I have a date-time object of class POSIXct. I need to adjust the values by adding several hours. I understand that I can do this using basic addition. For example, I can add 5 hours to a POSIXct object like so:
x <- as.POSIXct("2009-08-02 18:00:00", format="%Y-%m-%d %H:%M:%S")
x
[1] "2009-08-02 18:00:00 PDT"
x + (5*60*60)
[1] "2009-08-02 23:00:00 PDT"
Now I have a data frame in which some times are ok and some are bad.
> df
set_time duration up_time
1 2009-05-31 14:10:00 3 2009-05-31 11:10:00
2 2009-08-02 18:00:00 4 2009-08-02 23:00:00
3 2009-08-03 01:20:00 5 2009-08-03 06:20:00
4 2009-08-03 06:30:00 2 2009-08-03 11:30:00
Note that the first data frame entry has an 'up_time' less than the 'set_time'. So in this context a 'good' time is one where the set_time < up_time. And a 'bad' time is one in which set_time > up_time. I want to leave the good entries alone and fix the bad entries. The bad entries should be fixed by creating an 'up_time' that is equal to the 'set_time' + duration. I do this with the following dplyr pipe:
df1 <- tbl_df(df) %>% mutate(up_time = ifelse(set_time > up_time, set_time +
(duration*60*60), up_time))
df1
# A tibble: 4 x 3
set_time duration up_time
<dttm> <dbl> <dbl>
1 2009-05-31 14:10:00 3. 1243815000.
2 2009-08-02 18:00:00 4. 1249279200.
3 2009-08-03 01:20:00 5. 1249305600.
4 2009-08-03 06:30:00 2. 1249324200.
Up time has been coerced to numeric:
> str(df1)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 4 obs. of 3 variables:
$ set_time: POSIXct, format: "2009-05-31 14:10:00" "2009-08-02 18:00:00"
"2009-08-03 01:20:00" "2009-08-03 06:30:00"
$ duration: num 3 4 5 2
$ up_time : num 1.24e+09 1.25e+09 1.25e+09 1.25e+09
I can convert it back to the desired POSIXct format using:
> as.POSIXct(df1$up_time,origin="1970-01-01")
[1] "2009-05-31 17:10:00 PDT" "2009-08-02 23:00:00 PDT" "2009-08-03 06:20:00
PDT" "2009-08-03 11:30:00 PDT"
But I feel like this last step shouldn't be necessary. Can I/How can I avoid having dplyr change my variable formatting?
Related
Consider the following example
library(lubridate)
library(tidyverse)
> hour(ymd_hms('2008-01-04 00:00:00'))
[1] 0
Now,
dataframe <- data_frame(time = c(ymd_hms('2008-01-04 00:00:00'),
ymd_hms('2008-01-04 00:01:00'),
ymd_hms('2008-01-04 00:02:00'),
ymd_hms('2008-01-04 00:03:00')),
value = c(1,2,3,4))
mutate(dataframe,hour = strftime(time, format="%H:%M:%S"),
hour2 = hour(time))
# A tibble: 4 × 4
time value hour hour2
<dttm> <dbl> <chr> <int>
1 2008-01-03 19:00:00 1 19:00:00 19
2 2008-01-03 19:01:00 2 19:01:00 19
3 2008-01-03 19:02:00 3 19:02:00 19
4 2008-01-03 19:03:00 4 19:03:00 19
What is going on here? Why are the dates converted into some local time which I dont event know?
This is not an issue with lubridate, but with the way POSIXct values are combined into a vector.
You have
> ymd_hms('2008-01-04 00:01:00')
[1] "2008-01-04 00:01:00 UTC"
But when combining into a vector you get
> c(ymd_hms('2008-01-04 00:01:00'), ymd_hms('2008-01-04 00:01:00'))
[1] "2008-01-03 19:01:00 EST" "2008-01-03 19:01:00 EST"
The reason is that the tzone attribute gets lost when combining POSIXct values (see c.POSIXct).
