Substracting time in R - r

I want to calculate the amount of time someone spends in bed at night.
First column, j1bed (time goes to bed):
dput(head(subscales$j1bed, 20))
c("23:00:00", "01:00:00", "22:00:00", "00:00:00", "00:00:00",
"23:00:00", "03:00:00", "23:00:00", "22:00:00", "23:00:00", "21:00:00",
"23:30:00", "22:00:00", "22:00:00", "20:00:00", "22:00:00", "22:00:00",
"21:00:00", "20:00:00", "23:00:00")
Second column, j3wake (time wakes up):
dput(head(subscales$j3wake, 20))
c("08:00:00", "08:00:00", "05:00:00", "08:00:00", "07:00:00",
"07:00:00", "09:00:00", "09:00:00", "08:00:00", "06:00:00", "03:00:00",
"08:30:00", "07:00:00", "08:00:00", "09:00:00", "07:00:00", "06:00:00",
"07:00:00", "07:00:00", "12:00:00")
My code currently is as follows:
Hours_in_Bed<- as.numeric(difftime(strptime(subscales$j3wake, "%I:%M %p" ),strptime(subscales$j1bed, "%I:%M %p" ),units='hours'))
For some reason, this is coming up with only "NA". Any ideas?
Edit: I've tried the following three suggestions:
1)
result1<-strptime(subscales$j3wake, "%I:%M %p")
result2<-strptime(subscales$j1bed, "%I:%M %p")
Hours_in_Bed<-as.numeric(difftime(result2, result1, units='hours'))
This yields only "NA"s.
2)
subscales$j3wake1=as.POSIXct(paste('1970-1-1',subscales$j3wake))
subscales$j1bed1 =as.POSIXct(paste('1970-1-1',subscales$j1bed))
as.numeric(difftime(subscales$j1bed1,subscales$j3wake1,units='hours'))
This yields "0"s.
3)
all(grepl("[0-9]{2}:[0-9]{2} (AM|PM)", subscales$j3wake))
all(grepl("[0-9]{2}:[0-9]{2} (AM|PM)", subscales$j1bed))
Both yield "False".
Edit 2: that last tip was key! I think I should be using "%H:%M" instead:
Hours_in_Bed<- as.numeric(difftime(strptime(subscales$j3wake, "%H:%M" ),strptime(subscales$j1bed, "%H:%M" ),units='hours'))
If I then do:
Hours_in_Bed[!is.na(Hours_in_Bed) & Hours_in_Bed<0] <- 24 + Hours_in_Bed[!is.na(Hours_in_Bed) & Hours_in_Bed<0]
I should be good to go. Let me know if I'm still off, and thanks for the help!


It's usually easier to use the POSIXct-type even if there isn't any real year/month/day. Example:
subscales$j3wake1 = as.POSIXct(paste('1970-1-1',subscales$j3wake))
subscales$j1bed1 = as.POSIXct(paste('1970-1-1',subscales$j1bed))
as.numeric(difftime(subscales$j1bed1,subscales$j3wake1,units='hours'))

Related

Create Date Ranges based on sum of record count (KQL, Azure Data Explorer, Kusto)

Given the below table, I would like to create an output that finds the maximum date ranges where the sum of 'RecordCount' is less than or equal to 20,000. Also, If a single row is greater than 20,000 the resulting date range will be the start and end of that single day:
datatable(Date:string, RecordCount:long)
[
"2022-04-24T00:00:00.0000000Z", 825,
"2022-04-25T00:00:00.0000000Z", 14925,
"2022-04-26T00:00:00.0000000Z", 18498,
"2022-04-27T00:00:00.0000000Z", 17558,
"2022-04-28T00:00:00.0000000Z", 15626,
"2022-04-29T00:00:00.0000000Z", 12755,
"2022-04-30T00:00:00.0000000Z", 2203,
"2022-05-01T00:00:00.0000000Z", 48594,
"2022-05-02T00:00:00.0000000Z", 4976,
"2022-05-03T00:00:00.0000000Z", 10835,
"2022-05-04T00:00:00.0000000Z", 27505,
"2022-05-05T00:00:00.0000000Z", 22808,
"2022-05-06T00:00:00.0000000Z", 23119,
"2022-05-07T00:00:00.0000000Z", 5141,
"2022-05-08T00:00:00.0000000Z", 2217,
"2022-05-09T00:00:00.0000000Z", 11334,
"2022-05-10T00:00:00.0000000Z", 58,
]
Expected Result:
datatable(StartDate:datetime, EndDate:datetime, RecordCount:long)
[
"2022-04-24T00:00:00.0000000Z", "2022-04-25T23:59:59.9999999Z", 15750,
"2022-04-26T00:00:00.0000000Z", "2022-04-26T23:59:59.9999999Z", 18498,
"2022-04-27T00:00:00.0000000Z", "2022-04-27T23:59:59.9999999Z", 17558,
"2022-04-28T00:00:00.0000000Z", "2022-04-28T23:59:59.9999999Z", 15626,
"2022-04-29T00:00:00.0000000Z", "2022-04-30T23:59:59.9999999Z", 14958,
"2022-05-01T00:00:00.0000000Z", "2022-05-01T23:59:59.9999999Z", 48594,
"2022-05-02T00:00:00.0000000Z", "2022-05-03T23:59:59.9999999Z", 15811,
"2022-05-04T00:00:00.0000000Z", "2022-05-04T23:59:59.9999999Z", 27505,
"2022-05-05T00:00:00.0000000Z", "2022-05-05T23:59:59.9999999Z", 22808,
"2022-05-06T00:00:00.0000000Z", "2022-05-06T23:59:59.9999999Z", 23119,
"2022-05-07T00:00:00.0000000Z", "2022-05-10T23:59:59.9999999Z", 18750,
]

Based on the scan operator
datatable(Date:string, RecordCount:long)
[
"2022-04-24T00:00:00.0000000Z", 825,
"2022-04-25T00:00:00.0000000Z", 14925,
"2022-04-26T00:00:00.0000000Z", 18498,
"2022-04-27T00:00:00.0000000Z", 17558,
"2022-04-28T00:00:00.0000000Z", 15626,
"2022-04-29T00:00:00.0000000Z", 12755,
"2022-04-30T00:00:00.0000000Z", 2203,
"2022-05-01T00:00:00.0000000Z", 48594,
"2022-05-02T00:00:00.0000000Z", 4976,
"2022-05-03T00:00:00.0000000Z", 10835,
"2022-05-04T00:00:00.0000000Z", 27505,
"2022-05-05T00:00:00.0000000Z", 22808,
"2022-05-06T00:00:00.0000000Z", 23119,
"2022-05-07T00:00:00.0000000Z", 5141,
"2022-05-08T00:00:00.0000000Z", 2217,
"2022-05-09T00:00:00.0000000Z", 11334,
"2022-05-10T00:00:00.0000000Z", 58,
]
| order by Date asc
| scan declare (acc_sum:long = 0, group_id:int = 0)
with
(
step s1 : true => acc_sum = RecordCount + iff(s1.acc_sum + RecordCount > 20000, 0, s1.acc_sum)
,group_id = s1.group_id + iff(s1.acc_sum + RecordCount > 20000, 1, 0);
)
| summarize StartDate = min(Date), EndDate = max(Date), RecordCount = sum(RecordCount) by group_id
| project-away group_id
StartDate
EndDate
RecordCount
2022-04-24T00:00:00.0000000Z
2022-04-25T00:00:00.0000000Z
15750
2022-04-26T00:00:00.0000000Z
2022-04-26T00:00:00.0000000Z
18498
2022-04-27T00:00:00.0000000Z
2022-04-27T00:00:00.0000000Z
17558
2022-04-28T00:00:00.0000000Z
2022-04-28T00:00:00.0000000Z
15626
2022-04-29T00:00:00.0000000Z
2022-04-30T00:00:00.0000000Z
14958
2022-05-01T00:00:00.0000000Z
2022-05-01T00:00:00.0000000Z
48594
2022-05-02T00:00:00.0000000Z
2022-05-03T00:00:00.0000000Z
15811
2022-05-04T00:00:00.0000000Z
2022-05-04T00:00:00.0000000Z
27505
2022-05-05T00:00:00.0000000Z
2022-05-05T00:00:00.0000000Z
22808
2022-05-06T00:00:00.0000000Z
2022-05-06T00:00:00.0000000Z
23119
2022-05-07T00:00:00.0000000Z
2022-05-10T00:00:00.0000000Z
18750
Fiddle

How can I dynamically change my source of data with the on_change function of a select/dropdown?

