I have two data.tables:
original <- data.frame(id = c(rep("RE01",5),rep("RE02",5)),date.time = head(seq.POSIXt(as.POSIXct("2015-11-01 01:00:00"),as.POSIXct("2015-11-05 01:00:00"),60*60*10),10))
compare <- data.frame(id = c("RE01","RE02"),seq = c(1,2),start = as.POSIXct(c("2015-11-01 20:00:00","2015-11-04 08:00:00")),end = as.POSIXct(c("2015-11-02 08:00:00","2015-11-04 20:00:00")))
setDT(original)
setDT(compare)
I would like to check the date in each row of original and see if it lies between the start and finish dates of compare whilst respecting the id. If it does lie between the two elements, a variable should be passed to original (compare$diff.seq). The output should look like this:
original
id date.time diff.seq
1 RE01 2015-11-01 01:00:00 NA
2 RE01 2015-11-01 11:00:00 NA
3 RE01 2015-11-01 21:00:00 1
4 RE01 2015-11-02 07:00:00 1
5 RE01 2015-11-02 17:00:00 NA
6 RE02 2015-11-03 03:00:00 NA
7 RE02 2015-11-03 13:00:00 NA
8 RE02 2015-11-03 23:00:00 NA
9 RE02 2015-11-04 09:00:00 2
10 RE02 2015-11-04 19:00:00 2
I've been reading the manual and SO for hours and trying "on", "by" and so on.. without any success. Can anybody point me in the right direction?
As said in the comments, this is very straight forward using data.table::foverlaps
You basically have to create an additional column in the original data set in order to set join boundaries, then key the two data sets by the columns you want to join on and then simply run forverlas and select the desired columns
original[, end := date.time]
setkey(original, id, date.time, end)
setkey(compare, id, start, end)
foverlaps(original, compare)[, .(id, date.time, seq)]
# id date.time seq
# 1: RE01 2015-11-01 01:00:00 NA
# 2: RE01 2015-11-01 11:00:00 NA
# 3: RE01 2015-11-01 21:00:00 1
# 4: RE01 2015-11-02 07:00:00 1
# 5: RE01 2015-11-02 17:00:00 NA
# 6: RE02 2015-11-03 03:00:00 NA
# 7: RE02 2015-11-03 13:00:00 NA
# 8: RE02 2015-11-03 23:00:00 NA
# 9: RE02 2015-11-04 09:00:00 2
# 10: RE02 2015-11-04 19:00:00 2
Alternatively, you can run foverlaps the other way around and then just update the original data set by reference while selecting the correct rows to update
indx <- foverlaps(compare, original, which = TRUE)
original[indx$yid, diff.seq := indx$xid]
original
# id date.time end diff.seq
# 1: RE01 2015-11-01 01:00:00 2015-11-01 01:00:00 NA
# 2: RE01 2015-11-01 11:00:00 2015-11-01 11:00:00 NA
# 3: RE01 2015-11-01 21:00:00 2015-11-01 21:00:00 1
# 4: RE01 2015-11-02 07:00:00 2015-11-02 07:00:00 1
# 5: RE01 2015-11-02 17:00:00 2015-11-02 17:00:00 NA
# 6: RE02 2015-11-03 03:00:00 2015-11-03 03:00:00 NA
# 7: RE02 2015-11-03 13:00:00 2015-11-03 13:00:00 NA
# 8: RE02 2015-11-03 23:00:00 2015-11-03 23:00:00 NA
# 9: RE02 2015-11-04 09:00:00 2015-11-04 09:00:00 2
# 10: RE02 2015-11-04 19:00:00 2015-11-04 19:00:00 2
Related
I've got a dataset with the following shape
ID Start Time End Time
1 01/01/2017 00:15:00 01/01/2017 07:15:00
2 01/01/2017 04:45:00 01/01/2017 06:15:00
3 01/01/2017 10:20:00 01/01/2017 20:15:00
4 01/01/2017 02:15:00 01/01/2017 00:15:00
5 02/01/2017 15:15:00 03/01/2017 00:30:00
6 03/01/2017 07:00:00 04/01/2017 09:15:00
I would like to count every 15 min for an entire year how many items have started but not finished, so count the number of times with a start time greater or equal than the time I'm looking at and an end time less or equal than the time I'm looking at.
