summary in R: How to Use summary() Function

To get a better idea of the distribution of your variables in the dataset, use the summary() function. If you need a quick survey of your dataset, you can, of course, always use the R str() function and look at the structure.

summary in R

The summary is a built-in R function used to produce result summaries of various model fitting functions. The summary() function implores specific methods that depend on the class of the first argument.


summary(object, maxsum = 7, digits = max(3, getOption("digits")-3), …)


object: It is an object for which a summary is desired.

maxsum: It is an integer indicating how many levels should be shown for factors.

digits: It is an integer used for number formatting with signif().

Return Value

The summary() function returns the value that depends on the class of its argument.


Let’s apply the summary() function to a vector that will act like the R object.

vec <- 1:5
cat("The summary() of vector is", "\n")


[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 that the summary() of a vector 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.

Applying summary() function on the List

To get the summary of the list in R, use the summary() function. To define a list, use the list() function and pass the elements as arguments.

vec <- 1:5
list <- list(vec)
cat("The summary() of list is", "\n")


The summary() of list is
Min. 1st Qu. Median Mean  3rd Qu. Max.
 1      2      3     3     4       5

summary() function on the Array

To get the summary of an array in R, use the summary() function. To create an array in Ruse the array() function. The array() function takes a vector as an argument and uses the dim parameter to create an array.

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")


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

summary() function on R Matrix

To get the summary of a matrix in R, use the summary() function. To create a matrix in R, use the matrix() function, and pass the vectornrow, and ncol parameters.

rv <- c(11, 18, 19, 21)
mtrx <- matrix(rv, nrow = 2, ncol = 2)
cat("The summary() of matrix is", "\n")


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

summary() function on R data frame

To get the summary of a data frame in R, use the summary() function. To create a data frame in Ruse data.frame() 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")


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

summary() function on 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 to be an explanatory variable, and the other is considered to be 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.

l_x <- rnorm(1000)
l_y <- rnorm(1000) + l_x
mod <- lm(l_y ~ l_x)


lm(formula = l_y ~ l_x)

 Min 1Q Median 3Q Max
-3.7337 -0.6964 -0.0047 0.7333 3.3489

 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

Our example data consists of two randomly distributed numeric vectors. As a result, we can estimate a linear regression model.

The data object mod contains the output of our linear regression. We have applied the summary() function to this model object to print summary statistics for this model.

That is it for the summary() function in the R tutorial.

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