResultSet,
dDataWeather2ResultSet, dDataWeather3ResultSet,dDataSeason1ResultSet,
dDataSeason2ResultSet, dDataSeason3ResultSet,dDataSeason4ResultSet)
colnames(resultData) <- c("N","Mean","SD" , "SEM","Lower_CI", "UPPER_CI")
rownames(resultData) <- c("Year-0", "Year-1", "Holiday-0", "Holiday-1", "WorkingDay-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["Value"] <- 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)]
library(knitr)
# print(xtable(df.resultData), type = "latex")
kable(df.resultData, format = "markdown")
write.csv(df.resultData, file = "D:\\X\\Study\\MSIS\\PredictiveLearning\\OutputResult.csv")

Answer 1

Your file path is wrong... There is a new line and lots of spaces in the middle of it.
> "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"
So the file is not getting read properly and hence the object is not available in the knitr session.

Answer 2

I downloaded your dataset from UCI Machine Learning Repository, saved your markdown in a new folder, adjusted the filenames by deleting the paths, ran it, and it worked fine.
So I maybe your session is corrupt, or the paths are wrong, or something. Try what I did and it should work.
Proof: