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Summarise distributional statistics by time series

Source: R/analyze_data.R
summarise_stats.Rd

Calculate descriptive statistics for one or more time series.

Usage

summarise_stats(.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 descriptive statistics.

Details

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

The function reports:

  • mean: arithmetic mean;

  • median: median;

  • mode: kernel-density based mode estimate;

  • sd: standard deviation;

  • p0: minimum;

  • p25: 25 percent quantile;

  • p75: 75 percent quantile;

  • p100: maximum;

  • skewness: moment-based skewness;

  • kurtosis: moment-based kurtosis.

Missing values are removed when calculating the statistics.

See also

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

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_stats(
  .data = data,
  context = context
)
#> # A tibble: 2 × 11
#>   series  mean median  mode    sd    p0   p25   p75  p100 skewness kurtosis
#>   <chr>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
#> 1 M14395 1422.  1369. 1113.  490.  586. 1031. 1697.  3253    0.786     3.62
#> 2 M23100 9059.  9050  9040.  394. 8160  8768. 9310  10040    0.222     2.74