Added dygraphs functionality. (#320)

* Added dygraphs functionality.

* reversed the unintentional inclusion of prophet_copy

* namespaced  functions and corrected %>% typo

R/DESCRIPTION

@@ -16,6 +16,7 @@ Depends:
     Rcpp (>= 0.12.0)
 Imports:
     dplyr (>= 0.5.0),
+    dygraphs,
     extraDistr,
     ggplot2,
     grid,
@@ -23,7 +24,8 @@ Imports:
     scales,
     stats,
     tidyr (>= 0.6.1),
-    utils
+    utils,
+    xts
 Suggests:
     knitr,
     testthat,

R/R/plot.R

@@ -0,0 +1,373 @@
+#
+#' Merge history and forecast for plotting.
+#'
+#' @param m Prophet object.
+#' @param fcst Data frame returned by prophet predict.
+#'
+#' @importFrom dplyr "%>%"
+#' @keywords internal
+df_for_plotting <- function(m, fcst) {
+  # Make sure there is no y in fcst
+  fcst$y <- NULL
+  df <- m$history %>%
+    dplyr::select(ds, y) %>%
+    dplyr::full_join(fcst, by = "ds") %>%
+    dplyr::arrange(ds)
+  return(df)
+}
+
+#' Plot the prophet forecast.
+#'
+#' @param x Prophet object.
+#' @param fcst Data frame returned by predict(m, df).
+#' @param uncertainty Boolean indicating if the uncertainty interval for yhat
+#'  should be plotted. Must be present in fcst as yhat_lower and yhat_upper.
+#' @param plot_cap Boolean indicating if the capacity should be shown in the
+#'  figure, if available.
+#' @param xlabel Optional label for x-axis
+#' @param ylabel Optional label for y-axis
+#' @param ... additional arguments
+#'
+#' @return A ggplot2 plot.
+#'
+#' @examples
+#' \dontrun{
+#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
+#'                       y = sin(1:366/200) + rnorm(366)/10)
+#' m <- prophet(history)
+#' future <- make_future_dataframe(m, periods = 365)
+#' forecast <- predict(m, future)
+#' plot(m, forecast)
+#' }
+#'
+#' @export
+plot.prophet <- function(x, fcst, uncertainty = TRUE, plot_cap = TRUE,
+                         xlabel = 'ds', ylabel = 'y', ...) {
+  df <- df_for_plotting(x, fcst)
+  gg <- ggplot2::ggplot(df, ggplot2::aes(x = ds, y = y)) +
+    ggplot2::labs(x = xlabel, y = ylabel)
+  if (exists('cap', where = df) && plot_cap) {
+    gg <- gg + ggplot2::geom_line(
+      ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)
+  }
+  if (x$logistic.floor && exists('floor', where = df) && plot_cap) {
+    gg <- gg + ggplot2::geom_line(
+      ggplot2::aes(y = floor), linetype = 'dashed', na.rm = TRUE)
+  }
+  if (uncertainty && exists('yhat_lower', where = df)) {
+    gg <- gg +
+      ggplot2::geom_ribbon(ggplot2::aes(ymin = yhat_lower, ymax = yhat_upper),
+                           alpha = 0.2,
+                           fill = "#0072B2",
+                           na.rm = TRUE)
+  }
+  gg <- gg +
+    ggplot2::geom_point(na.rm=TRUE) +
+    ggplot2::geom_line(ggplot2::aes(y = yhat), color = "#0072B2",
+                       na.rm = TRUE) +
+    ggplot2::theme(aspect.ratio = 3 / 5)
+  return(gg)
+}
+
+#' Plot the components of a prophet forecast.
+#' Prints a ggplot2 with panels for trend, weekly and yearly seasonalities if
+#' present, and holidays if present.
+#'
+#' @param m Prophet object.
+#' @param fcst Data frame returned by predict(m, df).
+#' @param uncertainty Boolean indicating if the uncertainty interval should be
+#'  plotted for the trend, from fcst columns trend_lower and trend_upper.