I'm looking for an approach using tidyverse/dplyr if possible.
Any help or guidance would be very much appreciated.
If I understand correctly, the OP wants to count the number of simultaneously active events.
One possibility to tackle this question is the coverage() function from Bioconductor's IRange package. Another one is to aggregate in a non-equi join which is available with the data.table package.
Non-equi join
# create sequence of datetimes (limited to 4 days for demonstration)
seq15 <- seq(lubridate::as_datetime("2017-01-01"),
lubridate::as_datetime("2017-01-05"), by = "15 mins")
# aggregate within a non-equi join
library(data.table)
result <- periods[.(time = seq15), on = .(Start.Time <= time, End.Time > time),
.(time, count = sum(!is.na(ID))), by = .EACHI][, .(time, count)]
result
time count
1: 2017-01-01 00:00:00 0
2: 2017-01-01 00:15:00 1
3: 2017-01-01 00:30:00 1
4: 2017-01-01 00:45:00 1
5: 2017-01-01 01:00:00 1
---
381: 2017-01-04 23:00:00 0
382: 2017-01-04 23:15:00 0
383: 2017-01-04 23:30:00 0
384: 2017-01-04 23:45:00 0
385: 2017-01-05 00:00:00 0
The result can be visualized graphically:
library(ggplot2)
ggplot(result) + aes(time, count) + geom_step()
Data
periods <- readr::read_table(
"ID Start.Time End.Time
1 01/01/2017 00:15:00 01/01/2017 07:15:00
2 01/01/2017 04:45:00 01/01/2017 06:15:00
3 01/01/2017 10:20:00 01/01/2017 20:15:00
4 01/01/2017 02:15:00 01/01/2017 00:15:00
5 02/01/2017 15:15:00 03/01/2017 00:30:00
6 03/01/2017 07:00:00 04/01/2017 09:15:00"
)
# convert date strings to class Date
library(data.table)
cols <- names(periods)[names(periods) %like% "Time$"]
setDT(periods)[, (cols) := lapply(.SD, lubridate::dmy_hms), .SDcols = cols]
periods
ID Start.Time End.Time
1: 1 2017-01-01 00:15:00 2017-01-01 07:15:00
2: 2 2017-01-01 04:45:00 2017-01-01 06:15:00
3: 3 2017-01-01 10:20:00 2017-01-01 20:15:00
4: 4 2017-01-01 02:15:00 2017-01-01 00:15:00
5: 5 2017-01-02 15:15:00 2017-01-03 00:30:00
6: 6 2017-01-03 07:00:00 2017-01-04 09:15:00
I want to fill some missing dates in large data.frames. I saw different posts, but nothing is working. I'm using merge, which I thought it would be easy, but the result is not what I expect.
My data consists on hourly data for the whole year, with the corresponding values of a variable. I show just a sample:
# sample of data
dput(head(x1))
structure(list(date = structure(c(14617, 14617, 14617, 14617,
14617, 14617), class = "Date"), value = c(-9999, -9999, -9999,
-9999, -9999, -9999)), .Names = c("date", "value"), row.names =
c(2923L, 6545L, 10167L, 13789L, 17411L, 21033L), class = "data.frame")
So, since I want to add the missing data I created the array with the correct and complete time series:
# Create hourly data
times <- seq(as.POSIXct("2010-01-01 00:00:00"), as.POSIXct("2010-12-31 23:00:00"), by="hour")
# Split into days and hours
nt <- as.Date(strptime(times, "%Y-%m-%d"))
ndays <- data.frame("date"=nt,"hour"=format(as.POSIXct(strptime(times,"%Y-%m-%d %H:%M:%S",tz="")) ,format = "%H:%M:%S"))
The, I tried to merge ndays and x1, to have a new data.frame with the whole dates (and hours):
newdata <- merge(ndays,x1,by="date",all.x = T)
But, I don't have the values of x1! I'd have NA, so I tried to combine different options of merge, but none it's working. If I use:
newdata <- merge(x1, ndays,by="date",all.x = T)
The result looks like:
head(newdata)
date value hour
1 2010-01-08 -9999 12:00:00
2 2010-01-08 -9999 01:00:00
3 2010-01-08 -9999 02:00:00
4 2010-01-08 -9999 03:00:00
5 2010-01-08 -9999 00:00:00
6 2010-01-08 -9999 05:00:00
.....
