The **summary()** function in R can be used to quickly summarize the values in a vector, data frame, regression model, or ANOVA model in R.

This syntax uses the following basic syntax:

summary(data)

The following examples show how to use this function in practice.

**Example 1: Using summary() with Vector**

The following code shows how to use the **summary()** function to summarize the values in a vector:

#define vector x <- c(3, 4, 4, 5, 7, 8, 9, 12, 13, 13, 15, 19, 21) #summarize values in vector summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 3.00 5.00 9.00 10.23 13.00 21.00

The **summary()** function automatically calculates the following summary statistics for the vector:

- Min: The minimum value
- 1st Qu: The value of the 1st quartile (25th percentile)
- Median: The median value
- 3rd Qu: The value of the 3rd quartile (75th percentile)
- Max: The maximum value

Note that if there are any missing values (NA) in the vector, the **summary()** function will automatically exclude them when calculating the summary statistics:

#define vector x <- c(3, 4, 4, 5, 7, 8, 9, 12, 13, 13, 15, 19, 21, NA, NA) #summarize values in vector summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 3.00 5.00 9.00 10.23 13.00 21.00 2

**Example 2: Using summary() with Data Frame**

The following code shows how to use the **summary()** function to summarize every column in a data frame:

#define data frame df <- data.frame(team=c('A', 'B', 'C', 'D', 'E'), points=c(99, 90, 86, 88, 95), assists=c(33, 28, 31, 39, 34), rebounds=c(30, 28, 24, 24, 28)) #summarize every column in data frame summary(df) team points assists rebounds Length:5 Min. :86.0 Min. :28 Min. :24.0 Class :character 1st Qu.:88.0 1st Qu.:31 1st Qu.:24.0 Mode :character Median :90.0 Median :33 Median :28.0 Mean :91.6 Mean :33 Mean :26.8 3rd Qu.:95.0 3rd Qu.:34 3rd Qu.:28.0 Max. :99.0 Max. :39 Max. :30.0

**Example 3: Using summary() with Specific Data Frame Columns**

The following code shows how to use the **summary()** function to summarize specific columns in a data frame:

#define data frame df <- data.frame(team=c('A', 'B', 'C', 'D', 'E'), points=c(99, 90, 86, 88, 95), assists=c(33, 28, 31, 39, 34), rebounds=c(30, 28, 24, 24, 28)) #summarize every column in data frame summary(df[c('points', 'rebounds')]) points rebounds Min. :86.0 Min. :24.0 1st Qu.:88.0 1st Qu.:24.0 Median :90.0 Median :28.0 Mean :91.6 Mean :26.8 3rd Qu.:95.0 3rd Qu.:28.0 Max. :99.0 Max. :30.0

**Example 4: Using summary() with Regression Model**

The following code shows how to use the **summary()** function to summarize the results of a linear regression model:

#define data df <- data.frame(y=c(99, 90, 86, 88, 95, 99, 91), x=c(33, 28, 31, 39, 34, 35, 36)) #fit linear regression model model <- lm(y~x, data=df) #summarize model fit summary(model) Call: lm(formula = y ~ x, data = df) Residuals: 1 2 3 4 5 6 7 6.515 -1.879 -6.242 -5.212 2.394 6.273 -1.848 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 88.4848 22.1050 4.003 0.0103 * x 0.1212 0.6526 0.186 0.8599 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 5.668 on 5 degrees of freedom Multiple R-squared: 0.006853, Adjusted R-squared: -0.1918 F-statistic: 0.0345 on 1 and 5 DF, p-value: 0.8599

**Related:** How to Interpret Regression Output in R

**Example 5: Using summary() with ANOVA Model**

The following code shows how to use the **summary()** function to summarize the results of an ANOVA model in R:

#make this example reproducible set.seed(0) #create data frame data <- data.frame(program = rep(c("A", "B", "C"), each = 30), weight_loss = c(runif(30, 0, 3), runif(30, 0, 5), runif(30, 1, 7))) #fit ANOVA model model <- aov(weight_loss ~ program, data = data) #summarize model fit summary(model) Df Sum Sq Mean Sq F value Pr(>F) program 2 98.93 49.46 30.83 7.55e-11 *** Residuals 87 139.57 1.60 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

**Related:** How to Interpret ANOVA Results in R

**Additional Resources**

The following tutorials offer more information on calculating summary statistics in R:

How to Calculate Five Number Summary in R

The Easiest Way to Create Summary Tables in R

How to Create Relative Frequency Tables in R