> attributes(ymd_hms('2008-01-04 00:01:00'))
$tzone
[1] "UTC"
$class
[1] "POSIXct" "POSIXt"
but
> attributes(c(ymd_hms('2008-01-04 00:01:00')))
$class
[1] "POSIXct" "POSIXt"
What you can use instead is
> ymd_hms(c('2008-01-04 00:01:00', '2008-01-04 00:01:00'))
[1] "2008-01-04 00:01:00 UTC" "2008-01-04 00:01:00 UTC"
which will use the default tz = "UTC" for all arguments.
You also need to pass tz = "UTC" into strftime because its default is your current time zone (unlike ymd_hms which defaults to UTC).
as I failed to solve my problem with PHP/MySQL or Excel due to the data size, I'm trying to do my very first steps with R now and struggle a bit. The problem is this: I have a second-by-second CSV-file with half a year of data, that looks like this:
metering,timestamp
123,2016-01-01 00:00:00
345,2016-01-01 00:00:01
243,2016-01-01 00:00:02
101,2016-01-01 00:00:04
134,2016-01-01 00:00:06
As you see, there are some seconds missing every once in a while (don't ask me, why the values are written before the timestamp, but that's how I received the data…). Now I try to calculate the amount of values (= seconds) that are missing.
So my idea was
to create a vector that is correct (includes all sec-by-sec timestamps),
match the given CSV file with that new vector, and
sum up all the timestamps with no value.
I managed to make step 1 happen with the following code:
RegularTimeSeries <- seq(as.POSIXct("2016-01-01 00:00:00", tz = "UTC"), as.POSIXct("2016-01-01 00:00:30", tz = "UTC"), by = "1 sec")
write.csv(RegularTimeSeries, file = "RegularTimeSeries.csv")
To have an idea what I did I also exported the vector to a CSV that looks like this:
"1",2016-01-01 00:00:00
"2",2016-01-01 00:00:01
"3",2016-01-01 00:00:02
"4",2016-01-01 00:00:03
"5",2016-01-01 00:00:04
"6",2016-01-01 00:00:05
"7",2016-01-01 00:00:06
Unfortunately I have no idea how to go on with step 2 and 3. I found some very similar examples (http://www.r-bloggers.com/fix-missing-dates-with-r/, R: Insert rows for missing dates/times), but as a total R noob I struggled to translate these examples to my given sec-by-sec data.
Some hints for the greenhorn would be very very helpful – thank you very much in advance :)
In the tidyverse,
library(dplyr)
library(tidyr)
# parse datetimes
df %>% mutate(timestamp = as.POSIXct(timestamp)) %>%
# complete sequence to full sequence from min to max by second
complete(timestamp = seq.POSIXt(min(timestamp), max(timestamp), by = 'sec'))
## # A tibble: 7 x 2
## timestamp metering
## <time> <int>
## 1 2016-01-01 00:00:00 123
## 2 2016-01-01 00:00:01 345
## 3 2016-01-01 00:00:02 243
## 4 2016-01-01 00:00:03 NA
## 5 2016-01-01 00:00:04 101
## 6 2016-01-01 00:00:05 NA
## 7 2016-01-01 00:00:06 134
If you want the number of NAs (i.e. the number of seconds with no data), add on
%>% tally(is.na(metering))
## # A tibble: 1 x 1
## n
## <int>
## 1 2
You can check which values of your RegularTimeSeries are in your broken time series using which and %in%. First create BrokenTimeSeries from your example:
RegularTimeSeries <- seq(as.POSIXct("2016-01-01 00:00:00", tz = "UTC"), as.POSIXct("2016-01-01 00:00:30", tz = "UTC"), by = "1 sec")
BrokenTimeSeries <- RegularTimeSeries[-c(3,6,9)] # remove some seconds
This will give you the indeces of values within RegularTimeSeries that are not in BrokenTimeSeries:
> which(!(RegularTimeSeries %in% BrokenTimeSeries))
[1] 3 6 9
This will return the actual values:
> RegularTimeSeries[which(!(RegularTimeSeries %in% BrokenTimeSeries))]
[1] "2016-01-01 00:00:02 UTC" "2016-01-01 00:00:05 UTC" "2016-01-01 00:00:08 UTC"
Maybe I'm misunderstanding your problem but you can count the number of missing seconds simply subtracting the length of your broken time series from RegularTimeSeries or getting the length of any of the two resulting vectors above.