But what I want is:
head(newdata)
date value hour
2010-01-01 NA 00:00:00
........
2010-01-08 -9999 12:00:00
2010-01-08 -9999 01:00:00
2010-01-08 -9999 02:00:00
To have the whole dates, and the final expected data.frame has to have a length (for each column) 8760 = the number of hours per year (timestep).
If I do:
newdata <- merge(ndays,x1,by="date",all = T)
Again, I'd have a new data.frame with a length of 193680, because all the data is merged. But I only want the values of x1 and days and hours for the whole year.
What am I missing to work with merge? Should I write another function to do it?
If I understand correctly, I believe this can be solved by updating in a join. This is a special kind of left join, i.e., take all rows of nday and copy value only in those rows where a matching date is found:
library(data.table)
setDT(ndays)[unique(setDT(x1)), on = "date", value := value]
Note that only unique rows of x1 are used assuming that there is only one distinct value per day.
# show some relevant rows
ndays[date %in% (as.IDate("2010-01-08") + (-1:+1))]
date hour value
1: 2010-01-07 00:00:00 NA
2: 2010-01-07 01:00:00 NA
3: 2010-01-07 02:00:00 NA
4: 2010-01-07 03:00:00 NA
5: 2010-01-07 04:00:00 NA
6: 2010-01-07 05:00:00 NA
7: 2010-01-07 06:00:00 NA
8: 2010-01-07 07:00:00 NA
9: 2010-01-07 08:00:00 NA
10: 2010-01-07 09:00:00 NA
11: 2010-01-07 10:00:00 NA
12: 2010-01-07 11:00:00 NA
13: 2010-01-07 12:00:00 NA
14: 2010-01-07 13:00:00 NA
15: 2010-01-07 14:00:00 NA
16: 2010-01-07 15:00:00 NA
17: 2010-01-07 16:00:00 NA
18: 2010-01-07 17:00:00 NA
19: 2010-01-07 18:00:00 NA
20: 2010-01-07 19:00:00 NA
21: 2010-01-07 20:00:00 NA
22: 2010-01-07 21:00:00 NA
23: 2010-01-07 22:00:00 NA
24: 2010-01-07 23:00:00 NA
25: 2010-01-08 00:00:00 -9999
26: 2010-01-08 01:00:00 -9999
27: 2010-01-08 02:00:00 -9999
28: 2010-01-08 03:00:00 -9999
29: 2010-01-08 04:00:00 -9999
30: 2010-01-08 05:00:00 -9999
31: 2010-01-08 06:00:00 -9999
32: 2010-01-08 07:00:00 -9999
33: 2010-01-08 08:00:00 -9999
34: 2010-01-08 09:00:00 -9999
35: 2010-01-08 10:00:00 -9999
36: 2010-01-08 11:00:00 -9999
37: 2010-01-08 12:00:00 -9999
38: 2010-01-08 13:00:00 -9999
39: 2010-01-08 14:00:00 -9999
40: 2010-01-08 15:00:00 -9999
41: 2010-01-08 16:00:00 -9999
42: 2010-01-08 17:00:00 -9999
43: 2010-01-08 18:00:00 -9999
44: 2010-01-08 19:00:00 -9999
45: 2010-01-08 20:00:00 -9999
46: 2010-01-08 21:00:00 -9999
47: 2010-01-08 22:00:00 -9999
48: 2010-01-08 23:00:00 -9999
49: 2010-01-09 00:00:00 NA
50: 2010-01-09 01:00:00 NA
51: 2010-01-09 02:00:00 NA
52: 2010-01-09 03:00:00 NA
53: 2010-01-09 04:00:00 NA
54: 2010-01-09 05:00:00 NA
55: 2010-01-09 06:00:00 NA
56: 2010-01-09 07:00:00 NA
57: 2010-01-09 08:00:00 NA
