Syntax and Basic Usage

The basic syntax of the table() function is as follows:

table(x)

Where x is a vector, factor, or a data frame.

Example: Frequency Table from a Vector

Let’s create a frequency table from a simple vector:

colors <- c("red", "blue", "red", "green", "blue", "blue")
color_table <- table(colors)
print(color_table)

colors
 blue green   red 
    3     1     2 

Example: Frequency Table from a Data Frame

Consider a data frame of survey responses:

survey_data <- data.frame(
  Gender = c("Male", "Female", "Female", "Male", "Female"),
  AgeGroup = c("18-25", "26-35", "18-25", "36-45", "18-25")
)

gender_table <- table(survey_data$Gender)
print(gender_table)

Female   Male 
     3      2 

Cross-Tabulation with table()

You can use table() to cross-tabulate data, which is helpful for contingency tables:

age_gender_table <- table(survey_data$Gender, survey_data$AgeGroup)
print(age_gender_table)

        
         18-25 26-35 36-45
  Female     2     1     0
  Male       1     0     1

Example: Contingency Table with Two Variables

The above code generates a contingency table showing the distribution of age groups across genders.

Adding Margins to Tables

Adding margin totals can be achieved using the addmargins() function:

age_gender_margins <- addmargins(age_gender_table)
print(age_gender_margins)

        
         18-25 26-35 36-45 Sum
  Female     2     1     0   3
  Male       1     0     1   2
  Sum        3     1     1   5

Customizing Table Output

You can customize table outputs by adjusting the parameters within table() and related functions to suit your analysis needs.

Example: Analyzing Survey Data

Suppose you have survey data about favorite fruits:

fruits <- c("apple", "banana", "apple", "orange", "banana", "apple")
fruit_table <- table(fruits)
print(fruit_table)

fruits
 apple banana orange 
     3      2      1 

Example: Demographic Data Analysis

Using demographic data, you can analyze age group distributions:

age_group_table <- table(survey_data$AgeGroup)
print(age_group_table)

18-25 26-35 36-45 
    3     1     1 

Handling NA Values

Use the useNA parameter to handle missing values:

table(survey_data$Gender, useNA = "ifany")

Female   Male 
     3      2 

Dealing with Large Datasets

For large datasets, consider summarizing data before using table() to improve performance.

Plotting Tables Using Base R

You can plot frequency tables directly using R’s built-in plotting functions:

barplot(fruit_table, main = "Fruit Preferences", col = "lightblue")

Using ggplot2 for Table Visualization

For more advanced visualizations, use ggplot2:

library(ggplot2)
ggplot(as.data.frame(fruit_table), aes(x = fruits, y = Freq)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  theme_minimal()

Combining table() with dplyr

You can integrate table() with dplyr for more complex data manipulations:

library(dplyr)
survey_data %>%
  count(Gender, AgeGroup) %>%
  table()

, , n = 1

        AgeGroup
Gender   18-25 26-35 36-45
  Female     0     1     0
  Male       1     0     1

, , n = 2

        AgeGroup
Gender   18-25 26-35 36-45
  Female     1     0     0
  Male       0     0     0

Using table() with tidyr

tidyr can help reshape data for table():

library(tidyr)
survey_data %>%
  complete(Gender, AgeGroup) %>%
  table()

        AgeGroup
Gender   18-25 26-35 36-45
  Female     2     1     1
  Male       1     1     1

Optimizing Table Creation for Speed

Consider using data.table for large datasets to optimize performance.

Memory Management Tips

Use gc() to manage memory effectively when working with large tables.

Case Study: Market Research Analysis

Create tables to analyze consumer preferences and trends.

Case Study: Academic Research Data

Use tables to summarize and interpret experimental data.