Resolving "Error in as.POSIXlt.character(x, tz, ...) : character string is not in a standard unambiguous format" error msg

[Beach_Life]

I am trying to split a column of date timestamp data into two individual columns. I have several columns that have date timestamp info. The first one, I was able to use the following and it worked with no problem:

delivered$order_date<-as.Date(delivered$order_purchase_timestamp)
delivered$order_time<-format(as.POSIXct(delivered$order_purchase_timestamp),
format = "%H:%M:%S")

The "order_purchase_timestamp" column is what I split into two separate columns called order_date and order_time. Again, this worked with no problem.

However, when I tried to split three other columns that had date timestamp info combined, I get this error:
Error in as.POSIXlt.character(x, tz, ...) :
character string is not in a standard unambiguous format

This seems to be only happening for the timestamp portion of the column; the "date" portion splits into its own column with no problem.

I watched a YouTube video on how to fix the error (convert the character string to a numeric string) but the example did NOT cover how to do this with a column of date timestamp data. This is the code the video suggested I use:
date<-as.numeric(as.character(date)), where "date" would be order_approved_numeric for my purposes. I entered the following code to fix my problem:

#to fix character string error msg, must convert character string to numeric
approved_time_numeric <- as.numeric(as.character(delivered$order_approved_at))
#order_approved_at is the column in the dataset that contains the date timestamp data

I got this error: Warning message:
NAs introduced by coercion

I then ran the entire code, both for the date and for the timestamp:
delivered$approved_date<-as.Date(delivered$order_approved_at)
delivered$approved_time<-format(as.POSIXct(approved_time_numeric),
format = "%H:%M:%S")

When I look at the revised dataset, it shows a new column called approved_time but it just has NA all the way down the column.

Any suggestions and advice is greatly appreciated.

[Flm]

Can you paste some example data using dput(head(yourdf, 10))? How would you like the final result to be?

[Beach_Life]

Will this work? As you can see, the order_date and order_time columns are good; I split this from another column not seen in the image. But you can get a good idea of what the date timestamp data looks like by looking at the order_estimated_delivery_date column, which is another column I need to split but am having the same issue with.

[Flm]

Run the code I provided in the previous post and copy and paste here the output

[Beach_Life]

dput(head(delivered, 10))
structure(list(order_status = c("delivered", "delivered", "delivered",
"delivered", "delivered", "delivered", "delivered", "delivered",
"delivered", "delivered"), order_purchase_timestamp = c("2017-10-02 10:56:33",
"2018-07-24 20:41:37", "2018-08-08 08:38:49", "2017-11-18 19:28:06",
"2018-02-13 21:18:39", "2017-07-09 21:57:05", "2017-05-16 13:10:30",
"2017-01-23 18:29:09", "2017-07-29 11:55:02", "2017-05-16 19:41:10"
), order_approved_at = c("2017-10-02 11:07:15", "2018-07-26 03:24:27",
"2018-08-08 08:55:23", "2017-11-18 19:45:59", "2018-02-13 22:20:29",
"2017-07-09 22:10:13", "2017-05-16 13:22:11", "2017-01-25 02:50:47",
"2017-07-29 12:05:32", "2017-05-16 19:50:18"), order_delivered_carrier_date = c("2017-10-04 19:55:00",
"2018-07-26 14:31:00", "2018-08-08 13:50:00", "2017-11-22 13:39:59",
"2018-02-14 19:46:34", "2017-07-11 14:58:04", "2017-05-22 10:07:46",
"2017-01-26 14:16:31", "2017-08-10 19:45:24", "2017-05-18 11:40:40"
), order_delivered_customer_date = c("2017-10-10 21:25:13", "2018-08-07 15:27:45",
"2018-08-17 18:06:29", "2017-12-02 00:28:42", "2018-02-16 18:17:02",
"2017-07-26 10:57:55", "2017-05-26 12:55:51", "2017-02-02 14:08:10",
"2017-08-16 17:14:30", "2017-05-29 11:18:31"), order_estimated_delivery_date = c("2017-10-18 00:00:00",
"2018-08-13 00:00:00", "2018-09-04 00:00:00", "2017-12-15 00:00:00",
"2018-02-26 00:00:00", "2017-08-01 00:00:00", "2017-06-07 00:00:00",
"2017-03-06 00:00:00", "2017-08-23 00:00:00", "2017-06-07 00:00:00"
), order_date = structure(c(17441, 17736, 17751, 17488, 17575,
17356, 17302, 17189, 17376, 17302), class = "Date"), order_time = c("10:56:33",
"20:41:37", "08:38:49", "19:28:06", "21:18:39", "21:57:05", "13:10:30",
"18:29:09", "11:55:02", "19:41:10"), approved_date = structure(c(17441,
17738, 17751, 17488, 17575, 17356, 17302, 17191, 17376, 17302
), class = "Date"), approved_time = c(NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_)), row.names = c(NA,
10L), class = "data.frame")

