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# Introduction

Understanding the distribution of your data is a fundamental step in any data analysis process. It gives you insights into the spread, central tendency, and overall shape of your data. In this blog post, we’ll explore two popular functions in R for visualizing data distribution: `density()` and `hist()`. We’ll use the classic Iris dataset for our examples. Additionally, we will introduce the `{TidyDensity}` library and show how it can be used to create distribution plots.

# Exampels

## Example 1. Visualizing Data Distribution using density()

The `density()` function in R is used to estimate the probability density function of a continuous random variable. This function calculates density curve, allowing us to see the underlying distribution of the data with the `plot()` function.

### Syntax:

`density(x, ...)`

Where `x` is the numeric vector for which the density will be estimated.

### Example:

```# Plot the density distribution of Sepal Length
plot(
density(iris\$Sepal.Length),
main="Density Plot of Sepal Length",
xlab="Sepal Length", ylab="Density"
)``` In this example, we load the Iris dataset and plot the density distribution of Sepal Length. The `main`, `xlab`, and `ylab` arguments are used to provide titles and labels to the plot.

## Example 2. Visualizing Data Distribution using hist()

The `hist()` function is another powerful tool for visualizing the distribution of data. It creates a histogram, which is a graphical representation of the frequency distribution of a dataset.

### Syntax:

`hist(x, ...)`

Where `x` is the numeric vector for which the histogram will be created.

### Example:

```# Create a histogram of Petal Width
hist(iris\$Petal.Width, main="Histogram of Petal Width",
xlab="Petal Width", ylab="Frequency", col="skyblue")``` Here, we create a histogram of the Petal Width from the Iris dataset. The `main`, `xlab`, `ylab`, and `col` arguments allow customization of the plot’s appearance.

### Add lines to a histogram

Here we will combine the density plot and the histogram together. Sometimes this helps.

```x <- iris\$Sepal.Length

hist(x, prob = TRUE)
lines(density(x))``` ## Using TidyDensity for Data Distribution Visualization

The `TidyDensity` library is a convenient way to visualize data distributions with a modern and tidy approach. Let’s take a look at how it works.

### Example:

```# Load the required library
library(TidyDensity)

# Extract the 'mpg' column
x <- mtcars\$mpg

# Use TidyDensity functions to visualize data distribution
tidy_empirical(x) |> tidy_autoplot()``` In this example, we load the `TidyDensity` library and the `mtcars` dataset. We extract the ‘mpg’ column and then utilize the `tidy_empirical()` function to compute the empirical density. The `tidy_autoplot()` function creates a visually appealing distribution plot.

# Conclusion

In conclusion, visualizing data distribution is crucial for understanding the characteristics of your dataset. R provides various functions like `density()` and `hist()` to help you with this task. Additionally, the `TidyDensity` library offers a modern approach to visualizing data distributions. With these tools at your disposal, you can gain valuable insights from your data and make informed decisions in your analysis.