I split the time from 2018-12-31 11:45:00 AM to 2018-12-31 and 11:45:00 aAM successfully.
However, I get difficulty that convert "11:45:00 AM" to 24 hours.
I know there are several ways to do that, for example, the most popular way is to use strptime and put format="%I:%M:%S %p. I did that several times and made double checked again and again... but still get N/A in my column. Here is, crimeData is my dataset name, toSplitHrs contains time which is "11:45:00 AM" just like what mentioned:
crimeData$toSplitHrs = strptime(crimeData$SplitHrs, format="%I:%M:%S %p")
Police.Beats SplitMs SplitHrs year month days hours mins sec toSplitHrs
1 28 2018-12-31 11:45:00 2018 12 31 11 45 00 <NA>
2 177 2018-12-31 11:42:00 2018 12 31 11 42 00 <NA>
3 233 2018-12-31 11:30:00 2018 12 31 11 30 00 <NA>
4 91 2018-12-31 11:30:00 2018 12 31 11 30 00 <NA>
5 73 2018-12-31 11:30:00 2018 12 31 11 30 00 <NA>
6 232 2018-12-31 11:27:00 2018 12 31 11 27 00 <NA>
but still, I got N/A result from that...
Also, this dataset contains over 10k observations, I really cannot change them one by one...any suggestions are appreciated!
You can try the format %r for the time, taking into account the am/pm specification (see ?strptime):
strptime("2018-12-31 11:45:00 am", format="%F %r")
#[1] "2018-12-31 11:45:00 CET"
strptime("2018-12-31 11:45:00 pm", format="%F %r")
#[1] "2018-12-31 23:45:00 CET"
Related
I have a data frame df1 with a datetime column in format UTC. I need to merge this dataframe with the data frame df2 by the column datetime. My problem is that df2 is in Europe/Paris format, and when I transform df2$datetime from Europe/Paris to UTC format, I lose or duplicate data at the moments in which is the time change between either summer/winter or winter/summer. As an example:
df1<- data.frame(datetime=c("2016-10-29 22:00:00","2016-10-29 23:00:00","2016-10-30 00:00:00","2016-10-30 01:00:00","2016-10-30 02:00:00","2016-10-30 03:00:00","2016-10-30 04:00:00","2016-10-30 05:00:00","2016-03-25 22:00:00","2016-03-25 23:00:00","2016-03-26 00:00:00","2016-03-26 01:00:00","2016-03-26 02:00:00","2016-03-26 03:00:00","2016-03-26 04:00:00"), Var1= c(4, 56, 76, 54, 34, 3, 4, 6, 78, 23, 12, 3, 5, 6, 7))
df1$datetime<- as.POSIXct(df1$datetime, format = "%Y-%m-%d %H", tz= "UTC")
df2<- data.frame(datetime=c("2016-10-29 22:00:00","2016-10-29 23:00:00","2016-10-30 00:00:00","2016-10-30 01:00:00","2016-10-30 02:00:00","2016-10-30 03:00:00","2016-10-30 04:00:00","2016-10-30 05:00:00","2016-03-25 22:00:00","2016-03-25 23:00:00","2016-03-26 00:00:00","2016-03-26 01:00:00","2016-03-26 02:00:00","2016-03-26 03:00:00","2016-03-26 04:00:00"), Var2=c(56, 43, 23, 14, 51, 27, 89, 76, 56, 4, 35, 23, 4, 62, 84))
df2$datetime<- as.POSIXct(df2$datetime, format = "%Y-%m-%d %H", tz= "Europe/Paris")
df1
datetime Var1
1 2016-10-29 22:00:00 4
2 2016-10-29 23:00:00 56
3 2016-10-30 00:00:00 76
4 2016-10-30 01:00:00 54
5 2016-10-30 02:00:00 34
6 2016-10-30 03:00:00 3
7 2016-10-30 04:00:00 4
8 2016-10-30 05:00:00 6
9 2017-03-25 22:00:00 78
10 2017-03-25 23:00:00 23
11 2017-03-26 00:00:00 12
12 2017-03-26 01:00:00 3
13 2017-03-26 02:00:00 5
14 2017-03-26 03:00:00 6
15 2017-03-26 04:00:00 7
df2
datetime Var2
1 2016-10-29 22:00:00 56
2 2016-10-29 23:00:00 43
3 2016-10-30 00:00:00 23
4 2016-10-30 01:00:00 14
5 2016-10-30 02:00:00 51
6 2016-10-30 03:00:00 27
7 2016-10-30 04:00:00 89
8 2016-10-30 05:00:00 76
9 2017-03-25 22:00:00 56
10 2017-03-25 23:00:00 4
11 2017-03-26 00:00:00 35
12 2017-03-26 01:00:00 23
13 2017-03-26 02:00:00 4
14 2017-03-26 03:00:00 62
15 2017-03-26 04:00:00 84
When I change df2$datetime format from Europe/Paris to UTC, this happens:
library(lubridate)
