Specify Time Series in R

Introduction

Time series data is a sequence of numerical values measured at regular time intervals. In this article, we’ll explore how to specify and manipulate time series data in R.

R provides several packages for handling time series data, including the base package, zoo, xts, and others. In this article, we’ll focus on using the zoo package to create time series objects and perform common operations on them.

Creating Time Series Data

To create a time series object in R, you can use the ts() function from the base package. However, if your data contains leap years or irregular intervals, you may need to use one of the alternative packages.

Let’s start by creating some sample data:

# create data set for testing
tt0 <- seq(as.Date("2013-01-01"), as.Date("2014-12-31"), by = "day")
lt <- as.POSIXlt(tt0)
DF <- data.frame(year = lt$year + 1900, month = lt$mon + 1, day = lt$mday, visits = 1:730)

In this example, we create a sequence of dates from January 1st, 2013 to December 31st, 2014, with daily intervals. We then transform the data into a data frame and add a visits column.

Converting Data to Time Series Class

To convert our sample data to a time series object, we can use the ts() function:

# convert to ts
tser <- ts(DF$visits, start = 2013, freq = 365)

In this example, we specify that our data starts in 2013 and has a frequency of 365 (days per year).

Handling Leap Years

If your data contains leap years, you may need to use an alternative package. The zoo package provides the as.yearmon() function, which can be used to create a time series object with monthly intervals:

# load zoo package
library(zoo)

# convert to ts
tser <- ts(DF$visits, start = 2013, freq = 12)

In this example, we specify that our data has a frequency of 12 (months per year).

Manipulating Time Series Data

Once you have created a time series object, you can perform various operations on it. For example, you can use the dcast() function from the reshape2 package to convert the time series to a data frame with day-of-month intervals:

# load reshape2 package
library(reshape2)

# append year_month column
DF2 <- transform(DF, year_month = I(sprintf("%d-%02d", year, month)))

# use dcast() to create 2D display
xtabs(visits ~ year_month + day, DF2, sparse = TRUE)

In this example, we add a year_month column to our data frame and then use the xtabs() function to count the number of visits for each month-day combination.

Alternative Methods

There are several alternative methods for creating time series objects in R. One approach is to use the as.yearmon() function from the zoo package:

# load zoo package
library(zoo)

# convert to ts
tser <- ts(DF$visits, start = 2013, freq = 12)

In this example, we specify that our data has a frequency of 12 (months per year).

Another approach is to use the transform() function and calculate day-of-month intervals manually:

# transform data frame
DF2 <- transform(DF, day = factor(day))

# use xtabs() to create 2D display
xtabs(visits ~ year_month + day, DF2, sparse = TRUE)

In this example, we add a day column to our data frame and then use the xtabs() function to count the number of visits for each month-day combination.

Conclusion

In this article, we explored how to specify and manipulate time series data in R. We discussed several approaches for creating time series objects, including using the base package, zoo, and xts packages. We also covered various operations that can be performed on time series data, such as converting it to a data frame with day-of-month intervals. By understanding how to work with time series data in R, you can more effectively analyze and visualize your data.

Last modified on 2024-06-07