timetk (version 2.9.0)

plot_time_series_regression: Visualize a Time Series Linear Regression Formula

Description

A wrapper for stats::lm() that overlays a linear regression fitted model over a time series, which can help show the effect of feature engineering

Usage

plot_time_series_regression(
  .data,
  .date_var,
  .formula,
  .show_summary = FALSE,
  ...
)

Value

A static ggplot2 plot or an interactive plotly plot

Arguments

.data

A tibble or data.frame with a time-based column

.date_var

A column containing either date or date-time values

.formula

A linear regression formula. The left-hand side of the formula is used as the y-axis value. The right-hand side of the formula is used to develop the linear regression model. See stats::lm() for details.

.show_summary

If TRUE, prints the summary.lm().

...

Additional arguments passed to plot_time_series()

Details

plot_time_series_regression() is a scalable function that works with both ungrouped and grouped data.frame objects (and tibbles!).

Time Series Formula

The .formula uses stats::lm() to apply a linear regression, which is used to visualize the effect of feature engineering on a time series.

• The left-hand side of the formula is used as the y-axis value.

• The right-hand side of the formula is used to develop the linear regression model.

Interactive by Default

plot_time_series_regression() is built for exploration using:

• Interactive Plots: plotly (default) - Great for exploring!

• Static Plots: ggplot2 (set .interactive = FALSE) - Great for PDF Reports

By default, an interactive plotly visualization is returned.

Scalable with Facets & Dplyr Groups

plot_time_series_regression() returns multiple time series plots using ggplot2 facets:

• group_by() - If groups are detected, multiple facets are returned

• plot_time_series_regression(.facet_vars) - You can manually supply facets as well.

Examples

library(dplyr)
library(lubridate)

# ---- SINGLE SERIES ----
m4_monthly %>%
    filter(id == "M750") %>%
    plot_time_series_regression(
        .date_var     = date,
        .formula      = log(value) ~ as.numeric(date) + month(date, label = TRUE),
        .show_summary = TRUE,
        .facet_ncol   = 2,
        .interactive  = FALSE
    )


# ---- GROUPED SERIES ----
m4_monthly %>%
    group_by(id) %>%
    plot_time_series_regression(
        .date_var    = date,
        .formula     = log(value) ~ as.numeric(date) + month(date, label = TRUE),
        .facet_ncol  = 2,
        .interactive = FALSE
    )