r calculating rolling average with window based on value (not number of rows or date/time variable)
Try out:
library(dplyr)
# count the number of values per ms
df <- df %>%
group_by(ms) %>%
mutate(Nb.values = n())
# consider a window of 1 ms and compute the percentage for each window
df2 <- setNames(aggregate(correct ~ factor(df$ms, levels = as.character(seq(min(df$ms), max(df$ms), 1))),
df, sum),
c("ms", "Count.correct"))
# complete data frame (including unused levels)
df2 <- tidyr::complete(df2, ms)
df2$ms <- as.numeric(levels(df2$ms))[df2$ms]
df2 <- df2 %>% left_join(distinct(df[, c(1, 3)]), "ms")
# compute a rolling mean of the percentage of correct, with a width of 5
df2 %>%
mutate(Window = paste(ms, ms+4, sep = "-"), # add windows
Rolling.correct = zoo::rollapply(Count.correct, 5, sum, na.rm = T,
partial = TRUE, fill = NA, align = "left") /
zoo::rollapply(Nb.values, 5, sum, na.rm = T, partial = TRUE,
fill = NA, align = "left")) # add rolling mean
# A tibble: 43 x 5
ms Count.correct Nb.values Window Rolling.correct
<dbl> <dbl> <int> <chr> <dbl>
1 300 2 3 300-304 0.40
2 301 0 1 301-305 0.00
3 302 NA NA 302-306 0.25
4 303 0 1 303-307 0.25
5 304 NA NA 304-308 0.25
6 305 0 2 305-309 0.25
7 306 1 1 306-310 0.25
8 307 NA NA 307-311 0.00
9 308 0 1 308-312 0.20
10 309 NA NA 309-313 0.25
# ... with 33 more rows
For the sake of completeness, here is an answer which uses data.table to aggregate in a non-equi join.
The OP has clarified in comments, that he is looking for a sliding window of 5 ms, i.e., windows that go 300-304, 301-305, 302-306 etc.
As there is no data point with 302 ms in OP's data set, the missing values need to be filled up.
library(data.table)
ws <- 5 # define window size
setDT(df)[SJ(start = seq(min(ms), max(ms), 1))[, end := start + ws - 1],
on = .(ms >= start, ms <= end),
.(share_correct = mean(correct)), by = .EACHI]
ms ms share_correct 1: 300 304 0.4000000 2: 301 305 0.0000000 3: 302 306 0.2500000 4: 303 307 0.2500000 5: 304 308 0.2500000 6: 305 309 0.2500000 7: 306 310 0.2500000 8: 307 311 0.0000000 9: 308 312 0.2000000 10: 309 313 0.2500000 11: 310 314 0.2000000 12: 311 315 0.4000000 13: 312 316 0.4285714 14: 313 317 0.2857143 15: 314 318 0.3750000 16: 315 319 0.4285714 17: 316 320 0.4285714 18: 317 321 0.4000000 19: 318 322 0.4000000 20: 319 323 0.2500000 21: 320 324 0.4000000 22: 321 325 0.3333333 23: 322 326 0.5000000 24: 323 327 1.0000000 25: 324 328 1.0000000 26: 325 329 0.5000000 27: 326 330 0.2000000 28: 327 331 0.2000000 29: 328 332 0.4285714 30: 329 333 0.3333333 31: 330 334 0.2857143 32: 331 335 0.5000000 33: 332 336 0.3750000 34: 333 337 0.2857143 35: 334 338 0.3000000 36: 335 339 0.3750000 37: 336 340 0.3750000 38: 337 341 0.4285714 39: 338 342 0.4000000 40: 339 343 0.4285714 41: 340 344 0.4285714 42: 341 345 0.4000000 43: 342 346 0.5000000 ms ms share_correct
If the OP would be interested only in windows where the starting point exist in the dataset the code can be simplified:
setDT(df)[SJ(start = unique(ms))[, end := start + ws - 1],
on = .(ms >= start, ms <= end),
.(share_correct = mean(correct)), by = .EACHI]
ms ms share_correct 1: 300 304 0.4000000 2: 301 305 0.0000000 3: 303 307 0.2500000 4: 305 309 0.2500000 5: 306 310 0.2500000 6: 308 312 0.2000000 7: 310 314 0.2000000 8: 311 315 0.4000000 9: 312 316 0.4285714 10: 314 318 0.3750000 11: 315 319 0.4285714 12: 316 320 0.4285714 13: 317 321 0.4000000 14: 318 322 0.4000000 15: 320 324 0.4000000 16: 321 325 0.3333333 17: 322 326 0.5000000 18: 324 328 1.0000000 19: 328 332 0.4285714 20: 329 333 0.3333333 21: 330 334 0.2857143 22: 332 336 0.3750000 23: 334 338 0.3000000 24: 335 339 0.3750000 25: 336 340 0.3750000 26: 337 341 0.4285714 27: 338 342 0.4000000 28: 340 344 0.4285714 29: 341 345 0.4000000 30: 342 346 0.5000000 ms ms share_correct
In both cases, a data.table containing the intervals [start, end] is created on the fly and right joined to df. During the non-equi join, the intermediate result is immediately grouped by the join parameters (by = .EACHI) and aggregated. Note that closed intervals are used to be in line with OP's expectations.