Fix merge issues

R/R/plot.R

@@ -1,4 +1,3 @@
-#
 #' Merge history and forecast for plotting.
 #'
 #' @param m Prophet object.
@@ -97,7 +96,7 @@ prophet_plot_components <- function(
  # Plot the trend
  panels <- list(plot_forecast_component(fcst, 'trend', uncertainty, plot_cap))
  # Plot holiday components, if present.
- if (!is.null(m$holidays) & ('holidays' %in% colnames(fcst))) {
+ if (!is.null(m$holidays) && ('holidays' %in% colnames(fcst))) {
  panels[[length(panels) + 1]] <- plot_forecast_component(
  fcst, 'holidays', uncertainty, FALSE)
  }
@@ -127,7 +126,7 @@ prophet_plot_components <- function(
  grid::grid.newpage()
  grid::pushViewport(grid::viewport(layout = grid::grid.layout(length(panels),
  1)))
- for (i in 1:length(panels)) {
+ for (i in seq_along(panels)) {
  print(panels[[i]], vp = grid::viewport(layout.pos.row = i,
  layout.pos.col = 1))
  }
@@ -181,7 +180,7 @@ plot_forecast_component <- function(
 #'
 #' @keywords internal
 seasonality_plot_df <- function(m, ds) {
- df_list <- list(ds = ds, cap = 1)
+ df_list <- list(ds = ds, cap = 1, floor = 0)
  for (name in names(m$extra_regressors)) {
  df_list[[name]] <- 0
  }
@@ -203,7 +202,8 @@ seasonality_plot_df <- function(m, ds) {
 #' @keywords internal
 plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
  # Compute weekly seasonality for a Sun-Sat sequence of dates.
- days <- seq(set_date('2017-01-01'), by='d', length.out=7) + weekly_start
+ days <- seq(set_date('2017-01-01'), by='d', length.out=7) + as.difftime(
+ weekly_start, units = "days")
  df.w <- seasonality_plot_df(m, days)
  seas <- predict_seasonal_components(m, df.w)
  seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))
@@ -236,7 +236,8 @@ plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
 #' @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
+ 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