Creating Quarterly `xts` Time-Series Objects for Use with Plot.XTS

Introduction

Time series data is a fundamental concept in various fields, including finance, economics, and statistics. In R, the xts package provides an efficient and flexible way to work with time series objects. This article will focus on transforming a monthly time series into a quarterly time series object using the xts package.

Understanding the Problem

The provided example data df represents monthly observations, but we need to convert it into a quarterly time series object for further analysis or plotting. The initial attempt using as.yearqtr results in an inconsistent date format, making it unsuitable for conversion.

Solution Overview

There are several approaches to achieve the desired outcome:

Using as.yearmon and to.quarterly
Indexing the time series object

We will explore both methods in detail, providing code examples and explanations to help you understand the process.

Method 1: Using `as.yearmon` and `to.quarterly`

This approach involves converting the monthly observations into a year-month format using as.yearmon, followed by applying the to.quarterly function to achieve the desired quarterly frequency.

Step 1: Convert Monthly Observations to Year-Month Format

# Load required libraries
library(xts)

# Define the data frame
df <- structure(list(yrQ = structure(1:7, .Label = c("2016-1", "2016-2",
    "2016-3", "2016-4", "2016-5", "2016-6", "2016-7"), class = "factor"),
    a = c(4.14, 2.83, 3.71, 4.15, 4.63, 4.91, 5.31), b = c(4.25,
        3.5, 3.5, 3.5, 3.5, 3.5, 5)), .Names = c("yrQ", "a", "b"
), row.names = c(NA, 7L), class = "data.frame")

# Convert monthly observations to year-month format
myxts <- xts(df[,-1], order.by = as.yearmon(as.character(df[,1])))

Step 2: Apply `to.quarterly` Function

# Apply to.quarterly function
myxtsQ <- to.quarterly(myxts$a)
plot(myxtsQ)

However, this approach has a limitation since to.quarterly cannot be used simultaneously with both columns. We need to find an alternative solution.

Method 2: Indexing the Time Series Object

Another approach is to use indexing to create a quarterly time series object. This method involves creating a list of indices that repeats every three rows and applying it to the original time series object.

Step 1: Create List of Indices

# Define the number of observations
nrow_myxts <- nrow(myxts)

# Create list of indices
indx <- rep(c(T,F,F), ceiling(nrow_myxts/3))

Step 2: Apply Indexing to Create Quarterly Time Series Object

# Apply indexing to create quarterly time series object
myxtsQ <- myxts[indx,]

# Plot the resulting time series object
plot.xts(myxtsQ)

Conclusion

In this article, we have explored two methods for creating a quarterly time series object from a monthly data frame using the xts package in R. The first approach involves using as.yearmon and to.quarterly, while the second method relies on indexing to achieve the desired outcome.

By choosing the most suitable approach, you can efficiently transform your data into a quarterly time series format, making it easier to analyze and visualize your data using various plotting functions like plot.xts.

Last modified on 2024-08-16

Creating Quarterly xts Time-Series Objects for Use with Plot.XTS