58: 2010-01-09 09:00:00 NA
59: 2010-01-09 10:00:00 NA
60: 2010-01-09 11:00:00 NA
61: 2010-01-09 12:00:00 NA
62: 2010-01-09 13:00:00 NA
63: 2010-01-09 14:00:00 NA
64: 2010-01-09 15:00:00 NA
65: 2010-01-09 16:00:00 NA
66: 2010-01-09 17:00:00 NA
67: 2010-01-09 18:00:00 NA
68: 2010-01-09 19:00:00 NA
69: 2010-01-09 20:00:00 NA
70: 2010-01-09 21:00:00 NA
71: 2010-01-09 22:00:00 NA
72: 2010-01-09 23:00:00 NA
date hour value
I have a time series data of two columns, and I want a graph with averaged hourly pattern for each month, like the graph attached but with two time series.
timestamp ET_control ET_treatment
1 2016-01-01 00:00:00 NA NA
2 2016-01-01 00:30:00 NA NA
3 2016-01-01 01:00:00 NA NA
4 2016-01-01 01:30:00 NA NA
5 2016-01-01 02:00:00 NA NA
6 2016-01-01 02:30:00 NA NA
7 2016-01-01 03:00:00 NA NA
8 2016-01-01 03:30:00 NA NA
9 2016-01-01 04:00:00 NA NA
10 2016-01-01 04:30:00 NA NA
11 2016-01-01 05:00:00 NA NA
12 2016-01-01 05:30:00 NA NA
13 2016-01-01 06:00:00 NA NA
14 2016-01-01 06:30:00 NA NA
15 2016-01-01 07:00:00 NA NA
16 2016-01-01 07:30:00 NA NA
17 2016-01-01 08:00:00 NA NA
18 2016-01-01 08:30:00 NA NA
19 2016-01-01 09:00:00 NA NA
20 2016-01-01 09:30:00 NA NA
21 2016-01-01 10:00:00 NA NA
22 2016-01-01 10:30:00 NA NA
23 2016-01-01 11:00:00 NA NA
24 2016-01-01 11:30:00 0.09863437 NA
25 2016-01-01 12:00:00 0.11465258 NA
26 2016-01-01 12:30:00 0.12356855 NA
27 2016-01-01 13:00:00 0.09246215 0.085398782
28 2016-01-01 13:30:00 0.08843156 0.072877001
29 2016-01-01 14:00:00 0.08536019 0.081885947
30 2016-01-01 14:30:00 0.08558541 NA
31 2016-01-01 15:00:00 0.05571436 NA
32 2016-01-01 15:30:00 0.04087248 0.038582547
33 2016-01-01 16:00:00 0.04233724 NA
34 2016-01-01 16:30:00 0.02150660 0.019560578
35 2016-01-01 17:00:00 0.01803765 0.019691155
36 2016-01-01 17:30:00 NA 0.005190489
37 2016-01-01 18:00:00 NA NA
38 2016-01-01 18:30:00 NA NA
39 2016-01-01 19:00:00 NA NA
40 2016-01-01 19:30:00 NA NA
41 2016-01-01 20:00:00 NA NA
42 2016-01-01 20:30:00 NA NA
43 2016-01-01 21:00:00 NA NA
44 2016-01-01 21:30:00 NA NA
45 2016-01-01 22:00:00 NA NA
46 2016-01-01 22:30:00 NA NA
47 2016-01-01 23:00:00 NA NA
48 2016-01-01 23:30:00 NA NA
49 2016-01-02 00:00:00 NA NA
50 2016-01-02 00:30:00 NA NA
given t is your data.frame with packages dplyr and ggplot2:
t <- t %>% mutate(
month = format(strptime(timestamp, "%Y-%m-%d %H:%M:%S"), "%b"),
hour=format(strptime(timestamp, "%Y-%m-%d %H:%M:%S"), "%H"))
tm <- t %>% group_by(month, hour) %>%
summarize(ET_control_mean=mean(ET_control, na.rm=T))
ggplot(tm, aes(x=hour, y=ET_control_mean)) + geom_point() + facet_wrap(~ month)
if you want to have both columns in your graph, you should transform your data into the 'long' format.
