R Language Time Series Objects Ts Xts Zoo Complete Guide

1. What are ts, xts, and zoo objects in R, and how do they differ?

Answer:
In R, ts, xts, and zoo objects are used to represent time series data.

• ts Object: This is the basic time series object provided by the base R package. It's suitable for regular time series data where observations are collected at fixed intervals.
• xts Object: Part of the xts package, it stands for "extensible time series." It extends the capabilities of base ts by allowing for irregular time series and non-numeric matrices.
• zoo Object: Part of the zoo package, it is designed for handling irregular time series but can also manage regular ones. It supports different types of time indices (e.g., numeric, character, Date), making it very flexible.

2. How do you create a ts object from a vector or dataframe?

Answer:
Creating a ts object in R is straightforward when dealing with regularly spaced time series data. Here’s an example using a vector:

# Create a simple time series object
data <- c(1, 2, 3, 4, 5)
ts_data <- ts(data, start = c(2000, 1), frequency = 12) # Monthly data starting from January 2000

# Using dataframe
df <- data.frame(data = rnorm(12), month = seq(as.Date('2020/01/01'), as.Date('2020/12/01'), by="month"))
ts_df <- ts(df$data, start = c(2020, 1), frequency = 12) 

Ensure that the start and frequency parameters correctly define your time series interval.

3. What does it mean to have frequency 12 in a ts object?

Answer:
Setting the frequency parameter to 12 in a ts object indicates that the data are monthly (12 months per year). The frequency parameter defines the number of observations per unit time. Hence, for quarterly data, it would be 4, and for daily, it might be 365 or 366, depending on leap years.

4. How can I convert an xts object to a zoo object?

Answer:
Conversion from xts to zoo is seamless because xts is built on top of zoo. You simply need to use the as.zoo function:

library(xts)

# Create an xts object
xts_data <- xts(data, order.by=seq(as.Date('2020/01/01'), as.Date('2020/12/01'), by="month"))

# Convert to zoo
zoo_data <- as.zoo(xts_data)

5. How do I create an xts object from a date dataframe?

Answer:
To create an xts object, your dataframe should contain both a date column and one or more columns containing the actual data:

library(xts)

# Sample data
dates <- seq(as.Date("2020-01-01"), as.Date("2020-12-31"), by = "day")
values <- rnorm(length(dates))  # Random data values
df <- data.frame(Date = dates, Value = values)

# Creating xts object
xts_data <- xts(df$Value, order.by=df$Date)

# Checking the structure
str(xts_data)

6. How do I create a weekly ts object in R?

Answer:
For creating a weekly ts object, you would set the frequency to 52 (assuming 52 weeks in a year):

# Example: Weekly time series
weekly_data <- c(20, 24, 27, 25, 24, 28, 29)
week_start <- ISOdate(2020, 01, 04) # Start from Sunday, January 4, 2020, which is the first week

ts_weekly <- ts(weekly_data, frequency = 52, start = as.numeric(format(week_start, "%Y.%W")))

Note that %W gives the week number from 00 to 52, so sometimes adjustments might be necessary depending on the specific requirements.

7. What is the difference between rollmean in zoo and runmean in xts?

Answer:
Both functions are designed to compute rolling means over a time series window but differ slightly in syntax and additional options:

• rollmean (from zoo):

  library(zoo)
  
  # Rolling mean using zoo
  z <- zoo(c(1, 2, 3, 4, 5, 6, 7, 8))
  roll_mean_z <- rollmean(z, k = 3) # Roll mean with window size 3
  

• runmean (from xts):

  library(xts)
  
  # Rolling mean using xts
  x <- xts(c(1, 2, 3, 4, 5, 6, 7, 8), order.by=seq(as.Date('2020-01-01'), by="day", length.out=8))
  run_mean_xts <- runMean(x, n = 3) # Run mean with window size 3
  

The primary difference lies in their default behaviors regarding end points and missing values.

8. How do I handle irregularly spaced time series in R?

Answer:
Irregularly spaced time series can be effectively managed using zoo and xts packages rather than the base ts.

• Example with zoo:

  library(zoo)
  
  # Irregular time series with zoo
  dates <- as.Date(c("2020-01-01", "2020-01-05", "2020-01-10", "2020-01-18"))
  values <- rnorm(length(dates))
  zoo_irregular <- zoo(values, dates)
  

• Example with xts:

  library(xts)
  
  # Irregular time series with xts
  dates <- as.Date(c("2020-01-01", "2020-01-05", "2020-01-10", "2020-01-18"))
  values <- rnorm(length(dates))
  xts_irregular <- xts(values, order.by=dates)
  

9. How do you merge two time series objects of different frequencies?

Answer:
Merging time series objects of different frequencies involves aligning their timestamps, usually by up-sampling or down-sampling. Both xts and zoo offer methods for this:

• Using xts:

  library(xts)
  
  # Daily data
  daily_data <- xts(1:10, order.by=seq(as.Date('2020-01-01'), by="day", length.out=10))
  
  # Weekly data for the same range up-sampled
  weekly_dates <- seq(as.Date('2020-01-01'), as.Date('2020-01-10'), by="week")
  weekly_data <- xts(11:13, order.by=weekly_dates)
  
  # Merge them
  merged_series_xts <- period.apply(daily_data, endpoints(daily_data, "weeks"), FUN = mean)
  merged_series_xts <- na.omit(merge(daily_data, merged_series_xts))[weekly_dates]
  colnames(merged_series_xts) <- c("Daily", "Weekly")
  print(merged_series_xts)
  

• Using zoo:

  library(zoo)
  
  # Daily data
  daily_data <- zoo(1:14, order.by=seq(as.Date('2020-01-01'), by="day", length.out=14))
  
  # Weekly data
  weekly_dates <- as.Date(c("2020-01-01", "2020-01-08", "2020-01-15", "2020-01-22"))
  weekly_data <- zoo(21:24, order.by=weekly_dates)
  
  # Aggregating daily to match weekly dates
  daily_to_weekly <- aggregate(daily_data, as.yearweek(daily_data@index), mean)
  daily_to_weekly <- daily_to_weekly[weekly_dates]
  
  # Merge them
  merged_series_zoo <- merge(daily_to_weekly, weekly_data)
  

10. How can I extract specific periods from time series objects?

Answer:
Extracting specific periods can be done using indexing for each package:

• Extracting from ts objects:

  # Extracting specific period (e.g., January 2000 to June 2000)
  ts_period <- ts_data["2000-01-01/2000-06-30"]
  

• Extracting from xts objects:

  # Extracting specific period
  xts_period <- xts_data['2020-01-01/2020-06-30']
  

• Extracting from zoo objects:

  # Extracting specific period using zoo
  zoo_period <- zoo_irregular[format.index(index(zoo_irregular), "%Y-%m") %in% c("2020-01", "2020-02", "2020-03", "2020-04", "2020-05", "2020-06")]
  

Each method allows you to subset the time series based on your specified date range or other conditions. The xts package provides additional flexibility with its powerful indexing capabilities.

Additional Tips:

• Use head() and summary() to inspect time series objects.
• For more advanced operations like lagging, rolling averages, merging with different time bases, and aggregations, explore the functionalities within the xts and zoo packages.
• Convert irregular time series to a more regular form if needed using interpolation methods provided by zoo.