# 1. Introduction

A scatterplot is a graphical representation of the relationship between two sets of data. Individual data points are represented as dots plotted on a 2D graph with the x and y-axis representing the two sets of data. Scatterplots are essential for spotting trends, correlations, or anomalies in datasets. In this tutorial, we'll see how to create a scatterplot using R.

# 2. Program Overview

The program will:

1. Create two numeric datasets representing the x and y coordinates.

2. Use the plot() function from base R to generate and display a scatterplot.

3. Customize the appearance of the scatterplot.

# 3. Code Program

``````# Create two numeric datasets
x_data <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
y_data <- c(1.1, 2.4, 2.8, 3.5, 5.1, 5.8, 7.3, 8.1, 9.5, 10.2)

# Generate and display a scatterplot with title, axis labels, and blue-colored points
plot(x_data, y_data,
main = "Scatterplot of x_data vs y_data",
xlab = "X Data",
ylab = "Y Data",
col = "blue",
pch = 19)
``````

### Output:

```[A scatterplot will be displayed with the title "Scatterplot of x_data vs y_data". The x-axis is labeled "X Data" and the y-axis is labeled "Y Data". The data points are colored blue.]
```

# 4. Step By Step Explanation

1. We start by creating two numeric datasets: "x_data" and "y_data". The "x_data" represents the x-coordinates, and "y_data" represents the y-coordinates of our scatterplot. For this example, we use fictional datasets.

2. The plot() function is employed to create the scatterplot. We provide it with various parameters:

- The first two parameters are our datasets, "x_data" and "y_data".

- The "main" parameter is used to set the title of the scatterplot.

- "xlab" and "ylab" are used to label the x and y-axes respectively.

- The "col" parameter determines the color of the points, which we set to blue in this case.

- The "pch" parameter dictates the type of point to use. Here, "19" refers to a solid circle.

3. When we run the code, R will display a scatterplot in a separate graphical window, showing the relationship between "x_data" and "y_data".

Remember, the actual visuals of the scatterplot can differ depending on your R graphical settings and the specific data employed.