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 cross 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.lvel = 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.

Value

A tibble::tibble with one row for each observation in the zoo time series and columns:

index

Index (usually date) for the zoo object

series

Name of the series

value

Value of the observation

See also

tidy(), zoo::zoo()

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

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 2004-02-06 Aa     -0.586 
#>  3 2004-02-08 Aa     -0.289 
#>  4 2004-02-12 Aa      1.85  
#>  5 2004-02-14 Aa      0.113 
#>  6 2004-02-18 Aa      0.313 
#>  7 2004-02-19 Aa      0.0950
#>  8 2004-02-25 Aa      0.489 
#>  9 2004-03-03 Aa      0.397 
#> 10 2004-03-17 Aa      0.0196
#> # … 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()