I want convert numeric values to time without the date for the data like 1215,1423,1544,1100,0645,1324 in R.
These data has to read like 12:15,14:23,15:44.
I was trying as.POSIXct.
We can use strptime with format
format(strptime(sprintf("%04d", v1), "%H%M"), "%H:%M")
The above output is character class, but if we needed a times class, then we can use times from chron on a "HH:MM:SS" format created with sub or from the above code
library(chron)
times(sub("(.{2})(.{2})","\\1:\\2:", sprintf("%04d00", v1)))
#[1] 12:15:00 14:23:00 15:44:00 11:00:00 06:45:00 13:24:00
Or
times(format(strptime(sprintf("%04d", v1), "%H%M"), "%H:%M:%S"))
data
v1 <- c( 1215,1423,1544,1100,0645,1324)
Related
I'm having trouble converting my date variable in my data frame to a new date-time variable. I know parse_date_time(x, orders="ymd HMS") but I don't know what code is needed to say: use this dataframe (workHours) and grab this column (date) now change to a new column named (date_time) and convert to "ymd HMS"
The date column already has the date and time in this format: mm/dd/yyyy hh:mm:ss and its a fct or factor.
If you have data like this -
workHours <- data.frame(date = c('3/22/2020 04:51:12', '3/15/2019 10:12:32'))
workHours
# date
#1 3/22/2020 04:51:12
#2 3/15/2019 10:12:32
You can use as.POSIXct in base R
workHours$date_time <- as.POSIXct(workHours$date, format = '%m/%d/%Y %T', tz = 'UTC')
Or lubridate::mdy_hms
workHours$date_time <- lubridate::mdy_hms(workHours$date)
Both of which would return -
# date date_time
#1 3/22/2020 04:51:12 2020-03-22 04:51:12
#2 3/15/2019 10:12:32 2019-03-15 10:12:32
class(workHours$date_time)
#[1] "POSIXct" "POSIXt"
We can use anytime
library(anytime)
workHours <- data.frame(date = c('3/22/2020 04:51:12', '3/15/2019 10:12:32'))
anytime(workHours$date)
[1] "2020-03-22 04:51:12 EDT" "2019-03-15 10:12:32 EDT"
I have a date column in a dataframe. I have read this df into R using openxlsx. The column is 'seen' as a character vector when I use typeof(df$date).
The column contains date information in several formats and I am looking to get this into the one format.
#Example
date <- c("43469.494444444441", "12/31/2019 1:41 PM", "12/01/2019 16:00:00")
#What I want -updated
fixed <- c("2019-04-01", "2019-12-31", "2019-12-01")
I have tried many work arounds including openxlsx::ConvertToDate, lubridate::parse_date_time, lubridate::date_decimal
openxlsx::ConvertToDateso far works best but it will only take 1 format and coerce NAs for the others
update
I realized I actually had one of the above output dates wrong.
Value 43469.494444444441 should convert to 2019-04-01.
Here is one way to do this in two-step. Change excel dates separately and all other dates differently. If you have some more formats of dates that can be added in parse_date_time.
temp <- lubridate::parse_date_time(date, c('mdY IMp', 'mdY HMS'))
temp[is.na(temp)] <- as.Date(as.numeric(date[is.na(temp)]), origin = "1899-12-30")
temp
#[1] "2019-01-04 11:51:59 UTC" "2019-12-31 13:41:00 UTC" "2019-12-01 16:00:00 UTC"
as.Date(temp)
#[1] "2019-01-04" "2019-12-31" "2019-12-01"
You could use a helper function to normalize the dates which might be slightly faster than lubridate.
There are weird origins in MS Excel that depend on platform. So if the data are imported from different platforms, you may want to work woth dummy variables.
normDate <- Vectorize(function(x) {
if (!is.na(suppressWarnings(as.numeric(x)))) # Win excel
as.Date(as.numeric(x), origin="1899-12-30")
else if (grepl("A|P", x))
as.Date(x, format="%m/%d/%Y %I:%M %p")
else
as.Date(x, format="%m/%d/%Y %R")
})
For additional date formats just add another else if. Format specifications can be found with ?strptime.
