Recode "date & time variable" into two separate variables

Question

I'm a PhD student (not that experienced in R), and I'm trying to recode a string variable, called RecordedDate into two separate variables: a Date variable and a Time variable. I am using RStudio.

An example of values are:

8/6/2018 18:56
7/26/2018 10:43
7/28/2018 8:36  

I would like to you the first part of the value (example: 08/6/2018) to reformat this into a date variable, and the second part of the value (example: 18:56) into a time variable.

1. I'm thinking the first step would be to create code that can break this up into two variables, based on some rule. I’m thinking maybe I can separate separate everything before the "space" into the Date variable, and after the "space" in the Time variable. I am not able to figure this out.

2. Then, I'm looking for code that would change the Date from a "string" variable to a "date" type variable. I’m not sure if this is correct, but I’m thinking something like: better_date <- as.Date(Date, "%m/%d/%Y")

3. Finally, then I would like to change theTime variable to a "time" type format (if this exists). Not sure how to do this part either, but something that indicates hours and minutes. This part is less important than getting the date variable.

Answer 1

Two immediate ways:

1. strsplit() on the white space

2. The proper ways: parse, and then format back out.

Only 2. will guarantee you do not end up with hour 27 or minute 83 ...

Examples:

R> data <- c("8/6/2018 18:56", "7/26/2018 10:43", "7/28/2018 8:36")
R> strsplit(data, " ")
[[1]]
[1] "8/6/2018" "18:56"   

[[2]]
[1] "7/26/2018" "10:43"    

[[3]]
[1] "7/28/2018" "8:36"     

R> 

And:

R> data <- c("8/6/2018 18:56", "7/26/2018 10:43", "7/28/2018 8:36")
R> df <- data.frame(data)
R> df$pt <- anytime::anytime(df$data)   ## anytime package used
R> df$time <- format(df$pt, "%H:%M")
R> df$day <- format(df$pt, "%Y-%m-%d")
R> df
             data                  pt  time        day
1  8/6/2018 18:56 2018-08-06 18:56:00 18:56 2018-08-06
2 7/26/2018 10:43 2018-07-26 10:43:00 10:43 2018-07-26
3  7/28/2018 8:36 2018-07-28 00:00:00 00:00 2018-07-28
R> 

I often collect data in a data.frame (or data.table) and then add column by column.