Time Series Plots
Learn how to build time series plots with ggplot2.
We'll cover the following...
- Introduction to time series plots
- Preparing time series data for visualization
- Basic time series plot in ggplot2
- Handling date formats for time series plotting
- Adding axis breaks to a time series plot
- Setting limits in a time series plot
- Modifying time series plots with colors and points
- Adding confidence intervals with trend lines
Introduction to time series plots
Time series plots are a type of line chart used to visualize time series data and help identify patterns, trends, and seasonality in the data. Both line charts and time series plots are commonly used to visualize data in R and can be created using the geom_line() function in the ggplot2 package. However, line charts and time series charts are different, and it is essential to understand these key differences before building the time series plots in ggplot2.
Let’s compare the differences between both in terms of usage, data requirement, and additional features.
Differences Between Time Series Plots and Line Charts
Attribute | Time Series Plots | Line Charts |
Use | Time series plots visualize data collected at regular intervals, such as daily, monthly, or quarterly, allowing us to show the trends and patterns in the data over time. | Line charts visualize the correlation between two variables. They don’t necessarily have a time component. |
Data Requirement | These charts typically require more data preparation, as the time variable may need to be formatted in a specific way to plot in R. | These are generally easier to create as they don’t have a time component and do not require much data preparation. |
Additional Features | Time series plots often include additional features, such as trend lines and confidence intervals, to help analyze and interpret the data better. These features enable us to identify long-term trends and patterns in the data and can be easily added to the plot. | Regular line charts may not include these additional features and may focus more on visualizing the relationship between the two variables being plotted. |
Preparing time series data for visualization
We need to prepare the data before we can create time series plots. It’s important to note that the time series data should be in a date format, with a single column for the time variable and a separate column for the corresponding values of the time variable.
We’ll create a data frame named df with the time series data and print the first few rows of the dataset using the code below:
df <- tibble(date = seq(as.Date("2022-01-01"), as.Date("2022-07-19"), "day"),value = rnorm(200))head(df)
- Line 1: We use
tibble A tibble is the tidyverse object type for a data frame. It is a collection of columns and variables that carry data values. A tibble preserves all the variable types and is created using the tibble() function in R. df. - Line 2: The data frame
dfcontains two columns,dateandvalue. We use theas.Date()function to format the date in theyyyy-mm-ddformat. With this function, we can convert character data to dates. Thedatecolumn will have a sequence of dates from January , to July