+#' @param plot_cap Boolean indicating if the capacity should be shown in the
+#'  figure, if available.
+#' @param weekly_start Integer specifying the start day of the weekly
+#'  seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
+#'  to Monday, and so on.
+#' @param yearly_start Integer specifying the start day of the yearly
+#'  seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
+#'  to Jan 2, and so on.
+#'
+#' @return Invisibly return a list containing the plotted ggplot objects
+#'
+#' @export
+#' @importFrom dplyr "%>%"
+prophet_plot_components <- function(
+  m, fcst, uncertainty = TRUE, plot_cap = TRUE, weekly_start = 0,
+  yearly_start = 0
+) {
+  # Plot the trend
+  panels <- list(plot_forecast_component(fcst, 'trend', uncertainty, plot_cap))
+  # Plot holiday components, if present.
+  if (!is.null(m$holidays) & ('holidays' %in% colnames(fcst))) {
+    panels[[length(panels) + 1]] <- plot_forecast_component(
+      fcst, 'holidays', uncertainty, FALSE)
+  }
+  # Plot weekly seasonality, if present
+  if ("weekly" %in% colnames(fcst)) {
+    panels[[length(panels) + 1]] <- plot_weekly(m, uncertainty, weekly_start)
+  }
+  # Plot yearly seasonality, if present
+  if ("yearly" %in% colnames(fcst)) {
+    panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty, yearly_start)
+  }
+  # Plot other seasonalities
+  for (name in names(m$seasonalities)) {
+    if (!(name %in% c('weekly', 'yearly')) &&
+        (name %in% colnames(fcst))) {
+      panels[[length(panels) + 1]] <- plot_seasonality(m, name, uncertainty)
+    }
+  }
+  # Plot extra regressors
+  if ((length(m$extra_regressors) > 0)
+      & ('extra_regressors' %in% colnames(fcst))) {
+    panels[[length(panels) + 1]] <- plot_forecast_component(
+      fcst, 'extra_regressors', uncertainty, FALSE)
+  }
+
+  # Make the plot.
+  grid::grid.newpage()
+  grid::pushViewport(grid::viewport(layout = grid::grid.layout(length(panels),
+                                                               1)))
+  for (i in 1:length(panels)) {
+    print(panels[[i]], vp = grid::viewport(layout.pos.row = i,
+                                           layout.pos.col = 1))
+  }
+  return(invisible(panels))
+}
+
+#' Plot a particular component of the forecast.
+#'
+#' @param fcst Dataframe output of `predict`.
+#' @param name String name of the component to plot (column of fcst).
+#' @param uncertainty Boolean to plot uncertainty intervals.
+#' @param plot_cap Boolean indicating if the capacity should be shown in the
+#'  figure, if available.
+#'
+#' @return A ggplot2 plot.
+#'
+#' @export
+plot_forecast_component <- function(
+  fcst, name, uncertainty = TRUE, plot_cap = FALSE
+) {
+  gg.comp <- ggplot2::ggplot(
+      fcst, ggplot2::aes_string(x = 'ds', y = name, group = 1)) +
+    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE)
+  if (exists('cap', where = fcst) && plot_cap) {
+    gg.comp <- gg.comp + ggplot2::geom_line(
+      ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)
+  }
+  if (exists('floor', where = fcst) && plot_cap) {
+    gg.comp <- gg.comp + ggplot2::geom_line(
+      ggplot2::aes(y = floor), linetype = 'dashed', na.rm = TRUE)
+  }
+  if (uncertainty) {
+    gg.comp <- gg.comp +
+      ggplot2::geom_ribbon(
+        ggplot2::aes_string(
+          ymin = paste0(name, '_lower'), ymax = paste0(name, '_upper')
+        ),
+        alpha = 0.2,
+        fill = "#0072B2",
+        na.rm = TRUE)
+  }
+  return(gg.comp)
+}
+
+#' Prepare dataframe for plotting seasonal components.
+#'
+#' @param m Prophet object.
+#' @param ds Array of dates for column ds.