df2$datetime<-with_tz(df2$datetime,"UTC")
df2
datetime Var2
1 2016-10-29 20:00:00 56
2 2016-10-29 21:00:00 43
3 2016-10-29 22:00:00 23
4 2016-10-29 23:00:00 14
5 2016-10-30 00:00:00 51
6 2016-10-30 02:00:00 27 # Data at 01:00:00 is missing
7 2016-10-30 03:00:00 89
8 2016-10-30 04:00:00 76
9 2017-03-25 21:00:00 56
10 2017-03-25 22:00:00 4
11 2017-03-25 23:00:00 35
12 2017-03-26 00:00:00 23
13 2017-03-26 00:00:00 4 # There is a duplicate at 00:00:00
14 2017-03-26 01:00:00 62
15 2017-03-26 02:00:00 84
16 2017-03-26 03:00:00 56
Is there another way to transform df2$datetime from Europe/Paris format to UTC format that allows me to merge two data frames without this problem of having either lost or duplicated data? I don't understand why I have to lose or duplicate info in df2.
Is the transformation I did right in df2$datetime in order to merge this data frame with df1? What I've done so far to solve this is to add a new row in df2 on 2016-10-30 at 01:00:00 that is the mean between 2016-10-30 00:00:00and 2016-10-30 02:00:00 and to remove one row on 2017-03-26 at 00:00:00.
Thanks for your help.
I found out that my original df2 should be like this:
df2
datetime Var1
1 2016-10-29 22:00:00 4 # This is time in format "GMT+2". It corresponds to 20:00 UTC
2 2016-10-29 23:00:00 56 # This is time in format "GMT+2". It corresponds to 21:00 UTC
3 2016-10-30 00:00:00 76 # This is time in format "GMT+2". It corresponds to 22:00 UTC
4 2016-10-30 01:00:00 54 # This is time in format "GMT+2". It corresponds to 23:00 UTC
5 2016-10-30 02:00:00 34 # This is time in format "GMT+2". It corresponds to 00:00 UTC
6 2016-10-30 02:00:00 3 # This is time in format "GMT+1". It corresponds to 01:00 UTC
7 2016-10-30 03:00:00 4 # This is time in format "GMT+1". It corresponds to 02:00 UTC
8 2016-10-30 04:00:00 6 # This is time in format "GMT+1". It corresponds to 03:00 UTC
9 2016-10-30 05:00:00 78 # This is time in format "GMT+1". It corresponds to 04:00 UTC
10 2017-03-25 22:00:00 23 # This is time in format "GMT+1". It corresponds to 21:00 UTC
11 2017-03-25 23:00:00 12 # This is time in format "GMT+1". It corresponds to 22:00 UTC
12 2017-03-26 00:00:00 3 # This is time in format "GMT+1". It corresponds to 23:00 UTC
13 2017-03-26 01:00:00 5 # This is time in format "GMT+1". It corresponds to 00:00 UTC
14 2017-03-26 03:00:00 6 # This is time in format "GMT+2". It corresponds to 01:00 UTC
15 2017-03-26 04:00:00 7 # This is time in format "GMT+2". It corresponds to 02:00 UTC
16 2017-03-26 05:00:00 76 # This is time in format "GMT+2". It corresponds to 03:00 UTC
However, my original df2 doesn't have duplicated or lost time data. It is like this:
df2
datetime Var1
1 2016-10-29 22:00:00 4
2 2016-10-29 23:00:00 56
3 2016-10-30 00:00:00 76
4 2016-10-30 01:00:00 54
5 2016-10-30 02:00:00 34
6 2016-10-30 03:00:00 3
7 2016-10-30 04:00:00 4
8 2016-10-30 05:00:00 6
9 2017-03-25 22:00:00 78
10 2017-03-25 23:00:00 23
11 2017-03-26 00:00:00 12
12 2017-03-26 01:00:00 3
13 2017-10-30 02:00:00 5
14 2017-03-26 03:00:00 6
15 2017-03-26 04:00:00 7
16 2017-03-26 05:00:00 76
When I applied the R code df2$datetime<-with_tz(df2$datetime,"UTC"), this happens:
df2
datetime Var1
1 2016-10-29 20:00:00 4
2 2016-10-29 21:00:00 56
3 2016-10-29 22:00:00 76
4 2016-10-29 23:00:00 54
5 2016-10-30 00:00:00 34
6 2016-10-30 02:00:00 3 # I have to add mannually a new row between the times "00:00" and "02:00"
7 2016-10-30 03:00:00 4
8 2016-10-30 04:00:00 6
9 2017-03-25 21:00:00 78
10 2017-03-25 22:00:00 23
11 2017-03-25 23:00:00 12
12 2017-03-26 00:00:00 3
13 2017-10-30 01:00:00 5 # I have to remove mannually one of the rows refered to the time "01:00".
