# 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.

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

`data(iris)`

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
summary(iris)

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  ```

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
dim(iris)

 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
names(iris)

 "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
hist(iris\$Sepal.Length,
col='steelblue',
main='Histogram',
xlab='Length',
ylab='Frequency')
``` 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,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)``` We can also use the boxplot() function to create a boxplot by group:

```#create scatterplot of sepal width vs. sepal length
boxplot(Sepal.Length~Species,
data=iris,
main='Sepal Length by Species',
xlab='Species',
ylab='Sepal Length',
col='steelblue',
border='black')``` 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.