seolmatrix: Correlations suite for jamovi

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


#' Spearman Analysis
#'
#' @importFrom R6 R6Class
#' @import jmvcore
#' @import psych
#' @importFrom psych corr.test
#' @importFrom psych partial.r
#' @importFrom psych tetrachoric
#' @importFrom qgraph EBICglasso
#' @importFrom qgraph centralityPlot
#' @importFrom corrplot corrplot
#' @import qgraph
#' @export

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

rankClass <- if (requireNamespace('jmvcore')) R6::R6Class(
    "rankClass",
    inherit = rankBase,
    private = list(
        
        #==========================================================
        .init = function() {
           
          
          
          if(is.null(self$data) | is.null(self$options$vars)){
            self$results$instructions$setVisible(visible = TRUE)
            
            self$results$instructions$setContent(
              "<html>
            <head>
            </head>
            <body>
            <div class='instructions'>
            <p>____________________________________________________________________________________</p>
            <p>Feature requests and bug reports can be made on my <a href='https://github.com/hyunsooseol/seolmatrix/issues'  target = '_blank'>GitHub</a>.</p>
            <p>____________________________________________________________________________________</p>
            </div>
            </body>
            </html>"
            )
            
          
            
          }
        },
            
        
        #==========================================================
        .run = function() {
            
          
          if (length(self$options$vars)<2) return() 
        
          if(length(self$options$vars>2)){
            
            # get variables---------------------------------
            
            vars <- self$options$vars
            type <- self$options$type
            
            mydata <- self$data
            
            mydata <- jmvcore::naOmit(mydata)
            
            for(v in vars)
              mydata[[v]] <- jmvcore::toNumeric(mydata[[v]])
            
            
            if(isTRUE(self$options$plot)){
              width <- self$options$width
              height <- self$options$height
              self$results$plot$setSize(width, height)
            }
            
            if(isTRUE(self$options$plot1)){
              width <- self$options$width1
              height <- self$options$height1
              self$results$plot1$setSize(width, height)
            }  
            
            if(isTRUE(self$options$plot2)){
              width <- self$options$width2
              height <- self$options$height2
              self$results$plot2$setSize(width, height)
            }  
            
            if(isTRUE(self$options$plot3)){
              width <- self$options$width3
              height <- self$options$height3
              self$results$plot3$setSize(width, height)
            }  
            
            
            if(self$options$type=='spearman'){
            
              #  compute spearman correlation with psych package--------
            
            spear <- psych::corr.test(mydata, method="spearman")
            spearman <- spear$r
            
            spearman<- as.data.frame(spearman)
            
            
            names <- dimnames(spearman)[[1]] 
            dims <- dimnames(spearman)[[2]]
            
            table <- self$results$matrix
            
            # creating table----------------
            
            for (dim in dims) {
              
              table$addColumn(name = paste0(dim),
                              type = 'number')
            }
            
            for (name in names) {
              
              row <- list()
              
              for(j in seq_along(dims)){
                
                row[[dims[j]]] <- spearman[name,j]
                
              }
              
              table$addRow(rowKey=name, values=row)
              
            }
            
            # partial plot---------
            res<- psych::partial.r(spearman)
            
            image1 <- self$results$plot1
            image1$setState(res)
           
           # EBIC PLOT------------
              
              rank <- spear$r
              
              # Compute graph with tuning = 0.5 (EBIC)
              EBICgraph <- qgraph::EBICglasso(rank,nrow(mydata), 0.5, threshold = TRUE)
              
              # Prepare Data For Plot -------
              image <- self$results$plot
              image$setState(EBICgraph)
              
              # Centrality plot-------
              image2 <- self$results$plot2
              image2$setState(EBICgraph)  
           
           # Matrix plot----------
              
              image3 <- self$results$plot3
              image3$setState(as.matrix(spearman))
              
           
            }
            
            if(self$options$type=='polychoric'){
            
              # compute polychoric correlation-------
              
              poly <- psych::polychoric(mydata)$rho
              poly<- as.data.frame(poly)
              
              
              names <- dimnames(poly)[[1]] 
              dims <- dimnames(poly)[[2]]
            