+#'
+#' @return A dataframe with seasonal components on ds.
+#'
+#' @keywords internal
+seasonality_plot_df <- function(m, ds) {
+  df_list <- list(ds = ds, cap = 1)
+  for (name in names(m$extra_regressors)) {
+    df_list[[name]] <- 0
+  }
+  df <- as.data.frame(df_list)
+  df <- setup_dataframe(m, df)$df
+  return(df)
+}
+
+#' Plot the weekly component of the forecast.
+#'
+#' @param m Prophet model object
+#' @param uncertainty Boolean to plot uncertainty intervals.
+#' @param weekly_start Integer specifying the start day of the weekly
+#'  seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
+#'  to Monday, and so on.
+#'
+#' @return A ggplot2 plot.
+#'
+#' @keywords internal
+plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
+  # Compute weekly seasonality for a Sun-Sat sequence of dates.
+  days <- seq(set_date('2017-01-01'), by='d', length.out=7) + weekly_start
+  df.w <- seasonality_plot_df(m, days)
+  seas <- predict_seasonal_components(m, df.w)
+  seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))
+
+  gg.weekly <- ggplot2::ggplot(seas, ggplot2::aes(x = dow, y = weekly,
+                                                  group = 1)) +
+    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE) +
+    ggplot2::labs(x = "Day of week")
+  if (uncertainty) {
+    gg.weekly <- gg.weekly +
+      ggplot2::geom_ribbon(ggplot2::aes(ymin = weekly_lower,
+                                        ymax = weekly_upper),
+                           alpha = 0.2,
+                           fill = "#0072B2",
+                           na.rm = TRUE)
+  }
+  return(gg.weekly)
+}
+
+#' Plot the yearly component of the forecast.
+#'
+#' @param m Prophet model object.
+#' @param uncertainty Boolean to plot uncertainty intervals.
+#' @param yearly_start Integer specifying the start day of the yearly
+#'  seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
+#'  to Jan 2, and so on.
+#'
+#' @return A ggplot2 plot.
+#'
+#' @keywords internal
+plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
+  # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
+  days <- seq(set_date('2017-01-01'), by='d', length.out=365) + yearly_start
+  df.y <- seasonality_plot_df(m, days)
+  seas <- predict_seasonal_components(m, df.y)
+  seas$ds <- df.y$ds
+
+  gg.yearly <- ggplot2::ggplot(seas, ggplot2::aes(x = ds, y = yearly,
+                                                  group = 1)) +
+    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE) +
+    ggplot2::labs(x = "Day of year") +
+    ggplot2::scale_x_datetime(labels = scales::date_format('%B %d'))
+  if (uncertainty) {
+    gg.yearly <- gg.yearly +
+      ggplot2::geom_ribbon(ggplot2::aes(ymin = yearly_lower,
+                                        ymax = yearly_upper),
+                           alpha = 0.2,
+                           fill = "#0072B2",
+                           na.rm = TRUE)
+  }
+  return(gg.yearly)
+}
+
+#' Plot a custom seasonal component.
+#'
+#' @param m Prophet model object.
+#' @param name String name of the seasonality.
+#' @param uncertainty Boolean to plot uncertainty intervals.
+#'
+#' @return A ggplot2 plot.
+#'
+#' @keywords internal
+plot_seasonality <- function(m, name, uncertainty = TRUE) {
+  # Compute seasonality from Jan 1 through a single period.
