Removing Duplicated Rows within and between Data Frames Stored in a List

In this blog post, we’ll explore how to remove duplicated rows both within and between data frames stored in a list. We’ll delve into the details of using R programming language’s functionality for this task.

Introduction

Data manipulation is an essential aspect of data science. One common problem that arises when working with data frames is duplicate rows. Duplicate rows can lead to inaccurate results, incorrect conclusions, and even misrepresentations of data. In this article, we’ll discuss how to remove duplicated rows both within a single data frame and between multiple data frames stored in a list.

The Problem

We’re given an example where we have a list of data frames:

my_list <- list(
  structure(list("_uuid" = c("xxxyz", "xxxyz", "zzuio", "iiopz"), country = c("USA", "USA", "Canada", "Switzerland")), class = "data.frame", row.names = c(NA, -4L)),
  structure(list("_uuid" = c("xxxyz", "ppuip", "zzuio"), country = c("USA", "Canada", "Canada")), class = "data.frame", row.names = c(NA, -3L))
)

We want to remove duplicated rows both within and between data frames stored in my_list. We can achieve this by first removing duplicates within each individual data frame using duplicated() function.

# Remove duplicates within each individual data frame
my_list <- lapply(my_list, function(z) z[!duplicated(z[["_uuid"]]),])

However, this approach leaves duplicated rows between different data frames. To remove these as well, we need to find common IDs across all data frames.

Finding Common IDs Across Data Frames

We can use Reduce() function in combination with rev() and accumulate() to achieve this:

# Find the common IDs across all data frames
previous_ids <- rev(Reduce(
  function(prev, this) unique(c(prev, this[["_uuid"]])),
  rev(my_list), init = character(0), accumulate = TRUE))[-1]

Removing Duplicates

Now that we have found the common IDs to be removed, we can use a Map() function to apply the removal operation to each data frame in the list.

# Remove duplicates across all data frames using Map()
my_list <- Map(my_list, previous_ids,
  f = function(dat, rmid) {
    dat[!duplicated(dat[["_uuid"]], fromLast = TRUE) &amp; !dat[["_uuid"]] %in% rmid,]
  })

Example

Let’s apply this solution to the given example:

# Apply the solution to the given example
my_list <- list(
  structure(list("_uuid" = c("xxxyz", "xxxyz", "zzuio", "iiopz"), country = c("USA", "USA", "Canada", "Switzerland")), class = "data.frame", row.names = c(NA, -4L)),
  structure(list("_uuid" = c("xxxyz", "ppuip", "zzuio"), country = c("USA", "Canada", "Canada")), class = "data.frame", row.names = c(NA, -3L))
)

previous_ids <- rev(Reduce(
  function(prev, this) unique(c(prev, this[["_uuid"]])),
  rev(my_list), init = character(0), accumulate = TRUE))[-1]

my_list <- Map(my_list, previous_ids,
  f = function(dat, rmid) {
    dat[!duplicated(dat[["_uuid"]], fromLast = TRUE) &amp; !dat[["_uuid"]] %in% rmid,]
  })

# Print the resulting list
print(my_list)

When you run this code, it will output:

$[[1]]
   _uuid     country
iiopz Switzerland

$[[2]]
   _uuid      country
xxxyz         USA
ppuip        Canada
zzuio       Canada

As expected, the first data frame has been reduced to only one row (with duplicated _uuid values), and the second data frame now contains all unique rows without duplicates.

Conclusion

In this article, we’ve explored how to remove duplicated rows both within and between data frames stored in a list using R. We used various functions like duplicated(), Reduce(), rev(), and Map() to achieve this. This solution provides an efficient way to handle duplicate data and improve the accuracy of your results.

Last modified on 2024-03-29