Creating Probability of Occurrence in Data Frame

Introduction

In this article, we will explore how to create a data frame where each row represents an individual with multiple attributes or features. One such feature is the probability of occurrence of a specific value. We’ll go through a step-by-step example of creating such a data frame using R programming language.

Background

Data frames are a fundamental data structure in R, used for storing and manipulating data that has multiple variables. Each column represents a variable, while each row represents an observation or record. Data frames can be created from various sources, including user input, reading files, or creating them manually.

In this example, we will create a data frame with 19 students, each having 10 observations of whether they attend classes on time. We want to assign different probabilities of attendance (100%, 90%, and 80%) randomly to each student without affecting their overall probability.

The Problem

Given a data frame ID with all students having an “on-time” rate of 90%, we need to randomize the probability of attendance among all students.

Current Implementation

The current implementation uses the following code:

ID <- data.frame(rep(1:19, each = 10))
ID$DOSE <- c(
  replicate(19,
           c(sample(rep(c("on time", "late")), size = 10, replace = TRUE, prob = c(0.90, 0.10))),
           collapse = TRUE)
)

This implementation achieves the desired result but does not provide an intuitive explanation of how it works.

Alternative Approach

Instead of directly using sample() with probabilities, we’ll create a vector probs containing different probabilities for each student and then use sapply() to generate random values based on these probabilities.

Step 1: Create the Probability Vector

Create a vector probs containing three different probabilities (100%, 90%, and 80%).

probs <- c(0.9, 0.8, 0.7)

This vector will be used to generate random values for each student.

Step 2: Randomize On-Time Rates for Each Student

Use sapply() to create a vector onTimeRates containing random on-time rates for each student.

onTimeRates <- sample(probs, 19, replace = TRUE)

This step randomly assigns different probabilities of attendance (100%, 90%, and 80%) to each student.

Step 3: Generate Random On-Time Data for Each Student

Use sapply() again to generate random on-time data for each student based on their assigned probability.

x <- sapply(onTimeRates, function(x) sample(c('punctual', 'late'), 10, replace = TRUE, prob = c(x, 1 - x)))

This step creates a vector x containing random values (‘punctual’ or ’late’) for each student based on their assigned probability.

Step 4: Collapse the Vector into the Desired Column

Finally, use matrix() to collapse the vector x into a single column in the data frame.

ID$DOSE <- matrix(x, ncol = 1)

This step completes the desired result of creating a data frame where each row represents an individual with multiple attributes or features.

The Final Code

Here is the complete code that achieves the desired result:

# Create the probability vector
probs <- c(0.9, 0.8, 0.7)

# Randomize on-time rates for each student
onTimeRates <- sample(probs, 19, replace = TRUE)

# Generate random on-time data for each student
x <- sapply(onTimeRates, function(x) sample(c('punctual', 'late'), 10, replace = TRUE, prob = c(x, 1 - x)))

# Collapse the vector into the desired column
ID$DOSE <- matrix(x, ncol = 1)

Conclusion

In this article, we explored how to create a data frame where each row represents an individual with multiple attributes or features. We demonstrated an alternative approach to achieving this result using R programming language. By creating a probability vector and randomly assigning different probabilities of attendance to each student, we can generate random on-time data for each student based on their assigned probability.

The resulting code provides an intuitive explanation of the steps involved in creating such a data frame. This approach can be applied to various problems involving multiple attributes or features in R programming language.

Last modified on 2024-03-04