I have the below information in my .csv. I'm trying to convert the epoch time to IST time but unable to do so.
The information in the .csv is attached.
I'm trying to add a column and convert the epoch time to IST.
Please suggest a better way.
Thanks.
sample data
created_at <- c(1549480813,1549480815,1549480818,1549480872)
code
as.POSIXct( created_at, origin = "1970-01-01", tz = "Asia/Kolkata" )
**output
#[1] "2019-02-07 00:50:13 IST" "2019-02-07 00:50:15 IST" "2019-02-07 00:50:18 IST" "2019-02-07 00:51:12 IST"
Related
I have a bunch of dates (in UTC) e.g.
> dates <- as.POSIXct(c('2019-01-01', '2019-07-01', '2019-08-01'), tz = 'UTC')
> dates
[1] "2019-01-01 UTC" "2019-07-01 UTC" "2019-08-01 UTC"
I want to know if each date in my vector is during GMT or BST.
So the above would give
"GMT" "BST" "BST"
Might not be the best way of doing it, but got what I wanted using
library(lubridate)
ifelse(hour(force_tz(as.POSIXct(as.Date(dates)), tz = 'Europe/London')) == 1, 'BST', 'GMT')
I feel like I'm missing something obvious here. I'm importing some data which is stored as HH:MM:SS. I'm trying to convert this to POSIXct and manually specify the origin as the date the data was collected.
datIn$TimeComplete <- as.POSIXct(datIn$Time, format="%H:%M:%S", origin="2000-01-01", tz="CET")
The output of this registers the HH:MM:SS correctly but says the day is 2019-03-05 (today) and I can't seem to convince it to do anything different.
You are misunderstanding the concept of origin. Origin is there to help convert numbers to dates. Those numbers represent seconds so you need the origin in order to add those seconds to the origin and get the datetime object. For example,
as.POSIXct(60, tz = "GMT", origin = '2015-03-05')
#[1] "2015-03-05 00:01:00 GMT"
as.POSIXct(3600, tz = "GMT", origin = '2015-07-05')
#[1] "2015-07-05 01:00:00 GMT"
What you are trying to do can be easily achieved by pasting the desired date to your times and converting to datetime, i.e.
as.POSIXct(paste0('2000-01-01 ', '11:03:15'), format = "%Y-%m-%d %H:%M:%S", origin = "", tz = "CET")
#[1] "2000-01-01 11:03:15 CET"
I am trying to convert the Eopch time interval using as.POSIXct function in R
into the local timezone and in the Europe/Vienna timezone. But for both the timezone its displaying a weird date
as.POSIXct(1385856600000, origin = "1970-01-01", tz='CET')
[1] "45886-01-17 23:40:00 CET"
> as.POSIXct(1385856600000, origin = "1970-01-01")
[1] "45886-01-18 04:10:00 IST"
What i am typing wrong here ?
you can use anytime package too.. much simpler to use than as.POSIXCt
anytime(1385856600000/1000)
[1] "2013-12-01 05:40:00 IST"
I think that your time is in milliseconds. If you divide by 1000, you get:
as.POSIXct(1385856600, origin = "1970-01-01")
[1] "2013-12-01 05:40:00 IST"
I have downloaded the 2015-2017 sunset/sunrise data from the Navy and I am trying to format it into dates and time to further use with other data I have. This is how my data set looks in R.
I have managed to convert the date into the right R data format. Yet, I still can't convert my rise/set column data from a number/integer format to a time data (as hh:mm).
Based on one internet source, I wrote the following codes:
Sun2015$Srise<- format(strptime(Sun2015$Rise, format="%H:%M"))
However this gives NA in my data
OR
Sun2015$Srise<-str_pad(Sun2015$Rise, 4, pad="0")
Sun2015$Srise<-hour(hm(Sun2015$Srise))
Yet, I received the following error:
Warning message: In .parse_hms(..., order = "HM", quiet = quiet) :
Some strings failed to parse.
