Added functions to merge factor vairables. Tested funModeling package.

funs.R

@@ -17,4 +17,16 @@ plot_freq <- function(df, fac_var, target = "Exited") {
     geom_line(aes(x = 1:nrow(count_frame), y = ExitedFreq * multi), color = "red", lwd = 2) + 
     scale_y_continuous(sec.axis = sec_axis(trans = ~ . / multi)) +
     xlab("Country")
+}
+
+merge_factor_vars <- function(var1, ...) {
+  UseMethod("merge_factor_vars", var1)
+}
+
+merge_factor_vars.factor <- function(var1, ...) {
+  factor(str_c(var1, ...))
+}
+
+merge_factor_vars.tbl <- function(var1, ...) {
+  do.call(function(...) factor(str_c(...)), as.list(var1))
 }

work1.R

@@ -5,6 +5,7 @@ library(stringr)
 library(ggplot2)
 library(GGally)
 library(scorecard)
+library(funModeling)
 # library(caret)
 # library(ranger)
 
@@ -40,4 +41,17 @@ iv(data1, "Exited", "NotSpain", positive = "No")
 
 opt_bin <- woebin(data1, "Exited", "Age", positive = "No")#[[1]]$breaks
 
-data2 <- data1 %>% woebin_ply(opt_bin, to = "bin") %>% mutate(Age_bin = as.factor(Age_bin))
+data2 <- data1 %>% mutate(Age_bin2 = discretize_rgr(Age, Exited, max_n_bins = 6))
+data2 <- data2 %>% woebin_ply(opt_bin, to = "bin") %>% mutate(Age_bin = as.factor(Age_bin))
+
+# opt_bin2 <- discretize_get_bins(data1, 4, input = "Age")
+
+head(data2$Age_bin2)
+plot(data2$Age_bin)
+plot(data2$Age_bin2)
+
+iv(data2, y = "Exited", positive = "No")
+
+#### łączenie zmiennych w celu uzyskania zbliżonej liczby kategorii (nieobciążone oceny ważności parametrów i IV)
+
+# dodałem funkcję w pliku funs.R

work2.R

@@ -0,0 +1,4 @@
+##### work file 2 - tidymodels + ranger
+
+library(tidyverse)
+library(stringr)