I want to compare all scores in a certain range (from i until the index that belongs to the range of i) to the last baseline score, and update the baseline score in a recursive way. The range is based on the corresponding index that meets the minimal time difference required to be able to confirm a new baseline. If all scores in this range are lower than the last baseline score, then I want the new baseline to become the highest value of all scores in the range (ie, the closest to the old baseline).
df <- tibble(
i = c("1", "2", "3", "4", "5", "6", "7", "8", "9"),
range_index = c("2", "4", "4", "5", "7", "7", "9", "9", "NA"),
score = c("5", "4", "4", "3", "2", "2", "3", "1", "1"))
I am looking to do something like this in sapply or a for loop:
df <- df %>%
mutate(
baseline = first(score),
baseline = sapply(1:n(), function(i) {
if (all(score[i]:score[range_index[i]]) < baseline[i-1]) {return(max(score[i]:score[range_index[i]]))}
else {return(baseline[i-1])}}))
But I think score[i]:score[range[i]] doesn't compare all scores to the last baseline. How can I create a condition that is true if each of these scores are lower than the last baseline?
The desired outcome is:
baseline = c("5", "4", "4", "3", "3", "3", "3", "1", "1")
Explanation: the first baseline is 5. At i=2 the new baseline is set to 4, as all scores between i=2 and i=4 (corresponding range) are lower than 5. The new baseline is 4, and not 3, because 4 is the greatest score out of i=2 until i=4. At i=4 we obtain the new baseline 3, because all scores in the range (score[4]=3, score[5]=2) are lower than the last baseline, which was 4. At i=5, we don't obtain a new baseline despite the decrease, because the range includes i=7, and score[7] (==3) is not lower than the last baseline (==3). The new baseline at i=8 is obtained as all scores i[8:9] are lower than the last baseline of 3.
I think it's best to iterate with a for loop.
library(tidyverse)
df <- tibble(id = rep(1:2, each = 9),
range_index = rep(c(2,4,4,5,7,7,9,9,NA), 2),
score = c(5,4,4,3,2,2,3,1,1,5,4,4,3,2,2,3,1,0))
df %>%
group_by(id) %>%
mutate(
baseline = {
baseline <- score
for (i in 2:(n() - 1)) {
baseline[i] <- min(baseline[i - 1], max(score[i]:score[range_index[i]]))
}
baseline[n()] <- baseline[n() - 1]
baseline
}
)
#> # A tibble: 18 × 4
#> # Groups: id [2]
#> id range_index score baseline
#> <int> <dbl> <dbl> <dbl>
#> 1 1 2 5 5
#> 2 1 4 4 4
#> 3 1 4 4 4
#> 4 1 5 3 3
#> 5 1 7 2 3
#> 6 1 7 2 3
#> 7 1 9 3 3
#> 8 1 9 1 1
#> 9 1 NA 1 1
#> 10 2 2 5 5
#> 11 2 4 4 4
#> 12 2 4 4 4
#> 13 2 5 3 3
#> 14 2 7 2 3
#> 15 2 7 2 3
#> 16 2 9 3 3
#> 17 2 9 1 1
#> 18 2 NA 0 1