I am trying to fill in the gaps in one of my time series by merging a full day time series into my original time series. But for some reason I get duplicate entries and all the rest of my data is NA.
My data looks like this:
> head(data)
TIME Water_Temperature
1 2016-08-22 00:00:00 81.000
2 2016-08-22 00:01:00 80.625
3 2016-08-22 00:02:00 85.000
4 2016-08-22 00:03:00 80.437
5 2016-08-22 00:04:00 85.000
6 2016-08-22 00:05:00 80.375
> tail(data)
TIME Water_Temperature
1398 2016-08-22 23:54:00 19.5
1399 2016-08-22 23:55:00 19.5
1400 2016-08-22 23:56:00 19.5
1401 2016-08-22 23:57:00 19.5
1402 2016-08-22 23:58:00 19.5
1403 2016-08-22 23:59:00 19.5
In between are some minutes missing (1403 rows instead of 1440). I tried to fill them in using:
data.length <- length(data$TIME)
time.min <- data$TIME[1]
time.max <- data$TIME[data.length]
all.dates <- seq(time.min, time.max, by="min")
all.dates.frame <- data.frame(list(TIME=all.dates))
merged.data <- merge(all.dates.frame, data, all=T)
But that gives me a result of 1449 rows instead of 1440. The first eight minutes are duplicates in the time stamp column and all other values in Water_Temperature are NA. Looks like this:
> merged.data[1:25,]
TIME Water_Temperature
1 2016-08-22 00:00:00 NA
2 2016-08-22 00:00:00 81.000
3 2016-08-22 00:01:00 NA
4 2016-08-22 00:01:00 80.625
5 2016-08-22 00:02:00 NA
6 2016-08-22 00:02:00 85.000
7 2016-08-22 00:03:00 NA
8 2016-08-22 00:03:00 80.437
9 2016-08-22 00:04:00 NA
10 2016-08-22 00:04:00 85.000
11 2016-08-22 00:05:00 NA
12 2016-08-22 00:05:00 80.375
13 2016-08-22 00:06:00 NA
14 2016-08-22 00:06:00 80.812
15 2016-08-22 00:07:00 NA
16 2016-08-22 00:07:00 80.812
17 2016-08-22 00:08:00 NA
18 2016-08-22 00:08:00 80.937
19 2016-08-22 00:09:00 NA
20 2016-08-22 00:10:00 NA
21 2016-08-22 00:11:00 NA
22 2016-08-22 00:12:00 NA
23 2016-08-22 00:13:00 NA
24 2016-08-22 00:14:00 NA
25 2016-08-22 00:15:00 NA
> tail(merged.data)
TIME Water_Temperature
1444 2016-08-22 23:54:00 NA
1445 2016-08-22 23:55:00 NA
1446 2016-08-22 23:56:00 NA
1447 2016-08-22 23:57:00 NA
1448 2016-08-22 23:58:00 NA
1449 2016-08-22 23:59:00 NA
Does anyone has an idea whats going wrong?
