healthyR.ts (version 0.3.1)

ts_time_event_analysis_tbl: Event Analysis

Description

Given a tibble/data.frame, you can get information on what happens before, after, or in both directions of some given event, where the event is defined by some percentage increase/decrease in values from time t to t+1

Usage

ts_time_event_analysis_tbl(
  .data,
  .date_col,
  .value_col,
  .percent_change = 0.05,
  .horizon = 12,
  .precision = 2,
  .direction = "forward",
  .filter_non_event_groups = TRUE
)

Value

A tibble.

Arguments

.data

The date.frame/tibble that holds the data.

.date_col

The column with the date value.

.value_col

The column with the value you are measuring.

.percent_change

This defaults to 0.05 which is a 5% increase in the .value_col.

.horizon

How far do you want to look back or ahead.

.precision

The default is 2 which means it rounds the lagged 1 value percent change to 2 decimal points. You may want more for more finely tuned results, this will result in fewer groupings.

.direction

The default is forward. You can supply either forward, backwards or both.

.filter_non_event_groups

The default is TRUE, this drops groupings with no events on the rare occasion it does occur.

Author

Steven P. Sanderson II, MPH

Details

This takes in a data.frame/tibble of a time series. It requires a date column, and a value column. You can convert a ts/xts/zoo/mts object into a tibble by using the ts_to_tbl() function.

You will provide the function with a percentage change in the form of -1 to 1 inclusive. You then provide a time horizon in which you want to see. For example you may want to see what happens to AirPassengers after a 0.1 percent increase in volume.

The next most important thing to supply is the direction. Do you want to see what typically happens after such an event, what leads up to such an event, or both.

See Also

Other Time_Filtering: ts_compare_data()

Examples

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))

df_tbl <- ts_to_tbl(AirPassengers) %>% select(-index)

tst <- ts_time_event_analysis_tbl(df_tbl, date_col, value, .direction = "both",
.horizon = 6)

glimpse(tst)

tst %>%
  ggplot(aes(x = x, y = mean_event_change)) +
  geom_line() +
  geom_line(aes(y = event_change_ci_high), color = "blue", linetype = "dashed") +
  geom_line(aes(y = event_change_ci_low), color = "blue", linetype = "dashed") +
  geom_vline(xintercept = 7, color = "red", linetype = "dashed") +
  theme_minimal() +
  labs(
    title = "'AirPassengers' Event Analysis at 5% Increase",
    subtitle = "Vertical Red line is normalized event epoch - Direction: Both",
    x = "",
    y = "Mean Event Change"
  )