change correlation function name, also added the documentation

pablo14 · Mar 27, 2016 · 0570b9f · 0570b9f
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diff --git a/.Rproj.user/D07DC6CE/pcs/source-pane.pper b/.Rproj.user/D07DC6CE/pcs/source-pane.pper
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diff --git a/NEWS b/NEWS
@@ -1,5 +1,7 @@
 funModeling v2.0 (Release date: 2016-xxxxx)
 ==============
+
+
 Minor improvements:
 - New parameter in df_status: print_results, if FALSE then there isn't a print console. 
 - If str_input is missing in cross_plot , then it runs for all variables

diff --git a/R/numbers.R b/R/numbers.R
@@ -1,21 +1,17 @@
-#' @title Plot
-#' @description For each variable it returns: Quantity and percentage of zeros (q_zeros and p_zeros respectevly). Same metrics for NA values (q_NA and p_na). Last two columns indicates data type and quantity of unique values.
-#' This function print and return the results.
+#' @title Correlation index against target variable
+#' @description Obtain correlation table of all variables that belongs to data against target variable
 #' @param data data frame
+#' @param str_target string variable to predict
 #' @examples
-#' df_status(heart_disease)
-#' @return Metrics data frame
+#' correlation_table(data=heart_disease, str_target="has_heart_disease")
+#' @return Correlation index for all data input variable
 #' @export
-get_cor_matrix <- function(data, str_target)
+correlation_table <- function(data, str_target)
 {
-	str_target="has_heart_disease"
-	data=heart_disease
-
 	data[, str_target]=as.numeric(data[, str_target])
 
 	data=data[, c(give_me_num_vars(data, str_target), str_target)]
 
-
   df_cor=as.data.frame(round(cor(data, use="complete.obs"	),2))
   df_cor$Variable = rownames(df_cor)
   df_cor=df_cor[, names(df_cor) %in% c(str_target, "Variable")]