I have the following dataframe:
>dput(df)
structure(list(Date = structure(c(18724, 18724, 18725, 18725, 18726, 18726, 18726, 18731, 18731,
18731), class = "Date"),
Time = structure(c(0.490277777777778, 0.490277777777778, 0.552083333333333,
0.552083333333333, 0.461805555555556, 0.461805555555556,
0.461805555555556, 0.647916666666667, 0.647916666666667,
0.647916666666667), format = "h:m:s", class = "times"),
Date.1 = structure(c(18725, 18725, 18726, 18726, 18727, 18727, 18727, NA, NA, NA),
class = "Date"),
Time.1 = structure(c(0.754861111111111, 0.754861111111111, 0.9375, 0.9375,
0.472222222222222, 0.472222222222222, 0.472222222222222, NA,
NA, NA), format = "h:m:s", class = "times")), row.names = c(NA,
10L), class = "data.frame")
I would like to merge Date and Time columns (Date with Time, and Date.1 with Time.1) to obtain a single column in POSIXct format (i.e. one column from Date - Time columns, and another from Date.1 - Time.1 columns).
library(data.table)
setDT(df)
df[, datetime := as.POSIXct( as.ITime(Time), as.IDate(Date))]
df[, datetime.1 := as.POSIXct( as.ITime(Time.1), as.IDate(Date.1))]
# Date Time Date.1 Time.1 datetime datetime.1
# 1: 2021-04-07 0.4902778 2021-04-08 0.7548611 2021-04-07 11:46:00 2021-04-08 18:06:59
# 2: 2021-04-07 0.4902778 2021-04-08 0.7548611 2021-04-07 11:46:00 2021-04-08 18:06:59
# 3: 2021-04-08 0.5520833 2021-04-09 0.9375000 2021-04-08 13:14:59 2021-04-09 22:30:00
# 4: 2021-04-08 0.5520833 2021-04-09 0.9375000 2021-04-08 13:14:59 2021-04-09 22:30:00
# 5: 2021-04-09 0.4618056 2021-04-10 0.4722222 2021-04-09 11:05:00 2021-04-10 11:19:59
# 6: 2021-04-09 0.4618056 2021-04-10 0.4722222 2021-04-09 11:05:00 2021-04-10 11:19:59
# 7: 2021-04-09 0.4618056 2021-04-10 0.4722222 2021-04-09 11:05:00 2021-04-10 11:19:59
# 8: 2021-04-14 0.6479167 <NA> NA 2021-04-14 15:33:00 <NA>
# 9: 2021-04-14 0.6479167 <NA> NA 2021-04-14 15:33:00 <NA>
#10: 2021-04-14 0.6479167 <NA> NA 2021-04-14 15:33:00 <NA>