Pivoting Dataframe / Self Joining Based on Column Within DataFrame in R

In this article, we will explore a common data manipulation technique used in R: pivoting or self-joining based on a column within a dataframe. We’ll start by explaining the basics of pivot tables and then move on to more advanced topics.

Introduction to Pivot Tables

A pivot table is a summary table that shows the total value for each unique combination of two variables, called columns, in a dataset. The most common type of pivot table is the wide format pivot table, which displays each unique value in one column (called the “index” or “row”) and the corresponding values in another column.

Pivot Wider Function

The pivot_wider function from the tidyverse package is used to create a wide format pivot table. It takes two main arguments:

• names_from: specifies the name of the column(s) that contains the unique values to be displayed as rows.
• values_from: specifies the column(s) containing the data to be summed.

Self Joining

Self joining refers to the process of combining a dataset with itself based on a common column. In this article, we’ll explore self joining using the pivot_wider function.

Creating a Sample Dataset

To illustrate the concepts discussed above, let’s create a sample dataset df.

library(tidyverse)

# Create a sample dataframe
df <- data.frame(
  item_number = c(1, 1, 1, 2, 2, 2),
  scales = c(1, 5, 10, 2, 15, 20),
  prices = c(1, 1.50, 2, 3, 4, 5),
  product_name = c("Cheese", "Cheese", "Cheese", "Ham", "Ham", "Ham")
)

Desired Output

The desired output is a dataset where each item number and corresponding scales are pivoted into separate columns. The output would look something like this:

Using Pivot Wider Function

To achieve the desired output, we can use the pivot_wider function.

df %>% 
  group_by(item_number) %>% 
  mutate(row = row_number()) %>% 
  ungroup() %>% 
  pivot_wider(names_from = row, values_from = c(scales, prices))

This code performs the following steps:

• Groups the data by item_number.
• Assigns a new column row containing the row number of each observation within each group.
• Ungroups the data.
• Creates a wide format pivot table where rows is the unique values from the original row column, and values are the corresponding values from the scales and prices columns.

Explanation

The key to this solution is using the group_by function to group the data by item_number. This allows us to apply the pivot_wider function to each row within a group. The row column created during the grouping step serves as the index for our pivot table.

Advanced Topics: Handling Missing Values and Non-Numeric Columns

While the pivot_wider function is powerful, there are cases where it may not be suitable due to missing values or non-numeric columns in the data. Let’s explore how to handle these scenarios:

Handling Missing Values

When working with pivot tables, missing values can often lead to incorrect results. One approach to handle missing values is to use the drop_na argument within the pivot_wider function.

df %>% 
  group_by(item_number) %>% 
  mutate(row = row_number()) %>% 
  ungroup() %>% 
  pivot_wider(names_from = row, values_from = c(scales, prices), drop_na = TRUE)

In this code snippet, drop_na is set to TRUE, which means that any rows with missing values in the specified columns will be dropped from the resulting pivot table.

Handling Non-Numeric Columns

When working with pivot tables, non-numeric columns can also lead to incorrect results. One approach to handle non-numeric columns is to convert them into numeric columns using functions such as as.numeric() or mutate.

df %>% 
  mutate(scales = as.numeric(scales), prices = as.numeric(prices)) %>% 
  group_by(item_number) %>% 
  mutate(row = row_number()) %>% 
  ungroup() %>% 
  pivot_wider(names_from = row, values_from = c(scales, prices))

In this code snippet, mutate is used to convert the scales and prices columns into numeric columns.

Conclusion

Pivoting dataframes or self joining based on a column within a dataframe is a powerful technique for transforming and summarizing datasets. In this article, we explored how to use the pivot_wider function from the tidyverse package to achieve these transformations. We also discussed advanced topics such as handling missing values and non-numeric columns.

By mastering pivot tables and self joining, data analysts and scientists can unlock deeper insights into their datasets and gain a better understanding of complex relationships within their data.

Additional Resources

• Pivot Wider Function - A description of the pivot_wider function from the tidyverse package.
• Dplyr Documentation - The official documentation for the dplyr package, which includes a wide range of functions for data manipulation and analysis.

Last modified on 2023-06-17