Correlations: Correlations suite for jamovi

#' Correlation Analysis
#'
#' @importFrom R6 R6Class
#' @import jmvcore
#' @importFrom magrittr "%>%"
#' @importFrom correlation correlation
#' @import psych
#' @export

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


# `self$data` contains the data
# `self$options` contains the options
# ..corrvars - variables to calculate correlations for
# ..ctrlvars - variables to control for
# ..shwSig  - show significance level for correlations (p-values)
# ..flgSig  - flag significant correlations
# ..sidSig  - one- or two-tailed significance calculations
# `self$results` contains the results object (to populate)


# .run=function() {
#
#   formula <- constructFormula(self$options$dep, self$options$group)
#   formula <- as.formula(formula)
#
#   results <- t.test(formula, self$data)
#
#   table <- self$results$ttest
#
#   table$setRow(rowNo=1, values=list(
#     var=self$options$dep,
#     t=results$statistic,
#     df=results$parameter,
#     p=results$p.value


correlationClass <- if (requireNamespace('jmvcore')) R6::R6Class(
    "correlationClass",
    inherit = correlationBase,
    private = list(
# # ====================================================
#       .init = function() {
#             # get variables
#             matrix <- self$results$get('matrix')
#             varCrr <- self$options$get('corrvars')
#            varCtl <- self$options$get('ctrlvars')
#
#             # set title according to whether the procedure is controlling for variables or not
#             matrix$setTitle(ifelse(length(varCtl) > 0, 'Partial Correlation Matrix', 'Correlation Matrix'))
#
#
#
#
#     },
#
#
# #=====================================================

.init = function() {
  # get variables
  varCrr <- self$options$get('corrvars')

    tables <- list(self$results$pearsontable, self$results$spearmantable)
  for(table in tables) {
    table$getColumn("ci.low")$setSuperTitle(paste("95% CI"))
    table$getColumn("ci.high")$setSuperTitle(paste("95% CI"))
  }

},


        .run = function() {
            # get variables
            #matrix <- self$results$get('matrix')
            #varCrr <- self$options$get('corrvars')
            #varCtl <- self$options$get('ctrlvars')

          self$results$instructions$setContent(
            "<html>
            <head>
            </head>
            <body>
            <div class='instructions'>
            <p>Welcome to Correlations analysis. To get started:</p>
            <ol>
            <li> This module is still under development by Hyunsoo Seol<br /><br /></li>
            </ol>
            <p>If you encounter any errors, or have questions, please e-mail me: snow@cau.ac.kr</a></p>
            <p>This is the first release of the module and is subject to change until a peer-reviewed article documenting its use has been published. Anticipated final version release date is late 2020.</a></p>
            </div>
            </body>
            </html>")

          # Data definition--------------


            if (length(self$options$corrvars) < 2)
                return()

            if (nrow(self$data) == 0)
                stop('Data contains no (complete) rows')


          # Correlation-----------------------


          mydata <- self$data

          formula <- jmvcore::constructFormula(terms = self$options$corrvars)

          myvars <- jmvcore::decomposeFormula(formula = formula)

          myvars <- unlist(myvars)

###-----------------------------------

          #           library(dplyr)
          #           mydata<- tibble(mtcars)
          #            mydata
          #            class(mydata)

          #           library(correlation)

          #           cor1 <- mydata %>% select(mpg, wt, drat) %>% correlation(.)

          #           cor1
###--Pearson-------------------------------------

          results <- mydata %>%
            select(myvars) %>%
            correlation::correlation(method = "pearson")

          self$results$text1$setContent(results)

          # res<- correlation(iris, method="pearson")
          # mode(iris)
          # names(res)

#Populating a table---------------------------------


          table <- self$results$pearsontable

          table$setRow(rowNo=1, values=list(
            var1=results$Parameter1,
            var2=results$Parameter2,
            r=results$r,
            ci.low=results$CI_low,
            ci.high=results$CI_high,
            t=results$t,
            df=results$df,
            p=results$p,
            n=results$n_Obs
          ))





###-------------------------------------------

#           corx <- mydata %>%
#               dplyr::select(myvars)  %>%
#               stats::cor.test(method = "spearman", exact = FALSE) %>%
#               report::report()

#          cor2 <- cor1 %>%
#               report::report(.)


#           self$results$text2$setContent(cor2)

###-------Spearman------------------------------------------

#           cor2 <- mydata %>%
#             select(myvars) %>%
#             correlation::correlation(method = "spearman")
#
#           self$results$text2$setContent(cor2)
#
# ###-----Tetrachoric--------------------------
#
#           cor3 <- mydata %>%
#             select(myvars) %>%
#             correlation::correlation(method = "tetrachoric")
#
#           self$results$text3$setContent(cor3)




        }
    )
)