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.

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

Needs custom desciption.

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 2004-02-10 Aa      0.747
#>  2 2004-02-15 Aa      1.27
#>  3 2004-02-27 Aa      0.222
#>  4 2004-03-01 Aa      0.0211
#>  5 2004-03-05 Aa     -0.298
#>  6 2004-03-06 Aa     -2.08
#>  7 2004-03-11 Aa     -1.78
#>  8 2004-03-13 Aa      0.686
#>  9 2004-03-17 Aa     -0.195
#> 10 2004-03-19 Aa      1.94
#> # ... 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()