Table of Contents
The summary() function in R is used to generate a summary of various data types, such as vectors, matrices, data frames, and functions. It is a generic function that can be used to generate summaries for different types of variables such as numeric, factor, logical, and character. It can also be used to generate summary statistics, including mean, median, mode, standard deviation, variance, and quartiles. By using the summary() function, you can quickly and easily generate a summary of your data quickly and easily.
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:
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:
The following tutorials offer more information on calculating summary statistics in R: