A Complete Guide to the Iris Dataset in R

The iris dataset is a built-in dataset in R that contains measurements on 4 different attributes (in centimeters) for 50 flowers from 3 different species.

This tutorial explains how to explore and summarize a dataset in R, using the iris dataset as an example.

Related: A Complete Guide to the mtcars Dataset in R

Load the Iris Dataset

Since the iris dataset is a built-in dataset in R, we can load it by using the following command:


We can take a look at the first six rows of the dataset by using the head() function:

#view first six rows of iris dataset

  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

Summarize the Iris Dataset

We can use the summary() function to quickly summarize each variable in the dataset:

#summarize iris dataset

  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  
 setosa    :50  
 virginica :50  

For each of the numeric variables we can see the following information:

  • Min: The minimum value.
  • 1st Qu: The value of the first quartile (25th percentile).
  • Median: The median value.
  • Mean: The mean value.
  • 3rd Qu: The value of the third quartile (75th percentile).
  • Max: The maximum value.

For the only categorical variable in the dataset (Species) we see a frequency count of each value:

  • setosa: This species occurs 50 times.
  • versicolor: This species occurs 50 times.
  • virginica: This species occurs 50 times.

We can use the dim() function to get the dimensions of the dataset in terms of number of rows and number of columns:

#display rows and columns

[1] 150   5

We can see that the dataset has 150 rows and 5 columns.

We can also use the names() function to display the column names of the data frame:

#display column names

[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"     

Visualize the Iris Dataset

We can also create some plots to visualize the values in the dataset.

For example, we can use the hist() function to create a histogram of the values for a certain variable:

#create histogram of values for sepal length

We can also use the plot() function to create a scatterplot of any pairwise combination of variables:

#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
     xlab='Sepal Width',
     ylab='Sepal Length',

We can also use the boxplot() function to create a boxplot by group:

#create scatterplot of sepal width vs. sepal length
        main='Sepal Length by Species',
        ylab='Sepal Length',

The x-axis displays the three species and the y-axis displays the distribution of values for sepal length for each species.

This type of plot allows us to quickly see that the sepal length tends to be largest for the virginica species and smallest for the setosa species.

Additional Resources

The following tutorials further explain how to summarize datasets in R:

The Easiest Way to Create Summary Tables in R
How to Calculate Five Number Summary in R

3 Replies to “A Complete Guide to the Iris Dataset in R”

  1. Hi! How would I work out the strength of association between Species and Sepal.Length using the lm() function? Thank you.

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