The Loop in My R Function Appears to be Running Twice

As a data analyst, I have encountered numerous issues with my R functions. One such issue that has been plaguing me recently is the apparent duplication of rows in my dataframe when I run the function. In this article, we will delve into the code and identify the root cause of this problem.

Creating the DataFrame

We begin by creating a sample dataframe df with three rows:

a <- c("1.x", "2.xx", "3.1")
b <- c("single", "double", "nothing")
df <- data.frame(a, b, stringsAsFactors = FALSE)
names(df) <- c("code", "desc")

Our dataframe looks like this:

  code desc
1  1.x single
2 2.xx double
3  3.1 nothing

Defining the Function

Next, we define a function newdf that takes our dataframe as input and returns an expanded version of it.

newdf <- function(df) {
  # If I run through my code chunk by chunk it works as I want it.

  df$expanded <- 0 # a variable to let me know if the loop was run on the row

  emp <- function(){ # This function creates empty vectors for my loop
    assign("codes", c(), envir = .GlobalEnv)
    assign("desc", c(), envir = .GlobalEnv)
    assign("expanded", c(), envir = .GlobalEnv)
  }

  emp()

  # I want to expand xx with numbers 00 - 99 and 0 - 9. 
  # Note: 2.0 is different than 2.00

  # Identifies the rows to be expanded    
  xd <- grep("xx", df$code)

  # Create a vector to loop through
  tens <- formatC(c(0:99)); tens <- tens[11:100]
  ones <- c("00","01","02","03","04","05","06","07","08","09")
  single <- as.character(c(0:9))
  exp <- c(single, ones, tens)

  # This loop appears to run twice when I run the function: newdf(df) 
  # Each row is there twice: 2.00, 2.00, 2.01 2.01... 
  # It runs as I want it to if I just highlight the code. 

  for (i in xd){
    for (n in exp) {
      codes <- c(codes, gsub("xx", n, df$code[i])) #expanding the number
      desc <- c(desc, df$desc[i])  # repeating the description
      expanded <- c(expanded, 1) # assigning 1 to indicated the row has been expanded
    }
  }

  # Binds the df with the new expansion
  df <- df[-xd, ]
  df <- rbind(as.matrix(df),cbind(codes,desc,expanded))
  df <- as.data.frame(df, stringsAsFactors = FALSE)

  # Empties the vector to begin another expansion
  emp()
  xs <- grep("x", df$code) # This is for the single digit expansion

  # Expands the single digits. This part of the code works fine inside the function.
  for (i in xs){
    for (n in 0:9) {
      codes <- c(codes, gsub("x", n, df$code[i]))
      desc <- c(desc, df$desc[i])
      expanded <- c(expanded, 1)
    }
  }

  df <- df[-xs,]
  df <- rbind(as.matrix(df), cbind(codes,desc,expanded))
  df <- as.data.frame(df, stringsAsFactors = FALSE)

  assign("out", df, envir = .GlobalEnv) # This is how I view my dataframe after I run the function.
}

Calling the Function

Finally, we call our function newdf with our original dataframe as input:

newdf(df)

But instead of getting a beautifully expanded version of our dataframe, we get an error message indicating that there is something wrong with the code.

Identifying the Problem

After carefully examining the code, I realized that the issue lies in the use of assign function. The assign function is used to assign a value to a variable. However, when using assign inside a loop, it can lead to unexpected behavior and even crashes the R environment.

In our case, we are trying to modify the same vector codes within the inner loop. This causes the previous values to be lost, leading to incorrect results.

A Solution

To fix this issue, we can create a new vector for each iteration of the inner loop instead of modifying an existing one:

for (i in xd){
  for (n in exp) {
    codes <- c(codes, gsub("xx", n, df$code[i]))
    desc <- c(desc, df$desc[i])
    expanded <- c(expanded, 1)
  }
}

becomes:

for (i in xd){
  temp_codes <- c()
  for (n in exp) {
    temp_codes <- c(temp_codes, gsub("xx", n, df$code[i]))
  }
  codes <- c(codes, temp_codes)
  desc <- c(desc, df$desc[i])
  expanded <- c(expanded, 1)
}

By creating a new vector temp_codes for each iteration of the inner loop, we ensure that the values are not lost and the code produces the correct results.

Conclusion

In conclusion, the issue with the duplication of rows in our dataframe was caused by using the assign function inside a loop. By creating a new vector for each iteration of the inner loop, we can fix this problem and produce the desired output.

Last modified on 2024-04-20