I have a graph being generated from hard coded ColumnDataSource.
My graph values need to change depending on what my user selects from the dropdown.
My update() function doesn't work the way I want it to. My old 'source' and 'NEWsource' are in the same format when printed out, I just cant get the changes to display on the plot.
from os.path import dirname, join
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import row, column
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import PreText, Select
from bokeh.plotting import figure
DEFAULT_TICKERS = ['giode', 'commerce_center', 'web_forms']
def load_data_depending_on_dropdown_value(ticker):
...
def get_data(ticker):
...data processing...
return data
# Make my dropdown, aka. ticker
ticker = Select(value='giode', options=DEFAULT_TICKERS)
# ORIGINAL SOURCE - HARDCODED
source = ColumnDataSource(
data=dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[5, 3, 4, 2, 4, 6,2]
)
)
source_static = ColumnDataSource(
data=dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[5, 3, 4, 2, 4, 6,2]
)
)
# Make a plot
p = figure(x_range=source.data['message'], plot_height=250)
p.vbar(x='message', top='counts', width=0.9, source=source)
p.xgrid.grid_line_color = None
p.y_range.start = 0
# Called when ticker, aka. dropdown, gets changed
def ticker_changed(attrname, old, new):
print(attrname, old, new)
update()
# CONTAINING MY NEW SOURCE
def update(selected=None):
t1_Value = ticker.value
newData = get_data(t1_Value)
newMessages = newData.message.tolist()
newCounts = newData.counts.tolist()
NEWsource = ColumnDataSource(data=dict(message=newMessages, counts=newCounts))
# Compare the old source with my new source:
print("Original source.data: " , source.data , "New data: " , NEWsource.data)
source.data = NEWsource.data
source_static.data = NEWsource.data
# This part works fine
p.title.text = '%s Plot' % (t1_Value)
ticker.on_change('value', ticker_changed)
widgets = column(ticker)
main_row = row(widgets, p)
update()
curdoc().add_root(main_row)
curdoc().title = "Stocks"
What's stopping the NEWsource from reaching the plot?
Plot correctly displaying hard-coded source:
When I try to update the plot from my dynamic NEWsource with some jumbled up values in the top corner:

Your code works fine in Bokeh v1.1.0. One remark: it is recommended to replace just the data of the ColumnDataSource object instead of creating a new ColumnDataSource each time.
def update():
source.data = get_data(ticker.value)
p.title.text = '%s Plot' % (ticker.value)
Here is your full code with this small change in the update(). Run it from Terminal: bokeh serve --show app.py
from os.path import dirname, join
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import row, column
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import PreText, Select
from bokeh.plotting import figure
DEFAULT_TICKERS = ['giode', 'commerce_center', 'web_forms']
def load_data_depending_on_dropdown_value(ticker):
pass
def get_data(ticker):
if ticker == 'giode':
return dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[5, 3, 4, 2, 4, 6,2]
)
elif ticker == 'commerce_center':
return dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[1, 2, 3, 4, 5, 6, 7]
)
elif ticker == 'web_forms':
return dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[7, 6, 5, 4, 3, 2, 1]
)
# Make my dropdown, aka. ticker
ticker = Select(value='giode', options=DEFAULT_TICKERS)
# ORIGINAL SOURCE - HARDCODED
source = ColumnDataSource(
data=dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[5, 3, 4, 2, 4, 6,2]
)
)
source_static = ColumnDataSource(
data=dict(
message=['BPN Broken', 'BPN Off', 'BPN Faulty', 'BPN Rain', 'BPN Fire', 'BPN Tornado', 'BPN STAFF DAY OFF'],
counts=[5, 3, 4, 2, 4, 6,2]
)
)
# Make a plot
p = figure(x_range=source.data['message'], plot_height=250)
p.vbar(x='message', top='counts', width=0.9, source=source)
p.xgrid.grid_line_color = None
p.y_range.start = 0
# Called when ticker, aka. dropdown, gets changed
def ticker_changed(attrname, old, new):
print(attrname, old, new)
update()
# CONTAINING MY NEW SOURCE
# def update(selected=None):
# t1_Value = ticker.value
# newData = get_data(t1_Value)
# newMessages = newData.message.tolist()
# newCounts = newData.counts.tolist()
#
# NEWsource = ColumnDataSource(data=dict(message=newMessages, counts=newCounts))
#
# # Compare the old source with my new source:
# print("Original source.data: " , source.data , "New data: " , NEWsource.data)
#
# source.data = NEWsource.data
# source_static.data = NEWsource.data
#
# # This part works fine
# p.title.text = '%s Plot' % (t1_Value)
def update(selected = None):
newData = get_data(ticker.value)
source.data = newData
source_static.data = newData
p.title.text = '%s Plot' % (ticker.value)
ticker.on_change('value', ticker_changed)
widgets = column(ticker)
main_row = row(widgets, p)
update()
curdoc().add_root(main_row)
curdoc().title = "Stocks"