14 2017-03-26 01:00:00 6
15 2017-03-26 02:00:00 7
16 2017-03-26 03:00:00 76
If my original df2 had one duplication at "02:00:00" on 30th Octover and a gap on 26th March between "01:00" and "03:00", I would get with the R code df2$datetime<-with_tz(df2$datetime,"UTC") this:
df2
datetime Var1
1 2016-10-29 20:00:00 4
2 2016-10-29 21:00:00 56
3 2016-10-29 22:00:00 76
4 2016-10-29 23:00:00 54
5 2016-10-30 00:00:00 34
6 2016-10-30 00:00:00 3 # I just have to change "00:00:00" for "01:00"
7 2016-10-30 02:00:00 4
8 2016-10-30 03:00:00 6
9 2016-10-30 04:00:00 78
10 2017-03-25 21:00:00 23
11 2017-03-25 22:00:00 12
12 2017-03-25 23:00:00 3
13 2017-03-26 00:00:00 5
14 2017-03-26 01:00:00 6
15 2017-03-26 02:00:00 7
16 2017-03-26 03:00:00 76
#As there are some Versions of df2 I use the one shown in the Question
df2 <- read.table(text = "
datetime Var2
1 '2016-10-29 22:00:00' 56
2 '2016-10-29 23:00:00' 43
3 '2016-10-30 00:00:00' 23
4 '2016-10-30 01:00:00' 14
5 '2016-10-30 02:00:00' 51
6 '2016-10-30 03:00:00' 27
7 '2016-10-30 04:00:00' 89
8 '2016-10-30 05:00:00' 76
9 '2017-03-25 22:00:00' 56
10 '2017-03-25 23:00:00' 4
11 '2017-03-26 00:00:00' 35
12 '2017-03-26 01:00:00' 23
13 '2017-03-26 02:00:00' 4
14 '2017-03-26 03:00:00' 62
15 '2017-03-26 04:00:00' 84
", header = TRUE)
library(lubridate)
#When you define now the timezone the content of df2 is already changed
df2$datetimeEP <- as.POSIXct(df2$datetime, format = "%Y-%m-%d %H", tz= "Europe/Paris")
#df2[13,]
# datetime Var2 datetimeEP
#13 2017-03-26 02:00:00 4 2017-03-26 01:00:00
#For me it looks like that your recorded times don't consider "daylight savings time".
#So your have to uses e.g. "Etc/GMT-1" instead of "Europe/Paris"
df2$datetimeG1 <- as.POSIXct(df2$datetime, format = "%Y-%m-%d %H", tz= "Etc/GMT-1")
data.frame(datetime=df2$datetime, utc=with_tz(df2$datetimeG1,"UTC"))
# datetime utc
#1 2016-10-29 22:00:00 2016-10-29 21:00:00
#2 2016-10-29 23:00:00 2016-10-29 22:00:00
#3 2016-10-30 00:00:00 2016-10-29 23:00:00
#4 2016-10-30 01:00:00 2016-10-30 00:00:00
#5 2016-10-30 02:00:00 2016-10-30 01:00:00
#6 2016-10-30 03:00:00 2016-10-30 02:00:00
#7 2016-10-30 04:00:00 2016-10-30 03:00:00
#8 2016-10-30 05:00:00 2016-10-30 04:00:00
#9 2017-03-25 22:00:00 2017-03-25 21:00:00
#10 2017-03-25 23:00:00 2017-03-25 22:00:00
#11 2017-03-26 00:00:00 2017-03-25 23:00:00
#12 2017-03-26 01:00:00 2017-03-26 00:00:00
#13 2017-03-26 02:00:00 2017-03-26 01:00:00
#14 2017-03-26 03:00:00 2017-03-26 02:00:00
#15 2017-03-26 04:00:00 2017-03-26 03:00:00
#You can use "dst" to see if datetime of a time zone has "daylight savings time"
dst(df2$datetimeEP)
dst(df2$datetimeG1)
dst(with_tz(df2$datetimeEP,"UTC"))
dst(with_tz(df2$datetimeG1,"UTC"))
#If your recorded times consider "daylight savings time" then you HAVE a gap and an overlap.