+  start <- set_date('2017-01-01')
+  period <- m$seasonalities[[name]]$period
+  end <- start + period * 24 * 3600
+  plot.points <- 200
+  days <- seq(from=start, to=end, length.out=plot.points)
+  df.y <- seasonality_plot_df(m, days)
+  seas <- predict_seasonal_components(m, df.y)
+  seas$ds <- df.y$ds
+  gg.s <- ggplot2::ggplot(
+      seas, ggplot2::aes_string(x = 'ds', y = name, group = 1)) +
+    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE)
+  if (period <= 2) {
+    fmt.str <- '%T'
+  } else if (period < 14) {
+    fmt.str <- '%m/%d %R'
+  } else {
+    fmt.str <- '%m/%d'
+  }
+  gg.s <- gg.s +
+    ggplot2::scale_x_datetime(labels = scales::date_format(fmt.str))
+  if (uncertainty) {
+    gg.s <- gg.s +
+    ggplot2::geom_ribbon(
+      ggplot2::aes_string(
+        ymin = paste0(name, '_lower'), ymax = paste0(name, '_upper')
+      ),
+      alpha = 0.2,
+      fill = "#0072B2",
+      na.rm = TRUE)
+  }
+  return(gg.s)
+}
+
+#' Plot the prophet forecast.
+#'
+#' @param x Prophet object.
+#' @param fcst Data frame returned by predict(m, df).
+#' @param uncertainty Boolean indicating if the uncertainty interval for yhat
+#'  should be plotted. Must be present in fcst as yhat_lower and yhat_upper.
+#' @param ... additional arguments
+#' @importFrom magrittr "%>%"
+#' @return A dygraph plot.
+#'
+#' @examples
+#' \dontrun{
+#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
+#'                       y = sin(1:366/200) + rnorm(366)/10)
+#' m <- prophet(history)
+#' future <- make_future_dataframe(m, periods = 365)
+#' forecast <- predict(m, future)
+#' dyplot.prophet(m, forecast)
+#' }
+#'
+#' @export
+dyplot.prophet <- function(x, fcst, uncertainty=TRUE, 
+                           ...) 
+{
+  forecast.label='Predicted'
+  actual.label='Actual'
+  # create data.frame for plotting
+  df <- df_for_plotting(x, fcst)
+  
+  # build variables to include, or not, the uncertainty data
+  if(uncertainty && exists("yhat_lower", where = df))
+  {
+    colsToKeep <- c('y', 'yhat', 'yhat_lower', 'yhat_upper')
+    forecastCols <- c('yhat_lower', 'yhat', 'yhat_upper')
+  } else
+  {
+    colsToKeep <- c('y', 'yhat')
+    forecastCols <- c('yhat')
+  }
+  # convert to xts for easier date handling by dygraph
+  dfTS <- xts::xts(df %>% dplyr::select_(.dots=colsToKeep), order.by = df$ds)
+
+  # base plot
+  dyBase <- dygraphs::dygraph(dfTS)
+  
+  presAnnotation <- function(dygraph, x, text) {
+    dygraph %>%
+      dygraphs::dyAnnotation(x, text, text, attachAtBottom = TRUE)
+  }
+  
+  dyBase <- dyBase %>% 
+    dygraphs::dyOptions(colors = RColorBrewer::brewer.pal(3, "Set1")) %>%
+    # plot actual values
+    dygraphs::dySeries('y', label=actual.label) %>% 
+    # plot forecast and ribbon
+    dygraphs::dySeries(forecastCols, label=forecast.label) %>% 
+    # allow zooming
+    dygraphs::dyRangeSelector() %>% 
+    # make unzoom button
+    dygraphs::dyUnzoom()
+  if (!is.null(x$holidays)) {
+    for (i in 1:nrow(x$holidays)) {
+      # make a gray line
+      dyBase <- dyBase %>% dygraphs::dyEvent(x$holidays$ds[i],color = "rgb(200,200,200)", strokePattern = "solid")
+      dyBase <- dyBase %>% dygraphs::dyAnnotation(x$holidays$ds[i], x$holidays$holiday[i], x$holidays$holiday[i], attachAtBottom = TRUE)
+    }
+  }
+  return(dyBase)
+}
+

R/R/prophet.R

@@ -1383,310 +1383,6 @@ make_future_dataframe <- function(m, periods, freq = 'day',
   return(data.frame(ds = dates))
 }
 
-#' Merge history and forecast for plotting.
-#'
-#' @param m Prophet object.
-#' @param fcst Data frame returned by prophet predict.