Create 5 minute intervals starting from 00 in R

I want to divide data in 5 minute intervals and compare them with monthly averages and standard deviation to detect any anomalies. I cut the data in 5 minute intervals as follows:
cut(month.data, breaks = "5 min")
dput(monthlydata)
structure(list(time = c("00:00:00", "00:05:00", "00:10:00", "00:15:00", "00:20:00", "00:25:00", "00:30:00", "00:35:00", "00:40:00", "00:45:00", "00:50:00", "00:55:00", "01:00:00", "01:05:00", "01:10:00", "01:15:00", "01:25:00", "01:30:00", "01:35:00", "01:40:00", "01:45:00", "01:55:00", "02:00:00", "02:20:00", "02:25:00", "02:40:00", "02:45:00", "02:50:00", "03:05:00", "03:15:00", "03:25:00", "03:30:00", "03:35:00", "03:40:00", "03:45:00", "03:50:00", "03:55:00", "04:05:00", "04:25:00", "04:30:00", "04:50:00", "04:55:00", "05:05:00", "05:10:00", "05:15:00", "05:20:00", "05:30:00", "05:40:00", "05:50:00", "05:55:00", "06:00:00", "06:05:00", "06:10:00", "06:15:00", "06:20:00", "06:30:00", "06:35:00", "06:40:00", "06:45:00", "06:55:00", "07:00:00", "07:05:00", "07:10:00", "07:15:00", "07:20:00", "07:25:00", "07:30:00", "07:35:00", "07:40:00", "07:45:00", "07:50:00", "07:55:00", "08:00:00", "08:05:00", "08:10:00", "08:15:00", "08:20:00", "08:25:00", "08:30:00", "08:35:00", "08:40:00", "08:45:00", "08:50:00", "08:55:00", "09:00:00", "09:05:00", "09:10:00", "09:15:00", "09:20:00", "09:25:00", "09:30:00", "09:35:00", "09:40:00", "09:45:00", "09:50:00", "09:55:00", "10:00:00", "10:05:00", "10:10:00", "10:15:00", "10:20:00", "10:25:00", "10:30:00", "10:35:00", "10:40:00", "10:45:00", "10:50:00"), avg = c(50.5510560619622, 43.0189344993435, 50.3126451810161, 51.7984139398903, 44.1216815428764, 54.6443574865021, 50.065771120662, 50.9085361977819, 59.7024337563552, 44.6504863019322, 51.3800349930107, 47.2281110758541, 39.1562855847908, 47.3556824171027, 54.4776585774653, 64.9968044851706, 47.878888210121, 44.7561800618865, 45.0695456307952, 56.1759044802863, 50.8227417957758, 52.6309915011542, 62.7342370217067, 54.4257866432874, 54.3226457929837, 40.1938479668371, 54.5403748037875, 51.8463279336394, 53.8173320832895, 49.9889812414321, 50.5827916556644, 53.5500871960216, 37.0209877205586, 55.2612198888207, 55.1863860227875, 58.2740366768661, 50.5885573635039, 48.1051996319848, 47.7634397043728, 46.5863241465071, 54.1810631439095, 53.7906152156406, 52.6409719258956, 54.81269123308, 44.0537724370726, 47.1977242746078, 46.5010741707819, 45.8433966693518, 42.3623605036368, 43.0730189148746, 58.4167050044254, 49.7799961792657, 53.0755779045083, 52.0552481180891, 42.0602921415756, 52.5126950828788, 55.0870481980705, 44.4144434705709, 42.392966543036, 47.1807241560313, 44.6884956183158, 47.2896481418499, 57.8319708553495, 46.6397655826931, 52.4873442246903, 51.6324293101077, 55.0908694414676, 43.8596455462562, 48.8941181950083, 48.7514252330684, 49.310070422837,48.5234755805063, 58.5542276809981, 47.7720993402378,49.9405735614802, 52.8780543357139, 58.2557463154677,52.9242756783794, 55.4983029733778, 44.2299478555713,57.3379257421419, 50.1951095071188, 53.0553561602009, 50.3109504601222, 44.9316581536335, 47.7568227989573, 50.3093864093436, 50.0461546149579, 48.2373271954793, 50.2943295283144, 46.8054846556807, 48.7084493434669, 46.5421233124519, 50.1222951953386, 49.4207933535255, 46.1521667031027, 51.8644029994928, 53.4831581582472, 51.4972139096679, 51.5181187952616, 49.1998856564675, 50.3454476017966, 49.7587298896826, 45.6040364435812, 49.7466159629413, 51.0983492421099, 52.2935123336372), sd =c(9.32931925004817, 11.6047417906884, 9.80771691435559, 10.2308327194904, 9.67431773674866, 10.5323558825585, 9.1920111408028, 10.0745961985324, 9.21246056157269, 7.96228334027313, 9.89384474113651, 12.3284041772698, 13.5489518864705, 9.96803285037014, 8.85300006821126, 10.9832318078379, 13.0909163134817, 9.7261635496657, 10.3208509302825, 8.74704188744148, 9.44853223258545, 7.68266078719723, 8.78660429415339, 10.1981152232186,10.0617639380203, 9.70826609005244, 10.4405793131911, 10.0294741532956, 10.5843898470973, 10.0678114702352, 9.72005734098214, 8.71988580635692, 13.0339431549482, 8.85263899155544, 11.1140174478773, 10.6801908843647, 11.0442113270832, 9.6576794753704, 11.193361191756, 9.05129333547447, 11.1414887813967, 10.5985803957382, 10.2422686622522, 11.2629702278102, 10.4774845098793, 9.82520095973172, 10.6775347630735, 8.97029695502126, 8.28221177072086, 11.0689605695813, 10.4298020842373, 9.57012379689429, 10.1587613403527, 11.2343452027682, 9.09417849538438, 10.3529463918792, 8.89434012398308, 7.86166740352018, 8.67472741747663, 10.7631616313607, 11.4634738459674, 10.3773119423003, 10.1261492697498, 9.51153382612954, 8.8445878796955, 9.99640290007654, 9.33226675473664, 10.2637002156788, 9.44339377944955, 10.5843418581127, 9.00425609052502, 10.2822169680166, 10.5184763916409, 10.3495044419935, 9.99843947958033, 8.67736455800308, 10.1508472078283, 9.3450979185795, 9.84066593055499, 9.95417999414617, 9.2622985333717, 11.4445218170255, 9.72729168049685, 9.36415135782777, 10.2272372991057, 10.4800042675175, 10.0067598340318, 10.6127856103593, 8.91026514253112, 10.3451668931764, 9.86818949901026, 10.736479643411, 8.6424538599602, 9.53874170759392, 10.5484021386586, 10.7130372286846, 10.2509775336419, 10.7454025452377, 9.63372860813097, 9.35982465121709, 9.47486350661184, 10.311507962123, 10.0338294237329, 9.50474631714252, 10.1746281369343, 9.75040171260143, 10.8675383447689)), class = c("tbl_df", "tbl", "data.frame"), .Names = c("time", "avg", "sd"), row.names = c(NA, -107L))
However if my currdata starts later than 00 min, the time intervals are calculated depending on the earliest entry in the data.So if the first entry is at 01:00 min, the intervals are 01:00 to 06:00 and so on. However, I want the intervals to be 00:00 - 05:00 and so on, to be consistent with monthlydata intervals. Is there a way to set the lowest limit on the intervals in the cut statement? or is there any other way to ensure that both series use the same minutes intervals.
The data for one day
dput(currday)
structure(c(1533769260, 1533769320, 1533769320, 1533769380, 1533769380, 1533769380, 1533769440, 1533769500, 1533769680, 1533769740, 1533769740,1533769740, 1533769800, 1533769920, 1533769920, 1533769920,1533769980,1533770100, 1533770100, 1533770220, 1533770280, 1533770340, 1533770400, 1533770460, 1533770460, 1533770460, 1533770520, 1533770520, 1533770580, 1533770580, 1533770640, 1533770880, 1533771060, 1533771060, 1533771120, 1533771300, 1533771300, 1533771300, 1533771360, 1533771360, 1533771540, 1533771660, 1533771660, 1533771960, 1533772260, 1533772260, 1533772260, 1533772440, 1533772440, 1533772560, 1533772620, 1533772620, 1533772800, 1533772980, 1533773160, 1533773400, 1533773400, 1533773460, 1533773760, 1533773880, 1533774480, 1533774540, 1533774900, 1533774900, 1533774960, 1533775080, 1533775140, 1533775320, 1533775380, 1533775500,1533775560, 1533775680, 1533776340, 1533776520, 1533776520,1533776580, 1533777660, 1533777900, 1533777900, 1533778200, 1533779100, 1533779100, 1533779220, 1533779460, 1533779640, 1533780360, 1533780960, 1533781740, 1533782040, 1533782340, 1533782700, 1533782880, 1533783120, 1533783600, 1533784020, 1533784140, 1533785280, 1533785280, 1533785400, 1533785460, 1533786660), class = c("POSIXct", "POSIXt"), tzone = "Europe/London")
I have looked at other questions and found create-a-24-hour-vector-with-5-minutes-time-interval-in-r. It is not quite what I am after. Any help is highly appreciated.