> length(RegularTimeSeries) - length(BrokenTimeSeries)
[1] 3
> length(which(!(RegularTimeSeries %in% BrokenTimeSeries)))
[1] 3
> length(RegularTimeSeries[which(!(RegularTimeSeries %in% BrokenTimeSeries))])
[1] 3
If you want to merge the files together to see the missing values you can do something like this:
#data with regular time series and a "step"
df <- data.frame(
RegularTimeSeries
)
df$BrokenTimeSeries[RegularTimeSeries %in% BrokenTimeSeries] <- df$RegularTimeSeries
df$BrokenTimeSeries <- as.POSIXct(df$BrokenTimeSeries, origin="2015-01-01", tz="UTC")
resulting in:
> df[1:12,]
RegularTimeSeries BrokenTimeSeries
1 2016-01-01 00:00:00 2016-01-01 00:00:00
2 2016-01-01 00:00:01 2016-01-01 00:00:01
3 2016-01-01 00:00:02 <NA>
4 2016-01-01 00:00:03 2016-01-01 00:00:02
5 2016-01-01 00:00:04 2016-01-01 00:00:03
6 2016-01-01 00:00:05 <NA>
7 2016-01-01 00:00:06 2016-01-01 00:00:04
8 2016-01-01 00:00:07 2016-01-01 00:00:05
9 2016-01-01 00:00:08 <NA>
10 2016-01-01 00:00:09 2016-01-01 00:00:06
11 2016-01-01 00:00:10 2016-01-01 00:00:07
12 2016-01-01 00:00:11 2016-01-01 00:00:08
If all you want is the number of missing seconds, it can be done much more simply. First find the number of seconds in your timerange, and then subtract the number of rows in your dataset. This could be done in R along these lines:
n.seconds <- difftime("2016-06-01 00:00:00", "2016-01-01 00:00:00", units="secs")
n.rows <- nrow(my.data.frame)
n.missing.values <- n.seconds - n.rows
You might change the time range and the variable of your data frame.
Hope it helps
d <- (c("2016-01-01 00:00:01",
"2016-01-01 00:00:02",
"2016-01-01 00:00:03",
"2016-01-01 00:00:04",
"2016-01-01 00:00:05",
"2016-01-01 00:00:06",
"2016-01-01 00:00:10",
"2016-01-01 00:00:12",
"2016-01-01 00:00:14",
"2016-01-01 00:00:16",
"2016-01-01 00:00:18",
"2016-01-01 00:00:20",
"2016-01-01 00:00:22"))
d <- as.POSIXct(d)
for (i in 2:length(d)){
if(difftime(d[i-1],d[i], units = "secs") < -1 ){
c[i] <- d[i]
}
}
class(c) <- c('POSIXt','POSIXct')
c
[1] NA NA NA
NA NA
[6] NA "2016-01-01 00:00:10 EST" "2016-01-01 00:00:12
EST" "2016-01-01 00:00:14 EST" "2016-01-01 00:00:16 EST"
[11] "2016-01-01 00:00:18 EST" "2016-01-01 00:00:20 EST" "2016-01-01
00:00:22 EST"
I have a dataframe df with a certain number of columns. One of them, ts, is timestamps:
1462147403122 1462147412990 1462147388224 1462147415651 1462147397069 1462147392497
...
1463529545634 1463529558639 1463529556798 1463529558788 1463529564627 1463529557370.
I have also at my disposal the corresponding datetime in the datetime column:
"2016-05-02 02:03:23 CEST" "2016-05-02 02:03:32 CEST" "2016-05-02 02:03:08 CEST" "2016-05-02 02:03:35 CEST" "2016-05-02 02:03:17 CEST" "2016-05-02 02:03:12 CEST"
...
"2016-05-18 01:59:05 CEST" "2016-05-18 01:59:18 CEST" "2016-05-18 01:59:16 CEST" "2016-05-18 01:59:18 CEST" "2016-05-18 01:59:24 CEST" "2016-05-18 01:59:17 CEST"
As you can see my dataframe contains data accross several day. Let's say there are 3. I would like to add a column containing number 1, 2 or 3. 1 if the line belongs to the first day, 2 for the second day, etc...
Thank you very much in advance,
Clement
One way to do this is to keep track of total days elapsed each time the date changes, as demonstrated below.
