Example 2 Step Line Plot with Multiple Series
R
# Sample datatime_points <- c(1, 2, 3, 4, 5, 6, 7)series_a <- c(10, 15, 12, 18, 22, 20, 25)series_b <- c(5, 8, 7, 12, 14, 11, 18)# Create a step line plot with multiple seriesplot(x = time_points, y = series_a, type = "s", col = "blue", main = "Step Line Plot with Multiple Series", xlab = "Time", ylab = "Value")lines(x = time_points, y = series_b, type = "s", col = "red")legend("topright", legend = c("Series A", "Series B"), col = c("blue", "red"), lty = 1, cex = 0.8) |
Output:
Step Line Plot
- We have defined three vectors: time_points, which represents time points, and series_a and series_b, which represent the values of two hypothetical variables, “Series A” and “Series B,” respectively.
- We use the plot() function to create the initial step line plot for “Series A.” The x argument specifies the x-axis values (time_points), and the y argument specifies the y-axis values (series_a). We set the line color to blue (col = “blue”).
- We use the lines() function to add a second step line plot for “Series B.” We specify the line color as red (col = “red”).
- The legend() function is used to add a legend to the plot, distinguishing between “Series A” and “Series B.”.
Step Line Plot Using R
Step line plots, also known as step plots or step charts, are a type of data visualization used to display data points that change abruptly at specific time intervals or discrete data points. They are particularly useful for showing changes over time in a visually intuitive manner. In this article, we will explore the theory behind step-line plots and provide multiple examples with explanations using R.
In R Programming Language A step line plot is a variation of a line chart where data points are connected with horizontal and vertical line segments, creating a series of steps. Each step corresponds to a data point, and the horizontal line segments indicate that the data remains constant until the next data point.
Step line plots are commonly used in various fields, including finance (e.g., stock price charts), engineering (e.g., response time plots), and data analysis (e.g., time series analysis). They are particularly effective for visualizing data with discrete or irregularly spaced time intervals.
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