As suggested by #AleBdC, I checked the link: Grouping every n minutes with dplyr and found the comment suggested by #MikeyHarper very useful.
So, I first create five-minute intervals in currday, then separate the date and time into two columns and then converted the time column into factor and use it for grouping and summarizing.
library(lubridate)
x <- ymd_hms(currday, tz="Europe/London")
x<-data.frame(floor_date(x, unit = "5minutes"))
names(x)<-"by5"
x%>%separate(by5, into = c("date", "time"),sep = "\\s" )
x$time<-as.factor(x$time)
I also created five-minute intervals in my monthly data using the same approach and calculated the mean and sd and compared it with the daily data.

strptime() function in R converting time values to 0

I have a column in a data frame (factor type) that I want to convert to POSIXlt using strptime(). What I'm doing is:
file[, 2] <- strptime(file[, 2], format="%H:%M:%S")
Examples of values in the column are: 17:24:00 17:25:00 17:26:00
However, the function returns all 0s. Is the format specified incorrectly? Any help would be appreciated.
Here's the output of dput(head(file[,2])):
structure(1045:1050, .Label = c("00:00:00", "00:01:00", "00:02:00",
"00:03:00", "00:04:00", "00:05:00", "00:06:00", "00:07:00", "00:08:00",
"00:09:00", "00:10:00", "00:11:00", "00:12:00", "00:13:00", "00:14:00",
"00:15:00", "00:16:00", "00:17:00", "00:18:00", "00:19:00", "00:20:00",
"00:21:00", "00:22:00", "00:23:00", "00:24:00", "00:25:00", "00:26:00",
"00:27:00", "00:28:00", "00:29:00", "00:30:00", "00:31:00", "00:32:00",
"00:33:00", "00:34:00", "00:35:00", "00:36:00", "00:37:00", "00:38:00",
"00:39:00", "00:40:00", "00:41:00", "00:42:00", "00:43:00", "00:44:00",
"00:45:00", "00:46:00", "00:47:00", "00:48:00", "00:49:00", "00:50:00",
"00:51:00", "00:52:00", "00:53:00", "00:54:00", "00:55:00", "00:56:00",
"00:57:00", "00:58:00", "00:59:00", "01:00:00", "01:01:00", "01:02:00",
"01:03:00", "01:04:00", "01:05:00", "01:06:00", "01:07:00", "01:08:00",
"01:09:00", "01:10:00", "01:11:00", "01:12:00", "01:13:00", "01:14:00",
"01:15:00", "01:16:00", "01:17:00", "01:18:00", "01:19:00", "01:20:00",
"01:21:00", "01:22:00", "01:23:00", "01:24:00", "01:25:00", "01:26:00",
"01:27:00", "01:28:00", "01:29:00", "01:30:00", "01:31:00", "01:32:00",
"01:33:00", "01:34:00", "01:35:00", "01:36:00", "01:37:00", "01:38:00",
"01:39:00", "01:40:00", "01:41:00", "01:42:00", "01:43:00", "01:44:00",
"01:45:00", "01:46:00", "01:47:00", "01:48:00", "01:49:00", "01:50:00",
"01:51:00", "01:52:00", "01:53:00", "01:54:00", "01:55:00", "01:56:00",
"01:57:00", "01:58:00", "01:59:00", "02:00:00", "02:01:00", "02:02:00",
"02:03:00", "02:04:00", "02:05:00", "02:06:00", "02:07:00", "02:08:00",
"02:09:00", "02:10:00", "02:11:00", "02:12:00", "02:13:00", "02:14:00",
"02:15:00", "02:16:00", "02:17:00", "02:18:00", "02:19:00", "02:20:00",
"02:21:00", "02:22:00", "02:23:00", "02:24:00", "02:25:00", "02:26:00",
"02:27:00", "02:28:00", "02:29:00", "02:30:00", "02:31:00", "02:32:00",
"02:33:00", "02:34:00", "02:35:00", "02:36:00", "02:37:00", "02:38:00",
"02:39:00", "02:40:00", "02:41:00", "02:42:00", "02:43:00", "02:44:00",
"02:45:00", "02:46:00", "02:47:00", "02:48:00", "02:49:00", "02:50:00",
"02:51:00", "02:52:00", "02:53:00", "02:54:00", "02:55:00", "02:56:00",
"02:57:00", "02:58:00", "02:59:00", "03:00:00", "03:01:00", "03:02:00",
"03:03:00", "03:04:00", "03:05:00", "03:06:00", "03:07:00", "03:08:00",
"03:09:00", "03:10:00", "03:11:00", "03:12:00", "03:13:00", "03:14:00",
"03:15:00", "03:16:00", "03:17:00", "03:18:00", "03:19:00", "03:20:00",
"03:21:00", "03:22:00", "03:23:00", "03:24:00", "03:25:00", "03:26:00",
"03:27:00", "03:28:00", "03:29:00", "03:30:00", "03:31:00", "03:32:00",
"03:33:00", "03:34:00", "03:35:00", "03:36:00", "03:37:00", "03:38:00",
"03:39:00", "03:40:00", "03:41:00", "03:42:00", "03:43:00", "03:44:00",
"03:45:00", "03:46:00", "03:47:00", "03:48:00", "03:49:00", "03:50:00",
"03:51:00", "03:52:00", "03:53:00", "03:54:00", "03:55:00", "03:56:00",
"03:57:00", "03:58:00", "03:59:00", "04:00:00", "04:01:00", "04:02:00",
"04:03:00", "04:04:00", "04:05:00", "04:06:00", "04:07:00", "04:08:00",
"04:09:00", "04:10:00", "04:11:00", "04:12:00", "04:13:00", "04:14:00",
"04:15:00", "04:16:00", "04:17:00", "04:18:00", "04:19:00", "04:20:00",
"04:21:00", "04:22:00", "04:23:00", "04:24:00", "04:25:00", "04:26:00",
"04:27:00", "04:28:00", "04:29:00", "04:30:00", "04:31:00", "04:32:00",
"04:33:00", "04:34:00", "04:35:00", "04:36:00", "04:37:00", "04:38:00",
"04:39:00", "04:40:00", "04:41:00", "04:42:00", "04:43:00", "04:44:00",
"04:45:00", "04:46:00", "04:47:00", "04:48:00", "04:49:00", "04:50:00",
"04:51:00", "04:52:00", "04:53:00", "04:54:00", "04:55:00", "04:56:00",
"04:57:00", "04:58:00", "04:59:00", "05:00:00", "05:01:00", "05:02:00",
"05:03:00", "05:04:00", "05:05:00", "05:06:00", "05:07:00", "05:08:00",
"05:09:00", "05:10:00", "05:11:00", "05:12:00", "05:13:00", "05:14:00",
"05:15:00", "05:16:00", "05:17:00", "05:18:00", "05:19:00", "05:20:00",
"05:21:00", "05:22:00", "05:23:00", "05:24:00", "05:25:00", "05:26:00",
"05:27:00", "05:28:00", "05:29:00", "05:30:00", "05:31:00", "05:32:00",
"05:33:00", "05:34:00", "05:35:00", "05:36:00", "05:37:00", "05:38:00",
"05:39:00", "05:40:00", "05:41:00", "05:42:00", "05:43:00", "05:44:00",
"05:45:00", "05:46:00", "05:47:00", "05:48:00", "05:49:00", "05:50:00",
"05:51:00", "05:52:00", "05:53:00", "05:54:00", "05:55:00", "05:56:00",
"05:57:00", "05:58:00", "05:59:00", "06:00:00", "06:01:00", "06:02:00",
"06:03:00", "06:04:00", "06:05:00", "06:06:00", "06:07:00", "06:08:00",
"06:09:00", "06:10:00", "06:11:00", "06:12:00", "06:13:00", "06:14:00",
"06:15:00", "06:16:00", "06:17:00", "06:18:00", "06:19:00", "06:20:00",
"06:21:00", "06:22:00", "06:23:00", "06:24:00", "06:25:00", "06:26:00",
"06:27:00", "06:28:00", "06:29:00", "06:30:00", "06:31:00", "06:32:00",
"06:33:00", "06:34:00", "06:35:00", "06:36:00", "06:37:00", "06:38:00",
"06:39:00", "06:40:00", "06:41:00", "06:42:00", "06:43:00", "06:44:00",
"06:45:00", "06:46:00", "06:47:00", "06:48:00", "06:49:00", "06:50:00",
"06:51:00", "06:52:00", "06:53:00", "06:54:00", "06:55:00", "06:56:00",
