Calculate cumulative sum after a set period of time - r

Question

I have a data frame with COVID data and I'm trying to make a column calculating the number of recovered people based off of the number of positive tests.
My data has a location, a date, and the number of tests administered/positive results/negative results each day. Here's a few lines using one location as an example (the real data has several months worth of dates):
loc date tests pos neg active
spot1 2020-04-10 1 1 0 5
spot1 2020-04-11 2 1 1 6
spot1 2020-04-12 0 0 0 6
spot1 2020-04-13 11 1 10 7
I want to make a new column that cumulatively counts each positive test in each location 14 days after it is recorded. On 2020-04-24, the 5 active classes are not active anymore, so I want a recovered column with 5. For each date I want the newly nonactive cases to be added.
My first thought was to try it in a loop:
df1 <- df %>%
mutate(date = as.Date(date)) %>%
group_by(loc) %>%
mutate(rec = for (i in 1:nrow(df)) {
#getting number of new cases
x <- df$pos[i]
#add 14 days to the date
d <- df$date + 14
df$rec <- sum(x)
})
As you can see, I'm not the best at writing for loops. That gives me a bunch of numbers, but bear very little meaningful relationship to the data.
Also tried it with map_dbl:
df1 <- df %>%
mutate(date = as.Date(date)) %>%
group_by(loc) %>%
mutate(rec = map_dbl(date, ~sum(pos[(date <= . + 14) & date >= .])))
Which resulted in the same number printed on the entire rec column.
Any suggestions? (Sorry for the lengthy explanation, just want to make sure this all makes sense)

Answer 1

Your sample data shows that -
you have all continuous dates despite 0 tests (12 April)
Active column seems like already a cumsum
Therefore I think you can simply use lag function with argument 14
example code
df %>% group_by(loc) %>% mutate(recovered = lag(active, 14)) %>% ungroup()

Answer 2

You could use aggregate to sum the specific column and then applying
cut in order to set a 14 day time frame for each sum:
df <- data.frame(loc = rep("spot1", 30),
date = seq(as.Date('2020-04-01'), as.Date('2020-04-30'),by = 1),
test = seq(1:30),
positive = seq(1:30),
active = seq(1:30))
output <- aggregate(positive ~ cut(date, "14 days"), df, sum)
output
Console output:
cut(date, "14 days") positive
1 2020-04-01 105
2 2020-04-15 301
3 2020-04-29 59

Answer 3

my solution:
library(dplyr)
date_seq <- seq(as.Date("2020/04/01"), by = "day", length.out = 30)
pos <- rpois(n = 60, lambda = 10)
mydf <-
data.frame(loc = c(rep('loc1', 30), rep('loc2', 30)),
date = date_seq,
pos = pos)
head(mydf)
getPosSum <- function(max, tbl, myloc, daysBack = 14) {
max.Date <- as.Date(max)
sum(tbl %>%
filter(date >= max.Date - (daysBack - 1) &
date <= max.Date & loc == myloc) %>%
select(pos))
}
result <-
mydf %>%
group_by(date, loc) %>%
mutate(rec = getPosSum(max = date, tbl = mydf, myloc = loc))

Answer 4

library(tidyverse)
library(lubridate)
data %>%
mutate(date = as_date(date),
cut = cut(date, '14 days') %>%
group_by(loc) %>%
arrange(cut) %>%
mutate(cum_pos = accumulate(pos, `+`)) # accumulate(pos, sum) should also work
As a general rule of thumb, avoid loops, especially within mutate - that won't work. Instead of map_dbl you should check out purrr::accumulate. There's specialized functions for this in R's base library such as cumsum and cummin but their behavior is a lot less predictable in relation to purrr's.