I have 4 different Columns. The first is ID’s, the second is Names, the third is Start_Date and the fourth is End_Date. For ID 1 there can be multiple rows with different names and dates. I am trying to write a code that starts out by examining for ID 1, if the name A comes before name B, what is the difference between End_Date for A and Start_Date for B. For example:
ID 1, A, 20-4-2021, 20-5-2021
ID 1, B, 21-4-2021, 22-5-2021
ID 1, C, 22-5-2021, 25-5-2021
So in this example a new column should say 1 day under ID 1.
I was thinking about using a IF AND function, but I am unsure of how to write in R, and whether R is the right program for the task.
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If I understand your questions correctly, what you want can be solved by some functions from the tidyverse package (especially the lag / lead ones).
myData = data.frame(
ID = rep(1:2, each = 3),
Names = LETTERS[1:3],
Start_Date = as.Date(c("20-4-2021", "21-4-2021", "22-5-2021"), format = "%d-%m-%Y"),
End_Date = as.Date(c("20-5-2021", "22-5-2021", "25-5-2021"), format = "%d-%m-%Y")
myData = myData %>%
timeDiff = difftime(lag(Start_Date), End_Date, units = "days")
#> # A tibble: 6 x 5
#> # Groups: ID 
#> ID Names Start_Date End_Date timeDiff
#> <int> <chr> <date> <date> <drtn>
#> 1 1 A 2021-04-20 2021-05-20 NA days
#> 2 1 B 2021-04-21 2021-05-22 -32 days
#> 3 1 C 2021-05-22 2021-05-25 -34 days
#> 4 2 A 2021-04-20 2021-05-20 NA days
#> 5 2 B 2021-04-21 2021-05-22 -32 days
#> 6 2 C 2021-05-22 2021-05-25 -34 days
The group_by function makes sure we only evaluate the data per ID (I created an ID 2 to show this)
I assume you always want to make the difference between the current and previous row, so this can be done using the lag function which shifts data of certain columns by any amount (default 1). Not the NA values where there is no previous row.
The difftime function actually calculated the different in days
If you like to learn more about the Tidyverse, check this link