```
iris <- read.csv("Iris_Data.csv")
# Display the first few rows of the iris data.
head(iris)
# Rename the column name Species to Type.
names(iris)[5] <- "Type"
# Display the first 5 rows and last 3 columns of the iris data frame.
iris[1:5, 3:5]
# What is the data type of each column in this data frame of iris data?
str(iris)
# Draw a box plot of Sepal Length.
boxplot(iris$Sepal.Length)
# Draw a scatter plot of Sepal Length vs Sepal Width.
plot(Sepal.Width ~ Sepal.Length, data = iris)
# Create a new column in the iris data frame which is the sum of Sepal Length and Sepal Width.
iris$sum <- iris$Sepal.Length + iris$Sepal.Width
# What are the means, medians, and standard deviations of the four predictor columns in this data frame?
summary(iris[, 1:4])
sd(iris$Sepal.Length)
sd(iris$Sepal.Width)
sd(iris$Petal.Length)
sd(iris$Petal.Width)
# Draw the pair-wise correlations between the features of the iris data set.
pairs(iris[,1:4])
```

Another way to visualize the distributions of the variables in the data which can be Length and Width is to use the **ggplot** library in R. Histograms can also be plotted using this library. To get to know about these features in detail here is the link to the documentation: https://www.rdocumentation.org/packages/ggplot2/versions/3.1.1/topics/ggplot