ProcessTimeseriesResponse = function(tsResponse, tsName)
{
  if (!is.null(tsResponse))
  {
    # Any request dealing with a single user created item returns a
    # DSUserObjectResponse. This has ResponseStatus property that indicates
    # success or failure
    if (tsResponse$ResponseStatus != DSUserObjectResponseStatus$UserObjectSuccess)
      print(paste('Request failed for timeseries', tsName, 'with error ', names(DSUserObjectResponseStatus)[tsResponse$ResponseStatus + 1], ': ', tsResponse$ErrorMessage, '\n\n'))
    else if (!is.null(tsResponse$UserObject))  # The timeseries item won't be returned if you set SkipItem true in CreateItem or UpdateItem
    {
      # Here we simply display the timeseries data using a dataframe.
      tsItem = tsResponse$UserObject
      metadata = c (Id = tsItem$Id,
                    Desc = tsItem$Description,
                    LastModified = as.character(tsItem$LastModified),
                    StartDate = ifelse (!is.null(tsItem$DateInfo), as.character(tsItem$DateInfo$StartDate), NULL),
                    EndDate = ifelse(!is.null(tsItem$DateInfo),as.character(tsItem$DateInfo$EndDate), NULL),
                    Frequency = ifelse(!is.null(tsItem$DateInfo), names(DSUserObjectFrequency)[tsItem$DateInfo$Frequency + 1], NULL),
                    NoOfValues = ifelse(!is.null(tsItem$DateRange), tsItem$DateRange$ValuesCount , 0))
      df = data.frame(metadata)
      print(df)

      if (!is.null(tsItem$DateRange))
      {
        df = data.frame(Dates = sapply(tsItem$DateRange$Dates,
                                       FUN = function(x)
                                       { return (as.character(x)) }),
                        Values = sapply(tsItem$DateRange$Values,
                                        FUN = function(x)
                                        { ifelse (is.null(x), return (NA_character_ ), return (x) )} ))
        # Values if NULL, is printed as <NA> because, while
        # convertind list to vector either by using as.vector or sapply,
        # the NULL values in the list are deleted. and thus there will
        # be mismatch in no of rows and cannot be put in a dataframe
        print(df)
      }
    }
  }
  else
    print(paste('Timeseries ', tsName, ' successfully updated but item details not returned.'))
}

testID = 'TSVVV023'

# Modifying 'TSVVV023' it to be a daily frequency
# series with data from 2000 until 2020.

# we'll get the list of supported dates between the start and end date.
startDate = as.Date('2000-01-01')
endDate = as.Date('2020-12-31')
freq = DSUserObjectFrequency$Daily
print('Requesting GetTimeseriesDateRange to get supported dates from
      2001-01-0 to 2020-12-31 at daily frequency.')
dateRangeResp = timeseriesClient$GetTimeseriesDateRange(startDate, endDate, freq)

# With the list of supported dates returned from GetTimeseriesDateRange,
# you would then create an array of values associated with the given dates
# from your data source.

# For this example, we'll just populate an array of test values randomly
# between 10.00 and 200.00 with two decimal places
if (!(is.null(dateRangeResp)) &
    dateRangeResp$ResponseStatus == DSUserObjectResponseStatus$UserObjectSuccess &
    !(is.null(dateRangeResp$Dates)))
{
  # We'll just take the count of the number of dates and generate random
  # data to simulate us retrieving corresponding values from your data source.
  datesCount = length(dateRangeResp$Dates)
  values = runif(datesCount, 10, 200) # an array datesCount long with random values between 10 and 200 with 2 decimal places
}

# set the new dates, frequency and values on our existing timeseries object
# Note: Datastream takes the StartDate, Frequency and Values and creates the
# timeseries based only using these parameters. The EndDate is not actually used internally
# other than for logging purposes. The true end date is calculated based on the
# start date, frequency and list of values. Supply too few or too many values
# and the mainframe will accept them and set the end date accordingly based
# on the given frequency for the item.

# To demonstrate this, we deliberately set the enddate to be a date other than
# 2020-12-31. The response item will give the true end date as calculated
# (2020-12-31)
testTs$DataInput = DSTimeSeriesDataInput$new(startDate, as.Date('2005-12-31'), freq, values)
testTs$DisplayName = 'Modifying timeseries to be daily frequency'
testTs$DataInput$Frequency = DSUserObjectFrequency$Daily

# and finally call UpdateItem
print(paste('Updating our timeseries', testID, 'to have daily frequency.'))
tsResp = timeseriesClient$UpdateItem(testTs)
# we will just display the updated timeseries object returned
# from the mainframe.
ProcessTimeseriesResponse(tsResp, testID)