In the R language, there is much graphical representation of qualitative and quantitative data. We will only discuss the histogram, bar plot, and box plot in this post.

**Histogram**

To visualize a single variable, the histogram can be drawn using the `hist( )`

function.

Let use the data from `iris`

dataset.

attach(iris) head(iris) hist(Petal.Width)

We can enhance the histogram by using some arguments/parameters related to `hist( )`

function. For example,

hist(Petal.Width, xlab = "Petal Width", ylab = "Frequency", main = "Histogram of Petal Width from Iris Data set", breaks =10, col = "dodgerblue", border = "orange")

If these arguments are not provided, R will attempt to intelligently guess them, especially the number of `breaks`

. See the YouTube tutorial for a graphical representation of the histogram.

**Barplots**

The bar plots are the best choice for visual inspection of a categorical variable (or a numeric variable with a finite number of values), or a rank variable. For example,

library(mtcars) barplot( table(cyl) )

barplot(table(cyl), ylab = "Frequency", xlab = "Cylinders (4, 6, 8)", main = "Number of cylinders ", col = "green", border = "blue")

**Boxplots**

Boxplots are used to visualize the normality, skewness, and existence of outliers in the data based on five-number summary statistics.

boxplot(mpg) boxplot(Petal.Width) boxplot(Petal.Length)

However, we often compare a numerical variable for different values of a categorical variable. For example,

boxplot(mpg ~ cyl, data = mtcars)

The reads the formula `mpg ~ cyl`

as: “plot the `mpg`

variable against the `cyl`

variable using the dataset `mtcars`

. The symbol `~`

used to specify a formula in R.

boxplot(mpg ~ cyl, data =mtcars, xlab = "Cylinders", ylab = "Miles per Gallon", pch = 20, cex = 2, col = "pink", border = "black")

See How to perform descriptive statistics

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