Visualize Distribution for an "optimal_bins" Object

It generates plots for understand distribution, frequency, bad rate, and weight of evidence using optimal_bins.

See vignette("transformation") for an introduction to these concepts.

# S3 method for optimal_bins
plot(
  x,
  type = c("all", "dist", "freq", "posrate", "WoE"),
  typographic = TRUE,
  base_family = NULL,
  rotate_angle = 0,
  ...
)

Arguments

x	an object of class "optimal_bins", usually, a result of a call to binning_by().
type	character. options for visualization. Distribution ("dist"), Relateive Frequency ("freq"), Positive Rate ("posrate"), and Weight of Evidence ("WoE"). and default "all" draw all plot.
typographic	logical. Whether to apply focuses on typographic elements to ggplot2 visualization. The default is TRUE. if TRUE provides a base theme that focuses on typographic elements using hrbrthemes package.
base_family	character. The name of the base font family to use for the visualization. If not specified, the font defined in dlookr is applied. (See details)
rotate_angle	integer. specifies the rotation angle of the x-axis label. This is useful when the x-axis labels are long and overlap. The default is 0 to not rotate the label.
...	further arguments to be passed from or to other methods.

Details

The base_family is selected from "Roboto Condensed", "Liberation Sans Narrow", "NanumSquare", "Noto Sans Korean". If you want to use a different font, use it after loading the Google font with import_google_font().

See also

binning_by, summary.optimal_bins

Examples

# \donttest{
# Generate data for the example
heartfailure2 <- heartfailure
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "creatinine"] <- NA

# optimal binning using binning_by()
bin <- binning_by(heartfailure2, "death_event", "creatinine")
#> Warning: The factor y has been changed to a numeric vector consisting of 0 and 1.
#> 'Yes' changed to 1 (positive) and 'No' changed to 0 (negative).
bin
#> binned type: optimal
#> number of bins: 3
#> x
#> [0.5,0.9] (0.9,1.8] (1.8,9.4]      <NA> 
#>        80       167        47         5 

# summary optimal_bins class.
summary(bin)
#> ── Binning Table ──────────────────────── Several Metrics ── 
#>         Bin CntRec CntPos CntNeg RatePos RateNeg    Odds      WoE      IV
#> 1 [0.5,0.9]     80      9     71 0.09375 0.34975 0.12676 -1.31660 0.33705
#> 2 (0.9,1.8]    167     52    115 0.54167 0.56650 0.45217 -0.04483 0.00111
#> 3 (1.8,9.4]     47     34     13 0.35417 0.06404 2.61538  1.71027 0.49620
#> 4      <NA>      5      1      4 0.01042 0.01970 0.25000 -0.63744 0.00592
#> 5     Total    299     96    203 1.00000 1.00000 0.47291       NA 0.84028
#>       JSD     AUC
#> 1 0.03933 0.01639
#> 2 0.00014 0.20654
#> 3 0.05542 0.05203
#> 4 0.00073 0.01960
#> 5 0.09562 0.29457
#> 
#> ── General Metrics ───────────────────────────────────────── 
#> • Gini index                       :  -0.41087
#> • IV (Jeffrey)                     :  0.84028
#> • JS (Jensen-Shannon) Divergence   :  0.09562
#> • Kolmogorov-Smirnov Statistics    :  0.28084
#> • HHI (Herfindahl-Hirschman Index) :  0.40853
#> • HHI (normalized)                 :  0.21137
#> • Cramer's V                       :  0.41553 
#> 
#> ── Significance Tests ──────────────────── Chisquare Test ── 
#>       Bin A     Bin B statistics      p_value
#> 1 [0.5,0.9] (0.9,1.8]   11.50352 6.946445e-04
#> 2 (0.9,1.8] (1.8,9.4]   25.90425 3.587780e-07
#> 

# visualize all information for optimal_bins class
plot(bin)

# rotate the x-axis labels by 45 degrees so that they do not overlap.
plot(bin, rotate_angle = 45)

# visualize WoE information for optimal_bins class
plot(bin, type = "WoE")

# visualize all information with typographic
plot(bin)
# }