I have the arrival time and departure time and date of different customers to a system. I want to count the number of people in the system in every 30 min. How can I do this R?
Here are my data
If I understand your question, here's an example with fake data:
library(tidyverse)
library(lubridate)
# Fake data
set.seed(2)
dat = data.frame(id=1:1000, type=rep(c("A","B"), 500),
arrival=as.POSIXct("2013-08-21 05:00:00") + sample(-10000:10000, 1000, replace=TRUE))
dat$departure = dat$arrival + sample(100:5000, 1000, replace=TRUE)
# Times when we want to check how many people are still present
times = seq(round_date(min(dat$arrival), "hour"), ceiling_date(max(dat$departure), "hour"), "30 min")
# Count number of people present at each time
map_df(times, function(x) {
dat %>%
group_by(type) %>%
summarise(Time = x,
Count=sum(arrival < x & departure > x)) %>%
spread(type, Count) %>%
mutate(Total = A + B)
})
Time A B Total
<dttm> <int> <int> <int>
1 2013-08-21 02:00:00 0 0 0
2 2013-08-21 02:30:00 26 31 57
3 2013-08-21 03:00:00 54 53 107
4 2013-08-21 03:30:00 75 81 156
5 2013-08-21 04:00:00 58 63 121
6 2013-08-21 04:30:00 66 58 124
7 2013-08-21 05:00:00 55 60 115
8 2013-08-21 05:30:00 52 63 115
9 2013-08-21 06:00:00 57 62 119
10 2013-08-21 06:30:00 62 51 113
11 2013-08-21 07:00:00 60 67 127
12 2013-08-21 07:30:00 72 54 126
13 2013-08-21 08:00:00 66 46 112
14 2013-08-21 08:30:00 19 12 31
15 2013-08-21 09:00:00 1 2 3
16 2013-08-21 09:30:00 0 0 0
17 2013-08-21 10:00:00 0 0 0
I'm not sure what you mean by counting the number of people "in the system", but I'm assuming you mean "the number of people who have arrived but not yet departed". To do this, you can apply a simple logical condition on the relevant columns of your dataframe, e.g.
logicVec <- df$arrival_time <= dateTimeObj & dateTimeObj < df$departure_time
LogicVec will evidently be a logical vector of TRUEs and FALSEs. Because TRUE == 1 and FALSE == 0, you can then simply use the sum(logicVec) function to get the the total number of people/customers/rows who fulfill the condition written above.
You can then simply repeat this line of code for every dateTimeObj (of class e.g. POSIXct) you want. In your case, it would be every dateTimeObj where each are 30 minutes apart.
I hope this helps.
I have a data frame with date , name and values ,here some of the dates are in 15-mins interval and some are not , how to get average by grouping that are not in 15-mins in to regular intervals eg. if min lies between 0-14 then 15min, 15-29 then 15 mins ,30-44 then 30 mins , 45-59 then 45 mins
df
date_time name value
2014-02-01 00:01:00 sam 23
2014-02-01 00:14:00 sam 24
2014-02-01 01:00:00 sam 24
2014-02-01 01:15:00 sam 12
2014-02-01 01:30:00 sam 27
2014-02-01 01:45:00 sam 20
2014-02-01 02:00:00 sunny 23
2014-02-01 02:15:00 sunny 33
2014-02-01 02:17:00 sunny 41
2014-02-01 02:30:00 sunny 23
2014-02-01 02:46:00 sunny 43
o/p
date_time name value
2014-02-01 00:00:00 sam 23.5
2014-02-01 01:00:00 sam 24
2014-02-01 01:15:00 sam 12
2014-02-01 01:30:00 sam 27
2014-02-01 01:45:00 sam 20
2014-02-01 02:00:00 sunny 23
2014-02-01 02:15:00 sunny 37
2014-02-01 02:30:00 sunny 23
2014-02-01 02:45:00 sunny 43
I guess you meant that if min lies between 0-14 then new variable should be 0min rather than 15min. If so, here's my suggestion:
newvar <- floor(as.numeric(format(dt$date_time, "%M"))/15)*15
I'm supposing that your date_time has POSIX format. Otherwise, you just need to make a simple conversion before using my solution.
