column value changes in R group find date difference - r

Question

Suppose I have the following DataFrame
ID Result Date
1 Pos 4th Jan, 2020
1 Pos 20th Jan, 2020
1 Neg 21st Jan, 2020
2 Pos 5th Jan, 2020
2 Neg 7th Jan, 2020
I want to record the delta (between days) by ID when the result changes from positive to negative ONLY.
so I would like an answer for this test case as:
ID Result Date Delta Time_Spent_Pos
1 Pos 4th Jan, 2020 0 17
1 Pos 20th Jan, 2020 16 17
1 Neg 21st Jan, 2020 17 17
2 Pos 5th Jan, 2020 0 2
2 Neg 7th Jan, 2020 2 2
Where I plan to use the time_spent_pos column for further analysis.
Further Testing Case
I also would like to point out the data could look like
ID Result Date
1 Neg 12th Dec, 2019
1 Pos 4th Jan, 2020
1 Pos 20th Jan, 2020
1 Neg 21st Jan, 2020
2 Neg 2nd Jan, 2020
2 Pos 5th Jan, 2020
2 Neg 7th Jan, 2020
In which case I would still like the old output. So it is important to find the first time an ID was positive (Record that forever) -> then find the first time it changed to negative. And push the delta to a column.
Any tips + help is appreciated.

Answer 1

You can write a function to do this calculation. Get the first date where result = 'Pos' and subtract it from the immediate next 'Neg' date.
get_delta <- function(res, date) {
d1 <- date[match('Pos', res)]
as.integer(min(date[res == 'Neg' & date > d1]) - d1)
}
library(dplyr)
df %>%
mutate(Date = lubridate::dmy(Date)) %>%
group_by(ID) %>%
mutate(Time_Spent_Pos = get_delta(Result, Date)) %>%
ungroup
# ID Result Date Time_Spent_Pos
# <int> <chr> <date> <int>
#1 1 Pos 2020-01-04 17
#2 1 Pos 2020-01-20 17
#3 1 Neg 2020-01-21 17
#4 2 Pos 2020-01-05 2
#5 2 Neg 2020-01-07 2

Answer 2

So a simple idea is to create two separate columns one whenever the value is positive and another whenever the value is negative then grouping and getting minimum/maximum values for each of these columns.
Here is how you can do it;
# Reading required libraries
library(dplyr)
library(lubridate)
# Create sample dataframes
df <-
data.frame(ID = c(1,1,1,1,2,2,2),
Result = c("Neg", "Pos", "Pos", "Neg", "Neg", "Pos", "Neg"),
Date = c("12th Dec, 2019", "4th Jan, 2020", "20th Jan, 2020",
"21st Jan, 2020", "2nd Jan, 2020", "5th Jan, 2020",
"7th Jan, 2020"))
df %>%
# Convert date into yyyy-mm-dd to easily manipulate it
mutate(Date = dmy(Date),
# In case positive/negative then create a column with value
POSITIVE = as.Date(ifelse(Result == "Pos", Date, NA), origin = lubridate::origin),
NEGATIVE = as.Date(ifelse(Result == "Neg", Date, NA), origin = lubridate::origin)) %>%
# Grouping by ID
group_by(ID) %>%
# Getting firs positive and last negative
mutate(POSITIVE = min(POSITIVE, na.rm = TRUE),
NEGATIVE = max(NEGATIVE, na.rm = TRUE)) %>%
ungroup() %>%
# Calculating difference between positive and negative
mutate(Time_Spent_Pos = NEGATIVE - POSITIVE)