timetk (version 2.6.1)

plot_time_series: Interactive Plotting for One or More Time Series

Description

A workhorse time-series plotting function that generates interactive plotly plots, consolidates 20+ lines of ggplot2 code, and scales well to many time series.

Usage

plot_time_series(
  .data,
  .date_var,
  .value,
  .color_var = NULL,
  .facet_vars = NULL,
  .facet_ncol = 1,
  .facet_scales = "free_y",
  .facet_collapse = TRUE,
  .facet_collapse_sep = " ",
  .line_color = "#2c3e50",
  .line_size = 0.5,
  .line_type = 1,
  .line_alpha = 1,
  .y_intercept = NULL,
  .y_intercept_color = "#2c3e50",
  .smooth = TRUE,
  .smooth_period = "auto",
  .smooth_message = FALSE,
  .smooth_span = NULL,
  .smooth_degree = 2,
  .smooth_color = "#3366FF",
  .smooth_size = 1,
  .smooth_alpha = 1,
  .legend_show = TRUE,
  .title = "Time Series Plot",
  .x_lab = "",
  .y_lab = "",
  .color_lab = "Legend",
  .interactive = TRUE,
  .plotly_slider = FALSE
)

Arguments

.data

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

.date_var

A column containing either date or date-time values

.value

A column containing numeric values

.color_var

A categorical column that can be used to change the line color

.facet_vars

One or more grouping columns that broken out into ggplot2 facets. These can be selected using tidyselect() helpers (e.g contains()).

.facet_ncol

Number of facet columns.

.facet_scales

Control facet x & y-axis ranges. Options include "fixed", "free", "free_y", "free_x"

.facet_collapse

Multiple facets included on one facet strip instead of multiple facet strips.

.facet_collapse_sep

The separator used for collapsing facets.

.line_color

Line color. Overrided if .color_var is specified.

.line_size

Line size.

.line_type

Line type.

.line_alpha

Line alpha (opacity). Range: (0, 1).

.y_intercept

Value for a y-intercept on the plot

.y_intercept_color

Color for the y-intercept

.smooth

Logical - Whether or not to include a trendline smoother. Uses See smooth_vec() to apply a LOESS smoother.

.smooth_period

Number of observations to include in the Loess Smoother. Set to "auto" by default, which uses tk_get_trend() to determine a logical trend cycle.

.smooth_message

Logical. Whether or not to return the trend selected as a message. Useful for those that want to see what .smooth_period was selected.

.smooth_span

Percentage of observations to include in the Loess Smoother. You can use either period or span. See smooth_vec().

.smooth_degree

Flexibility of Loess Polynomial. Either 0, 1, 2 (0 = lest flexible, 2 = more flexible).

.smooth_color

Smoother line color

.smooth_size

Smoother line size

.smooth_alpha

Smoother alpha (opacity). Range: (0, 1).

.legend_show

Toggles on/off the Legend

.title

Title for the plot

.x_lab

X-axis label for the plot

.y_lab

Y-axis label for the plot

.color_lab

Legend label if a color_var is used.

.interactive

Returns either a static (ggplot2) visualization or an interactive (plotly) visualization

.plotly_slider

If TRUE, returns a plotly date range slider.

Value

A static ggplot2 plot or an interactive plotly plot

Details

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

Interactive by Default

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

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

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

Can Transform Values just like ggplot

The .values argument accepts transformations just like ggplot2. For example, if you want to take the log of sales you can use a call like plot_time_series(date, log(sales)) and the log transformation will be applied.

Smoother Period / Span Calculation

The .smooth = TRUE option returns a smoother that is calculated based on either:

1. A .smooth_period: Number of observations

2. A .smooth_span: A percentage of observations

By default, the .smooth_period is automatically calculated using 75% of the observertions. This is the same as geom_smooth(method = "loess", span = 0.75).

A user can specify a time-based window (e.g. .smooth_period = "1 year") or a numeric value (e.g. smooth_period = 365).

Time-based windows return the median number of observations in a window using tk_get_trend().

Examples

# NOT RUN {
library(tidyverse)
library(tidyquant)
library(lubridate)
library(timetk)

# Works with individual time series
FANG %>%
    filter(symbol == "FB") %>%
    plot_time_series(date, adjusted, .interactive = FALSE)

# Works with groups
FANG %>%
    group_by(symbol) %>%
    plot_time_series(date, adjusted,
                     .facet_ncol  = 2,     # 2-column layout
                     .interactive = FALSE)

# Can also group inside & use .color_var
FANG %>%
    mutate(year = year(date)) %>%
    plot_time_series(date, adjusted,
                     .facet_vars   = c(symbol, year), # add groups/facets
                     .color_var    = year,            # color by year
                     .facet_ncol   = 4,
                     .facet_scales = "free",
                     .interactive  = FALSE)

# Can apply transformations to .value or .color_var
# - .value = log(adjusted)
# - .color_var = year(date)
FANG %>%
    plot_time_series(date, log(adjusted),
                     .color_var    = year(date),
                     .facet_vars   = contains("symbol"),
                     .facet_ncol   = 2,
                     .facet_scales = "free",
                     .y_lab        = "Log Scale",
                     .interactive  = FALSE)



# }