#Introduction The data used in the following examples comes from the heart disease dataset found at the UCI Machine Learning Repository.

#Load packages
require(tidyverse); require(cheese)
## Loading required package: tidyverse
## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.1     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.2     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## Loading required package: cheese
#Look at the top ten rows
heart_disease
## # A tibble: 303 x 9
##      Age Sex   ChestPain    BP Cholesterol BloodSugar MaximumHR
##    <dbl> <fct> <fct>     <dbl>       <dbl> <lgl>          <dbl>
##  1    63 Male  Typical …   145         233 TRUE             150
##  2    67 Male  Asymptom…   160         286 FALSE            108
##  3    67 Male  Asymptom…   120         229 FALSE            129
##  4    37 Male  Non-angi…   130         250 FALSE            187
##  5    41 Fema… Atypical…   130         204 FALSE            172
##  6    56 Male  Atypical…   120         236 FALSE            178
##  7    62 Fema… Asymptom…   140         268 FALSE            160
##  8    57 Fema… Asymptom…   120         354 FALSE            163
##  9    63 Male  Asymptom…   130         254 FALSE            147
## 10    53 Male  Asymptom…   140         203 TRUE             155
## # … with 293 more rows, and 2 more variables: ExerciseInducedAngina <fct>,
## #   HeartDisease <fct>

#Creating a univariate table The function univariate_table allows flexible summarization and presentation of variables in a dataset. Arguments are available to customize the statistics that are computed, association metrics, stratification variables, variable labels, etc. The format argument allows the user to render any table in “html”, “latex”, “markdown”, “pandoc”, “none” (i.e. return a data.frame). The following examples are rendered in “html” (default):

##Default By default, the median (iqr), count (%), and the number of distinct values are displayed for numeric, categorical, and 'other' data types,

#Default table
heart_disease %>%
    univariate_table
Variable Level Summary
Age 56 (13)
Sex Female 97 (32.01%)
Male 206 (67.99%)
ChestPain Typical angina 23 (7.59%)
Atypical angina 50 (16.5%)
Non-anginal pain 86 (28.38%)
Asymptomatic 144 (47.52%)
BP 130 (20)
Cholesterol 241 (64)
BloodSugar 2
MaximumHR 153 (32.5)
ExerciseInducedAngina No 204 (67.33%)
Yes 99 (32.67%)
HeartDisease No 164 (54.13%)
Yes 139 (45.87%)