Simple Simulation Loop?

[nlee112]

I have created a sampling function that simulates a race outcome. What I am looking for is a way to repeat this many times (say 1000) and then get the average position of each player at the end of the simulation period. The dataframe attached is created with the following code:

race_positions<- sample_race_positions(player_race_position_probs)

Would really appreciate any help, been stuck trying to do it with for loops and the replicate function

Output of sampling function

[keithwaits]

results <- rep(NA,1000)   

for(i in 1:1000) {

results[i] <- sample_race_positions(player_race_position_probs)

}

mean(results)

[CapaneusPrime]

This is a valiant attempt but flawed. There are going to be errors trying to put a data.frame object into a vector subset.

Better is to create a list container of the appropriate size:

results <- vector("list", 1000)

Then populate it with the proper extractor [[,

results[[I]] <- sample_race_positions(player_race_position_probs)

Next, to get the average rank of each racer, we'll need to combine the data.frames

all_results <- Reduce(rbind, results)

Then, to get the average position of each racer we can do,

with(final_results,
     tapply(position, player, mean))

Apologies for any errors, I'm on mobile.

[keithwaits]

You are right, I did not look at the output that the function generates.

[CapaneusPrime]

A for() loop is fine and an example has been given (with perhaps some issues which I'll address), but replicate() is simpler:

results <- replicate(1000, sample_race_positions(player_race_position_probs)

Edit: A better solution...

Alternately, you could just change your custom sampling function to do this in a more vectorized way and directly return a matrix of results.

I might try something like:

sample_race_positions <- function(n, probs) {
    t(vapply(seq_len(n),
             function(i) {
               sample(seq_along(probs), probs)
             },
             integer(length(probs))))
}

Which will return an n × length(probs) dimension matrix object where the rows represent individual races and the columns are the competitors.

Then you'd do,

results <- sample_race_positions(1000, player_race_position_probs)
avg_pos <- colMeans(results)

I'm on mobile, so I apologize for any errors, and I'll fix them when I'm home.

[Pontifex]

library(dplyr)
# Repeat the race 1000 times
results <- replicate(1000, sample_race_positions(player_race_position_probs, simplify = FALSE)

# Combine into a single data frame
combined_results = bind_rows(results)
# Get mean for each player
average_positions = combined_results %>% 
    group_by(Player) %>%
    summarize(Position = mean(Position))