"split" dataframe per month based on columns Start/End - r

Question

I need to "split" a 15 million line df of the following form:
library(lubridate)
dateStart <- c(lubridate::ymd("2010-01-01"))
dateEnd <- c(lubridate::ymd("2010-03-06"))
length <- c(65)
Amt <- c(348.80)
df1 <- data.frame(dateStart, dateEnd, length, Amt)
df1
# dateStart dateEnd length Amt
# 1 2010-01-01 2010-03-06 65 348.8
into something like:
dateStart dateEnd length Amt
1 2010-01-01 2010-01-31 31 166.35
2 2010-02-01 2010-02-28 28 150.55
3 2010-03-01 2010-03-06 6 32.19
Where length is the number of days and Amt is the pro-rata amount for the number of days. Does anybody know how to do this? Someone mentioned the padr package to me but I do not know how to use it for this specific purpose.
Thank you in advance

Answer 1

I'm going to assume you have an some sort of unique id field in your data set so you have a unique record. Otherwise this is not going to work. I also added 1 extra record so we can see everything works on multiple records.
Data:
library(lubridate)
id <- c(1:2) # added id field needed for unique record and needed for grouping
dateStart <- c(lubridate::ymd("2010-01-01", "2011-01-09"))
dateEnd <- c(lubridate::ymd("2010-03-06", "2011-04-09"))
length <- c(65, 91)
Amt <- c(348.80, 468.70)
df1 <- data.frame(id , dateStart, dateEnd, length, Amt)
First create a data.frame which has the id and missing months. We need dplyr, tidyr and padr. Create groups per unique id, gather the dates so we have start and end date in 1 column. For padr to extend months we first need to thicken the data.frame. Get rid of not needed columns and fill in the missing months.
library(dplyr)
library(tidyr)
library(padr)
#create last_day function for later use
last_day <- function(date) {
ceiling_date(date, "month") - days(1)
}
dates <- df1 %>%
select(id, dateStart, dateEnd) %>%
group_by(id) %>%
gather(names, dates, -id) %>%
arrange(id, dates) %>%
thicken(interval = "month") %>% # need to thicken first for month interval
select(-c(names, dates)) %>%
pad(interval = "month")
dates
# A tibble: 7 x 2
# Groups: id [2]
id dates_month
<int> <date>
1 1 2010-01-01
2 1 2010-02-01
3 1 2010-03-01
4 2 2011-01-01
5 2 2011-02-01
6 2 2011-03-01
7 2 2011-04-01
Next join back the data to the original data.frame
df_extended <- inner_join(dates, df1, by = "id")
df_extended
# A tibble: 7 x 6
# Groups: id [2]
id dates_month dateStart dateEnd length Amt
<int> <date> <date> <date> <dbl> <dbl>
1 1 2010-01-01 2010-01-01 2010-03-06 65 349.
2 1 2010-02-01 2010-01-01 2010-03-06 65 349.
3 1 2010-03-01 2010-01-01 2010-03-06 65 349.
4 2 2011-01-01 2011-01-09 2011-04-09 91 469.
5 2 2011-02-01 2011-01-09 2011-04-09 91 469.
6 2 2011-03-01 2011-01-09 2011-04-09 91 469.
7 2 2011-04-01 2011-01-09 2011-04-09 91 469.
Now to get to the end result. need to use case_when, ifelse doesn't return the data in date format for some reason. The case_when replace set the correct start and end dates (I assume you need the exact start date, not the first of the month, otherwise adjust code to use dates_month instead.) I create an amount per day (amt_pd) variable to be able to multiply this with the number of days in the month to get the pro-rata amount for the number of days in the month.
df_end <- df_extended %>%
mutate(dateEnd = case_when(last_day(dates_month) <= dateEnd ~ last_day(dates_month),
TRUE ~ dateEnd),
dateStart = case_when(dates_month <= dateStart ~ dateStart,
TRUE ~ dates_month),
amt_pd = Amt / length,
length = dateEnd - dateStart + 1,
Amt = amt_pd * length) %>%
select(-c(dates_month, amt_pd)) # get rid of not needed columns
df_end
# A tibble: 7 x 5
# Groups: id [2]
id dateStart dateEnd length Amt
<int> <date> <date> <time> <time>
1 1 2010-01-01 2010-01-31 31 166.350769230769
2 1 2010-02-01 2010-02-28 28 150.252307692308
3 1 2010-03-01 2010-03-06 6 32.1969230769231
4 2 2011-01-09 2011-01-31 23 118.462637362637
5 2 2011-02-01 2011-02-28 28 144.215384615385
6 2 2011-03-01 2011-03-31 31 159.667032967033
7 2 2011-04-01 2011-04-09 9 46.354945054945
All of this could be done in one go. But if you have 15 million rows it might be better to see if the intermediate steps work. Also note that pad has a break_above option.
This is a numeric value that indicates the number of rows in millions
above which the function will break. Safety net for situations where
the interval is different than expected and padding yields a very
large dataframe, possibly overflowing memory.