Here is the data i had
temp time_hour
1 37.04 2013-01-01 05:30:00
2 37.04 2013-01-01 06:30:00
3 37.94 2013-01-01 07:30:00
4 37.94 2013-01-01 08:30:00
5 37.94 2013-01-01 09:30:00
Now i need to separate the time_hour column to "date","month","year" and also to "Hours","minutes","seconds" using tidyr package in R programming languages. I used separate variables to separate date, month, year. i don't know how to separate time like hours, minutes, seconds. Thanks in advance...
We can use separate
library(tidyr)
df1 %>%
separate(time_hour, into = c("year", "month", "day", "hour", "minutes", "seconds"))
# temp year month day hour minutes seconds
#1 37.04 2013 01 01 05 30 00
#2 37.04 2013 01 01 06 30 00
#3 37.94 2013 01 01 07 30 00
#4 37.94 2013 01 01 08 30 00
#5 37.94 2013 01 01 09 30 00
So, here is my problem. I have a dataset of locations of radiotagged hummingbirds I’ve been following as part of my thesis. As you might imagine, they fly fast so there were intervals when I lost track of where they were until I eventually found them again.
Now I am trying to identify the segments where the bird was followed continuously (i.e., the intervals between “Lost” periods).
ID Type TimeStart TimeEnd Limiter Starter Ender
1 Observed 6:45:00 6:45:00 NO Start End
2 Lost 6:45:00 5:31:00 YES NO NO
3 Observed 5:31:00 5:31:00 NO Start NO
4 Observed 9:48:00 9:48:00 NO NO NO
5 Observed 10:02:00 10:02:00 NO NO NO
6 Observed 10:18:00 10:18:00 NO NO NO
7 Observed 11:00:00 11:00:00 NO NO NO
8 Observed 13:15:00 13:15:00 NO NO NO
9 Observed 13:34:00 13:34:00 NO NO NO
10 Observed 13:43:00 13:43:00 NO NO NO
11 Observed 13:52:00 13:52:00 NO NO NO
12 Observed 14:25:00 14:25:00 NO NO NO
13 Observed 14:46:00 14:46:00 NO NO End
14 Lost 14:46:00 10:47:00 YES NO NO
15 Observed 10:47:00 10:47:00 NO Start NO
16 Observed 10:57:00 11:00:00 NO NO NO
17 Observed 11:10:00 11:10:00 NO NO NO
18 Observed 11:19:00 11:27:55 NO NO NO
19 Observed 11:28:05 11:32:00 NO NO NO
20 Observed 11:45:00 12:09:00 NO NO NO
21 Observed 11:51:00 11:51:00 NO NO NO
22 Observed 12:11:00 12:11:00 NO NO NO
23 Observed 13:15:00 13:15:00 NO NO End
24 Lost 13:15:00 7:53:00 YES NO NO
25 Observed 7:53:00 7:53:00 NO Start NO
26 Observed 8:48:00 8:48:00 NO NO NO
27 Observed 9:25:00 9:25:00 NO NO NO
28 Observed 9:26:00 9:26:00 NO NO NO
29 Observed 9:32:00 9:33:25 NO NO NO
30 Observed 9:33:35 9:33:35 NO NO NO
31 Observed 9:42:00 9:42:00 NO NO NO
32 Observed 9:44:00 9:44:00 NO NO NO
33 Observed 9:48:00 9:48:00 NO NO NO
34 Observed 9:48:30 9:48:30 NO NO NO
35 Observed 9:51:00 9:51:00 NO NO NO
36 Observed 9:54:00 9:54:00 NO NO NO
37 Observed 9:55:00 9:55:00 NO NO NO
38 Observed 9:57:00 10:01:00 NO NO NO
39 Observed 10:02:00 10:02:00 NO NO NO
40 Observed 10:04:00 10:04:00 NO NO NO
41 Observed 10:06:00 10:06:00 NO NO NO
42 Observed 10:20:00 10:33:00 NO NO NO
43 Observed 10:34:00 10:34:00 NO NO NO
44 Observed 10:39:00 10:39:00 NO NO End
Note: When there is a “Start” and an “End” in the same row it’s because the non-lost period consists only of that record.
