Visualize mosaics plot by attribute of compare_category class.
# S3 method for compare_category plot( x, prompt = FALSE, na.rm = FALSE, typographic = TRUE, base_family = NULL, ... )
Arguments
| x | an object of class "compare_category", usually, a result of a call to compare_category(). |
|---|---|
| prompt | logical. The default value is FALSE. If there are multiple visualizations to be output, if this argument value is TRUE, a prompt is output each time. |
| na.rm | logical. Specifies whether to include NA when plotting mosaics plot. The default is FALSE, so plot NA. |
| 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) |
| ... | arguments to be passed to methods, such as graphical parameters (see par). However, it only support las parameter. las is numeric in 0,1; the style of axis labels.
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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
Examples
# \donttest{ # Generate data for the example heartfailure2 <- heartfailure heartfailure2[sample(seq(NROW(heartfailure2)), 5), "smoking"] <- NA library(dplyr) # Compare the all categorical variables all_var <- compare_category(heartfailure2) # Print compare_numeric class objects all_var#> $`anaemia vs diabetes` #> # A tibble: 4 x 6 #> anaemia diabetes n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 98 0.328 0.576 0.563 #> 2 No Yes 72 0.241 0.424 0.576 #> 3 Yes No 76 0.254 0.589 0.437 #> 4 Yes Yes 53 0.177 0.411 0.424 #> #> $`anaemia vs hblood_pressure` #> # A tibble: 4 x 6 #> anaemia hblood_pressure n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 113 0.378 0.665 0.582 #> 2 No Yes 57 0.191 0.335 0.543 #> 3 Yes No 81 0.271 0.628 0.418 #> 4 Yes Yes 48 0.161 0.372 0.457 #> #> $`anaemia vs sex` #> # A tibble: 4 x 6 #> anaemia sex n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No Female 53 0.177 0.312 0.505 #> 2 No Male 117 0.391 0.688 0.603 #> 3 Yes Female 52 0.174 0.403 0.495 #> 4 Yes Male 77 0.258 0.597 0.397 #> #> $`anaemia vs smoking` #> # A tibble: 5 x 6 #> anaemia smoking n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 108 0.361 0.635 0.543 #> 2 No Yes 62 0.207 0.365 0.653 #> 3 Yes No 91 0.304 0.705 0.457 #> 4 Yes Yes 33 0.110 0.256 0.347 #> 5 Yes NA 5 0.0167 0.0388 1 #> #> $`anaemia vs death_event` #> # A tibble: 4 x 6 #> anaemia death_event n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 120 0.401 0.706 0.591 #> 2 No Yes 50 0.167 0.294 0.521 #> 3 Yes No 83 0.278 0.643 0.409 #> 4 Yes Yes 46 0.154 0.357 0.479 #> #> $`diabetes vs hblood_pressure` #> # A tibble: 4 x 6 #> diabetes hblood_pressure n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 112 0.375 0.644 0.577 #> 2 No Yes 62 0.207 0.356 0.590 #> 3 Yes No 82 0.274 0.656 0.423 #> 4 Yes Yes 43 0.144 0.344 0.410 #> #> $`diabetes vs sex` #> # A tibble: 4 x 6 #> diabetes sex n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No Female 50 0.167 0.287 0.476 #> 2 No Male 124 0.415 0.713 0.639 #> 3 Yes Female 55 0.184 0.44 0.524 #> 4 Yes Male 70 0.234 0.56 0.361 #> #> $`diabetes vs smoking` #> # A tibble: 6 x 6 #> diabetes smoking n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 106 0.355 0.609 0.533 #> 2 No Yes 65 0.217 0.374 0.684 #> 3 No NA 3 0.0100 0.0172 0.6 #> 4 Yes No 93 0.311 0.744 0.467 #> 5 Yes Yes 30 0.100 0.24 0.316 #> 6 Yes NA 2 0.00669 0.016 0.4 #> #> $`diabetes vs death_event` #> # A tibble: 4 x 6 #> diabetes death_event n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 118 0.395 0.678 0.581 #> 2 No Yes 56 0.187 0.322 0.583 #> 3 Yes No 85 0.284 0.68 0.419 #> 4 Yes Yes 40 0.134 0.32 0.417 #> #> $`hblood_pressure vs sex` #> # A tibble: 4 x 6 #> hblood_pressure sex n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No Female 61 0.204 0.314 0.581 #> 2 No Male 133 0.445 0.686 0.686 #> 3 Yes Female 44 0.147 0.419 0.419 #> 4 Yes Male 61 0.204 0.581 0.314 #> #> $`hblood_pressure vs smoking` #> # A tibble: 6 x 6 #> hblood_pressure smoking n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 126 0.421 0.649 0.633 #> 2 No Yes 66 0.221 0.340 0.695 #> 3 No NA 2 0.00669 0.0103 0.4 #> 4 Yes No 73 0.244 0.695 0.367 #> 5 Yes Yes 29 0.0970 0.276 0.305 #> 6 Yes NA 3 0.0100 0.0286 0.6 #> #> $`hblood_pressure vs death_event` #> # A tibble: 4 x 6 #> hblood_pressure death_event n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 137 0.458 0.706 0.675 #> 2 No Yes 57 0.191 0.294 0.594 #> 3 Yes No 66 0.221 0.629 0.325 #> 4 Yes Yes 39 0.130 0.371 0.406 #> #> $`sex vs smoking` #> # A tibble: 6 x 6 #> sex smoking n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 Female No 99 0.331 0.943 0.497 #> 2 Female Yes 4 0.0134 0.0381 0.0421 #> 3 Female NA 2 0.00669 0.0190 0.4 #> 4 Male No 100 0.334 0.515 0.503 #> 5 Male Yes 91 0.304 0.469 0.958 #> 6 Male NA 3 0.0100 0.0155 0.6 #> #> $`sex vs death_event` #> # A tibble: 4 x 6 #> sex death_event n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 Female No 71 0.237 0.676 0.350 #> 2 Female Yes 34 0.114 0.324 0.354 #> 3 Male No 132 0.441 0.680 0.650 #> 4 Male Yes 62 0.207 0.320 0.646 #> #> $`smoking vs death_event` #> # A tibble: 6 x 6 #> smoking death_event n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 134 0.448 0.673 0.660 #> 2 No Yes 65 0.217 0.327 0.677 #> 3 Yes No 65 0.217 0.684 0.320 #> 4 Yes Yes 30 0.100 0.316 0.312 #> 5 NA No 4 0.0134 0.8 0.0197 #> 6 NA Yes 1 0.00334 0.2 0.0104 #># Compare the two categorical variables two_var <- compare_category(heartfailure2, smoking, death_event) # Print compare_category class objects two_var#> $`smoking vs death_event` #> # A tibble: 6 x 6 #> smoking death_event n rate var1_rate var2_rate #> <fct> <fct> <int> <dbl> <dbl> <dbl> #> 1 No No 134 0.448 0.673 0.660 #> 2 No Yes 65 0.217 0.327 0.677 #> 3 Yes No 65 0.217 0.684 0.320 #> 4 Yes Yes 30 0.100 0.316 0.312 #> 5 NA No 4 0.0134 0.8 0.0197 #> 6 NA Yes 1 0.00334 0.2 0.0104 #># }