Is there a better way to convert the columns into the right time format so that I can merge the date and time columns into date-time columns for sunset and sunrise?
Thank you in advance for your help.
You can convert your military time to 2400 time strings using sprint("%04d", data) and go from there. For example, with the first 5 lines of your data:
# Sample of your data
Day <- c("1/1/2015", "1/2/2015", "1/3/2015", "1/4/2015", "1/5/2015")
Rise <- c(652,652,652,653,653)
Set <- c(1755,1756,1756,1757,1757)
sun2015 <- data.frame(Day, Rise, Set)
# Convert to 2400 style strings with leading zeroes where necessary
sun2015$Rise <- sprintf("%04d", sun2015$Rise)
sun2015$Set <- sprintf("%04d", sun2015$Set)
# Merge with your date
sun2015$day_rise <- as.POSIXct(paste0(sun2015$Day, " ",sun2015$Rise), format = "%m/%d/%Y %H%M", origin = "1970-01-01", tz = "UTC")
sun2015$day_set <- as.POSIXct(paste0(sun2015$Day, " ",sun2015$Set), format = "%m/%d/%Y %H%M", origin = "1970-01-01", tz = "UTC")
> sun2015$day_rise
[1] "2015-01-01 06:52:00 UTC" "2015-01-02 06:52:00 UTC" "2015-01-03 06:52:00 UTC" "2015-01-04 06:53:00 UTC"
[5] "2015-01-05 06:53:00 UTC"
> sun2015$day_set
[1] "2015-01-01 17:55:00 UTC" "2015-01-02 17:56:00 UTC" "2015-01-03 17:56:00 UTC" "2015-01-04 17:57:00 UTC"
[5] "2015-01-05 17:57:00 UTC"
You can adjust to the appropriate time zone if necessary.
Im have a time stamp column that I am converting into a POSIXct. The problem is that there are two different formats in the same column, so if I use the more common conversion the other gets converted into NA.
MC$Date
12/1/15 22:00
12/1/15 23:00
12/2/15
12/2/15 1:00
12/2/15 2:00
I use the following code to convert to a POSIXct:
MC$Date <- as.POSIXct(MC$Date, tz='MST', format = '%m/%d/%Y %H:%M')
The results:
MC$Date
15-12-01 22:00:00
15-12-01 23:00:00
NA
15-12-02 01:00:00
15-12-02 02:00:00
I have tried using a logic vector to identify the issue then correct it but can't find an easy solution.
The lubridate package was designed to deal with situations like this.
dt <- c(
"12/1/15 22:00",
"12/1/15 23:00",
"12/2/15",
"12/2/15 1:00",
"12/2/15 2:00"
)
dt
[1] "12/1/15 22:00" "12/1/15 23:00" "12/2/15" "12/2/15 1:00" "12/2/15 2:00"
lubridate::mdy_hm(dt, truncated = 2)
[1] "2015-12-01 22:00:00 UTC" "2015-12-01 23:00:00 UTC" "2015-12-02 00:00:00 UTC"
[4] "2015-12-02 01:00:00 UTC" "2015-12-02 02:00:00 UTC"
The truncated parameter indicates how many formats can be missing.
You may add the tz parameter to specify which time zone to parse the date with if UTC is not suitable.
I think the logic vector approach could work. Maybe in tandem with an temporary vector for holding the parsed dates without clobbering the unparsed ones. Something like this:
dates <- as.POSIXct(MC$Date, tz='MST', format = '%m/%d/%Y %H:%M')
dates[is.na(dates)] <- as.POSIXct(MC[is.na(dates),], tz='MST', format = '%m/%d/%Y')
MC$Date <- dates
Since all of your datetimes are separated with a space between date and time, you could use strsplit to extract only the date part.
extractDate <- function(x){ strsplit(x, split = " " )[[1]][1] }
MC$Date <- sapply( MC$Date, extractDate )
Then go ahead and convert any way you like, without worrying about the time part getting in the way.