Creating Three Time Series Plots in Two Faceted Grids Using ggplot in R

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As a data visualization enthusiast, it’s essential to understand the basics of ggplot and facet grids in R. In this article, we’ll explore how to create three time series plots in two faceted grids using ggplot.

Introduction to ggplot

ggplot is a powerful data visualization library in R that provides a consistent and intuitive way to create high-quality graphics. It’s built on top of the Grammar of Graphics, which provides a framework for creating complex visualizations.

Creating Time Series Plots with ggplot

To create time series plots using ggplot, we can use the geom_line() function, which is used to plot lines in our data.

# Create some fake data
stock1 = cumprod(1+c(0, rnorm(99, 0, .05)))
stock2 = cumprod(1+c(0, rnorm(99, 0, .075)))
indicator = sample(1:50, 100, replace = TRUE)
date_seq = seq.Date(as.Date("2023-01-01"), length.out = 100, by = 1)

df = data.frame(date = date_seq, stock1 = stock1, stock2 = stock2, indicator = indicator)

Converting to Long Format

To create a time series plot with ggplot, we need to convert our data from wide format to long format. We can use the pivot_longer() function from the tidyr package for this purpose.

library(ggplot2)
library(dplyr)
library(tidyr)

grid_df = pivot_longer(df, c(stock1, stock2, indicator), names_to = "underlying", values_to = "values")

Plotting Time Series Data

Now that we have our data in long format, we can create a time series plot using ggplot.

ggplot(grid_df, aes(x = date, y = values, colour = underlying)) +
  geom_line() +
  facet_grid(vars(underlying), scales = "free")

Problem: Creating Two Faceted Grids with Three Time Series Plots

However, we want to create a plot with two faceted grids, one for the stocks and one for the indicator. The problem is that we have three time series plots (two stocks and one indicator) and only two faceted grids.

To solve this problem, we can combine the two stocks into one facet by adding a new column to our data frame.

grid_df = pivot_longer(df, c(stock1, stock2, indicator), names_to = "underlying", values_to = "values") %&gt;%
  mutate(grids = ifelse(underlying == "indicator", "indicator", paste("stock", underlying)))

In this code snippet, we use the ifelse() function to assign a new value to the grids column. If the value is the indicator, it remains the same. Otherwise, we concatenate the string “stock” with the original stock name.

Now that we have our data in long format with the combined facet, we can plot the time series data using ggplot.

ggplot(grid_df, aes(x = date, y = values, colour = underlying)) +
  geom_line() +
  facet_grid(vars(grids), scales = "free")

Conclusion

In this article, we explored how to create three time series plots in two faceted grids using ggplot. We discussed the basics of ggplot and facet grids, and provided a solution for combining stocks into one facet.

Example Use Case: Financial Time Series Analysis

One common use case for creating multiple time series plots with ggplot is financial time series analysis. For example, you might want to visualize the stock prices of two different companies over time, along with their respective indicators (e.g., moving averages or RSI).

# Create some fake data for a company A and company B
company_a = cumprod(1+c(0, rnorm(99, 0, .05)))
company_b = cumprod(1+c(0, rnorm(99, 0, .075)))

# Combine the stock prices into one facet
grid_df = pivot_longer(data.frame(company_a, company_b), names_to = "underlying", values_to = "values") %&gt;%
  mutate(grids = ifelse(underlying == "company_a", "stock_a", paste("stock_", underlying)))

# Plot the time series data
ggplot(grid_df, aes(x = date, y = values, colour = underlying)) +
  geom_line() +
  facet_grid(vars(grids), scales = "free")

In this example, we create two fake datasets for company A and company B. We then combine their stock prices into one facet using the same approach as before.

By using ggplot to visualize multiple time series plots with faceted grids, you can gain a deeper understanding of your financial data and make more informed investment decisions.

Last modified on 2023-10-15

Understanding the Basics of ggplot and Facet Grids