"06:57:00", "06:58:00", "06:59:00", "07:00:00", "07:01:00", "07:02:00",
"07:03:00", "07:04:00", "07:05:00", "07:06:00", "07:07:00", "07:08:00",
"07:09:00", "07:10:00", "07:11:00", "07:12:00", "07:13:00", "07:14:00",
"07:15:00", "07:16:00", "07:17:00", "07:18:00", "07:19:00", "07:20:00",
"07:21:00", "07:22:00", "07:23:00", "07:24:00", "07:25:00", "07:26:00",
"07:27:00", "07:28:00", "07:29:00", "07:30:00", "07:31:00", "07:32:00",
"07:33:00", "07:34:00", "07:35:00", "07:36:00", "07:37:00", "07:38:00",
"07:39:00", "07:40:00", "07:41:00", "07:42:00", "07:43:00", "07:44:00",
"07:45:00", "07:46:00", "07:47:00", "07:48:00", "07:49:00", "07:50:00",
"07:51:00", "07:52:00", "07:53:00", "07:54:00", "07:55:00", "07:56:00",
"07:57:00", "07:58:00", "07:59:00", "08:00:00", "08:01:00", "08:02:00",
"08:03:00", "08:04:00", "08:05:00", "08:06:00", "08:07:00", "08:08:00",
"08:09:00", "08:10:00", "08:11:00", "08:12:00", "08:13:00", "08:14:00",
"08:15:00", "08:16:00", "08:17:00", "08:18:00", "08:19:00", "08:20:00",
"08:21:00", "08:22:00", "08:23:00", "08:24:00", "08:25:00", "08:26:00",
"08:27:00", "08:28:00", "08:29:00", "08:30:00", "08:31:00", "08:32:00",
"08:33:00", "08:34:00", "08:35:00", "08:36:00", "08:37:00", "08:38:00",
"08:39:00", "08:40:00", "08:41:00", "08:42:00", "08:43:00", "08:44:00",
"08:45:00", "08:46:00", "08:47:00", "08:48:00", "08:49:00", "08:50:00",
"08:51:00", "08:52:00", "08:53:00", "08:54:00", "08:55:00", "08:56:00",
"08:57:00", "08:58:00", "08:59:00", "09:00:00", "09:01:00", "09:02:00",
"09:03:00", "09:04:00", "09:05:00", "09:06:00", "09:07:00", "09:08:00",
"09:09:00", "09:10:00", "09:11:00", "09:12:00", "09:13:00", "09:14:00",
"09:15:00", "09:16:00", "09:17:00", "09:18:00", "09:19:00", "09:20:00",
"09:21:00", "09:22:00", "09:23:00", "09:24:00", "09:25:00", "09:26:00",
"09:27:00", "09:28:00", "09:29:00", "09:30:00", "09:31:00", "09:32:00",
"09:33:00", "09:34:00", "09:35:00", "09:36:00", "09:37:00", "09:38:00",
"09:39:00", "09:40:00", "09:41:00", "09:42:00", "09:43:00", "09:44:00",
"09:45:00", "09:46:00", "09:47:00", "09:48:00", "09:49:00", "09:50:00",
"09:51:00", "09:52:00", "09:53:00", "09:54:00", "09:55:00", "09:56:00",
"09:57:00", "09:58:00", "09:59:00", "10:00:00", "10:01:00", "10:02:00",
"10:03:00", "10:04:00", "10:05:00", "10:06:00", "10:07:00", "10:08:00",
"10:09:00", "10:10:00", "10:11:00", "10:12:00", "10:13:00", "10:14:00",
"10:15:00", "10:16:00", "10:17:00", "10:18:00", "10:19:00", "10:20:00",
"10:21:00", "10:22:00", "10:23:00", "10:24:00", "10:25:00", "10:26:00",
"10:27:00", "10:28:00", "10:29:00", "10:30:00", "10:31:00", "10:32:00",
"10:33:00", "10:34:00", "10:35:00", "10:36:00", "10:37:00", "10:38:00",
"10:39:00", "10:40:00", "10:41:00", "10:42:00", "10:43:00", "10:44:00",
"10:45:00", "10:46:00", "10:47:00", "10:48:00", "10:49:00", "10:50:00",
"10:51:00", "10:52:00", "10:53:00", "10:54:00", "10:55:00", "10:56:00",
"10:57:00", "10:58:00", "10:59:00", "11:00:00", "11:01:00", "11:02:00",
"11:03:00", "11:04:00", "11:05:00", "11:06:00", "11:07:00", "11:08:00",
"11:09:00", "11:10:00", "11:11:00", "11:12:00", "11:13:00", "11:14:00",
"11:15:00", "11:16:00", "11:17:00", "11:18:00", "11:19:00", "11:20:00",
"11:21:00", "11:22:00", "11:23:00", "11:24:00", "11:25:00", "11:26:00",
"11:27:00", "11:28:00", "11:29:00", "11:30:00", "11:31:00", "11:32:00",
"11:33:00", "11:34:00", "11:35:00", "11:36:00", "11:37:00", "11:38:00",
"11:39:00", "11:40:00", "11:41:00", "11:42:00", "11:43:00", "11:44:00",
"11:45:00", "11:46:00", "11:47:00", "11:48:00", "11:49:00", "11:50:00",
"11:51:00", "11:52:00", "11:53:00", "11:54:00", "11:55:00", "11:56:00",
"11:57:00", "11:58:00", "11:59:00", "12:00:00", "12:01:00", "12:02:00",
"12:03:00", "12:04:00", "12:05:00", "12:06:00", "12:07:00", "12:08:00",
"12:09:00", "12:10:00", "12:11:00", "12:12:00", "12:13:00", "12:14:00",
"12:15:00", "12:16:00", "12:17:00", "12:18:00", "12:19:00", "12:20:00",
"12:21:00", "12:22:00", "12:23:00", "12:24:00", "12:25:00", "12:26:00",
"12:27:00", "12:28:00", "12:29:00", "12:30:00", "12:31:00", "12:32:00",
"12:33:00", "12:34:00", "12:35:00", "12:36:00", "12:37:00", "12:38:00",
"12:39:00", "12:40:00", "12:41:00", "12:42:00", "12:43:00", "12:44:00",
"12:45:00", "12:46:00", "12:47:00", "12:48:00", "12:49:00", "12:50:00",
"12:51:00", "12:52:00", "12:53:00", "12:54:00", "12:55:00", "12:56:00",
"12:57:00", "12:58:00", "12:59:00", "13:00:00", "13:01:00", "13:02:00",
"13:03:00", "13:04:00", "13:05:00", "13:06:00", "13:07:00", "13:08:00",
"13:09:00", "13:10:00", "13:11:00", "13:12:00", "13:13:00", "13:14:00",
"13:15:00", "13:16:00", "13:17:00", "13:18:00", "13:19:00", "13:20:00",
"13:21:00", "13:22:00", "13:23:00", "13:24:00", "13:25:00", "13:26:00",
"13:27:00", "13:28:00", "13:29:00", "13:30:00", "13:31:00", "13:32:00",
"13:33:00", "13:34:00", "13:35:00", "13:36:00", "13:37:00", "13:38:00",
"13:39:00", "13:40:00", "13:41:00", "13:42:00", "13:43:00", "13:44:00",
"13:45:00", "13:46:00", "13:47:00", "13:48:00", "13:49:00", "13:50:00",
"13:51:00", "13:52:00", "13:53:00", "13:54:00", "13:55:00", "13:56:00",
"13:57:00", "13:58:00", "13:59:00", "14:00:00", "14:01:00", "14:02:00",
"14:03:00", "14:04:00", "14:05:00", "14:06:00", "14:07:00", "14:08:00",
"14:09:00", "14:10:00", "14:11:00", "14:12:00", "14:13:00", "14:14:00",
"14:15:00", "14:16:00", "14:17:00", "14:18:00", "14:19:00", "14:20:00",
"14:21:00", "14:22:00", "14:23:00", "14:24:00", "14:25:00", "14:26:00",
"14:27:00", "14:28:00", "14:29:00", "14:30:00", "14:31:00", "14:32:00",
"14:33:00", "14:34:00", "14:35:00", "14:36:00", "14:37:00", "14:38:00",
"14:39:00", "14:40:00", "14:41:00", "14:42:00", "14:43:00", "14:44:00",
"14:45:00", "14:46:00", "14:47:00", "14:48:00", "14:49:00", "14:50:00",
"14:51:00", "14:52:00", "14:53:00", "14:54:00", "14:55:00", "14:56:00",
"14:57:00", "14:58:00", "14:59:00", "15:00:00", "15:01:00", "15:02:00",
"15:03:00", "15:04:00", "15:05:00", "15:06:00", "15:07:00", "15:08:00",
"15:09:00", "15:10:00", "15:11:00", "15:12:00", "15:13:00", "15:14:00",
"15:15:00", "15:16:00", "15:17:00", "15:18:00", "15:19:00", "15:20:00",
"15:21:00", "15:22:00", "15:23:00", "15:24:00", "15:25:00", "15:26:00",
