Descriptive Statistics Using summary() Function in R (3 Examples)
This article explains how to compute descriptive statistics using the summary function in the R programming language.
Creation of Example Data
data(iris) # Iris flower data set head(iris) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa |
data(iris) # Iris flower data set head(iris) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa
Example 1: Compute Summary Statistics of Column or Vector
summary(iris$Sepal.Length) # Using summary function # Min. 1st Qu. Median Mean 3rd Qu. Max. # 4.300 5.100 5.800 5.843 6.400 7.900 |
summary(iris$Sepal.Length) # Using summary function # Min. 1st Qu. Median Mean 3rd Qu. Max. # 4.300 5.100 5.800 5.843 6.400 7.900
Example 2: Compute Summary Statistics of Data Frame
summary(iris) # Using summary function # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50 # 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50 # Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50 # Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 # 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 # Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 |
summary(iris) # Using summary function # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50 # 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50 # Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50 # Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 # 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 # Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
Example 3: Compute Summary Statistics of Linear Regression Model
my_model <- lm(Sepal.Length ~ ., iris) # Estimating model |
my_model <- lm(Sepal.Length ~ ., iris) # Estimating model
summary(my_model) # Using summary function # Call: # lm(formula = Sepal.Length ~ ., data = iris) # # Residuals: # Min 1Q Median 3Q Max # -0.79424 -0.21874 0.00899 0.20255 0.73103 # # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.17127 0.27979 7.760 1.43e-12 *** # Sepal.Width 0.49589 0.08607 5.761 4.87e-08 *** # Petal.Length 0.82924 0.06853 12.101 < 2e-16 *** # Petal.Width -0.31516 0.15120 -2.084 0.03889 * # Speciesversicolor -0.72356 0.24017 -3.013 0.00306 ** # Speciesvirginica -1.02350 0.33373 -3.067 0.00258 ** # --- # Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # # Residual standard error: 0.3068 on 144 degrees of freedom # Multiple R-squared: 0.8673, Adjusted R-squared: 0.8627 # F-statistic: 188.3 on 5 and 144 DF, p-value: < 2.2e-16 |
summary(my_model) # Using summary function # Call: # lm(formula = Sepal.Length ~ ., data = iris) # # Residuals: # Min 1Q Median 3Q Max # -0.79424 -0.21874 0.00899 0.20255 0.73103 # # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.17127 0.27979 7.760 1.43e-12 *** # Sepal.Width 0.49589 0.08607 5.761 4.87e-08 *** # Petal.Length 0.82924 0.06853 12.101 < 2e-16 *** # Petal.Width -0.31516 0.15120 -2.084 0.03889 * # Speciesversicolor -0.72356 0.24017 -3.013 0.00306 ** # Speciesvirginica -1.02350 0.33373 -3.067 0.00258 ** # --- # Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # # Residual standard error: 0.3068 on 144 degrees of freedom # Multiple R-squared: 0.8673, Adjusted R-squared: 0.8627 # F-statistic: 188.3 on 5 and 144 DF, p-value: < 2.2e-16