I am new in R, I have collected eye-tracking data that has the following structure:
Participant Trial Condition Fixation.Start Fixation.End Fixated.Area
P01 T01 Early 4 206 Outside
P01 T01 Early 258 476 Competitor
P01 T01 Early 496 882 Target
P01 T02 Late 4 794 Outside
P01 T02 Late 838 1026 Target
P01 T02 Late 1046 1328 Target
P02 T01 Early 4 168 Outside
P02 T01 Early 232 452 Competitor
P02 T01 Early 494 738 Target
P02 T02 Late 4 176 Outside
P02 T02 Late 238 466 Target
P02 T02 Late 524 632 Competitor
In it, the fixation time to the different areas shown on screen was measured in milliseconds form beginning (Fixiation Start) to end (Fixation End). Each row is a fixation.
What I would like to do is to reshape the data into time bins of 50ms in a new dataframe so that each time bin (row) reflects what area was being fixated at that moment. In other words, I want the new dataframe to look like this:
Participant Trial Condition Time.Bin Fixated.Area
P01 T01 Early 50 Outside
P01 T01 Early 100 Outside
P01 T01 Early 150 Outside
P01 T01 Early 200 Outside
P01 T01 Early 250 Competitor
P01 T01 Early 300 Competitor
P01 T01 Early 350 Competitor
P01 T01 Early 400 Competitor
P01 T01 Early 450 Competitor
P01 T01 Early 500 Target
P01 T01 Early 550 Target
P01 T01 Early 600 Target
P01 T01 Early 650 Target
I think this should be pretty easy to do in R. Any ideas?
Here's a technique that expands each timeframe into by=50 time bins.
Time.Bins <- Map(
function(a, b) seq(a, b, by = 50),
ceiling(dat$Fixation.Start / 50) * 50,
dat$Fixation.End)
out <- cbind(
dat[, c("Participant", "Trial", "Condition", "Fixated.Area")
][ rep(seq_len(nrow(dat)), lengths(Time.Bins)),],
Time.Bin = unlist(Time.Bins)
)
head(out, 20)
# Participant Trial Condition Fixated.Area Time.Bin
# 1 P01 T01 Early Outside 50
# 1.1 P01 T01 Early Outside 100
# 1.2 P01 T01 Early Outside 150
# 1.3 P01 T01 Early Outside 200
# 2 P01 T01 Early Competitor 300
# 2.1 P01 T01 Early Competitor 350
# 2.2 P01 T01 Early Competitor 400
# 2.3 P01 T01 Early Competitor 450
# 3 P01 T01 Early Target 500
# 3.1 P01 T01 Early Target 550
# 3.2 P01 T01 Early Target 600
# 3.3 P01 T01 Early Target 650
# 3.4 P01 T01 Early Target 700
# 3.5 P01 T01 Early Target 750
# 3.6 P01 T01 Early Target 800
# 3.7 P01 T01 Early Target 850
# 4 P01 T02 Late Outside 50
# 4.1 P01 T02 Late Outside 100
# 4.2 P01 T02 Late Outside 150
# 4.3 P01 T02 Late Outside 200
library(dplyr)
out <- dat %>%
rowwise() %>%
summarize(
Participant, Trial, Condition, Fixated.Area,
Time.Bin = seq(ceiling(Fixation.Start / 50) * 50, Fixation.End, by = 50),
.groups = "drop"
)
out
# # A tibble: 64 x 5
# Participant Trial Condition Fixated.Area Time.Bin
# <chr> <chr> <chr> <chr> <dbl>
# 1 P01 T01 Early Outside 50
# 2 P01 T01 Early Outside 100
# 3 P01 T01 Early Outside 150
# 4 P01 T01 Early Outside 200
# 5 P01 T01 Early Competitor 300
# 6 P01 T01 Early Competitor 350
# 7 P01 T01 Early Competitor 400
# 8 P01 T01 Early Competitor 450
# 9 P01 T01 Early Target 500
# 10 P01 T01 Early Target 550
# # ... with 54 more rows
Your expected output shows "Competitor" at time=250, but the data does not support that. If you need 250 (with or without an area), then you can interpolate this way.
expbins <- do.call(rbind, by(out, out[,c("Participant", "Trial", "Condition")],
FUN = function(z) {
rng <- seq(min(z$Time.Bin), max(z$Time.Bin), by = 50)
transform(z[rep(1, length(rng)),], Fixated.Area = NULL, Time.Bin = rng)
}))
out2 <- merge(expbins, out, by = c("Participant", "Trial", "Condition", "Time.Bin"), all = TRUE)
head(out2, 10)
# Participant Trial Condition Time.Bin Fixated.Area
# 1 P01 T01 Early 50 Outside
# 2 P01 T01 Early 100 Outside
# 3 P01 T01 Early 150 Outside
# 4 P01 T01 Early 200 Outside
# 5 P01 T01 Early 250 <NA>
# 6 P01 T01 Early 300 Competitor
# 7 P01 T01 Early 350 Competitor
# 8 P01 T01 Early 400 Competitor
# 9 P01 T01 Early 450 Competitor
# 10 P01 T01 Early 500 Target
which presents the time=250 as NA, an unknown state (which is better, imo).
Dplyr, same:
out %>%
group_by(Participant, Trial, Condition) %>%
summarize(
Time.Bin = seq(min(Time.Bin), max(Time.Bin), by = 50),
.groups = "drop"
) %>%
full_join(out, by = c("Participant", "Trial", "Condition", "Time.Bin"))
# # A tibble: 69 x 5
# Participant Trial Condition Time.Bin Fixated.Area
# <chr> <chr> <chr> <dbl> <chr>
# 1 P01 T01 Early 50 Outside
# 2 P01 T01 Early 100 Outside
# 3 P01 T01 Early 150 Outside
# 4 P01 T01 Early 200 Outside
# 5 P01 T01 Early 250 <NA>
# 6 P01 T01 Early 300 Competitor
# 7 P01 T01 Early 350 Competitor
# 8 P01 T01 Early 400 Competitor
# 9 P01 T01 Early 450 Competitor
# 10 P01 T01 Early 500 Target
# # ... with 59 more rows
Data:
dat <- structure(list(Participant = c("P01", "P01", "P01", "P01", "P01", "P01", "P02", "P02", "P02", "P02", "P02", "P02"), Trial = c("T01", "T01", "T01", "T02", "T02", "T02", "T01", "T01", "T01", "T02", "T02", "T02"), Condition = c("Early", "Early", "Early", "Late", "Late", "Late", "Early", "Early", "Early", "Late", "Late", "Late"), Fixation.Start = c(4L, 258L, 496L, 4L, 838L, 1046L, 4L, 232L, 494L, 4L, 238L, 524L), Fixation.End = c(206L, 476L, 882L, 794L, 1026L, 1328L, 168L, 452L, 738L, 176L, 466L, 632L), Fixated.Area = c("Outside", "Competitor", "Target", "Outside", "Target", "Target", "Outside", "Competitor", "Target", "Outside", "Target", "Competitor")), class = "data.frame", row.names = c(NA, -12L))