tk_get_timeseries: Get date features from a time-series index

Description

Get date features from a time-series index

Usage

tk_get_timeseries_signature(idx)
tk_get_timeseries_summary(idx)

Value

Returns a tibble object describing the timeseries.

Arguments

idx: A time-series index that is a vector of dates or datetimes.

Details

tk_get_timeseries_signature decomposes the timeseries into commonly needed features such as numeric value, differences, year, month, day, day of week, day of month, day of year, hour, minute, second.

tk_get_timeseries_summary returns the summary returns the start, end, units, scale, and a "summary" of the timeseries differences in seconds including the minimum, 1st quartile, median, mean, 3rd quartile, and maximum frequency. The timeseries differences give the user a better picture of the index frequency so the user can understand the level of regularity or irregularity. A perfectly regular time series will have equal values in seconds for each metric. However, this is not often the case.

Important Note: These functions only work with time-based indexes in datetime, date, yearmon, and yearqtr values. Regularized dates cannot be decomposed.

Examples

Run this code

library(dplyr)
library(lubridate)
library(zoo)

# Works with time-based tibbles
FB_tbl <- FANG %>% dplyr::filter(symbol == "FB")
FB_idx <- tk_index(FB_tbl)

tk_get_timeseries_signature(FB_idx)
tk_get_timeseries_summary(FB_idx)


# Works with dates in any periodicity
idx_weekly <- seq.Date(from = lubridate::ymd("2016-01-01"), by = 'week', length.out = 6)

tk_get_timeseries_signature(idx_weekly)
tk_get_timeseries_summary(idx_weekly)


# Works with zoo yearmon and yearqtr classes
idx_yearmon <- seq.Date(from       = lubridate::ymd("2016-01-01"),
                        by         = "month",
                        length.out = 12) %>%
    zoo::as.yearmon()

tk_get_timeseries_signature(idx_yearmon)
tk_get_timeseries_summary(idx_yearmon)