-#'
-#' @importFrom dplyr "%>%"
-#' @keywords internal
-df_for_plotting <- function(m, fcst) {
-  # Make sure there is no y in fcst
-  fcst$y <- NULL
-  df <- m$history %>%
-    dplyr::select(ds, y) %>%
-    dplyr::full_join(fcst, by = "ds") %>%
-    dplyr::arrange(ds)
-  return(df)
-}
-
-#' Plot the prophet forecast.
-#'
-#' @param x Prophet object.
-#' @param fcst Data frame returned by predict(m, df).
-#' @param uncertainty Boolean indicating if the uncertainty interval for yhat
-#'  should be plotted. Must be present in fcst as yhat_lower and yhat_upper.
-#' @param plot_cap Boolean indicating if the capacity should be shown in the
-#'  figure, if available.
-#' @param xlabel Optional label for x-axis
-#' @param ylabel Optional label for y-axis
-#' @param ... additional arguments
-#'
-#' @return A ggplot2 plot.
-#'
-#' @examples
-#' \dontrun{
-#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
-#'                       y = sin(1:366/200) + rnorm(366)/10)
-#' m <- prophet(history)
-#' future <- make_future_dataframe(m, periods = 365)
-#' forecast <- predict(m, future)
-#' plot(m, forecast)
-#' }
-#'
-#' @export
-plot.prophet <- function(x, fcst, uncertainty = TRUE, plot_cap = TRUE,
-                         xlabel = 'ds', ylabel = 'y', ...) {
-  df <- df_for_plotting(x, fcst)
-  gg <- ggplot2::ggplot(df, ggplot2::aes(x = ds, y = y)) +
-    ggplot2::labs(x = xlabel, y = ylabel)
-  if (exists('cap', where = df) && plot_cap) {
-    gg <- gg + ggplot2::geom_line(
-      ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)
-  }
-  if (x$logistic.floor && exists('floor', where = df) && plot_cap) {
-    gg <- gg + ggplot2::geom_line(
-      ggplot2::aes(y = floor), linetype = 'dashed', na.rm = TRUE)
-  }
-  if (uncertainty && exists('yhat_lower', where = df)) {
-    gg <- gg +
-      ggplot2::geom_ribbon(ggplot2::aes(ymin = yhat_lower, ymax = yhat_upper),
-                           alpha = 0.2,
-                           fill = "#0072B2",
-                           na.rm = TRUE)
-  }
-  gg <- gg +
-    ggplot2::geom_point(na.rm=TRUE) +
-    ggplot2::geom_line(ggplot2::aes(y = yhat), color = "#0072B2",
-                       na.rm = TRUE) +
-    ggplot2::theme(aspect.ratio = 3 / 5)
-  return(gg)
-}
-
-#' Plot the components of a prophet forecast.
-#' Prints a ggplot2 with panels for trend, weekly and yearly seasonalities if
-#' present, and holidays if present.
-#'
-#' @param m Prophet object.
-#' @param fcst Data frame returned by predict(m, df).
-#' @param uncertainty Boolean indicating if the uncertainty interval should be
-#'  plotted for the trend, from fcst columns trend_lower and trend_upper.
-#' @param plot_cap Boolean indicating if the capacity should be shown in the
-#'  figure, if available.
-#' @param weekly_start Integer specifying the start day of the weekly
-#'  seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
-#'  to Monday, and so on.
-#' @param yearly_start Integer specifying the start day of the yearly
-#'  seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
-#'  to Jan 2, and so on.
-#'
-#' @return Invisibly return a list containing the plotted ggplot objects
-#'
-#' @export
-#' @importFrom dplyr "%>%"
-prophet_plot_components <- function(
-  m, fcst, uncertainty = TRUE, plot_cap = TRUE, weekly_start = 0,
-  yearly_start = 0
-) {
-  # Plot the trend
-  panels <- list(plot_forecast_component(fcst, 'trend', uncertainty, plot_cap))
-  # Plot holiday components, if present.
