Absolute and Relative Frequency in R

In statistics, absolute frequency refers to the number of times a particular value or category appears in a dataset. Relative frequency, on the other hand, is the proportion or percentage of the data that falls in a particular category.

Using R, you can compute both absolute and relative frequencies with ease. Let's go over how to compute both of these frequencies using a simple example:

Suppose you have a vector of data that represents the grades of 10 students:

grades <- c("A", "B", "A", "C", "B", "A", "C", "B", "B", "A")

Absolute Frequency:

To compute the absolute frequency, you can use the table() function in R:

absolute_freq <- table(grades)
print(absolute_freq)

The output will show you how many times each grade appears in the dataset.

Relative Frequency:

To compute the relative frequency, you simply need to divide the absolute frequency by the total number of observations:

relative_freq <- absolute_freq / length(grades)
print(relative_freq)

If you want the relative frequencies as percentages, you can multiply them by 100:

relative_freq_percentage <- relative_freq * 100
print(relative_freq_percentage)

This will show you the percentage of students who received each grade.

In summary, R provides simple and intuitive functions to compute both absolute and relative frequencies. The table() function is especially useful for computing absolute frequencies, and simple arithmetic operations can be used to compute relative frequencies.

R absolute frequency calculation example: Calculate the absolute frequency of a variable:

# Example data
data <- c(1, 2, 2, 3, 3, 3, 4, 4, 4, 4)

# Absolute frequency calculation
abs_freq <- table(data)
print(abs_freq)

Relative frequency in R statistics: Calculate the relative frequency of a variable:

# Relative frequency calculation
rel_freq <- prop.table(table(data))
print(rel_freq)

Creating frequency tables in R: Create a frequency table for a categorical variable:

# Creating a frequency table
category <- c("A", "B", "A", "C", "B", "A", "C", "A", "B")
freq_table <- table(category)
print(freq_table)

Calculate absolute and relative frequency in R: Calculate both absolute and relative frequencies:

# Calculate absolute frequency
abs_freq <- table(data)

# Calculate relative frequency
rel_freq <- prop.table(abs_freq)

print("Absolute Frequency:")
print(abs_freq)
print("Relative Frequency:")
print(rel_freq)

Using table() function for frequency in R: Utilize the table() function for frequency analysis:
```
# Using table() function
freq_table <- table(category)
print(freq_table)
```

Bar plots for absolute and relative frequency in R: Create bar plots to visualize absolute and relative frequencies:

# Bar plot for absolute frequency
barplot(abs_freq, main="Absolute Frequency")

# Bar plot for relative frequency
barplot(rel_freq, main="Relative Frequency")

Summarizing data with table() function in R: Summarize data using the table() function:

# Summarizing data with table()
summary_table <- table(data, category)
print(summary_table)

R contingency tables for absolute and relative frequency: Create a contingency table for cross-tabulation:

# Contingency table for absolute frequency
contingency_abs <- table(data, category)

# Contingency table for relative frequency
contingency_rel <- prop.table(contingency_abs)

print("Contingency Table - Absolute Frequency:")
print(contingency_abs)
print("Contingency Table - Relative Frequency:")
print(contingency_rel)

Cross-tabulation in R for frequency analysis: Perform cross-tabulation for frequency analysis:
```
# Cross-tabulation
cross_tab <- xtabs(~data + category)
print(cross_tab)
```

Normalize data to relative frequency in R: Normalize data to obtain relative frequency:

# Normalize data to relative frequency
normalized_data <- prop.table(table(data))
print(normalized_data)