What Does duplicated Do?

• Identification: It identifies elements or rows that are duplicates of previous occurrences.
• Output: Returns a logical vector of the same length as the input, with TRUE for duplicates and FALSE for unique entries.

Syntax and Parameters

The basic syntax of the duplicated function is:

duplicated(x, incomparables = FALSE, fromLast = FALSE, ...)

• x: A vector, data frame, or array.
• incomparables: A vector of values that cannot be compared. Defaults to FALSE.
• fromLast: Logical indicating if duplication should be considered from the last. Defaults to FALSE.
• ...: Further arguments passed to or from other methods.

Identifying Duplicates in Numeric Vectors

# Example numeric vector
num_vec <- c(10, 20, 30, 20, 40, 10, 50)

# Identify duplicates
duplicated(num_vec)

Output:

[1] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE

Explanation:

• The function returns TRUE for the second occurrence of duplicates.
• In num_vec, the numbers 20 and 10 are duplicated.

Handling Character Vectors

# Example character vector
char_vec <- c("apple", "banana", "cherry", "apple", "date", "banana")

# Identify duplicates
duplicated(char_vec)

Output:

[1] FALSE FALSE FALSE  TRUE FALSE  TRUE

Explanation:

• “apple” and “banana” both appear twice in the vector.
• The function marks the second occurrences as duplicates.

Dealing with Logical and Factor Vectors

# Logical vector
log_vec <- c(TRUE, FALSE, TRUE, FALSE, TRUE)

# Identify duplicates
duplicated(log_vec)

Output:

[1] FALSE FALSE  TRUE  TRUE  TRUE

Factor vector

# Factor vector
fact_vec <- factor(c("low", "medium", "high", "medium", "low"))

# Identify duplicates
duplicated(fact_vec)

Output:

[1] FALSE FALSE FALSE  TRUE  TRUE

Explanation:

• The duplicated function works similarly with logical and factor vectors, identifying repeated values.

Detecting Duplicate Rows

[1] FALSE FALSE FALSE FALSE  TRUE

Output:

[1] FALSE FALSE FALSE FALSE  TRUE

Explanation:

• The fifth row is a duplicate of the second row in all columns.

Using duplicated on Entire Data Frames

You can use the function to find duplicates in the entire data frame:

# View duplicate rows
df[duplicated(df), ]

Output:

  ID Name Age
5  2  Bob  30

Checking for Duplicates in Specific Columns

If you need to check for duplicates based on specific columns:

# Identify duplicates based on 'Name' column
duplicated(df$Name)

[1] FALSE FALSE FALSE FALSE  TRUE

# Or for multiple columns
duplicated(df[, c("Name", "Age")])

[1] FALSE FALSE FALSE FALSE  TRUE

Explanation:

• By providing a subset of the data frame, you focus the duplicated function on certain columns.

Using duplicated to Filter Out Duplicates

# Remove duplicate rows
df_no_duplicates <- df[!duplicated(df), ]

# View the result
df_no_duplicates

Output:

  ID    Name Age
1  1   Alice  25
2  2     Bob  30
3  3 Charlie  35
4  4   David  40

Difference Between duplicated and unique

• duplicated: Returns a logical vector indicating duplicates.
• unique: Returns a vector or data frame with duplicate entries removed.

Example with unique:

unique(df)

Output:

  ID    Name Age
1  1   Alice  25
2  2     Bob  30
3  3 Charlie  35
4  4   David  40

When to Use Each:

• Use duplicated when you need to identify or index duplicates.
• Use unique for a quick way to remove duplicates.

The fromLast Argument

By setting fromLast = TRUE, the function considers duplicates from the reverse side.

Example:

# Using fromLast
duplicated(num_vec, fromLast = TRUE)

Output:

[1]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE

Explanation:

• Now, the first occurrences are marked as duplicates.

Managing Missing Values (NA)

The duplicated function treats NA values as equal.

# Vector with NAs
na_vec <- c(1, 2, NA, 2, NA, 3)

# Identify duplicates
duplicated(na_vec)

Output:

[1] FALSE FALSE FALSE  TRUE  TRUE FALSE

Tips for Accurate Results:

• If NA values should not be considered duplicates, use the incomparables argument.

# Exclude NAs from comparison
duplicated(na_vec, incomparables = NA)

Output:

[1] FALSE FALSE FALSE  TRUE FALSE FALSE

Cleaning Survey Data

Suppose you have survey data with potential duplicate responses.

# Sample survey data
survey_data <- data.frame(
  RespondentID = c(1, 2, 3, 2, 4),
  Response = c("Yes", "No", "Yes", "No", "Yes")
)

# Identify duplicates based on 'RespondentID'
duplicates <- duplicated(survey_data$RespondentID)

# Remove duplicates
clean_data <- survey_data[!duplicates, ]
print(clean_data)

  RespondentID Response
1            1      Yes
2            2       No
3            3      Yes
5            4      Yes

Explanation:

• Duplicate RespondentID entries are identified and removed to ensure each respondent is counted once.

Preprocessing Datasets for Analysis

When preparing data for modeling, it’s crucial to eliminate duplicates.

# Load dataset
data("mtcars")

# Introduce duplicates for demonstration
mtcars_dup <- rbind(mtcars, mtcars[1:5, ])

# Remove duplicate rows
mtcars_clean <- mtcars_dup[!duplicated(mtcars_dup), ]
print(mtcars_clean)

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Explanation:

• Ensures the dataset used for analysis contains unique observations.

Combining Datasets and Resolving Duplicates

Merging datasets can introduce duplicates that need to be resolved.

# Sample datasets
df1 <- data.frame(ID = 1:3, Value = c(10, 20, 30))
df2 <- data.frame(ID = 2:4, Value = c(20, 40, 50))

# Merge datasets
merged_df <- rbind(df1, df2)

# Remove duplicates based on 'ID'
merged_df_unique <- merged_df[!duplicated(merged_df$ID), ]
print(merged_df_unique)

  ID Value
1  1    10
2  2    20
3  3    30
6  4    50

Explanation:

• After combining, duplicates based on ID are removed to maintain data integrity.

Tips for Efficient Duplicate Detection

• Specify Columns: When working with data frames, specify columns to focus on relevant data.
• Use fromLast: Consider the fromLast argument to control which duplicates are marked.
• Handle NA Values: Be mindful of how NA values are treated in your data.

Common Pitfalls to Avoid

• Assuming unique and duplicated Are the Same: They serve different purposes.
• Ignoring Data Types: Ensure that data types are appropriate for comparison.

Performance Considerations with Large Datasets

• For large datasets, operations can be time-consuming.
• Consider data.table or dplyr packages for optimized functions like duplicated.