What is the Collapse Function?
The collapse function in R Programming Language is mainly used to combine data frames by grouping values according to specified factors. It lets us summarize data and condense it into a single row for each unique combination of factors. This reduces the dataset’s size while keeping important information intact. The collapse function performs aggregation operations like sum, mean, and median across groups defined by one or more variables.
collapse(x, by, FUN = NULL)
- x: The data frame or matrix to be collapsed.
- by: A vector or list of variables by which the data should be collapsed. It defines the grouping criteria for the collapse operation.
- FUN: The function to be applied during the collapsing process. This can be any built-in or user-defined aggregation function, such as sum, mean, median, etc.
Usage of Collapse Function
- Use collapse functions to summarize data based on specific criteria, like grouping by gender or age.
- Aggregate values across dimensions, such as rows or columns, or multiple variables at once.
- Collapse data to simplify analysis by reducing its complexity while retaining important information.
- Create concise visualizations by collapsing data into summary statistics, highlighting trends or patterns.
- Prepare data for statistical analysis by aggregating it to identify relationships or patterns.
- Make reports clearer by summarizing data into digestible formats for easier understanding.
- Improve computational efficiency by collapsing large datasets, leading to faster processing and better memory management.
Collapse by Gender and Calculate Mean Scores
# Sample dataset
students <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eve"),
Gender = c("Female", "Male", "Male", "Female", "Female"),
Age = c(20, 22, 21, 19, 20),
Math_Score = c(85, 75, 90, 80, 95),
Science_Score = c(78, 82, 88, 75, 90)
)
students
# Collapse by gender and calculate mean scores
collapsed_data <- aggregate(cbind(Math_Score, Science_Score) ~ Gender,
data = students, FUN = mean)
print(collapsed_data)
Output:
Name Gender Age Math_Score Science_Score
1 Alice Female 20 85 78
2 Bob Male 22 75 82
3 Charlie Male 21 90 88
4 David Female 19 80 75
5 Eve Female 20 95 90
Gender Math_Score Science_Score
1 Female 86.66667 81
2 Male 82.50000 85
In this output, we can see that the average math score for females is approximately 86.67, while for males it’s approximately 82.50. Similarly, the average science score for females is 81, and for males it’s 85.
Collapse by Age Group and Calculate Sum of Math Scores
# Sample dataset
students <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eve"),
Gender = c("Female", "Male", "Male", "Female", "Female"),
Age = c(20, 22, 21, 19, 20),
Math_Score = c(85, 75, 90, 80, 95),
Science_Score = c(78, 82, 88, 75, 90)
)
students
# Collapse by age group and calculate sum of math scores
age_groups <- cut(students$Age, breaks = c(0, 20, 25, 30),
labels = c("Under 20", "20-25", "25-30"))
collapsed_data <- aggregate(Math_Score ~ age_groups, data = students, FUN = sum)
print(collapsed_data)
Output:
Name Gender Age Math_Score Science_Score
1 Alice Female 20 85 78
2 Bob Male 22 75 82
3 Charlie Male 21 90 88
4 David Female 19 80 75
5 Eve Female 20 95 90
age_groups Math_Score
1 Under 20 260
2 20-25 165
In this example, we have aggregated the data by age group, and we can see that individuals under 20 years old have a total math score of 260, while those aged between 20 and 25 have a total math score of 165. It provides a summarized view of the math scores based on different age groups, allowing for easier interpretation and analysis of the data.
Collapse by Gender and Calculate Median Age
# Sample dataset
students <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David", "Eve"),
Gender = c("Female", "Male", "Male", "Female", "Female"),
Age = c(20, 22, 21, 19, 20),
Math_Score = c(85, 75, 90, 80, 95),
Science_Score = c(78, 82, 88, 75, 90)
)
students
# Collapse by gender and calculate median age
collapsed_data <- aggregate(Age ~ Gender, data = students, FUN = median)
print(collapsed_data)
Output:
Name Gender Age Math_Score Science_Score
1 Alice Female 20 85 78
2 Bob Male 22 75 82
3 Charlie Male 21 90 88
4 David Female 19 80 75
5 Eve Female 20 95 90
Gender Age
1 Female 20.0
2 Male 21.5
Here , we aggregated the data by gender and calculated the median age for each gender group. The output provides insights into the central tendency of age within each gender category. It shows that the median age of males is slightly higher than that of females in the dataset.
Collapse Function In R
In the world of working with data, R is an important tool for people who study statistics, work with data, or do research. One of the important things R can do is called the “collapse” function. It helps to make big piles of data smaller and easier to work with. This makes it quicker to analyze and visualize the data. Here, we’ll talk about how the collapse function works in R, what it does, and how people use it in real life.