Exploratory Data Analysis in R Programming (6 Examples)
In this R programming tutorial you’ll learn how to explore a data frame.
Example Data
data(iris) # Example data frame |
data(iris) # Example data frame
Example 1: Apply nrow() Function to Get Number of Rows
nrow(iris) # Number of rows # [1] 150 |
nrow(iris) # Number of rows # [1] 150
Example 2: Apply ncol() Function to Get Number of Columns
ncol(iris) # Number of columns # [1] 5 |
ncol(iris) # Number of columns # [1] 5
Example 3: Apply names() Function to Get Column Names
names(iris) # Column names # [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" |
names(iris) # Column names # [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
Example 4: Apply head() Function to Display First Six Data Frame Rows
head(iris) # Display first six rows # 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 |
head(iris) # Display first six rows # 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 5: Apply str() Function to Show Structure of Data Frame Variables
str(iris) # Column structure # 'data.frame': 150 obs. of 5 variables: # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... # $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... |
str(iris) # Column structure # 'data.frame': 150 obs. of 5 variables: # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... # $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Example 6: Apply summary() Function to Compute Descriptive Statistics
summary(iris) # Summary statistics # Sepal.Length Sepal.Width Petal.Length Petal.Width # Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 # 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 # Median :5.800 Median :3.000 Median :4.350 Median :1.300 # 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 # Species # setosa :50 # versicolor:50 # virginica :50 # # # |
summary(iris) # Summary statistics # Sepal.Length Sepal.Width Petal.Length Petal.Width # Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 # 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 # Median :5.800 Median :3.000 Median :4.350 Median :1.300 # 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 # Species # setosa :50 # versicolor:50 # virginica :50 # # #