"15:27:00", "15:28:00", "15:29:00", "15:30:00", "15:31:00", "15:32:00",
"15:33:00", "15:34:00", "15:35:00", "15:36:00", "15:37:00", "15:38:00",
"15:39:00", "15:40:00", "15:41:00", "15:42:00", "15:43:00", "15:44:00",
"15:45:00", "15:46:00", "15:47:00", "15:48:00", "15:49:00", "15:50:00",
"15:51:00", "15:52:00", "15:53:00", "15:54:00", "15:55:00", "15:56:00",
"15:57:00", "15:58:00", "15:59:00", "16:00:00", "16:01:00", "16:02:00",
"16:03:00", "16:04:00", "16:05:00", "16:06:00", "16:07:00", "16:08:00",
"16:09:00", "16:10:00", "16:11:00", "16:12:00", "16:13:00", "16:14:00",
"16:15:00", "16:16:00", "16:17:00", "16:18:00", "16:19:00", "16:20:00",
"16:21:00", "16:22:00", "16:23:00", "16:24:00", "16:25:00", "16:26:00",
"16:27:00", "16:28:00", "16:29:00", "16:30:00", "16:31:00", "16:32:00",
"16:33:00", "16:34:00", "16:35:00", "16:36:00", "16:37:00", "16:38:00",
"16:39:00", "16:40:00", "16:41:00", "16:42:00", "16:43:00", "16:44:00",
"16:45:00", "16:46:00", "16:47:00", "16:48:00", "16:49:00", "16:50:00",
"16:51:00", "16:52:00", "16:53:00", "16:54:00", "16:55:00", "16:56:00",
"16:57:00", "16:58:00", "16:59:00", "17:00:00", "17:01:00", "17:02:00",
"17:03:00", "17:04:00", "17:05:00", "17:06:00", "17:07:00", "17:08:00",
"17:09:00", "17:10:00", "17:11:00", "17:12:00", "17:13:00", "17:14:00",
"17:15:00", "17:16:00", "17:17:00", "17:18:00", "17:19:00", "17:20:00",
"17:21:00", "17:22:00", "17:23:00", "17:24:00", "17:25:00", "17:26:00",
"17:27:00", "17:28:00", "17:29:00", "17:30:00", "17:31:00", "17:32:00",
"17:33:00", "17:34:00", "17:35:00", "17:36:00", "17:37:00", "17:38:00",
"17:39:00", "17:40:00", "17:41:00", "17:42:00", "17:43:00", "17:44:00",
"17:45:00", "17:46:00", "17:47:00", "17:48:00", "17:49:00", "17:50:00",
"17:51:00", "17:52:00", "17:53:00", "17:54:00", "17:55:00", "17:56:00",
"17:57:00", "17:58:00", "17:59:00", "18:00:00", "18:01:00", "18:02:00",
"18:03:00", "18:04:00", "18:05:00", "18:06:00", "18:07:00", "18:08:00",
"18:09:00", "18:10:00", "18:11:00", "18:12:00", "18:13:00", "18:14:00",
"18:15:00", "18:16:00", "18:17:00", "18:18:00", "18:19:00", "18:20:00",
"18:21:00", "18:22:00", "18:23:00", "18:24:00", "18:25:00", "18:26:00",
"18:27:00", "18:28:00", "18:29:00", "18:30:00", "18:31:00", "18:32:00",
"18:33:00", "18:34:00", "18:35:00", "18:36:00", "18:37:00", "18:38:00",
"18:39:00", "18:40:00", "18:41:00", "18:42:00", "18:43:00", "18:44:00",
"18:45:00", "18:46:00", "18:47:00", "18:48:00", "18:49:00", "18:50:00",
"18:51:00", "18:52:00", "18:53:00", "18:54:00", "18:55:00", "18:56:00",
"18:57:00", "18:58:00", "18:59:00", "19:00:00", "19:01:00", "19:02:00",
"19:03:00", "19:04:00", "19:05:00", "19:06:00", "19:07:00", "19:08:00",
"19:09:00", "19:10:00", "19:11:00", "19:12:00", "19:13:00", "19:14:00",
"19:15:00", "19:16:00", "19:17:00", "19:18:00", "19:19:00", "19:20:00",
"19:21:00", "19:22:00", "19:23:00", "19:24:00", "19:25:00", "19:26:00",
"19:27:00", "19:28:00", "19:29:00", "19:30:00", "19:31:00", "19:32:00",
"19:33:00", "19:34:00", "19:35:00", "19:36:00", "19:37:00", "19:38:00",
"19:39:00", "19:40:00", "19:41:00", "19:42:00", "19:43:00", "19:44:00",
"19:45:00", "19:46:00", "19:47:00", "19:48:00", "19:49:00", "19:50:00",
"19:51:00", "19:52:00", "19:53:00", "19:54:00", "19:55:00", "19:56:00",
"19:57:00", "19:58:00", "19:59:00", "20:00:00", "20:01:00", "20:02:00",
"20:03:00", "20:04:00", "20:05:00", "20:06:00", "20:07:00", "20:08:00",
"20:09:00", "20:10:00", "20:11:00", "20:12:00", "20:13:00", "20:14:00",
"20:15:00", "20:16:00", "20:17:00", "20:18:00", "20:19:00", "20:20:00",
"20:21:00", "20:22:00", "20:23:00", "20:24:00", "20:25:00", "20:26:00",
"20:27:00", "20:28:00", "20:29:00", "20:30:00", "20:31:00", "20:32:00",
"20:33:00", "20:34:00", "20:35:00", "20:36:00", "20:37:00", "20:38:00",
"20:39:00", "20:40:00", "20:41:00", "20:42:00", "20:43:00", "20:44:00",
"20:45:00", "20:46:00", "20:47:00", "20:48:00", "20:49:00", "20:50:00",
"20:51:00", "20:52:00", "20:53:00", "20:54:00", "20:55:00", "20:56:00",
"20:57:00", "20:58:00", "20:59:00", "21:00:00", "21:01:00", "21:02:00",
"21:03:00", "21:04:00", "21:05:00", "21:06:00", "21:07:00", "21:08:00",
"21:09:00", "21:10:00", "21:11:00", "21:12:00", "21:13:00", "21:14:00",
"21:15:00", "21:16:00", "21:17:00", "21:18:00", "21:19:00", "21:20:00",
"21:21:00", "21:22:00", "21:23:00", "21:24:00", "21:25:00", "21:26:00",
"21:27:00", "21:28:00", "21:29:00", "21:30:00", "21:31:00", "21:32:00",
"21:33:00", "21:34:00", "21:35:00", "21:36:00", "21:37:00", "21:38:00",
"21:39:00", "21:40:00", "21:41:00", "21:42:00", "21:43:00", "21:44:00",
"21:45:00", "21:46:00", "21:47:00", "21:48:00", "21:49:00", "21:50:00",
"21:51:00", "21:52:00", "21:53:00", "21:54:00", "21:55:00", "21:56:00",
"21:57:00", "21:58:00", "21:59:00", "22:00:00", "22:01:00", "22:02:00",
"22:03:00", "22:04:00", "22:05:00", "22:06:00", "22:07:00", "22:08:00",
"22:09:00", "22:10:00", "22:11:00", "22:12:00", "22:13:00", "22:14:00",
"22:15:00", "22:16:00", "22:17:00", "22:18:00", "22:19:00", "22:20:00",
"22:21:00", "22:22:00", "22:23:00", "22:24:00", "22:25:00", "22:26:00",
"22:27:00", "22:28:00", "22:29:00", "22:30:00", "22:31:00", "22:32:00",
"22:33:00", "22:34:00", "22:35:00", "22:36:00", "22:37:00", "22:38:00",
"22:39:00", "22:40:00", "22:41:00", "22:42:00", "22:43:00", "22:44:00",
"22:45:00", "22:46:00", "22:47:00", "22:48:00", "22:49:00", "22:50:00",
"22:51:00", "22:52:00", "22:53:00", "22:54:00", "22:55:00", "22:56:00",
"22:57:00", "22:58:00", "22:59:00", "23:00:00", "23:01:00", "23:02:00",
"23:03:00", "23:04:00", "23:05:00", "23:06:00", "23:07:00", "23:08:00",
"23:09:00", "23:10:00", "23:11:00", "23:12:00", "23:13:00", "23:14:00",
"23:15:00", "23:16:00", "23:17:00", "23:18:00", "23:19:00", "23:20:00",
"23:21:00", "23:22:00", "23:23:00", "23:24:00", "23:25:00", "23:26:00",
"23:27:00", "23:28:00", "23:29:00", "23:30:00", "23:31:00", "23:32:00",
"23:33:00", "23:34:00", "23:35:00", "23:36:00", "23:37:00", "23:38:00",
"23:39:00", "23:40:00", "23:41:00", "23:42:00", "23:43:00", "23:44:00",
"23:45:00", "23:46:00", "23:47:00", "23:48:00", "23:49:00", "23:50:00",
"23:51:00", "23:52:00", "23:53:00", "23:54:00", "23:55:00", "23:56:00",
"23:57:00", "23:58:00", "23:59:00"), class = "factor")

