每次迭代都调用新的数据框：遍历数据框？

Question

I am designing a simulation that iteratively runs a block of code using inputs from two separate data frames ( df and year ). The resulting data frame is a modified version of df , which is then saved on my hard-drive under two separate file names: one that is permanently stored for future analysis, and another that is recalled for the next iteration.

Here is my problem: The data frame year needs to be a completely new data frame for each iteration (ie, the following year's data).

Could this be accomplished with something like a for loop, where the index [i] is the next year's data frame (rather than a row within a data frame, which is how I understand for loops to operate)? I suspect the answer involves a list? Here are some dummy data attempting to demonstrate the issue:

df <- tibble(
  x = 1:25,
  y = rnorm(25, 22, 8))

year1990 <- tibble(
  Year = 1990, 
  DayOfYear = 1:6, 
  temp = seq(0, 20, 4))

year1991 <- tibble(
  Year = 1991, 
  DayOfYear = 1:6, 
  temp = seq(0, 25, 5))

year1992 <- tibble(
  Year = 1992, 
  DayOfYear = 1:6, 
  temp = seq(0, 15, 3))

#### Beginning of Code to Be Repeated ####

year <- year1990         # Start with this year, BUT each subsequent iteration needs the following year's data
df$survive <- ifelse(max(year$temp) <= df$y, "Dead", "Live")
write.csv(df, "location/f.csv",row.names=FALSE)  # Write temporary CSV to be recalled
write.csv(df, paste(year[1,1], ".csv", sep = ""), row.names = FALSE)      # Write permanent CSV for storage

#### End of Code to Be Repeated ####

# Reload the newly modified data frame
setwd()
df <- read.csv("df.csv")  

Currently, I manually reload df and reset the year for each iteration (eg, I would reassign year using year1991 for the second iteration in this example), but I'm certain there's a better way to automate the entire process. Thank you!

Answer 1

Simply save objects in a named list (which can be created if they originally were in one data frame with split or by ). Then loop elementwise with Map (wrapper to mapply ) through list's names and objects through a defined process for looping

year_list <- list(
  year1990 = tibble(Year = 1990, DayOfYear = 1:6, temp = seq(0, 20, 4)),    
  year1991 = tibble(Year = 1991, DayOfYear = 1:6, temp = seq(0, 25, 5)),    
  year1992 = tibble(Year = 1992, DayOfYear = 1:6, temp = seq(0, 15, 3))
)

proc <- function(n, d) {    
    year <- d        
    df$survive <- ifelse(max(year$temp) <= df$y, "Dead", "Live")
    write.csv(df, "location/f.csv", row.names = FALSE)  

    # Write temporary CSV to be recalled
    write.csv(df, paste0(n, ".csv"), row.names = FALSE)  

    return(df)
}

# ITERATIVELY SAVES CSVs AND RETURNS dfs WITH UPDATED survive COLUMN
output_list <- Map(proc, names(year_list), year_list)