# Example dataset
Data <- data.frame(
ID = sample(c("A", "B", "C", "D"), 50, replace = TRUE),
Act = sample(c("eat", "sleep", "play"), 50, replace = TRUE),
Period = sample(c("pre", "post"), 50, replace = TRUE)
)

# Separate my data by period
DataPre <- as.data.frame(Data[ which(Data $Period == "pre"), ])
DataPost <- as.data.frame(Data[ which(Data $Period == "post"), ])

# Get the minimum # observations for each ID across both periods
Num <- Data %>%
group_by(ID, Period) %>%
summarise(number=n()) %>%
group_by(ID) %>%
summarise(min=min(number))

# Function to get the mean proportion per ID
meanAct <- function(x){
x %>%
group_by(ID, Act) %>%
summarise (n = n()) %>%
mutate(freq = n / sum(n))
}

# See "8888" Here I want to subsample the Num$Min for each ID
DataResults <- function(x, rep){
reps <- replicate(rep, meanAct(x[sample(1:nrow(x), 8888, replace=FALSE),]))
meanfreq <- apply(simplify2array(reps[3, 1:2]), 1, mean)
sd <- apply(simplify2array(reps[3, 1:2]), 1, sd)
lower <- meanfreq - 1.96*(sd/sqrt(8888))
upper <- meanfreq + 1.96*(sd/sqrt(8888))
meanAct <- as.vector(reps[[1]])
output <- data.frame(meanAct, meanfreq, sd, lower, upper)
print(output)
}

# Print results
DataResults(DataPre, 1000)
DataResults(DataPost, 1000)

# Somehow I get the mean for the population by averaging across all IDs
DataMeanGroup <- DataMean %>%
group_by(Period) %>%
summarise (mean = mean(prop))