# Fake data
dat = data.frame(datetime = c(seq(as.POSIXct("2016-05-02 01:03:11"),
as.POSIXct("2016-05-05 01:03:11"), length.out=6),
seq(as.POSIXct("2016-05-09 01:09:11"),
as.POSIXct("2016-05-16 02:03:11"), length.out=4)))
tz(dat$datetime) = "UTC"
Note, if your datetime column is not already in a datetime format, convert it to one using as.POSIXct.
Now, create a new column with the day number, counting the first day in the sequence as day 1.
dat$day = c(1, cumsum(as.numeric(diff(as.Date(dat$datetime, tz="UTC")))) + 1)
dat
datetime day
1 2016-05-02 01:03:11 1
2 2016-05-02 15:27:11 1
3 2016-05-03 05:51:11 2
4 2016-05-03 20:15:11 2
5 2016-05-04 10:39:11 3
6 2016-05-05 01:03:11 4
7 2016-05-09 01:09:11 8
8 2016-05-11 09:27:11 10
9 2016-05-13 17:45:11 12
10 2016-05-16 02:03:11 15
I specified the timezone in the code above to avoid getting tripped up by potential silent shifts between my local timezone and UTC. For example, note the silent shift from my default local time zone ("America/Los_Angeles") to UTC when converting a POSIXct datetime to a date:
# Fake data
datetime = seq(as.POSIXct("2016-05-02 01:03:11"), as.POSIXct("2016-05-05 01:03:11"), length.out=6)
tz(datetime)
[1] ""
date = as.Date(datetime)
tz(date)
[1] "UTC"
data.frame(datetime, date)
datetime date
1 2016-05-02 01:03:11 2016-05-02
2 2016-05-02 15:27:11 2016-05-02
3 2016-05-03 05:51:11 2016-05-03
4 2016-05-03 20:15:11 2016-05-04 # Note day is different due to timezone shift
5 2016-05-04 10:39:11 2016-05-04
6 2016-05-05 01:03:11 2016-05-05
My problem is as follows: I've got a time series with 5-Minute precipitation data like:
Datum mm
1 2004-04-08 00:05:00 NA
2 2004-04-08 00:10:00 NA
3 2004-04-08 00:15:00 NA
4 2004-04-08 00:20:00 NA
5 2004-04-08 00:25:00 NA
6 2004-04-08 00:30:00 NA
With this structure:
'data.frame': 1098144 obs. of 2 variables:
$ Datum: POSIXlt, format: "2004-04-08 00:05:00" "2004-04-08 00:10:00" "2004-04-08 00:15:00" "2004-04-08 00:20:00" ...
$ mm : num NA NA NA NA NA NA NA NA NA NA ...
As you can see, the time series begins with a lot of NA's, but there is measured precipitation further down, although riddled with single, less common NA's due to malfunction of the measuring station.
What I'm trying to achieve, is summing up the measured precipitation to hourly sums, not considering NA's.
This is what I tried so far:
sums <- aggregate(precip["mm"],
list(cut(precip$Datum, "1 hour")), sum)
Even though the timestamps are correctly aggregated to hours, all sums are 0 or NA. The sums are not even calculated if there is no NA at all.
additionally to be taken into account:
Hourly precipitation sums in meteorology always describe the cumulative sum until a certain hour: The amount of precipitation at 0:00 o'clock describes the sum from 23:00 the previous day until 0:00. So I always need to sum up the previous hour.