-  if (!is.null(m$holidays) && ('holidays' %in% colnames(fcst))) {
-    panels[[length(panels) + 1]] <- plot_forecast_component(
-      fcst, 'holidays', uncertainty, FALSE)
-  }
-  # Plot weekly seasonality, if present
-  if ("weekly" %in% colnames(fcst)) {
-    panels[[length(panels) + 1]] <- plot_weekly(m, uncertainty, weekly_start)
-  }
-  # Plot yearly seasonality, if present
-  if ("yearly" %in% colnames(fcst)) {
-    panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty, yearly_start)
-  }
-  # Plot other seasonalities
-  for (name in names(m$seasonalities)) {
-    if (!(name %in% c('weekly', 'yearly')) &&
-        (name %in% colnames(fcst))) {
-      panels[[length(panels) + 1]] <- plot_seasonality(m, name, uncertainty)
-    }
-  }
-  # Plot extra regressors
-  if ((length(m$extra_regressors) > 0)
-      & ('extra_regressors' %in% colnames(fcst))) {
-    panels[[length(panels) + 1]] <- plot_forecast_component(
-      fcst, 'extra_regressors', uncertainty, FALSE)
-  }
-
-  # Make the plot.
-  grid::grid.newpage()
-  grid::pushViewport(grid::viewport(layout = grid::grid.layout(length(panels),
-                                                               1)))
-  for (i in seq_along(panels)) {
-    print(panels[[i]], vp = grid::viewport(layout.pos.row = i,
-                                           layout.pos.col = 1))
-  }
-  return(invisible(panels))
-}
-
-#' Plot a particular component of the forecast.
-#'
-#' @param fcst Dataframe output of `predict`.
-#' @param name String name of the component to plot (column of fcst).
-#' @param uncertainty Boolean to plot uncertainty intervals.
-#' @param plot_cap Boolean indicating if the capacity should be shown in the
-#'  figure, if available.
-#'
-#' @return A ggplot2 plot.
-#'
-#' @export
-plot_forecast_component <- function(
-  fcst, name, uncertainty = TRUE, plot_cap = FALSE
-) {
-  gg.comp <- ggplot2::ggplot(
-      fcst, ggplot2::aes_string(x = 'ds', y = name, group = 1)) +
-    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE)
-  if (exists('cap', where = fcst) && plot_cap) {
-    gg.comp <- gg.comp + ggplot2::geom_line(
-      ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)
-  }
-  if (exists('floor', where = fcst) && plot_cap) {
-    gg.comp <- gg.comp + ggplot2::geom_line(
-      ggplot2::aes(y = floor), linetype = 'dashed', na.rm = TRUE)
-  }
-  if (uncertainty) {
-    gg.comp <- gg.comp +
-      ggplot2::geom_ribbon(
-        ggplot2::aes_string(
-          ymin = paste0(name, '_lower'), ymax = paste0(name, '_upper')
-        ),
-        alpha = 0.2,
-        fill = "#0072B2",
-        na.rm = TRUE)
-  }
-  return(gg.comp)
-}
-
-#' Prepare dataframe for plotting seasonal components.
-#'
-#' @param m Prophet object.
-#' @param ds Array of dates for column ds.
-#'
-#' @return A dataframe with seasonal components on ds.
-#'
-#' @keywords internal
-seasonality_plot_df <- function(m, ds) {
-  df_list <- list(ds = ds, cap = 1, floor = 0)
-  for (name in names(m$extra_regressors)) {
-    df_list[[name]] <- 0
-  }
-  df <- as.data.frame(df_list)
-  df <- setup_dataframe(m, df)$df
-  return(df)
-}
-
-#' Plot the weekly component of the forecast.
-#'
-#' @param m Prophet model object
-#' @param uncertainty Boolean to plot uncertainty intervals.
-#' @param weekly_start Integer specifying the start day of the weekly
-#'  seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
-#'  to Monday, and so on.
-#'
-#' @return A ggplot2 plot.
-#'
-#' @keywords internal
-plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
-  # Compute weekly seasonality for a Sun-Sat sequence of dates.