Then just use as.Date() with usual origin.
res <- as.Date(normDate(date), origin="1970-01-01")
# 43469.494444444441 12/31/2019 1:41 PM 12/01/2019 16:00:00
# "2019-01-04" "2019-12-31" "2019-12-01"
class(res)
# [1] "Date"
Edit: To achieve a specific output format, use format, e.g.
format(res, "%Y-%d-%m")
# 43469.494444444441 12/31/2019 1:41 PM 12/01/2019 16:00:00
# "2019-04-01" "2019-31-12" "2019-01-12"
format(res, "%Y/%d/%m")
# 43469.494444444441 12/31/2019 1:41 PM 12/01/2019 16:00:00
# "2019/04/01" "2019/31/12" "2019/01/12"
To lookup the codes type ?strptime.
I have two dataframes:
dat is a 9752x8 dataframe that contains some POSIXlt dates
trips.df is a 35772x28 dataframe that contains hourly temperature
data
I would like to save the corresponding temperature for each dates in dat.
I have tried:
trips.df$temperature<-lapply(trips.df$fin, function(x){
dat_meteo[dat_meteo$Date.Heure==round(x,"hours"),7]})
But I got this error, which makes me think that x is not passed as a datetime variable
Error in round(x, "hours") :
non-numeric argument to mathematical function
I have also tried this:
merge(trips.df,dat_meteo[,c(1,7)])
But I also got an error:
Error: cannot allocate vector of size 653.8 Mb
Any advice on how to retrieve data on dat_meteo by dates?
I am using R version 3.4.0 with RStudio Version 1.0.143 on Windows 10
And here are an exercpt of my data:
> head(trips.df$fin)
[1] "2013-06-25 16:34:16 EDT" "2013-06-25 16:34:16 EDT" "2013-06-26 13:00:05 EDT"
[4] "2013-06-29 12:52:21 EDT" "2013-06-29 15:34:13 EDT" "2013-06-29 17:39:29 EDT"
> dat_meteo[1870:1875,c(1,7)]
Date.Heure Temp...C.
1870 2013-03-19 18:00:00 -1,2
1871 2013-03-19 19:00:00 -1,7
1872 2013-03-19 20:00:00 -2,1
1873 2013-03-19 21:00:00 -2,8
1874 2013-03-19 22:00:00 -3,0
1875 2013-03-19 23:00:00 -3,7
You may want to take a slightly different approach and use data.table.
trips.dt <- data.table(trips.df)
dat <- data.table(dat)
trips.dt <- trips.dt[ , dates.a := strptime(as.POSIXct(fin,format='%m/%d/%Y %H:%M:%S'),format='%m/%d/%Y')][,dates.b := dates.a]
dat <- dat[dates.dat.a := strptime(as.POSIXct(Date.Heure, format = '%m/%d/%Y %H:%M:%S'),format='%m/%d/%Y')][, dates.dat.b := dates.dat.a]
setkey(trips.dt, id, dates.a, dates.b)
setkey(dat , id, dates.dat.a, dates.dat.b)
combo <- foverlaps(trips.df, dat, type = "within")
This creates date ranges for both trip.df and dat after converting it to a data.table, then merges trips.df to dat and stores the result as combo
Make sure that the two time columns you want to match have the same format (POSIXct). It is more straightforward to use the POSIXct format within a dataframe, as the POSIXlt format actually corresponds to a list of named elements whereas POSIXct is in vector form.
dat_meteo$Date.Heure=as.POSIXct(dat_meteo$Date.Heure,format="%Y-%m-%d %H:%M:%S")
Create a column in trips.df of times rounded to the closest hours, converting it to POSIXct too, as round converts POSIXct to POSIXlt:
trips.df$fin_r=as.POSIXct(round(trips.df$fin,"hours"))
Then use merge:
res=merge(trips.df,dat_meteo[,c(1,7)],by.x="fin_r",by.y ="Date.Heure")
Im have a time stamp column that I am converting into a POSIXct. The problem is that there are two different formats in the same column, so if I use the more common conversion the other gets converted into NA.
MC$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.
I have a column in which time is in the following format: 0815A. I need it to be converted into a time format.
I have tried poxscit but there are some errors.
We can use as.POSIXct specifying the correct format
as.POSIXct(paste0(v1, "M"), format = '%I%M%p')
#[1] "2016-07-27 08:15:00 IST" "2016-07-27 21:20:00 IST"
data
v1 <- c("0815A", "0920P")