Unlocking the Power of Time: Transforming Data Frames into Time Series in R

rtip
timeseries
Author

Steven P. Sanderson II, MPH

Published

December 29, 2023

Introduction

Hey there, fellow R enthusiasts! Today, we’re diving into the realm of time series, where data dances along the temporal dimension. To join this rhythmic analysis, we’ll first learn how to convert our trusty data frames into time series objects—the heart of time-based exploration in R.

Ready to Time Warp? Let’s Get Started!

Every journey begins with preparation. Here’s our sample data frame containing daily sales:

``````df <- data.frame(date = as.Date('2022-01-01') + 0:9,
sales = runif(10, 10, 500) + seq(50, 59)^2)``````

2. Choose Your Time Series Destination

R offers two primary time series classes:

• “ts”: Base R’s classic time series object, designed for regularly spaced data.
• “xts”: Part of the ‘xts’ package, offering enhanced flexibility and features.

3. Embark on the Conversion Quest

A. Transforming into “ts”:

``````library(stats)  # Package for 'ts' class

# Unleash the time series magic!
ts_sales <- ts(df\$sales, start = c(2022, 1), frequency = 365)  # Daily data

print(ts_sales)``````
``````Time Series:
Start = c(2022, 1)
End = c(2022, 10)
Frequency = 365
[1] 2728.713 3026.967 2769.227 2928.872 3401.730 3129.780 3303.479 3414.551
[9] 3584.525 3922.348``````

Explanation:

• `ts()` function creates the time series object.
• `df\$sales` specifies the data for conversion.
• `start = c(2022, 1)` sets the starting year and month.
• `frequency = 365` indicates daily observations (365 days per year).

B. Shaping into “xts”:

``````library(xts)  # Package for 'xts' class

# Time to shine!
xts_sales <- xts(df\$sales, order.by = df\$date)

print(xts_sales)``````
``````               [,1]
2022-01-01 2728.713
2022-01-02 3026.967
2022-01-03 2769.227
2022-01-04 2928.872
2022-01-05 3401.730
2022-01-06 3129.780
2022-01-07 3303.479
2022-01-08 3414.551
2022-01-09 3584.525
2022-01-10 3922.348``````

Explanation:

• `xts()` function constructs the time series object.
• `df\$sales` provides the data.
• `order.by = df\$date` sets the time-based ordering.

Now that you’ve mastered the conversion, unleash your creativity:

• Visualize trends with plots.
• Forecast future values.
• Analyze patterns and seasonality.
• Decompose time series into components.
• And much more!

The possibilities are as boundless as time itself.

Remember:

• Choose the time series class that best suits your analysis needs.
• Always ensure your data frame has a column with valid date or time values.
• Explore the rich functionalities of R’s time series packages.