R语言中基于排序变量的状态变更：暴露人群状态更新的代码条件实现

First, let's clarify and correct some initial setup steps, then implement the conditional logic in your loop.

Step 1: Proper Initial Setup #

First, define your initial data frame and the sum_exposed vector correctly (your original syntax for sum(exposed[i]) was invalid):

# Initial data frame
df <- data.frame(
  i = 1:5,
  exposed = c("y", "y", "y", "n", "n"),
  index = c(22, 12, 6, 54, 3)
)

t <- 5
# Total exposed individuals at each time point (t=1 to t=5)
sum_exposed <- c(3, 4, 1, 4, 5)

# Initialize evolution list
evol <- list()
for(i in 1:t){evol[[i]] <- df}

Step 2: Implement the Update Logic #

Now, fill in the loop with conditional logic that handles both increasing and decreasing exposed counts:

for (i in 2:t) { 
  # Get the previous time point's data
  prev_df <- evol[[i-1]]
  
  # Calculate the change in exposed count
  prev_sum <- sum_exposed[i-1]
  current_sum <- sum_exposed[i]
  delta <- current_sum - prev_sum
  
  # Make a copy of the previous data to modify
  new_df <- prev_df
  
  if (delta > 0) {
    # Case 1: Need to add delta exposed individuals
    # Get non-exposed individuals, sort by index descending, pick top delta
    non_exposed <- prev_df[prev_df$exposed == "n", ]
    non_exposed_sorted <- non_exposed[order(-non_exposed$index), ]
    to_convert <- non_exposed_sorted$i[1:delta]
    
    # Update their exposed status
    new_df$exposed[new_df$i %in% to_convert] <- "y"
  } else if (delta < 0) {
    # Case 2: Need to remove (-delta) exposed individuals
    num_to_convert <- -delta
    # Get exposed individuals, sort by index ascending, pick top num_to_convert
    exposed_ind <- prev_df[prev_df$exposed == "y", ]
    exposed_sorted <- exposed_ind[order(exposed_ind$index), ]
    to_convert <- exposed_sorted$i[1:num_to_convert]
    
    # Update their exposed status
    new_df$exposed[new_df$i %in% to_convert] <- "n"
  }
  
  # Save the updated data to the evolution list
  evol[[i]] <- new_df
}

How It Works #

Let's break down the logic to match your examples:

• From evol[[1]] to evol[[2]]: sum_exposed increases from 3 to 4 (delta=1). We take the non-exposed individual with the highest index (individual 4, index=54) and change their status to "y".
• From evol[[2]] to evol[[3]]: sum_exposed decreases from 4 to 1 (delta=-3). We take the 3 exposed individuals with the lowest indices (individuals 3, 2, 1 with indices 6, 12, 22) and change their status to "n".

Verification #

After running the code, you can check the results with:

# View evol[[2]]
evol[[2]]
# View evol[[3]]
evol[[3]]

These will match the exact examples you provided.

内容的提问来源于stack exchange，提问作者Angelo_231