How to Create Tables in R

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Creating tables in R is a common task for researchers, analysts, and data scientists. Tables are a crucial element for summarizing data, presenting results, or even just for better data manipulation. This article aims to provide an exhaustive guide on how to create tables in R, covering a range of methods and packages that cater to different needs.

Table of Contents

  1. Introduction
  2. Base R Methods
    • matrix
    • data.frame
    • table
  3. Using Packages
    • tibble
    • data.table
  4. Cross-Tabulation
    • xtabs
    • ftable
  5. Creating Presentation Tables
    • kable
    • flextable
    • formattable
  6. Conclusion

1. Introduction

Tables are essential tools for data analysts and scientists. They serve as a powerful means to summarize, structure, and represent data for easier interpretation and visualization. This article walks you through various methods to create tables in R, from basic techniques in Base R to more advanced methods using popular R packages.

2. Base R Methods


In R, a matrix is a two-dimensional array that can hold a single type of data (either numeric, character, or logical). You can create a matrix using the matrix function:

# Create a numeric matrix
numeric_matrix <- matrix(1:9, nrow = 3, ncol = 3)


The data.frame is one of the most commonly used methods for table creation in R. Unlike matrices, data frames can contain multiple types of variables (e.g., numeric, character, factor).

# Create a data frame
df <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(24, 27, 22),
  Gender = c("F", "M", "M")


The table function in Base R is used for creating contingency tables, which can be useful for summarizing categorical variables.

# Create a simple contingency table
table_data <- table(df$Gender)

3. Using Packages


The tibble package is part of the tidyverse, and it provides an updated approach to data frames with easier printing and subsetting.

# Create a tibble
tb <- tibble(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(24, 27, 22),
  Gender = c("F", "M", "M")


The data.table package offers enhanced data frames that are designed for efficient data manipulation and transformation.

# Create a data.table
dt <- data.table(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(24, 27, 22),
  Gender = c("F", "M", "M")

4. Cross-Tabulation


The xtabs function is useful for creating contingency tables from data frames and includes the ability to specify formulas.

# Create a cross-tabulation
xt <- xtabs(~ Age + Gender, data = df)


The ftable function provides a more flexible approach to contingency tables, including the ability to create multi-dimensional tables.

# Create an ftable
ft <- ftable(df$Age, df$Gender)

5. Creating Presentation Tables


The kable function from the knitr package is commonly used for creating Markdown or HTML tables.

# Create a kable table


The flextable package allows for the creation of highly customizable tables suitable for reports and presentations.

# Create a flextable
ft <- flextable(df)


The formattable package enables the creation of tables with advanced formatting options, like color scales and bars.

# Create a formattable table
ft <- formattable(df)

6. Conclusion

Creating tables in R can be as simple or as complex as you need it to be. For quick analyses, Base R functions like data.frame and table are straightforward and effective. For more complex tasks that involve data manipulation and presentation-quality tables, packages like data.table, tibble, knitr, and flextable offer a range of advanced features.

With this guide, you should now have a comprehensive understanding of how to create tables in R to suit a wide variety of needs. Whether you are performing basic data analysis or preparing detailed reports, R offers a range of solutions to help you structure and present your data effectively.

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