I was able to identify the records that start or end these “non-lost” periods (under the columns “Starter” and “Ender”), but now I want to be able to identify those periods by giving them unique identifiers (period A,B,C or 1,2,3, etc).
Ideally, the name of the identifier would be the name of the start point for that period (i.e., ID[ Starter==”Start”])
I'm looking for something like this:
ID Type TimeStart TimeEnd Limiter Starter Ender Period
1 Observed 6:45:00 6:45:00 NO Start End 1
2 Lost 6:45:00 5:31:00 YES NO NO Lost
3 Observed 5:31:00 5:31:00 NO Start NO 3
4 Observed 9:48:00 9:48:00 NO NO NO 3
5 Observed 10:02:00 10:02:00 NO NO NO 3
6 Observed 10:18:00 10:18:00 NO NO NO 3
7 Observed 11:00:00 11:00:00 NO NO NO 3
8 Observed 13:15:00 13:15:00 NO NO NO 3
9 Observed 13:34:00 13:34:00 NO NO NO 3
10 Observed 13:43:00 13:43:00 NO NO NO 3
11 Observed 13:52:00 13:52:00 NO NO NO 3
12 Observed 14:25:00 14:25:00 NO NO NO 3
13 Observed 14:46:00 14:46:00 NO NO End 3
14 Lost 14:46:00 10:47:00 YES NO NO Lost
15 Observed 10:47:00 10:47:00 NO Start NO 15
16 Observed 10:57:00 11:00:00 NO NO NO 15
17 Observed 11:10:00 11:10:00 NO NO NO 15
18 Observed 11:19:00 11:27:55 NO NO NO 15
19 Observed 11:28:05 11:32:00 NO NO NO 15
20 Observed 11:45:00 12:09:00 NO NO NO 15
21 Observed 11:51:00 11:51:00 NO NO NO 15
22 Observed 12:11:00 12:11:00 NO NO NO 15
23 Observed 13:15:00 13:15:00 NO NO End 15
24 Lost 13:15:00 7:53:00 YES NO NO Lost
Would this be too hard to do in R?
Thanks!
> d <- data.frame(Limiter = rep("NO", 44), Starter = rep("NO", 44), Ender = rep("NO", 44), stringsAsFactors = FALSE)
> d$Starter[c(1, 3, 15, 25)] <- "Start"
> d$Ender[c(1, 13, 23, 44)] <- "End"
> d$Limiter[c(2, 14, 24)] <- "Yes"
> d$Period <- ifelse(d$Limiter == "Yes", "Lost", which(d$Starter == "Start")[cumsum(d$Starter == "Start")])
> d
Limiter Starter Ender Period
1 NO Start End 1
2 Yes NO NO Lost
3 NO Start NO 3
4 NO NO NO 3
5 NO NO NO 3
6 NO NO NO 3
7 NO NO NO 3
8 NO NO NO 3
9 NO NO NO 3
10 NO NO NO 3
11 NO NO NO 3
12 NO NO NO 3
13 NO NO End 3
14 Yes NO NO Lost
15 NO Start NO 15
16 NO NO NO 15
17 NO NO NO 15
18 NO NO NO 15
19 NO NO NO 15
20 NO NO NO 15
21 NO NO NO 15
22 NO NO NO 15
23 NO NO End 15
24 Yes NO NO Lost
25 NO Start NO 25
26 NO NO NO 25
27 NO NO NO 25
28 NO NO NO 25
29 NO NO NO 25
30 NO NO NO 25
31 NO NO NO 25
32 NO NO NO 25
33 NO NO NO 25
34 NO NO NO 25
35 NO NO NO 25
36 NO NO NO 25
37 NO NO NO 25
38 NO NO NO 25
39 NO NO NO 25
40 NO NO NO 25
41 NO NO NO 25
42 NO NO NO 25
43 NO NO NO 25
44 NO NO End 25