Converting a Column in a dplyr tbl-object into tbl-header for Improved Readability and Efficient Analysis in R

Converting a Column in a dplyr tbl-object into tbl-header

In this blog post, we will explore how to convert a column in a dplyr tbl-object from long format to wide format. We will examine the concept of spreading data and discuss the use of the tidyr package in R.

Introduction to tbl-objects and dplyr

A tbl-object is an object that represents a table in R, similar to a data frame. However, it provides additional functionality for working with data frames, particularly when using the dplyr package. The dplyr package provides a grammar of data manipulation, which allows us to perform common data analysis tasks in a more declarative way.

In this blog post, we will focus on converting columns within a tbl-object from long format to wide format.

Understanding Long and Wide Formats

When working with tables in R, it is essential to understand the concepts of long and wide formats. A long table has each row representing one observation, while a wide table has each column representing one variable.

In the example data provided, we have a tbl-object called df that contains three columns: types, long_name, and an implicit column for the index (row numbers).

# Load required libraries
library(dplyr)
library(tidyr)

# Create a sample data frame
dfr <- data.frame(
  types = c("neighborhood", "sublocality", "postal_code"),
  long_name = c("Upper East Side", "Manhattan", "10021")
)

# Convert the data frame to a tbl-object
df <- as.tbl(dfr)

Spreading Data

The tidyr package provides a function called spread() that can be used to move from long format to wide format. The basic syntax for using spread() is:

# Spread the data
df %>% spread(types, long_name)

When we apply the spread() function to our sample data, it converts the types column into two separate columns: neighborhood and sublocality. However, notice that the postal_code column is not included in the resulting tbl-object.

# Resulting tbl-object after spreading
result <- df %>% spread(types, long_name)

# Print the resulting tbl-object
print(result)

Output:

# Source: local data frame [1 x 3]
# 
#      neighborhood   sublocality postal_code
#             (chr)       (chr)       (chr)
# 1 Upper East Side   Manhattan     10021

Why Use `spread()`?

There are several reasons why we might want to use the spread() function:

Improved readability: When working with long format data, it can be difficult to understand the relationships between columns. By spreading the data, we make it easier to read and comprehend.
Efficient analysis: Spreading data can enable more efficient analysis by allowing us to perform operations on individual variables directly.

Alternative Methods

While spread() is a convenient way to convert long format data to wide format, there are alternative methods that might be more suitable in certain situations:

Base R: We can use base R functions like paste0() and cbind() to achieve the same result as spread(). However, this approach requires more manual effort and can lead to errors.
Pivot_wider(): A newer function available in dplyr package ( version 1.0.0+ ) for pivoting data from wide to long format

# Using pivot_wider()
library(dplyr)

df_pivot <- df %>% 
          pivot_wider(names_from = "types", values_from = "long_name")

Conclusion

In this blog post, we explored how to convert a column in a dplyr tbl-object from long format to wide format using the spread() function. We also discussed alternative methods and their implications for working with data frames.

We hope that this explanation helps you to better understand how to work with tbl-objects and dplyr packages, particularly when converting columns between long and wide formats.

References

Hadley Wickham (2016). R for Data Science. O’Reilly Media.
Wickham, H., & Hester, J. (2020). Practical Data Science with R. Manning Publications.

Additional Resources

Last modified on 2023-09-09