EDIT:
Using the xts and zoo package now to do the job by doing:
library(xts)
library(zoo)
df1.zoo<-zoo(data[,-1],data[,1])
df2 <- as.data.frame(as.zoo(merge(as.xts(df1.zoo), as.xts(zoo(,seq(start(df1.zoo),end(df1.zoo),by="min"))))))
Very easy and effective!
Instead of merge use rbind which gives you an irregular time series without NAs to start with. If you really want a regular time series with a frequency of say 1 minute you can build a time based sequence as an index and merge it with your data after ( after using rbind) and fill the resulting NAs with na.locf. Hope this helps.
you can try merging with full_join from tidyverse
This works for me with two dataframes (daily values) sharing a column named date.
big_data<-my_data %>%
reduce(full_join, by="Date")
My dataset is a bit noisy at 1-min interval. So, I'd like to get an average value every hour from 25 min to 35 min to stand for that hour at 30 min.
For example, an average average at: 00:30 (average from 00:25 to 00:35), 01:30 (average from 01:25 to 01:35), 02:30 (average from 02:25 to 02:35), etc.
Can you good way to do this in R?
Here is my dataset:
set.seed(1)
DateTime <- seq(as.POSIXct("2010/1/1 00:00"), as.POSIXct("2010/1/5 00:00"), "min")
value <- rnorm(n=length(DateTime), mean=100, sd=1)
df <- data.frame(DateTime, value)
Thanks a lot.
Here's one way
library(dplyr)
df %>%
filter(between(as.numeric(format(DateTime, "%M")), 25, 35)) %>%
group_by(hour=format(DateTime, "%Y-%m-%d %H")) %>%
summarise(value=mean(value))
I think that the existing answers are not general enough as they do not take into account that a time interval could fall within multiple midpoints.
I would instead use shift from the data.table package.
library(data.table)
setDT(df)
First set the interval argument based on the sequence you chose above. This calculates an average ten rows (minutes) around every row in your table:
df[, ave_val :=
Reduce('+',c(shift(value, 0:5L, type = "lag"),shift(value, 1:5L, type = "lead")))/11
]
Then generate the midpoints you want:
mids <- seq(as.POSIXct("2010/1/1 00:00"), as.POSIXct("2010/1/5 00:00"), by = 60*60) + 30*60 # every hour starting at 0:30
Then filter accordingly:
setkey(df,DateTime)
df[J(mids)]
Since you want to average on just a subset of each period, I think it makes sense to first subset the data.frame, then aggregate:
aggregate(
value~cbind(time=strftime(DateTime,'%Y-%m-%d %H:30:00')),
subset(df,{ m <- strftime(DateTime,'%M'); m>='25' & m<='35'; }),
mean
);
## time value
## 1 2010-01-01 00:30:00 99.82317
## 2 2010-01-01 01:30:00 100.58184
## 3 2010-01-01 02:30:00 99.54985
## 4 2010-01-01 03:30:00 100.47238
## 5 2010-01-01 04:30:00 100.05517
## 6 2010-01-01 05:30:00 99.96252
## 7 2010-01-01 06:30:00 99.79512
## 8 2010-01-01 07:30:00 99.06791
## 9 2010-01-01 08:30:00 99.58731
## 10 2010-01-01 09:30:00 100.27202
## 11 2010-01-01 10:30:00 99.60758
## 12 2010-01-01 11:30:00 99.92074
## 13 2010-01-01 12:30:00 99.65819
