I have this problem only on the web app and not the desktop. I made an R visual to take in a time series (daily data) and produce a forecasting graph and table visual. When I publish the app on the web, I get the following error:
Error in as.POSIXlt.POSIXct(x, tz) :
(converted from warning) unable to identify current timezone 'C':
please set environment variable 'TZ'
The source code is pasted at the bottom, although it is a bit convoluted... I believe one of the function calls uses the as.POSIXct function to coerce a date object from a string, and the data given is in the form of "YYYY-MM-DD" so I'm not sure if it's the lack of a timestamp and timezone in the string that's giving it an error, maybe the function's call to receive the tz variable isn't returning anything meaningful because it works fine on desktop. I've even tried setting the tz variable and giving the extra tz parameters in some of the function calls. What can I do to resolve this weird error?
R forecasting script (graph):
### R Script used for displaying a graph containing 6-month forecast
### aggregated by day.
# Libraries
library('ggplot2')
library('forecast')
library('tseries')
library('lubridate')
Sys.setenv(TZ='EST')
# Determine which column is the date column
firstCol = dataset[1,1]
secondCol = dataset[1,2]
isFirstDate <- grepl("[0-9]+-[0-9]+-[0-9]+", toString(firstCol))
isSecondDate <- grepl("[0-9]+-[0-9]+-[0-9]+", toString(secondCol))
dateColumn <- 1
valuesColumn <- 2
if(!isFirstDate){
dateColumn <- 2
valuesColumn <- 1
}
theDates <- data.frame(dataset[dateColumn])
names(theDates) <- c("date")
theValues <- data.frame(dataset[valuesColumn])
names(theValues) <- c("values")
dataset <- cbind(theDates, theValues)
# Get information on dataset, create date sequence
count <- nrow(dataset)
firstdate = dataset$date[1]
firstyear = as.double(substring(dataset$date[1], 1, 4))
lastdate = dataset$date[count]
lastyear = as.double(substring(dataset$date[count], 1, 4))
dataset$date <- lubridate::ymd(substring(dataset$date, 1,10))
dataset <- dplyr::arrange(dataset, date)
# Fill in the empty dates with zeros
# Get base values
i <- 2
x <- data.frame(dataset$values[1])
d <- data.frame(dataset$date[1])
names(x) <- c("values")
names(d) <- c("date")
# Loop through each value
while(i <= count){
# Get current day, month values
thisMonth <- substring(dataset$date[i],6,7)
lastMonth <- substring(dataset$date[i-1],6,7)
today <- substring(dataset$date[i],9,10)
yesterday <- substring(dataset$date[i-1],9,10)
# Add missing dates if applicable
# Add as many days as differenced from last date entry
if( ((as.numeric(yesterday) + 1) != as.numeric(today)) & (thisMonth == lastMonth)){
# How many days do we need to fill in?
daysToFill <- as.numeric(today)-as.numeric(yesterday)-1
# Fill in the days
while(daysToFill > 0){
# Add to new dataset
zeroValue <- data.frame(0)
names(zeroValue) <- c("values")
x <- rbind(x, zeroValue)
dateValue <- sprintf("%s-%s-%s",substring(dataset$date[i],1,4), thisMonth, toString(as.numeric(substring(dataset$date[i],9,10)) - daysToFill))
dateValue <- data.frame(as.character(as.Date(dateValue, tz = "EST")))
names(dateValue) <- c("date")
d <- rbind(d, dateValue)
# decrement the daysToFill counter
daysToFill <- daysToFill - 1
}
# Case when the first days of month are skipped
} else if(thisMonth != lastMonth){
# Get amount of days to fill
daysToFill <- as.numeric(today)-1
# Add each day missing
while (daysToFill > 0){
# Append dataset
zeroValue <- data.frame(0)
names(zeroValue) <- c("values")
x <- rbind(x, zeroValue)
dateValue <- sprintf("%s-%s-%s",substring(dataset$date[i],1,4), thisMonth, as.character(daysToFill))
dateValue <- data.frame(as.character(as.Date(dateValue, tz = "EST")))
names(dateValue) <- c("date")
d <- rbind(d, dateValue)
daysToFill <- daysToFill - 1
}
}
# If no missing values exist, then continue and then add to original dataset
i <- i + 1
}
# Append the dataset, then sort by date
dataset <- rbind(dataset, cbind(d,x))
dataset$date <- lubridate::ymd(dataset$date)
dataset <- dplyr::arrange(dataset, date)
#### Aggregate the Data (by day) ####
# Get base values
i <- 2
x <- data.frame(dataset$values[1])
d <- data.frame(dataset$date[1])
names(x) <- c("values")
names(d) <- c("date")
ptr <- 1
# update after transforming the dataset
count <- nrow(dataset)
# Loop through each value
while(i <= count){
# Get current and previous months and years
thisMonth <- substring(dataset$date[i],6,7)
lastMonth <- substring(dataset$date[i-1],6,7)
today <- substring(dataset$date[i],9,10)
yesterday <- substring(dataset$date[i-1],9,10)
# Compare month values, either greater or lower
# Also compare year
if( (today > yesterday) || (thisMonth != lastMonth)){
# Increment pointer
ptr <- ptr + 1
# Add new row to Date representing new day
de <- data.frame(dataset$date[i])
names(de) <- c("date")
d <- rbind(d, de)
# Add new row to Values representing new aggregate
dx <- data.frame(dataset$values[i])
names(dx) <- c("values")
x <- rbind(x, dx)
# Otherwise, aggregate data
}
else if( today == yesterday){
x$values[ptr] <- x$values[ptr] + dataset$values[i]
}
# Finish loop, increment i
i <- i + 1
}
dataset <- cbind(d,x)
# update after transforming the dataset
count <- nrow(dataset)
count
dataset
# Create time series object and forecasted ARIMA fit model
y = ts(dataset$values, start=c(firstyear, yday(firstdate)), frequency=365)
y <- tsclean(y)
fit = forecast(auto.arima(y), 183)
# Plot everything and format axis
plot(fit, xaxt="n")
a = seq(as.Date(firstdate, tz = "EST"), by="month", length=183 + count)
axis(1, at = decimal_date(a), labels = format(a, "%Y %b %d"), cex.axis=0.6, ylab = 'value')
abline(v = decimal_date(a), col='grey', lwd=0.5)