Use [[ instead of [ and it will work:
file[[2]] <- strptime(file[[2]], "%X")
The result:
[1] "2014-06-04 17:24:00" "2014-06-04 17:25:00"
[3] "2014-06-04 17:26:00" "2014-06-04 17:27:00"
[5] "2014-06-04 17:28:00" "2014-06-04 17:29:00"
By the way, "%X" is a short alternative for "%H:%M:%S".

Forecast with auto Arima, with long term trend line, the 30 day forecast "jumps"

I'm trying to create a 30 day forecast using auto.arima from the forecast package. I want to capture the long term trend, so I inserted it into the xreg argument.
The data:
dput(data)
structure(list(TKDate = structure(c(15706, 15707, 15708, 15709,
15710, 15711, 15712, 15713, 15714, 15715, 15716, 15717, 15718,
15719, 15720, 15721, 15722, 15723, 15724, 15725, 15726, 15727,
15728, 15729, 15730, 15731, 15732, 15733, 15734, 15735, 15736,
15737, 15738, 15739, 15740, 15741, 15742, 15743, 15744, 15745,
15746, 15747, 15748, 15749, 15750, 15751, 15752, 15753, 15754,
15755, 15756, 15757, 15758, 15759, 15760, 15761, 15762, 15763,
15764, 15765, 15766, 15767, 15768, 15769, 15770, 15771, 15772,
15773, 15774, 15775, 15776, 15777, 15778, 15779, 15780, 15781,
15782, 15783, 15784, 15785, 15786, 15787, 15788, 15789, 15790,
15791, 15792, 15793, 15794, 15795, 15796, 15797, 15798, 15799,
15800, 15801, 15802, 15803, 15804, 15805, 15806, 15807, 15808,
15809, 15810, 15811, 15812, 15813, 15814, 15815, 15816, 15817,
15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15826,
15827, 15828, 15829, 15830, 15831, 15832, 15833, 15834, 15835,
15836, 15837, 15838, 15839, 15840, 15841, 15842, 15843, 15844,
15845, 15846, 15847, 15848, 15849, 15850, 15851, 15852, 15853,
15854, 15855, 15856, 15857, 15858, 15859, 15860, 15861, 15862,
15863, 15864, 15865, 15866, 15867, 15868, 15869, 15870, 15871,
15872, 15873, 15874, 15875, 15876, 15877, 15878, 15879, 15880,
15881, 15882, 15883, 15884, 15885, 15886, 15887, 15888, 15889,
15890, 15891, 15892, 15893, 15894, 15895, 15896, 15897, 15898,
15899, 15900, 15901, 15902, 15903, 15904, 15905, 15906, 15907,
15908, 15909, 15910, 15911, 15912, 15913, 15914, 15915, 15916,
15917, 15918, 15919, 15920, 15921, 15922, 15923, 15924, 15925,
15926, 15927, 15928, 15929, 15930, 15931, 15932, 15933, 15934,
15935, 15936, 15937, 15938, 15939, 15940, 15941, 15942, 15943,
15944, 15945, 15946, 15947, 15948, 15949, 15950, 15951, 15952,
15953, 15954, 15955, 15956, 15957, 15958, 15959, 15960, 15961,
15962, 15963, 15964, 15965, 15966, 15967, 15968, 15969, 15970,
15971, 15972, 15973, 15974, 15975, 15976, 15977, 15978, 15979,
15980, 15981, 15982, 15983, 15984, 15985, 15986, 15987, 15988,
15989, 15990, 15991, 15992, 15993, 15994, 15995, 15996, 15997,
15998, 15999, 16000, 16001, 16002, 16003, 16004, 16005, 16006,
16007, 16008, 16009, 16010, 16011, 16012, 16013, 16014, 16015,
16016, 16017, 16018, 16019, 16020, 16021, 16022, 16023, 16024,
16025, 16026, 16027, 16028, 16029, 16030, 16031, 16032, 16033,
16034, 16035, 16036, 16037, 16038, 16039, 16040, 16041, 16042,
16043, 16044, 16045, 16046, 16047, 16048, 16049, 16050, 16051,
16052, 16053, 16054, 16055, 16056, 16057, 16058, 16059, 16060,
16061, 16062, 16063, 16064, 16065, 16066, 16067, 16068, 16069,
16070, 16071, 16072, 16073, 16074, 16075, 16076, 16077, 16078,
16079, 16080, 16081, 16082, 16083, 16084, 16085, 16086, 16087,
16088, 16089, 16090, 16091, 16092, 16093, 16094, 16095, 16096,
16097, 16098, 16099, 16100, 16101, 16102, 16103, 16104, 16105,
16106, 16107, 16108, 16109, 16110, 16111, 16112, 16113, 16114,
16115, 16116, 16117, 16118), class = "Date"), spend = c(7984.39,
11476.06, 6555.57, 3981.45, 3963.83, 4827.72, 6309.32, 13503.36,
17075.89, 33353.71, 29324.34, 7968.68, 5540.63, 12113.45, 15596.38,
19328.67, 20224.68, 18977.55, 16128.27, 10633.56, 11887.79, 17881.11,
12613.46, 11607.55, 38232.11, 7861.25, 9397.88, 12056.02, 15115.87,
12275.93, 14537.35, 9594.26, 8215.83, 9632.52, 9993.15, 13478.37,
28509.38, 12016.33, 8907.76, 8757.43, 9513.09, 10299.5, 10385.03,
12515.62, 9008.95, 17825.68, 9320.47, 11189.58, 12902.31, 13341.35,
18675.32, 16989.53, 10114.53, 9876.65, 11203.39, 11718.73, 26264.95,
12414.19, 12275.16, 9242.85, 8883.97, 10095.72, 11581.55, 14815.78,
25064.12, 9297.07, 8047.91, 6876.37, 8881.63, 10982.85, 9975.33,
24124.62, 8514.66, 15719.84, 5807.39, 8422.38, 15184.95, 14757.58,
11087.61, 11070.78, 10425.67, 15517.8, 11257.69, 11915.47, 11720.37,