## 14 2010-01-01 13:30:00 100.04202
## 15 2010-01-01 14:30:00 100.04461
## 16 2010-01-01 15:30:00 100.11609
## 17 2010-01-01 16:30:00 100.08631
## 18 2010-01-01 17:30:00 100.41956
## 19 2010-01-01 18:30:00 99.98065
## 20 2010-01-01 19:30:00 100.07341
## 21 2010-01-01 20:30:00 100.20281
## 22 2010-01-01 21:30:00 100.86013
## 23 2010-01-01 22:30:00 99.68170
## 24 2010-01-01 23:30:00 99.68097
## 25 2010-01-02 00:30:00 99.58603
## 26 2010-01-02 01:30:00 100.10178
## 27 2010-01-02 02:30:00 99.78766
## 28 2010-01-02 03:30:00 100.02220
## 29 2010-01-02 04:30:00 99.83427
## 30 2010-01-02 05:30:00 99.74934
## 31 2010-01-02 06:30:00 99.99594
## 32 2010-01-02 07:30:00 100.08257
## 33 2010-01-02 08:30:00 99.47077
## 34 2010-01-02 09:30:00 99.81419
## 35 2010-01-02 10:30:00 100.13294
## 36 2010-01-02 11:30:00 99.78352
## 37 2010-01-02 12:30:00 100.04590
## 38 2010-01-02 13:30:00 99.91061
## 39 2010-01-02 14:30:00 100.61730
## 40 2010-01-02 15:30:00 100.18539
## 41 2010-01-02 16:30:00 99.45165
## 42 2010-01-02 17:30:00 100.09894
## 43 2010-01-02 18:30:00 100.04131
## 44 2010-01-02 19:30:00 99.58399
## 45 2010-01-02 20:30:00 99.75524
## 46 2010-01-02 21:30:00 99.94079
## 47 2010-01-02 22:30:00 100.26533
## 48 2010-01-02 23:30:00 100.35354
## 49 2010-01-03 00:30:00 100.31141
## 50 2010-01-03 01:30:00 100.10709
## 51 2010-01-03 02:30:00 99.41102
## 52 2010-01-03 03:30:00 100.07964
## 53 2010-01-03 04:30:00 99.88183
## 54 2010-01-03 05:30:00 99.91112
## 55 2010-01-03 06:30:00 99.71431
## 56 2010-01-03 07:30:00 100.48585
## 57 2010-01-03 08:30:00 100.35096
## 58 2010-01-03 09:30:00 100.00060
## 59 2010-01-03 10:30:00 100.03858
## 60 2010-01-03 11:30:00 99.95713
## 61 2010-01-03 12:30:00 99.18699
## 62 2010-01-03 13:30:00 99.49216
## 63 2010-01-03 14:30:00 99.37762
## 64 2010-01-03 15:30:00 99.68642
## 65 2010-01-03 16:30:00 99.84921
## 66 2010-01-03 17:30:00 99.84039
## 67 2010-01-03 18:30:00 99.90989
## 68 2010-01-03 19:30:00 99.95421
## 69 2010-01-03 20:30:00 100.01276
## 70 2010-01-03 21:30:00 100.14585
## 71 2010-01-03 22:30:00 99.54110
## 72 2010-01-03 23:30:00 100.02526
## 73 2010-01-04 00:30:00 100.04476
## 74 2010-01-04 01:30:00 99.61132
## 75 2010-01-04 02:30:00 99.94782
## 76 2010-01-04 03:30:00 99.44863
## 77 2010-01-04 04:30:00 99.91305
## 78 2010-01-04 05:30:00 100.25428
## 79 2010-01-04 06:30:00 99.86279
## 80 2010-01-04 07:30:00 99.63516
## 81 2010-01-04 08:30:00 99.65747
## 82 2010-01-04 09:30:00 99.57810
## 83 2010-01-04 10:30:00 99.77603
## 84 2010-01-04 11:30:00 99.85140
## 85 2010-01-04 12:30:00 100.82995
## 86 2010-01-04 13:30:00 100.26138
## 87 2010-01-04 14:30:00 100.25851
## 88 2010-01-04 15:30:00 99.92685
## 89 2010-01-04 16:30:00 100.00825
## 90 2010-01-04 17:30:00 100.24437
## 91 2010-01-04 18:30:00 99.62711
## 92 2010-01-04 19:30:00 99.93999
## 93 2010-01-04 20:30:00 99.82477
## 94 2010-01-04 21:30:00 100.15321
## 95 2010-01-04 22:30:00 99.88370
## 96 2010-01-04 23:30:00 100.06657