Summary statistics in R Programming

Introduction

Summary statistics help us quickly understand the main features of data by showing simple numbers like average, minimum, and maximum.

To get a quick overview of a dataset before deeper analysis.

To compare groups by their average or spread.

To check if data has any unusual values or outliers.

To summarize survey results like average ratings.

To report key numbers in a simple way for presentations.

Syntax

R Programming

summary(data)

data can be a vector, data frame, or other R object.

The function returns minimum, 1st quartile, median, mean, 3rd quartile, and maximum for numeric data.

Examples

Summary of a numeric vector showing min, 1st quartile, median, mean, 3rd quartile, and max.

R Programming

x <- c(5, 10, 15, 20, 25)
summary(x)

Summary statistics for each column in a data frame.

R Programming

df <- data.frame(age = c(21, 25, 30, 22, 28), score = c(80, 90, 85, 88, 92))
summary(df)

Summary for a factor shows counts of each category.

R Programming

summary(factor(c('red', 'blue', 'red', 'green')))

Sample Program

This program creates two numeric vectors for ages and scores, puts them in a data frame, and prints summary statistics for each.

R Programming

ages <- c(23, 29, 31, 22, 27, 35, 28)
scores <- c(88, 92, 85, 90, 87, 95, 91)
data <- data.frame(ages, scores)

print("Summary of ages:")
print(summary(data$ages))

print("Summary of scores:")
print(summary(data$scores))

Important Notes

Summary statistics give a quick look but do not replace detailed analysis.

For large datasets, summary() is fast and helpful to spot data issues.

Non-numeric data like factors show counts of each category instead of numbers.

Summary

Summary statistics show key numbers like min, max, mean, and median.

Use summary() in R to get these numbers easily for vectors or data frames.

They help understand data quickly and find unusual values.

Practice

1. What does the summary() function in R provide when applied to a numeric vector?

easy

A. Minimum, 1st quartile, median, mean, 3rd quartile, and maximum values

B. Only the mean and median values

C. The sum and product of all elements

D. The count of unique values

Solution

1. Step 1: Understand what summary() does for numeric vectors

   The summary() function returns key statistics including minimum, quartiles, median, mean, and maximum.

2. Step 2: Compare options with the known output

   Minimum, 1st quartile, median, mean, 3rd quartile, and maximum values lists all these statistics correctly, while others miss important parts or give unrelated info.

3. Final Answer:

   Minimum, 1st quartile, median, mean, 3rd quartile, and maximum values -> Option A

4. Quick Check:

   summary() = min, Q1, median, mean, Q3, max [OK]

Hint: Remember summary() shows min, quartiles, median, mean, max [OK]

Common Mistakes:

• Thinking summary() only gives mean and median
• Confusing summary() with sum() or length()
• Expecting summary() to count unique values

2. Which of the following is the correct way to get summary statistics of a data frame named df in R?

easy

A. stats(df)

B. summarize(df)

C. summary(df)

D. summary_stats(df)

Solution

1. Step 1: Recall the correct function name for summary statistics in R

   The built-in function to get summary statistics is summary().

2. Step 2: Check the options for correct syntax

   Only summary(df) uses the correct function summary() applied to the data frame df.

3. Final Answer:

   summary(df) -> Option C

4. Quick Check:

   summary() is the correct function [OK]

Hint: Use summary() for data frames, not summarize() or others [OK]

Common Mistakes:

• Using summarize() which is from dplyr, not base R
• Typing summary_stats() which doesn't exist
• Using stats() which is unrelated

3. What will be the output of the following R code?

vec <- c(5, 10, 15, 20, 25)
summary(vec)

medium

A. Min. 5, 1st Qu. 10, Median 15, Mean 15, 3rd Qu. 20, Max. 25

B. Min. 5, Median 10, Mean 15, Max. 25

C. Min. 5, Max. 25, Sum 75

D. Error: summary() cannot be applied to vectors

Solution

1. Step 1: Calculate summary statistics for the vector

   The vector is 5, 10, 15, 20, 25. Minimum is 5, 1st quartile is 10, median is 15, mean is (5+10+15+20+25)/5 = 15, 3rd quartile is 20, maximum is 25.

2. Step 2: Match calculated values with options

   Min. 5, 1st Qu. 10, Median 15, Mean 15, 3rd Qu. 20, Max. 25 matches all these values exactly. Other options miss quartiles or give errors.

3. Final Answer:

   Min. 5, 1st Qu. 10, Median 15, Mean 15, 3rd Qu. 20, Max. 25 -> Option A

4. Quick Check:

   summary(vec) = min 5, Q1 10, median 15, mean 15, Q3 20, max 25 [OK]

Hint: Calculate min, quartiles, median, mean, max for vector [OK]

Common Mistakes:

• Forgetting to calculate quartiles
• Confusing median with mean
• Expecting an error for summary() on vectors

4. Identify the error in this R code that tries to get summary statistics of a numeric vector nums:

summary(nums, digits=2)

medium

A. The vector nums must be sorted before summary()

B. The vector nums must be converted to a data frame first

C. The function should be summarize() instead of summary()

D. The summary() function does not have a digits argument

Solution

1. Step 1: Check the arguments of summary() in R

   The base R summary() function does not accept a digits argument. This causes an error.

2. Step 2: Verify other options

   Converting to data frame or sorting is not required. summarize() is from dplyr, not base R.

3. Final Answer:

   The summary() function does not have a digits argument -> Option D

4. Quick Check:

   summary() has no digits argument [OK]

Hint: Check function arguments carefully to avoid errors [OK]

Common Mistakes:

• Assuming summary() accepts digits argument
• Thinking vector must be data frame
• Confusing summary() with dplyr's summarize()

5. You have a data frame df with a numeric column age. You want to find the median age but exclude any missing values (NA). Which R code correctly does this?

hard

A. median(df$age)

B. median(df$age, na.rm = TRUE)

C. summary(df$age, na.rm = TRUE)["Median"]

D. summary(df$age)$median

Solution

1. Step 1: Understand how to calculate median excluding NA values

   The median() function has an argument na.rm = TRUE to remove missing values before calculation.

2. Step 2: Check each option for correctness

   median(df$age, na.rm = TRUE) correctly uses median() with na.rm = TRUE. summary(df$age, na.rm = TRUE)["Median"] tries to extract median from summary but summary() does not accept na.rm. median(df$age) ignores NA values causing NA result. summary(df$age)$median tries to access median as a list element which is invalid.

3. Final Answer:

   median(df$age, na.rm = TRUE) -> Option B

4. Quick Check:

   Use median() with na.rm=TRUE to exclude NA [OK]

Hint: Use median(..., na.rm=TRUE) to ignore missing values [OK]

Common Mistakes:

• Not using na.rm=TRUE causing NA result
• Trying to get median from summary() output incorrectly
• Assuming summary() removes NA automatically