Multiple Line Plots or Time Series Plots with ggplot2 in R

A multiple line plot or time series plot shows how values change over time, like crime rates or sales across years. In R, we use the ggplot2 package with the geom_line() function to create these plots. By adding a third variable, we can draw separate lines for different groups, making it easy to compare trends.

Syntax:

ggplot( df, aes( x, y ) ) + geom_line()

Parameters:

• df: data frame used.
• x and y: axis variables.

Example

A basic line plot is created to show how violent crime rates (violent_per_100k) changed over the years (year) from 1975 to 2015 using the city crime dataset. Since only two variables are used, it shows a single overall trend line without separating by city or department.

You can download the dataset from here.

• library(): Loads the necessary packages like ggplot2 and tidyverse.
• read_csv(): Reads the data from a CSV file.
• ggplot(): Initializes the plot with data and aesthetics.
• aes(): Specifies the x, y and color mappings for the plot.
• geom_line(): Adds lines to the plot for each department's trend.

Output:

The plot shows the overall trend of violent crime rates per 100,000 people from 1975 to 2015. It displays a single combined line without separating by department, resulting in overlapping vertical lines due to multiple records per year.

To better visualize trends for each department, a grouping variable like department_name should be added.

Syntax:

ggplot( df, aes( x, y, color ) ) + geom_line()

Parameters:

• df: data frame used
• x and y: axis variable
• color: variable according to which plot is grouped and colored

Example

In this example, we create a line plot grouped and colored according to department_name, which separates the data into multiple time series. This allows us to compare the trends of violent crime rates for different departments over the years.

• theme(legend.position = "right"): used to customize the plot appearance by positioning the legend on the right side of the plot, which is also a common default location.

Output:

This plot shows the trends in violent crime rates per 100,000 people over time across multiple departments, with each line representing a different department.

Use Cases for Multiple Line Plots

We use multiple line plots to visualize how a numeric variable changes over time across different categories. They are ideal when:

• We have a time-based variable (e.g., year or month) on the x-axis.
• We compare trends across multiple categories or groups (e.g., departments or cities).
• The data values are continuous or numerical.

We should avoid them when there are too many groups, as the chart can become cluttered. Filtering or faceting can simplify the visualization in such cases.