Calculate largest value for multiple overlapping events in a specific range - r

Question

I have multiple large data frames that capture events that last a certain amount of time. This example gives a simplified version of my data set
Data frame 1:
ID Days Date Value
1 10 80 30
1 10 85 30
2 20 75 20
2 10 80 20
3 5 90 30
Data frame 2:
ID Days Date Value
1 20 0 30
1 10 3 20
2 20 5 30
3 20 1 10
3 10 10 10
The same ID is used for the same person in all datasets
Days specifies the length of the event (if Days has the value 10 then the event lasts 10 days)
Date specifies the date at which the event starts. In this case,Date can be any number between 0 and 90 or 91 (the data represent days in quarter)
Value is an attribute that is repeated for the number of Days specified. For example, for the first row in df1, the value 30 is repeated for 10 times starting from day 80 ( 30 is repeated for 10 days)
What I am interested in is to give for each ID in each data frame the highest value per day. Keep in mind that multiple events can overlap and values then have to be summed.
The final data frame should look like this:
ID HighestValuedf1 HighestValuedf2
1 60 80
2 40 30
3 30 20
For example, for ID 1 three events overlapped and resulted in the highest value of 80 in data frame 2. There was no overlap between the events of df1 and df1 for ID 3, only an overlap withing df2.
I would prefer a solution that avoids merging all data frames into one data frame because of the size of my files.
EDIT
I rearranged my data so that all events that overlap are in one data frame. I only need the highest overlap value for every data frame.
Code to reproduce the data frames:
ID = c(1,1,2,2,3)
Date = c(80,85,75,80,90)
Days = c(10,10,20,10,5)
Value = c(30,30,20,20,30)
df1 = data.frame(ID,Days, Date,Value)
ID = c(1,1,2,3,3)
Date = c(1,3,5,1,10)
Days = c(20,10,20,20,10 )
Value =c(30,20,30,10,10)
df2 = data.frame(ID,Days, Date,Value)
ID= c(1,2,3)
HighestValuedf1 = c(60,40,30)
HighestValuedf2 = c(80,30,20)
df3 = data.frame(ID, HighestValuedf1, HighestValuedf2)

Answer 1

I am interpreting highest value per day to mean highest value on a single day throughout the time period. This is probably not the most efficient solution, since I expect something can be done with map or apply functions, but I didn't see how on a first look. Using df1 and df2 as defined above:
EDIT: Modified code upon understanding that df1 and df2 are supposed to represent sequential quarters. I think the easiest way to do this is simply to stack the dataframes so anything that overlaps will automatically be caught (i.e. day 1 of df2 is day 91 overall). You will probably need to either adjust this code manually because of the different length of quarters, or preferably simply convert days of quarters into actual dates of the year with a date formate ((df1 day 1 becomes January 1st 2017, for example). The code below just rearranges to achieve this and then produces the results desired for each quarter by filtering on days 1:90, 91:180 as shown)
ID = c(1,1,2,2,3)
Date = c(80,85,75,80,90)
Days = c(10,10,20,10,5)
Value = c(30,30,20,20,30)
df1 = data.frame(ID,Days, Date,Value)
ID = c(1,1,2,3,3)
Date = c(1,3,5,1,10)
Days = c(20,10,20,20,10 )
Value =c(30,20,30,10,10)
df2 = data.frame(ID,Days, Date,Value)
library(tidyverse)
#> -- Attaching packages --------------------------------------------------------------------- tidyverse 1.2.1 --
#> v ggplot2 2.2.1.9000 v purrr 0.2.4
#> v tibble 1.4.2 v dplyr 0.7.4
#> v tidyr 0.7.2 v stringr 1.2.0
#> v readr 1.1.1 v forcats 0.2.0
#> -- Conflicts ------------------------------------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
df2 <- df2 %>%
mutate(Date = Date + 90)
# Make a dataframe with complete set of day-ID combinations
df_completed <- df1 %>%
mutate(day = factor(Date, levels = 1:180)) %>% # set to total day length
complete(ID, day) %>%
mutate(daysum = 0) %>%
select(ID, day, daysum)
# Function to apply to each data frame containing events
# Should take each event and add value to the appropriate days
sum_df_daily <- function(df_complete, df){
for (i in 1:nrow(df)){
event_days <- seq(df[i, "Date"], df[i, "Date"] + df[i, "Days"] - 1)
df_complete <- df_complete %>%
mutate(
to_add = case_when(
ID == df[i, "ID"] & day %in% event_days ~ df[i, "Value"],
!(ID == df[i, "ID"] & day %in% event_days) ~ 0
),
daysum = daysum + to_add
)
}
return(df_complete)
}
df_filled <- df_completed %>%
sum_df_daily(df1) %>%
sum_df_daily(df2) %>%
mutate(
quarter = case_when(
day %in% 1:90 ~ "q1",
day %in% 91:180 ~ "q2"
)
)
df_filled %>%
group_by(quarter, ID) %>%
summarise(maxsum = max(daysum))
#> # A tibble: 6 x 3
#> # Groups: quarter [?]
#> quarter ID maxsum
#> <chr> <dbl> <dbl>
#> 1 q1 1.00 60.0
#> 2 q1 2.00 40.0
#> 3 q1 3.00 30.0
#> 4 q2 1.00 80.0
#> 5 q2 2.00 30.0
#> 6 q2 3.00 40.0