Tidy a(n) zoo object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for zoo
tidy(x, ...)

Arguments

x

A zoo object such as those created by zoo::zoo().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.level = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

tidy(), zoo::zoo()

Other time series tidiers: tidy.acf(), tidy.spec(), tidy.ts()

Value

A tibble::tibble() with columns:

index

Index (i.e. date or time) for a `ts` or `zoo` object.

series

Name of the series (present only for multivariate time series).

value

The value/estimate of the component. Results from data reshaping.

Examples

library(zoo)
library(ggplot2)

set.seed(1071)

# data generated as shown in the zoo vignette
Z.index <- as.Date(sample(12450:12500, 10))
Z.data <- matrix(rnorm(30), ncol = 3)
colnames(Z.data) <- c("Aa", "Bb", "Cc")
Z <- zoo(Z.data, Z.index)

tidy(Z)
#> # A tibble: 30 x 3
#>    index      series   value
#>    <date>     <chr>    <dbl>
#>  1 2004-02-02 Aa     -0.537 
#>  2 2004-02-02 Bb      0.746 
#>  3 2004-02-02 Cc     -0.634 
#>  4 2004-02-06 Aa     -0.586 
#>  5 2004-02-06 Bb     -0.0779
#>  6 2004-02-06 Cc      0.0397
#>  7 2004-02-08 Aa     -0.289 
#>  8 2004-02-08 Bb     -1.11  
#>  9 2004-02-08 Cc     -0.341 
#> 10 2004-02-12 Aa      1.85  
#> # … with 20 more rows

ggplot(tidy(Z), aes(index, value, color = series)) +
  geom_line()

ggplot(tidy(Z), aes(index, value)) +
  geom_line() +
  facet_wrap(~series, ncol = 1)

Zrolled <- rollmean(Z, 5)
ggplot(tidy(Zrolled), aes(index, value, color = series)) +
  geom_line()