How to Find Unique Values in a Column in R

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Data analysis in R often requires dealing with large datasets with numerous columns. One common task during such analysis is identifying unique values within a specific column. This can be critical in situations where you need to know the distinct elements of a column, for example, to understand the variety of categorical variables, to eliminate duplicates, or to identify unique cases in your dataset.

R provides multiple ways to identify these unique values, and each method has its pros and cons. In this article, we will explore several methods to find unique values in a column using base R and the tidyverse package dplyr.

Method 1: Using unique() Function

The simplest way to find unique values in R is by using the unique() function. This function takes a vector as an argument and returns another vector with all duplicates removed. Here is an example of its use:

# create a dataframe
df <- data.frame("Color" = c("Red", "Blue", "Green", "Red", "Green", "Blue", "Blue", "Red"))

# use unique() to find unique values
unique_values <- unique(df$Color)


In the output, you will see all the distinct values from the “Color” column.

Method 2: Using distinct() from dplyr

Another method of finding unique values in a column involves the use of the distinct() function from the dplyr package. The distinct() function works with data frames and allows for the specification of columns.

Here is how you can use the distinct() function:

# install and load dplyr

# use distinct() to find unique values
unique_values <- distinct(df, Color)


Unlike unique(), distinct() will return a dataframe with unique values, rather than a vector. This could be helpful if you’re working with multiple columns and want to retain the dataframe structure.

Method 3: Using table() Function

The table() function in R provides a tabular count of categorical variables. It’s typically used to create frequency tables, but it can also be employed to find unique values.

Here’s how to use table() for this purpose:

# create a frequency table
freq_table <- table(df$Color)

# extract names of the frequency table
unique_values <- names(freq_table)


In this method, table() counts the frequency of each value, and names() extracts the unique values from the table. This method also returns a vector of unique values.

Method 4: Using duplicated() Function

The duplicated() function in R identifies duplicate values in a vector or a dataframe. It returns a logical vector where TRUE indicates that the element is a duplicate, and FALSE indicates that the element is unique (it appears for the first time).

# use duplicated() to find duplicate values
duplicates <- duplicated(df$Color)

# use logical indexing to find unique values
unique_values <- df$Color[!duplicates]


In this method, duplicated() identifies the duplicates, and logical indexing with the ! operator (which negates the duplicates) is used to extract the unique values.

Method 5: Using aggregate() Function

The aggregate() function is a powerful function in R used to compute summary statistics for subsets of data. Although not traditionally used for finding unique values, it can be employed for this purpose.

# use aggregate() to group by Color and find unique values
unique_values <- aggregate(x = df$Color, by = list(df$Color), FUN = function(x){x[1]})


In this case, aggregate() groups by “Color” and applies the anonymous function function(x){x[1]} to each group, returning the first value in each group. The result is a dataframe with the unique values.

Summary of Methods and Performance Considerations

Each of the methods discussed in this article provides a way to find unique values in a column in R. However, the choice of method depends on your specific requirements and the size of your data.

  • The unique() function is simple and straightforward, and works well for small to moderately large data.
  • dplyr::distinct() is also easy to use and maintains the dataframe structure. It is efficient and scales well for larger dataframes.
  • The table() function provides additional information about frequencies, which might be useful in some scenarios. However, it could be slower for very large data.
  • The duplicated() function is also quite efficient and offers flexibility in terms of choosing to remove all duplicates or just subsequent duplicates.
  • The aggregate() function offers a way to find unique values, but it might not be as efficient for larger datasets, and its main strength lies in more complex data aggregation tasks.


Finding unique values in a column is a common task in data analysis. R provides multiple ways to achieve this, each with its strengths and weaknesses. While the unique() function is the most straightforward, other functions like dplyr::distinct(), table(), duplicated(), and aggregate() offer additional flexibility and functionality.

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