Create a variable and apllying a function to another one - r

Question

I have a dataframe with a column Arrivo (formatted as date) and a column Giorni (formatted as integer) with number of days (es.: 2, 3, 6 etc..).
I would like to apply two function to these columns and precisely, I would like to duplicate a row for the number in the column Giorni and while duplicating these rows, I would like to create a new column called data.osservazione that is equal to Arrivo and augmented of one day iteratively.
From this:
No. Casa Anno Data Categoria Camera Arrivo Stornata.il Giorni
1 2.867 SEELE 2019 03/09/2019 CDV 316 28/03/2020 NA 3
2 148.000 SEELE 2020 20/01/2020 CDS 105 29/03/2020 NA 3
3 3.684 SEELE 2019 16/11/2019 CD 102 02/04/2020 NA 5
to this:
No. data.osservazione Casa Anno Data Categoria Camera Arrivo
1 2867 3/28/2020 SEELE 2019 03/09/2019 CDV 316 3/28/2020 0:00:00
2 2867 3/29/2020 SEELE 2019 03/09/2019 CDV 316 3/28/2020 0:00:00
3 2867 3/30/2020 SEELE 2019 03/09/2019 CDV 316 3/28/2020 0:00:00
4 148 3/29/2020 SEELE 2020 20/01/2020 CDS 105 3/29/2020 0:00:00
5 148 3/30/2020 SEELE 2020 20/01/2020 CDS 105 3/29/2020 0:00:00
6 148 3/31/2020 SEELE 2020 20/01/2020 CDS 105 3/29/2020 0:00:00
Stornata.il Giorni
1 #N/D 3
2 #N/D 3
3 #N/D 3
4 #N/D 3
I was able to duplicate the rows but I don't know how to concurrently create the new column with the values I need.
Please don't mind the date values in the columns, I'll fix them in the end.
Thanks in advance

Answer 1

Since I am a fan of the data.table package, I will propose a solution using data.table. You can install it by typing on the console: install.packages("data.table").
My approach was to create a separate data.frame with an index going from 0 to whatever number is in Giorni by row from the original data.frame and then merge this new data.frame with the original data that you have and, by virtue of many to one matches from the key specified, the resulting data.frame will "expand" to the size desired, therefore "duplicating" the rows when necessary.
For this, I used seq_len(). If you do seq_len(3L), you get: [1] 1 2 3 which is the sequence from 1L to whatever integer you've given in length.out when length.out >= 1L. Thus seq_len() will produce a sequence that ends in whatever is in Giorni, the challenge is to do by row since length.out in seq_len() needs to be a vector of size 1. We use by in data.table syntax to accomplish this.
So let's get started, first you load data.table:
library(data.table) # load data.table
setDT(data) # data.frame into data.table
In your example, it isn't clear whether Arrivo is in Date format, I am assuming it isn't so I convert to Date --you will need this to add the days later.
# is `Arrivo`` date? If no, into date fmt
data[["Arrivo"]] <- as.Date(data[["Arrivo"]], format = "%d/%m/%y")
The next bit is key, using seq_len() and by in data.table syntax, I create a separate data.table --which is always a data.frame, but not the other way around-- with the sequence by every single element of Giorni, therefore expanding the data to the desired size. I use by = "No." because I want to apply seq_len() to every value of Giorni associated with No. the values in No..
# create an index with the count from `Giorni`, subtract by 1 so the first day is 0.
d1 <- data[, seq_len(Giorni) - 1, by = "No."]
Check the result, you can see where I am going now:
> d1
No. V1
1: 2867 0
2: 2867 1
3: 2867 2
4: 148 0
5: 148 1
Lastly, you inner join d1 with the original data, I am using data.table join syntax here. Then you add the index V1 to Arrivo:
# merge with previous data
res <- d1[data, on = "No."]
# add days to `Arrivo``, create column data.osservazione
res[ , data.osservazione := V1 + Arrivo]
Result:
> res
No. V1 Casa Anno Data Categoria Camera Arrivo
1: 2867 0 SEELE 2019 03/09/2019 CDV 316 2020-03-28
2: 2867 1 SEELE 2019 03/09/2019 CDV 316 2020-03-28
3: 2867 2 SEELE 2019 03/09/2019 CDV 316 2020-03-28
4: 148 0 SEELE 2019 20/01/2020 CDS 105 2020-03-29
5: 148 1 SEELE 2019 20/01/2020 CDS 105 2020-03-29
Stornata.il Giorni data.osservazione
1: NA 3 2020-03-28
2: NA 3 2020-03-29
3: NA 3 2020-03-30
4: NA 2 2020-03-29
5: NA 2 2020-03-30
The next commands are just cosmetic, formatting dates and deleting columns:
# reformat `Arrivo` and `data.osservazione`
cols <- c("Arrivo", "data.osservazione")
res[, (cols) := lapply(.SD, function(x) format(x=x, format="%d/%m/%Y")), .SDcols=cols]
# remove index
res[, V1 := NULL]
Console:
> res
No. V1 Casa Anno Data Categoria Camera Arrivo
1: 2867 0 SEELE 2019 03/09/2019 CDV 316 2020-03-28
2: 2867 1 SEELE 2019 03/09/2019 CDV 316 2020-03-28
3: 2867 2 SEELE 2019 03/09/2019 CDV 316 2020-03-28
4: 148 0 SEELE 2019 20/01/2020 CDS 105 2020-03-29
5: 148 1 SEELE 2019 20/01/2020 CDS 105 2020-03-29
Stornata.il Giorni data.osservazione
1: NA 3 2020-03-28
2: NA 3 2020-03-29
3: NA 3 2020-03-30
4: NA 2 2020-03-29
5: NA 2 2020-03-30

Answer 2

Hi #JdeMello and really thank you for the quick answer!
Indeed it was what I was looking for, but in the mean time I kinda found a solution myself using lubridate and tidyverse and purrr.
What I did was transform variables from Posix to date (revenue is my df):
revenue <- revenue %>% mutate(Data = as_date(Data), Arrivo = as_date(Arrivo), `Stornata il` = as_date(`Stornata il`), Partenza = as_date(Partenza))
Then I created another data frame but included variables id and data_obs:
revenue_1 <- revenue %>% mutate(data_obs = Arrivo, id = 1:nrow(revenue))
I created another data frame with the variable data_obs iterated by the number of Giorni:
revenue_2 <- revenue_1 %>% group_by(id, data_obs) %>%
complete(Giorni = sequence(Giorni)) %>%
ungroup() %>%
mutate(data_obs = data_obs + Giorni -1)
I extracted data_obs:
data_obs <- revenue_2$data_obs
I created another data frame to duplicate the rows:
revenue_3 <- revenue %>% map_df(.,rep, .$Giorni)
And finally created the ultimate data frame that I needed:
revenue_finale <- revenue_3 %>% mutate(data_obs = data_obs)
I know it's kinda redundant having created all those data frame but I have very little knowledge of R at the moment and had to work around.
I wanted to merge data frames but for unknown reasons to me, it didn't work out.
What I found kinda fun is that you can play with many packages to get to your point instead of using just one.
I've never used data.table so your answer is very interesting and I'll try to memorize it.
So again, really really thank you!!