Reproducible Example
set.seed(1120)
s <- as.POSIXlt("2004-03-08 23:00:00")
r <- seq(s, s+1e4, "30 min")
precip <- data.frame(Datum=r, mm=sample(c(1:5,NA), 6, T))
Datum mm
2004-03-08 23:00:00 4
2004-03-08 23:30:00 1
2004-03-09 00:00:00 2
2004-03-09 00:30:00 4
2004-03-09 01:00:00 1
2004-03-09 01:30:00 4
With the above example, the result I am looking for is:
Datum mm
2004-03-09 00:00:00 5
2004-03-09 01:00:00 6
2004-03-09 02:00:00 5
Try adding na.rm=TRUE:
aggregate(precip['mm'], list(cut(precip$Datum, "1 hour")), sum, na.rm=TRUE)
# Group.1 mm
# 1 2004-04-08 00:00:00 26
# 2 2004-04-08 01:00:00 35
# 3 2004-04-08 02:00:00 25
Reproducible Example
set.seed(1120)
s <- as.POSIXlt("2004-04-08 00:05:00")
r <- seq(s, s+1e4, "5 min")
precip <- data.frame(Datum=r, mm=sample(c(1:5,NA), 34, T))
addendum
To your second question: If you would like measurements on the hour to be calculated with the lesser hour add right=TRUE:
aggregate(precip['mm'], list(cut(precip$Datum, "1 hour", right=TRUE)), sum, na.rm=TRUE)
Further Explanation
We will create another more detailed explanation to show how the solution works:
p <- c("2004-04-07 23:48:20", "2004-04-08 00:00:00", "2004-04-08 00:03:20")
ptime <- as.POSIXlt(p)
#[1] "2004-04-07 23:48:20 EDT" "2004-04-08 00:00:00 EDT" "2004-04-08 00:03:20 EDT"
We have three dates to separate into groups. If we use cut without any extra arguments, the second entry "2004-04-08 00:00:00 EDT" will be grouped with the third entry for hour "00:00":
cut(ptime, "1 hour")
#[1] 2004-04-07 23:00:00 2004-04-08 00:00:00 2004-04-08 00:00:00
But if we add the argument right=FALSE we can group it with the "23:00" hour:
cut(ptime, "1 hour", right=TRUE)
#[1] 2004-04-07 23:00:00 2004-04-07 23:00:00 2004-04-08 00:00:00
We can specify the behavior of edge cases.
edit
With your new data the original solution produces the desired output:
aggregate(precip['mm'], list(cut(precip$Datum, "1 hour")), sum, na.rm=TRUE)
Group.1 mm
1 2004-03-08 23:00:00 5
2 2004-03-09 00:00:00 6
3 2004-03-09 01:00:00 5
You can use dplyr to calculate sum like :
precip$hour <- strftime(precip$Datum,"%Y-%m-%d %H")
library(dplyr)
sum_hour <- precip %>% group_by(hour) %>% summarise(sum_hour = sum(mm,na.rm = T))
I have a POSIXct class vector containing am hours and I want to replace the values in a data frame containing a character class column. When I do the replacement the class changes to character. I'm proceeding as follows:
class(data2014.im.t[,2])
[1] "character"
class(horas.am)
[1] "POSIXct" "POSIXt"
head(horas.am)
[1] "1970-01-01 09:00:00 COT" "1970-01-01 10:00:00 COT" "1970-01-01 11:00:00 COT" "1970-01-01 12:00:00 COT"
[5] "1970-01-01 01:00:00 COT" "1970-01-01 02:00:00 COT"
data2014.im.t[grep("([a])", data2014.im.t[,2]), 2] <- horas.am
class(data2014.im.t[,2])
[1] "character"
head(data2014.im.t[,2])
[1] "50400" "54000" "57600" "104400" "64800" "68400"
Evidently I would like to have a POSIXct column containing hours. Any thoughts?
You should explicitly do the conversion yourself
#sample data
horas.am <- seq(as.POSIXct("2014-01-01 05:00:00"), length.out=10, by="2 hours")
data2014.im.t <- data.frame(a=1:10, b=rep("a",10), stringsAsFactors=FALSE)
class(data2014.im.t[,2])
# [1] "character"
class(horas.am)
# [1] "POSIXct" "POSIXt"
# NO:
data2014.im.t[grep("([a])", data2014.im.t[,2]), 2] <- horas.am
# YES
data2014.im.t[grep("([a])", data2014.im.t[,2]), 2] <- as.character(horas.am)
data2014.im.t
# a b
# 1 1 2014-01-01 05:00:00
# 2 2 2014-01-01 07:00:00
# 3 3 2014-01-01 09:00:00
# 4 4 2014-01-01 11:00:00
# 5 5 2014-01-01 13:00:00
# 6 6 2014-01-01 15:00:00
# 7 7 2014-01-01 17:00:00
# 8 8 2014-01-01 19:00:00
# 9 9 2014-01-01 21:00:00
# 10 10 2014-01-01 23:00:00
class(data2014.im.t[,2])
# [1] "character"