Classify table based on value 'moving window' range and proportions? - r

Question

I have a datasets of forest stands, each containing several tree layers of different age and volume.
I want to classify the stands as even- or uneven-aged, combining volume and age data. The forest is considered even-aged if more then 80% of the volume is allocated to age classes within 20 years apart. I wonder how to implement the 'within 20 years apart' condition? I can easily calculate the sum of volume and it's share for individual tree layers (strat). But how to check for 'how many years they are apart?' Is it some sort of moving window?
Dummy example:
# investigate volume by age classes?
library(dplyr)
df <- data.frame(stand = c("id1", "id1", "id1", "id1",
'id2', 'id2', 'id2'),
strat = c(1,2,3,4,
1,2,3),
v = c(4,10,15,20,
11,15,18),
age = c(5,10,65,80,
10,15,20))
# even age = if more of teh 80% of volume is allocated in layers in 20 years range
df %>%
group_by(stand) %>%
mutate(V_tot = sum(v)) %>%
mutate(V_share = v/V_tot*100)
Expected outcome:
stand strat v age V_tot V_share quality
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 id1 1 4 5 49 8.16 uneven-aged
2 id1 2 10 10 49 20.4 uneven-aged
3 id1 3 15 65 49 30.6 uneven-aged
4 id1 4 20 80 49 40.8 uneven-aged #* because age classes 65 and 80, even less then 20 years apart have only 70% of total volume
5 id2 1 11 10 44 25 even-aged
6 id2 2 15 15 44 34.1 even-aged
7 id2 3 18 20 44 40.9 even-aged

Answer 1

Another tidyverse solution implementing a moving average:
library(tidyverse)
df <- structure(list(stand = c("id1", "id1", "id1", "id1", "id2", "id2", "id2"), strat = c(1, 2, 3, 4, 1, 2, 3), v = c(4, 10, 15, 20, 11, 15, 18), age = c(5, 10, 65, 80, 10, 15, 20), V_tot = c(49, 49, 49, 49, 44, 44, 44), V_share = c(8.16326530612245, 20.4081632653061, 30.6122448979592, 40.8163265306122, 25, 34.0909090909091, 40.9090909090909)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -7L))
df %>%
group_by(stand) %>%
mutate(range20 = map_dbl(age, ~ sum(V_share[which(abs(age - .x) <= 20)])),
quality = ifelse(any(range20 > 80), "even-aged", "uneven-aged"))
#> # A tibble: 7 × 8
#> # Groups: stand [2]
#> stand strat v age V_tot V_share range20 quality
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 id1 1 4 5 49 8.16 28.6 uneven-aged
#> 2 id1 2 10 10 49 20.4 28.6 uneven-aged
#> 3 id1 3 15 65 49 30.6 71.4 uneven-aged
#> 4 id1 4 20 80 49 40.8 71.4 uneven-aged
#> 5 id2 1 11 10 44 25 100 even-aged
#> 6 id2 2 15 15 44 34.1 100 even-aged
#> 7 id2 3 18 20 44 40.9 100 even-aged
Created on 2021-09-08 by the reprex package (v2.0.1)

Answer 2

Interesting issue, I think I have a solution using the runner package
df %>%
group_by(stand) %>%
mutate(
V_tot = sum(v),
V_share = v/V_tot*100,
test = sum_run(
V_share,
k = 20L,
idx = age,
na_rm = TRUE,
na_pad = FALSE
),
quality = if_else(any(test >= 80), 'even-aged', 'uneven-aged')
) %>%
select(-test)