Information (version 0.0.9)

plot_infotables: Create bar charts for WOE or NWOE vectors

Description

plot_infotable creates WOE or NWOE bar charts for one or more variables. For multi-variable plots, bar charts will be displayed in a grid for comparison.

Usage

plot_infotables(information_object = NULL, variables = NULL, same_scales = FALSE, show_values = FALSE)

Arguments

information_object

object generated by the information package.

variables

vector of one more variables. For multi-variable plots, bar charts will be displayed in a grid.

same_scales

if set to TRUE, all plots will have the same limits on the y-axes (default is FALSE).

show_values

if set to TRUE, values will be displayed on the bar chart (default is FALSE).

Examples

 
##------------------------------------------------------------
## WOE plots
##------------------------------------------------------------
library(Information)
data(train, package="Information")
train <- subset(train, TREATMENT==1)
IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE)

# Plotting a single variable
Information::plot_infotables(IV, "N_OPEN_REV_ACTS")

# Plotting multiple variables in a grid for comparison
Information::plot_infotables(IV, IV$Summary$Variable[1:4], same_scale=TRUE)

# If the goal is to plot multiple variables individually, as opposed to a comparison-grid, we can
# loop through the variable names and create individual plots
## Not run: 
# names <- names(IV$Tables)
# plots <- list()
# for (i in 1:length(names)){
#   plots[[i]] <- plot_infotables(IV, names[i])
# }
# # Showing the top 18 variables
# plots[1:18]
# ## End(Not run)

# We can speed up the creation of the information tables by invoking the parallel option (default)
# If we leave ncore as the default, create_infotables() will set ncore to available clusters - 1
## Not run: 
# train <- subset(train, TREATMENT==1)
# IV <- Information::create_infotables(data=train, y="PURCHASE")
# ## End(Not run)
closeAllConnections()