R/correlation.b.R
#' @title Correlation Analysis
#' @importFrom R6 R6Class
#' @import jmvcore
#'
correlationClass <- if (requireNamespace('jmvcore')) R6::R6Class(
"correlationClass",
inherit = correlationBase,
private = list(
.run = function() {
# # Error Message ----
#
# if (nrow(self$data) == 0) stop("Data contains no (complete) rows")
#
# if ( (is.null(self$options$vars) || is.null(self$options$facs)) && is.null(self$options$target) ) {
# # ToDo Message ----
# todo <- "
# <br>Welcome to ClinicoPath
# <br><br>
# This tool will help you form an Alluvial Plots.
# "
# html <- self$results$todo
# html$setContent(todo)
#
# } else {
# todo <- ""
# html <- self$results$todo
# html$setContent(todo)
#
#
#
# }
# TODO
todo <- glue::glue(
"This Module is still under development
- Correlation matrix via ggplot
-
"
)
self$results$todo$setContent(todo)
####
if (length(self$options$vars) < 2)
return()
if (nrow(self$data) == 0)
stop('Data contains no (complete) rows')
# Correlation
mydata <- self$data
formula <- jmvcore::constructFormula(terms = self$options$vars)
myvars <- jmvcore::decomposeFormula(formula = formula)
myvars <- unlist(myvars)
cor1 <- mydata %>%
select(myvars) %>%
correlation::correlation(.)
self$results$text1$setContent(cor1)
# corx <- mydata %>%
# dplyr::select(myvars) %$%
# stats::cor.test(method = "spearman", exact = FALSE) %>%
# report::report()
# cor2 <- cor1 %>%
# report::report(.)
# self$results$text2$setContent(cor2)
# self$results$text3$setContent(cor3)
# ggplot(mydata, aes(x = PeritumoralTomurcukSayi, y = Yas)) +
# geom_point() +
# geom_smooth(method = lm, size = 1)
# mydata %>%
# ggstatsplot::ggscatterstats(x = PeritumoralTomurcukSayi, y = Yas, type = "np")
#
# iris %>%
# select(Species, starts_with("Sepal")) %>%
# group_by(Species) %>%
# correlation::correlation() %>%
# filter(r < 0.9)
#
# correlation::correlation(select(iris, Species, starts_with("Sepal")),
# select(iris, Species, starts_with("Petal")),
# partial=TRUE)
#
# correlation(iris, bayesian=TRUE)
#
#
# library(report)
# iris %>%
# select(starts_with("Sepal")) %>%
# correlation::correlation(bayesian=TRUE) %>%
# report()
#
#
# report::report(cor.test(iris$Sepal.Length, iris$Petal.Length))
#
#
# # https://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.test.html
#
#
# iris %>%
# group_by(Species) %>%
# correlation() %>%
# report() %>%
# to_table()
#
# iris %>% explore(Sepal.Length, Petal.Length)
#
# iris$is_versicolor <- ifelse(iris$Species == "versicolor", 1, 0)
# iris %>% explore(Sepal.Length, Petal.Length, target = is_versicolor)
#
#
# dlookr::correlate(carseats)
# dlookr::correlate(carseats, Sales, CompPrice, Income)
# dlookr::correlate(carseats, Sales:Income)
# dlookr::correlate(carseats, -(Sales:Income))
# carseats %>%
# dlookr::correlate(Sales:Income) %>%
# dplyr::filter(as.integer(var1) > as.integer(var2))
# carseats %>%
# dplyr::filter(ShelveLoc == "Good") %>%
# group_by(Urban, US) %>%
# dlookr::correlate(Sales) %>%
# dplyr::filter(abs(coef_corr) > 0.5)
#
#
# dlookr::plot_correlate(carseats)
# dlookr::plot_correlate(carseats, Sales, Price)
# carseats %>%
# dplyr::filter(ShelveLoc == "Good") %>%
# dplyr::group_by(Urban, US) %>%
# dlookr::plot_correlate(Sales)
#
#
# SmartEDA::ExpNumStat(
# Carseats,
# by = "A",
# gp = "Price",
# Qnt = seq(0, 1, 0.1),
# MesofShape = 1,
# Outlier = TRUE,
# round = 2
# )
#
#
# # https://alastairrushworth.github.io/inspectdf/articles/pkgdown/inspect_cor_exampes.html
# inspectdf::inspect_cor(storms)
#
# inspectdf::inspect_cor(storms) %>% inspectdf::show_plot()
#
# inspectdf::inspect_cor(storms, storms[-c(1:200), ])
#
# inspectdf::inspect_cor(storms, storms[-c(1:200), ]) %>%
# slice(1:20) %>%
# inspectdf::show_plot()
#
#
#
#
# ggplot(mydata, aes(x = tx_zamani_verici_yasi, y = trombosit)) +
# geom_point() +
# geom_smooth(method = lm, size = 1)
#
#
#
# # https://easystats.github.io/report/articles/interpret_metrics.html
#
# mydata %>%
# select(continuousVariables,
# -dateVariables) %>%
# visdat::vis_cor()
####
# cor <- self$data %>%
# select(myvars) %>%
# psycho::correlation(.)
# cor1 <- summary(cor) %>%
# knitr::kable(format = "latex") %>%
# kableExtra::kable_styling(latex_options="scale_down")
# cor2 <- print(cor)
# cor3 <- cor %>%
# report::to_values()
# Prepare Plot Data
plotData <- self$data %>%
dplyr::select(self$options$vars)
image <- self$results$plot
image$setState(plotData)
},
.plot = function(image, ggtheme, theme, ...) { # <-- the plot function
if (length(self$options$vars) < 2)
return()
plotData <- image$state
plot <- plot(plotData)
print(plot)
TRUE
},
.plot2 = function(image, ggtheme, theme, ...) { # <-- the plot2 function
if (length(self$options$vars) < 2)
return()
plotData <- image$state
plot2 <- ggplot(plotData) +
geom_point() +
geom_smooth(method = lm, size = 1)
print(plot2)
TRUE
}
)
)