data.table based function is altering the previous table - r

Question

I have a time series data with two columns: 1) a POSIX date time column of 30 minute intervals and 2) a value for each interval, as shown below:
read_date_time int_val
2013-01-15 15:00:00 2.3
2013-01-15 15:30:00 2.4
I've written a function that pivots the data.table so that there are 48 columns for each time interval for each row representing a day.
read_date 00:00 00:30 01:00 01:30 ...
2013-01-15 1.3 1.4 1.2 1.5 ...
The function involved creating two new columns (pure_date and interval) which are used as IDs as part of the reshape function. However I'm finding that the new columns are also added to the original table and the original read_date_time column is removed.
int_val pure_date interval
6.829986e-05 2013-08-31 00:00:00
6.887250e-05 2013-08-31 00:30:00
This causes numerous problems downstream as the original data set is reused in other functions. I'm aware that I could probably bypass some of these problems using data.frame operations instead, however as I'm handling very large quantities of data and efficiency is key, really I need a data.table solution.
What am I doing wrong?
Code for replication....
require(data.table)
require(reshape)
require(stringr)
# Create time_array for example
set.seed(1L) ## for reproducibility
dt_format = "%Y-%m-%d %H:%M"
time_seq <- seq.POSIXt(
as.POSIXct("2012-01-01 00:00:00", format=dt_format),
as.POSIXct("2013-12-31 00:00:00", format=dt_format),
by = "30 mins")
values <- runif(NROW(time_seq),0,1)
combined_data_set <- data.table(read_date_time = time_seq, int_val = values)
> head(combined_data_set) # Format wanted
# Define Pivoting Function
pivot_data <- function(A) {
con_data <- A
con_data[,pure_date := as.Date(read_date_time)]
con_data[,interval := str_sub(as.character(read_date_time),-8,-1)]
con_data[,read_date_time := NULL]
con_data <- data.table(read_date = as.character(con_data$pure_date),
interval = con_data$interval,
int_val = con_data$int_val)
pivoted <- recast(con_data, read_date ~ interval,
id.var = c("read_date","interval"))
return(pivoted)
}
# Apply to data set
pivoted_output <- pivot_data(combined_data_set)
# Original data has been altered, what's happened!!!!!
> head(combined_data_set)

Answer 1

pivot_data <- function(A) {
con_data <- copy(A)
con_data[,pure_date := as.Date(read_date_time)]
con_data[,interval := str_sub(as.character(read_date_time),-8,-1)]
con_data[,read_date_time := NULL]
con_data <- data.table(read_date = as.character(con_data$pure_date),
interval = con_data$interval,
int_val = con_data$int_val)
pivoted <- recast(con_data, read_date ~ interval,
id.var = c("read_date","interval"))
return(pivoted)
}
As mentioned, just a school-boy error, copy(A) does the job....