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Summarise time series data

Source: R/analyze_data.R
summarise_data.Rd

Calculate basic data-quality summary statistics for one or more time series.

Usage

summarise_data(.data, context)

Arguments

.data

A tibble in long format containing time series data.

context

A named list with the identifiers for series_id, value_id, and index_id.

Value

A tibble containing one row per time series and the calculated summary statistics.

Details

summarise_data() groups the input data by the series identifier supplied in context and returns one row per time series.

The function reports:

  • start: first time index;

  • end: last time index;

  • n_obs: number of observations;

  • n_missing: number of missing values;

  • pct_missing: percentage of missing values;

  • n_zeros: number of zero values;

  • pct_zeros: percentage of zero values.

See also

Other data analysis: acf_vec(), estimate_acf(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_split(), summarise_stats()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

summarise_data(
  .data = data,
  context = context
)
#> # A tibble: 2 × 8
#>   series    start      end n_obs n_missing pct_missing n_zeros pct_zeros
#>   <chr>     <mth>    <mth> <int>     <int>       <dbl>   <int>     <dbl>
#> 1 M14395 2001 Jul 2016 Dez   186         0           0       0         0
#> 2 M23100 2003 Jan 2016 Dez   168         0           0       0         0