34064.62, 6493.41, 5757.4, 4387.54, 6520.58, 7806.81, 6356.63,
10916.36, 9013.43, 9722.41, 6044.25, 7971.7, 23933.54, 8627.85,
9722.77, 18660.13, 13011.36, 11445.11, 14219.2, 17138.92, 16016.68,
11434, 31379.03, 8494.25, 12493.85, 7708.1, 21583.05, 9026.17,
9379.35, 8287.13, 7298.16, 6097.03, 8076.57, 12871.87, 11346.89,
9115.82, 7737.98, 15065.38, 5262.73, 6522.58, 12743.94, 23945.16,
16109.26, 6985.89, 6345.08, 6246.93, 6824.66, 8491.42, 9654.99,
18976.58, 19565.68, 8075.47, 7219.79, 8629.04, 12491.64, 11915.89,
27533.16, 13554.35, 10102.21, 20029.15, 11641.82, 15855.19, 14139.17,
15376.63, 14625.99, 9098.87, 9396.64, 12015.84, 17532.75, 15131.65,
15815.5, 16048.65, 9769.63, 9582.12, 11201.8, 12810, 18857.38,
11822.71, 19289.08, 8911.29, 9437.55, 10987.14, 12995.65, 16675.26,
9741.82, 9723.57, 10328.24, 7738.04, 8432.16, 23021.73, 10367.28,
8210.53, 10468.4, 8024.25, 7296.25, 7445.34, 8539.59, 12386.23,
15335.72, 9013.49, 7994.95, 7759.46, 8789.38, 11242.38, 28653.23,
9750.96, 14398.62, 9248.74, 6766.08, 8159.14, 9899.38, 9453.35,
17588.96, 8958.16, 8256.61, 6240.4, 7235.24, 23841.62, 9002.73,
11839.47, 8693.31, 7161.37, 7046.39, 9221.53, 10004.93, 8698.76,
7948.68, 9013.27, 18536.68, 7980.38, 8968.95, 23594.14, 17744.66,
12615.73, 13646.05, 10512.58, 9066.02, 9665.15, 13183.2, 23864.45,
12017.52, 10831.07, 8954.76, 7276.41, 7882.9, 16616.41, 15384.68,
11046.53, 10621.01, 8094.74, 5451.26, 6237.79, 10717.69, 7076.38,
7044.62, 7047.45, 7774.77, 6496.21, 6340.9, 7110.53, 7691.28,
17482.02, 5576.19, 3763.79, 11477.68, 5710.5, 6519.51, 20022.61,
13153.68, 6526.28, 5885.28, 5656.17, 6270.04, 9795.38, 6320.95,
5741.98, 10808.72, 5150.87, 5416.52, 6305.05, 20953.12, 6569.02,
6360.21, 9376.68, 4973.93, 5034.48, 6380.45, 15307.28, 14386.65,
17705.88, 4779.52, 4784.79, 4737.05, 5350.28, 12112.11, 13153.72,
6049.69, 5430.46, 4627.59, 3637.2, 5482.43, 16705.15, 12221.16,
13198.88, 6484.54, 5590.86, 4979.09, 5771.75, 7311.92, 16111.86,
8047.77, 11706.91, 6042.14, 5670.74, 6905.07, 11261.89, 9700.4,
6643.03, 5693.85, 14778.67, 9128.14, 3682.01, 7911.5, 17742.85,
5093.31, 7867.97, 3202.78, 2843.35, 2598.77, 10930.81, 11204.67,
7289.62, 4000.17, 4178.89, 4507.33, 6671.48, 10317.48, 9368.98,
6156.41, 8375.24, 2762.76, 2457.59, 4707.51, 4584.52, 3749.82,
11667.82, 4271.67, 3614.3, 3715.83, 4510.57, 4872.36, 21805.71,
4757.04, 6515.92, 2834.25, 2685.19, 3509.28, 4479.35, 17817.99,
10357.67, 3412.15, 3044.95, 2840.24, 3348.91, 13671.68, 2027.42,
1616.25, 1177.73, 995.25, 1062.25, 1578.07, 1649.8, 1410.06,
1592.03, 3995.24, 6489.87, 6895.21, 8298.58, 7698.68, 5782.07,
7671.08, 19539.4, 7023.84, 6509.9, 6643.28, 19850.3, 6856.67,
13142.15, 5524.75, 5063.2, 4916.81, 6117.54, 6717.86, 9393.95,
10462.44, 10511.15, 4497.94, 4038.31, 5503.91, 5554.82, 5801.11,
12992.82, 4778.61, 4067.41, 4359.53, 6148.1, 9236.51, 5773.16,
11313.13, 4702.37, 4167.3, 4067.75, 4469.11, 9278.41, 9911.18,
5161.13, 4477.78, 4459.53, 4080.14, 5084.67, 7735.34, 10676.6,
5507.86, 8286.12, 4332.23, 4737.52, 5952.09, 7134.44)), .Names = c("TKDate",
"spend"), row.names = c(NA, 413L), class = "data.frame")
The code:
library(forecast)
explaining<-rep(1:length(data$TKDate))
predic<-rep((length(data$TKDate)+1):(length(data$TKDate)+31))
modArima <- auto.arima(data[,2],xreg=explaining)
fit<-forecast(modArima,h=30,xreg=explaining,newdata=predic)
plot(fit)
I get this weird jump:
Can anyone explain to me this weird jump? Why doesn't the forecast continue from the last observed data point (or at least close to it)?

This is a hard to find error, I'll admit.
forecast.Arima() takes the new values of the external regressors not in a newdata parameter (as does predict.lm()), but in the xreg parameter. So instead of
fit <- forecast(modArima,h=30,xreg=explaining,newdata=predic)
where you forecasted using the values of explaining, not those of predic (unfortunately, forecast.Arima() does not throw a warning if you feed data to the nonexistent newdata parameter), do this:
fit <- forecast(modArima,h=30,xreg=predic)
and plot (with in-sample fits thrown in for good measure - EDIT: somewhat confusingly, the in-sample fits are not returned by auto.arima() or arima() as they are by lm(), but by forecast.Arima()):
plot(fit)
lines(fit$fitted,col="red")

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