-  days <- seq(set_date('2017-01-01'), by='d', length.out=7) + as.difftime(
-    weekly_start, units = "days")
-  df.w <- seasonality_plot_df(m, days)
-  seas <- predict_seasonal_components(m, df.w)
-  seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))
-
-  gg.weekly <- ggplot2::ggplot(seas, ggplot2::aes(x = dow, y = weekly,
-                                                  group = 1)) +
-    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE) +
-    ggplot2::labs(x = "Day of week")
-  if (uncertainty) {
-    gg.weekly <- gg.weekly +
-      ggplot2::geom_ribbon(ggplot2::aes(ymin = weekly_lower,
-                                        ymax = weekly_upper),
-                           alpha = 0.2,
-                           fill = "#0072B2",
-                           na.rm = TRUE)
-  }
-  return(gg.weekly)
-}
-
-#' Plot the yearly component of the forecast.
-#'
-#' @param m Prophet model object.
-#' @param uncertainty Boolean to plot uncertainty intervals.
-#' @param yearly_start Integer specifying the start day of the yearly
-#'  seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
-#'  to Jan 2, and so on.
-#'
-#' @return A ggplot2 plot.
-#'
-#' @keywords internal
-plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
-  # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
-  days <- seq(set_date('2017-01-01'), by='d', length.out=365) + as.difftime(
-    yearly_start, units = "days")
-  df.y <- seasonality_plot_df(m, days)
-  seas <- predict_seasonal_components(m, df.y)
-  seas$ds <- df.y$ds
-
-  gg.yearly <- ggplot2::ggplot(seas, ggplot2::aes(x = ds, y = yearly,
-                                                  group = 1)) +
-    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE) +
-    ggplot2::labs(x = "Day of year") +
-    ggplot2::scale_x_datetime(labels = scales::date_format('%B %d'))
-  if (uncertainty) {
-    gg.yearly <- gg.yearly +
-      ggplot2::geom_ribbon(ggplot2::aes(ymin = yearly_lower,
-                                        ymax = yearly_upper),
-                           alpha = 0.2,
-                           fill = "#0072B2",
-                           na.rm = TRUE)
-  }
-  return(gg.yearly)
-}
-
-#' Plot a custom seasonal component.
-#'
-#' @param m Prophet model object.
-#' @param name String name of the seasonality.
-#' @param uncertainty Boolean to plot uncertainty intervals.
-#'
-#' @return A ggplot2 plot.
-#'
-#' @keywords internal
-plot_seasonality <- function(m, name, uncertainty = TRUE) {
-  # Compute seasonality from Jan 1 through a single period.
-  start <- set_date('2017-01-01')
-  period <- m$seasonalities[[name]]$period
-  end <- start + period * 24 * 3600
-  plot.points <- 200
-  days <- seq(from=start, to=end, length.out=plot.points)
-  df.y <- seasonality_plot_df(m, days)
-  seas <- predict_seasonal_components(m, df.y)
-  seas$ds <- df.y$ds
-  gg.s <- ggplot2::ggplot(
-      seas, ggplot2::aes_string(x = 'ds', y = name, group = 1)) +
-    ggplot2::geom_line(color = "#0072B2", na.rm = TRUE)
-  if (period <= 2) {
-    fmt.str <- '%T'
-  } else if (period < 14) {
-    fmt.str <- '%m/%d %R'
-  } else {
-    fmt.str <- '%m/%d'
-  }
-  gg.s <- gg.s +
-    ggplot2::scale_x_datetime(labels = scales::date_format(fmt.str))
-  if (uncertainty) {
-    gg.s <- gg.s +
-    ggplot2::geom_ribbon(
-      ggplot2::aes_string(
-        ymin = paste0(name, '_lower'), ymax = paste0(name, '_upper')
-      ),
-      alpha = 0.2,
-      fill = "#0072B2",
-      na.rm = TRUE)
-  }
-  return(gg.s)
-}
-
 #' Copy Prophet object.
 #'
 #' @param m Prophet model object.