The summary() function in R is “used to produce result summaries of the results of various model fitting functions“. It is done by grouping observations by using categorical values at first, using the groupby() function.
The summary() function returns the following statistics.
- Minimum value
- The first quartile (25th percentile)
- Median (50th percentile)
- Mean
- Third quartile (75th percentile)
- Maximum value
Syntax
summary(data, maxsum)
Parameters
data: It is an R object for which you want a summary.
maxsum: An integer suggests how many levels should be shown for factors.
Return Value
The summary() function returns the value that depends on the class of its argument.
Example 1: Using summary() with data frame
To get the summary of a “data frame” in R, you can use the “summary()” function.
df <- data.frame(
service_id = c(1:5),
service_name = c("Netflix", "Disney+", "HBOMAX", "Hulu", "Peacock"),
service_price = c(18, 10, 15, 7, 12),
stringsAsFactors = FALSE
)
cat("The summary() of data frame is", "\n")
summary(df)
Output
The summary() of data frame is
service_id service_name service_price
Min. :1 Length:5 Min. : 7.0
1st Qu.:2 Class :character 1st Qu.:10.0
Median :3 Mode :character Median :12.0
Mean :3 Mean :12.4
3rd Qu.:4 3rd Qu.:15.0
Max. :5 Max. :18.0
Example 2: Using summary() with list
To get the summary of the list in R, you can use the “summary()” function.
vec <- 1:5
list <- list(vec)
cat("The summary() of list is", "\n")
summary(vec)
Output
The summary() of list is
Min. 1st Qu. Median Mean 3rd Qu. Max.
1 2 3 3 4 5
Example 3: Using summary() with an array
To get the summary of an “array” in R, use the “summary()” function.
rv <- c(19, 21)
rv2 <- c(46, 4)
arr <- array(c(rv, rv2), dim = c(2, 2, 2))
cat("The summary() of array is", "\n")
summary(arr)
Output
The summary() of array is
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.00 15.25 20.00 22.50 27.25 46.00
Example 4: Using summary() with matrix
To get a summary of a “matrix” in R, use the “summary()” function.
rv <- c(11, 18, 19, 21)
mtrx <- matrix(rv, nrow = 2, ncol = 2)
cat("The summary() of matrix is", "\n")
summary(mtrx)
Output
The summary() of matrix is
V1 V2
Min. :11.00 Min. :19.0
1st Qu.:12.75 1st Qu.:19.5
Median :14.50 Median :20.0
Mean :14.50 Mean :20.0
3rd Qu.:16.25 3rd Qu.:20.5
Max. :18.00 Max. :21.0
Example 5: Using summary() with vector
vec <- 1:5
vec
cat("The summary() of vector is", "\n")
summary(vec)
Output
[1] 1 2 3 4 5
The summary() of vector is
Min. 1st Quantile Median Mean 3rd Quantile Max.
1 2 3 3 4 5
As you can see from the output, a vector’s summary() returns descriptive statistics such as the minimum, the 1st quantile, the median, the mean, the 3rd quantile, and the maximum value of our input data.
Example 6: Using summary() with Linear Regression Model
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered an explanatory variable, and the other is a dependent variable.
A widespread application of the summary functions is the computation of summary statistics of statistical models. For example, let’s see the following code.
set.seed(93274)
l_x <- rnorm(1000)
l_y <- rnorm(1000) + l_x
mod <- lm(l_y ~ l_x)
summary(mod)
Output
Call:
lm(formula = l_y ~ l_x)
Residuals:
Min 1Q Median 3Q Max
-3.7337 -0.6964 -0.0047 0.7333 3.3489
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02159 0.03292 -0.656 0.512
l_x 1.00156 0.03262 30.707 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.041 on 998 degrees of freedom
Multiple R-squared: 0.4858, Adjusted R-squared: 0.4853
F-statistic: 942.9 on 1 and 998 DF, p-value: < 2.2e-16
We applied the summary() function to this model object to print summary statistics for this model.
That’s it.

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.