Pfthb

I'm quite new to all the packages meant for calculating rolling averages in R and I hope you can show me in the right direction.

I have the following data as an example:

ms <- c(300, 300, 300, 301, 303, 305, 305, 306, 308, 310, 310, 311, 312,
 314, 315, 315, 316, 316, 316, 317, 318, 320, 320, 321, 322, 324,
 328, 329, 330, 330, 330, 332, 332, 334, 334, 335, 335, 336, 336,
 337, 338, 338, 338, 340, 340, 341, 342, 342, 342, 342)
correct <- c(1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1,
 1, 0, 0, 1, 0, 0, 1, 1, 0, 0)
library(dplyr)
df <- as.data.frame(data_frame(ms, correct))

"ms" are time points in milliseconds and "correct" is whether a specific action is performed correctly (1= correct, 0=not correct).

My goal now is that I'd like to calculate the percentage correct (or average) over windows of a set number of milliseconds. As you can see, certain time points are missing and certain time points occur multiple times. I, therefore, do not want to do a filter based on row number. I've looked into some packages such as "tidyquant" but it seems to me that these kind of packages need a time/date variable instead of a numerical variable to determine the window over which values are averaged. Is there a way to specify the window on the numerical value of df$ms?

Many thanks!

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

You can try 'cut'. For example, if you want to divide ms such that you have 5 groups overall then you can do:

df$ms_factor <- cut(df$ms, 5)

df_new <- df %>% group_by(ms_factor) %>% summarise(mean = mean(correct)) 

This could be done with base R:

calculate_irregular_ratio <- function(df, time_var = "ms", window_var = 5, calc_var = "correct") 

sapply(df[[time_var]], function(x) round(mean(df[[calc_var]][df[[time_var]] >= (x - window_var) & df[[time_var]] <= x]), 2))

You can apply it as follows (the default is set to 5 ms, you can change it with changing the window_var parameter):

df$window_5_ratio <- calculate_irregular_ratio(df, window_var = 5)

In your case, you would get (first 10 rows shown only):

 ms correct window_5_ratio
1 300 1 0.67
2 300 1 0.67
3 300 0 0.67
4 301 0 0.50
5 303 0 0.40
6 305 0 0.29
7 305 0 0.29
8 306 1 0.20
9 308 0 0.20
10 310 0 0.17

It behaves like a rolling mean, however it does not rely on rows. Instead, it takes the window based on values in a column.

For instance, at rows 6 and 7, it takes the value of current row (305 ms), and calculates the ratio on all the values in dataframe that are 305 and - 5, i.e. between 305 and 300, yielding 0.29.

You can of course always modify the function yourself, e.g. if you'd like window 5 to actually mean 301 - 305 and not 300 - 305, you can set + 1 after x - window_var, etc.