R/prophet.b.R

In hyunsooseol/snowCluster: Multivariate Analysis

# This file is a generated template, your changes will not be overwritten

#' @importFrom magrittr %>%

prophetClass <- if (requireNamespace('jmvcore', quietly=TRUE)) R6::R6Class(
    "prophetClass",
    inherit = prophetBase,
    private = list(

      .htmlwidget = NULL,
      
      .init = function() {
        
        private$.htmlwidget <- HTMLWidget$new()
        
        if (is.null(self$options$dep)){
          
          
          self$results$instructions$setVisible(visible = TRUE)
          
        }
        self$results$instructions$setContent(
          private$.htmlwidget$generate_accordion(
            title="Instructions",
            content = paste(
              '<div style="border: 2px solid #e6f4fe; border-radius: 15px; padding: 15px; background-color: #e6f4fe; margin-top: 10px;">',
              '<div style="text-align:justify;">',
              '<ul>',
              '<li>Prophet analysis requires the date column to be in a specific format (%Y-%m-%d). Otherwise, an error occurs.</li>',
              '<li>Feature requests and bug reports can be made on my <a href="https://github.com/hyunsooseol/snowCluster/issues" target="_blank">GitHub</a>.</li>',
              '</ul></div></div>'
              
            )
            
          )
        )          

        if(isTRUE(self$options$plot1)){
          
          width <- self$options$width
          height <- self$options$height
       
          self$results$plot1$setSize(width, height)
        }  
        
        if(isTRUE(self$options$plot2)){
          
          width <- self$options$width1
          height <- self$options$height1
          
          self$results$plot2$setSize(width, height)
        }  
       
      },
      
      ################################################################## 
      .run = function() {
        
        if (is.null(self$options$dep) || length(self$options$covs) == 0)
          return()
        
        dep <- self$options$dep
        covs <- self$options$covs
        
        # get the data
        data <- self$data
        data <- jmvcore::naOmit(data)
        
        
        # Create a list to store the forecast data frames
        forecast_df_list <- list()
        
        # Loop through each variable and create a forecast data frame
        for (i in 1:length(covs)) {
          
         new_data <- data %>% dplyr::select(ds, all_of(covs[i])) %>% dplyr::rename(y = all_of(covs[i]))
          
          # you can use the following code to convert a column in data 
         # with a date format of yy-mm to yy-mm-dd format 
         # and perform a Prophet analysis on it:
          
         # # Example in R-----------------------------------
         # # Load the data from a CSV file
         # data <- read.csv("your_data_file.csv")
         # 
         # # Check the format of the ds column and process the data accordingly
         # if (nchar(as.character(data$ds[1])) == 7) {
         #   # If the date is in yy-mm format
         #   data$ds <- as.Date(paste0(data$ds, "-01"), format = "%y-%m-%d")
         # } else if (nchar(as.character(data$ds[1])) == 10) {
         #   # If the date is in yy-mm-dd format
         #   data$ds <- as.Date(data$ds)
         # }
          
         # Example converting yy-mm into yy-mm-dd
         # data<- read.csv("beer.csv")
         # head(data)
         # data$Month <- as.Date(paste0(data$Month, "-01"), format = "%Y-%m-%d")
         # head(data)
         # 
         
         ### yy-mm-dd-hh is no problem !!!
         
          # Fit the model
          model <- prophet::prophet(new_data,
                                    changepoint.prior.scale = 0.05,
                                    daily.seasonality = TRUE,
                                    yearly.seasonality = TRUE,
                                    weekly.seasonality = TRUE)
          
          
          # Make predictions for the next 365 days
          future <- prophet::make_future_dataframe(model, 
                                                   periods = self$options$periods,
                                                   freq = self$options$unit)
          
          forecast <- predict(model, future)
          
          # Add the forecast to the list
          forecast_df_list[[i]] <- data.frame(ds = forecast$ds, yhat = forecast$yhat, variable = covs[i])
        }
        
        # Combine the forecast data frames into one
        forecast_combined <- do.call(rbind, forecast_df_list)
        
        #self$results$text$setContent(head(forecast_combined ))
       
        # forecast plot----------
        
        image <- self$results$plot1
        image$setState(forecast_combined)
        
      # Smooth plot--------------
        
        image1 <- self$results$plot2
        image1$setState(forecast_combined)
        
        
        },
     

.plot1 = function(image,ggtheme, theme,...) {
  
  if (is.null(image$state))
    return(FALSE)
  
  
  forecast_combined <- image$state
  
  library(ggplot2)
  plot1 <- ggplot2::ggplot(forecast_combined, aes(x = ds, y = yhat, color = variable)) +
    geom_line() +
    labs(x = "Date", y = "Forecast", color = "Variable") +
  # ggtitle("Forecast of Multiple Variables") +
    theme_bw()
  
  plot1+ggtheme
  
  print(plot1)
  TRUE
},
        
.plot2 = function(image1,ggtheme, theme,...) {
  
  if (is.null(image1$state))
    return(FALSE)
 
  method <- self$options$method
  
  forecast_combined <- image1$state
  
 library(ggplot2)
  plot2<- ggplot2::ggplot(forecast_combined, aes(x = ds, y = yhat, color = variable)) +
    
    geom_smooth(method= method) +
    labs(x = "Date", y = "Forecast", color = "Variable") +
   # ggtitle("Forecast of Multiple Variables") +
    theme_bw()
  
  plot2+ggtheme
  
  print(plot2)
  TRUE
}    
        
        
        
)
)