              
              table <- self$results$matrix
              
              # creating table----------------
              
              for (dim in dims) {
                
                table$addColumn(name = paste0(dim),
                                type = 'number')
              }
              
              for (name in names) {
                
                row <- list()
                
                for(j in seq_along(dims)){
                  
                  row[[dims[j]]] <- poly[name,j]
                  
                }
                
                table$addRow(rowKey=name, values=row)
                
              }
              
              # paritial----------------------------------------------
              
              res<- psych::partial.r(poly)
              
              image1 <- self$results$plot1
              image1$setState(res)
              
              # EBIC PLOT------------
                
                poly <- psych::polychoric(mydata)$rho
                
                # Compute graph with tuning = 0.5 (EBIC)
                EBICgraph <- qgraph::EBICglasso(poly,nrow(mydata), 0.5, threshold = TRUE)
                
                image <- self$results$plot
                image$setState(EBICgraph)
                
                # Centrality plot-------
                image2 <- self$results$plot2
                image2$setState(EBICgraph)
                
                # Matrix plot----------
                
                image3 <- self$results$plot3
                image3$setState(as.matrix(poly))
                
                 
              }
              
            
            if(self$options$type=='tetrachoric'){
              
              # compute tetrachoric correlation with psych package--------
              
              tetrarho <- psych::tetrachoric(mydata)$rho
              
              tetrarho <- as.data.frame(tetrarho)
              
              
              names <- dimnames(tetrarho)[[1]] 
              dims <- dimnames(tetrarho)[[2]]
              
              
              table <- self$results$matrix
              
              # creating table----------------
              
              for (dim in dims) {
                
                table$addColumn(name = paste0(dim),
                                type = 'number')
              }
              
              for (name in names) {
                
                row <- list()
                
                for(j in seq_along(dims)){
                  
                  row[[dims[j]]] <- tetrarho[name,j]
                  
                }
                
                table$addRow(rowKey=name, values=row)
                
              }
              
              ### partial correlation--------
              
              res<- psych::partial.r(tetrarho)
             
              image1 <- self$results$plot1
              image1$setState(res)
             
              # EBIC PLOT------------
                
                tetrarho <- psych::tetrachoric(mydata)$rho
                
                # Compute graph with tuning = 0.5 (EBIC)
                EBICgraph <- qgraph::EBICglasso(tetrarho,nrow(mydata), 0.5, threshold = TRUE)
                
                image <- self$results$plot
                image$setState(EBICgraph)
                
                
              # Centrality plot-------
                image2 <- self$results$plot2
                image2$setState(EBICgraph)
                
              # Matrix plot----------
                
                image3 <- self$results$plot3
                image3$setState(as.matrix(tetrarho))
              
           
          }
          
          }
        },
        
        #================================================================
        .plot = function(image, ...) {

        
          if (is.null(image$state))
            return(FALSE)

          EBICgraph <- image$state

          plot <- qgraph(EBICgraph, layout = "spring", details = TRUE)

          print(plot)
          TRUE


        },
        
        
        # Centrality plot for EBIC------------
        
        .plot2 = function(image2, ggtheme, theme,...) {
          
          if (is.null(image2$state))
            return(FALSE)
          
          scale <- self$options$scale
          
          EBICgraph <- image2$state
          
          plot2<- qgraph::centralityPlot(EBIC = EBICgraph,
                                         scale=scale)
          
          plot2 <- plot2+ggtheme
          
          print(plot2)
          TRUE
          
        },
        
        
        .plot1 = function(image1, ...) {
          
          if (is.null(image1$state))
            return(FALSE)
          
          res <- image1$state
          
          plot1 <- qgraph(res, layout = "spring", details = TRUE)
          
          print(plot1)
          TRUE
          
        },
        
        .plot3 = function(image3, ggtheme, theme,...) {
          
          
          if (is.null(image3$state))
            return(FALSE)
          
          gram <- image3$state  
        
       
          plot3<- corrplot::corrplot(gram, 
                                     type="lower",
                                     col=c("black", "white"),
                                     bg="lightblue")
          
           print(plot3)
           TRUE
          
        }
        
        
        
    ))