Reshaping data.frame from wide to long format in R

Reshaping a data frame from wide to long format in R Programming Language is a common operation when dealing with data analysis and visualization. The process involves converting data that is spread across multiple columns (wide format) into a format where each row represents a single observation (long format).

Creating Sample Wide Data Frame

We create a wide-format data frame that stores test scores of students across multiple columns.

• data.frame : creates a data frame by combining all the columns (like ID, Name, Test1, etc.)

Output:

Method 1: Using melt from Reshape2 Package

We reshape the data using the melt() function from the reshape2 package. This method transforms the score columns into a single variable.

• melt(): Converts wide data into long format by gathering multiple columns into key-value pairs.
• id.vars: Columns that stay fixed (like ID and Name).
• variable.name: Name of the new column holding original column names (e.g., Test1, Test2).
• value.name: Name of the new column storing actual data values (scores).

Output:

Method 2: Using pivot_longer from Tidyr Package

We use pivot_longer() from the tidyr package, a modern and flexible function designed for reshaping tasks.

• pivot_longer(): A tidyverse function to reshape data from wide to long format.
• cols = starts_with("Test"): Selects all columns whose names begin with "Test".
• names_to: Name of the new column that stores the old column names.
• values_to: Name of the new column that holds the values.

Output:

Method 3: Using reshape from Base R

We use the base R reshape() function, which also allows us to convert the data to long format by specifying relevant columns and identifiers.

• reshape(): Built-in R function for converting data between wide and long formats.
• direction = "long": Indicates conversion from wide to long.
• idvar: Columns used to uniquely identify each record (like ID and Name).
• varying: List of columns to be reshaped.
• v.names: Name of the new column for values.
• timevar: Name of the new column to store original column names.
• times: Custom labels to use in the timevar column.

Output:

In this article, we explored three methods to reshape data from wide to long format in R programming language.