[Flm]

This should solve

library(tidyverse)
library(lubridate)
library(hms)

df <- structure(list(order_status=c("delivered","delivered","delivered",
"delivered","delivered","delivered","delivered","delivered",
"delivered","delivered"),order_purchase_timestamp=c("2017-10-0210:56:33",
"2018-07-2420:41:37","2018-08-0808:38:49","2017-11-1819:28:06",
"2018-02-1321:18:39","2017-07-0921:57:05","2017-05-1613:10:30",
"2017-01-2318:29:09","2017-07-2911:55:02","2017-05-1619:41:10"
),order_approved_at=c("2017-10-0211:07:15","2018-07-2603:24:27",
"2018-08-0808:55:23","2017-11-1819:45:59","2018-02-1322:20:29",
"2017-07-0922:10:13","2017-05-1613:22:11","2017-01-2502:50:47",
"2017-07-2912:05:32","2017-05-1619:50:18"),order_delivered_carrier_date=c("2017-10-0419:55:00",
"2018-07-2614:31:00","2018-08-0813:50:00","2017-11-2213:39:59",
"2018-02-1419:46:34","2017-07-1114:58:04","2017-05-2210:07:46",
"2017-01-2614:16:31","2017-08-1019:45:24","2017-05-1811:40:40"
),order_delivered_customer_date=c("2017-10-1021:25:13","2018-08-0715:27:45",
"2018-08-1718:06:29","2017-12-0200:28:42","2018-02-1618:17:02",
"2017-07-2610:57:55","2017-05-2612:55:51","2017-02-0214:08:10",
"2017-08-1617:14:30","2017-05-2911:18:31"),order_estimated_delivery_date=c("2017-10-1800:00:00",
"2018-08-1300:00:00","2018-09-0400:00:00","2017-12-1500:00:00",
"2018-02-2600:00:00","2017-08-0100:00:00","2017-06-0700:00:00",
"2017-03-0600:00:00","2017-08-2300:00:00","2017-06-0700:00:00"
),order_date=structure(c(17441,17736,17751,17488,17575,
17356,17302,17189,17376,17302),class="Date"),order_time=c("10:56:33",
"20:41:37","08:38:49","19:28:06","21:18:39","21:57:05","13:10:30",
"18:29:09","11:55:02","19:41:10"),approved_date=structure(c(17441,
17738,17751,17488,17575,17356,17302,17191,17376,17302
),class="Date"),approved_time=c(NA_character_,NA_character_,
NA_character_,NA_character_,NA_character_,NA_character_,NA_character_,
NA_character_,NA_character_,NA_character_)),row.names=c(NA,
10L),class="data.frame") %>% as_tibble()


# split order_purchase_timestamp
df$order_purchase_timestamp_splitdate <- as_date(df$order_purchase_timestamp)
df$order_purchase_timestamp_splittime <- as_hms(as_datetime(df$order_purchase_timestamp))

# split order_approved_at
df$order_approved_at_splitdate <- as_date(df$order_approved_at)
df$order_approved_at_splittime <- as_hms(as_datetime(df$order_approved_at))

# split order_delivered_carrier_date
df$order_delivered_carrier_date_splitdate <- as_date(df$order_delivered_carrier_date)
df$order_delivered_carrier_date_splittime <- as_hms(as_datetime(df$order_delivered_carrier_date))

# split order_delivered_customer_date
df$order_delivered_customer_date_splitdate <- as_date(df$order_delivered_customer_date)
df$order_delivered_customer_date_splittime <- as_hms(as_datetime(df$order_delivered_customer_date))

# split order_estimated_delivery_date
df$order_estimated_delivery_date_splitdate <- as_date(df$order_estimated_delivery_date)
df$order_estimated_delivery_date_splittime <- as_hms(as_datetime(df$order_estimated_delivery_date))

[Beach_Life]

That worked! Thanks so much! So, I'm assuming I need to use lubridate. I'll need to do more research into what all it does and when to use it. Thanks again for your help.