doudpackage (version 2.1.0)

descTab: Generic function to create a table of descriptive analysis of a dataset

Description

This function allows you to display all together all univariate analysis (median/mean; IQR/SD; proportions) and bivariates analysis (Wilcoxon, Chi² or Fisher). The univariate analysis can be sub-grouped by a variable of interest of n levels. Appropriate statistics test will be applied

Usage

descTab(
  data,
  group = NULL,
  quanti = TRUE,
  quali = TRUE,
  na.print = FALSE,
  pvalue = TRUE,
  digits.p = 3L,
  digits.qt = 1L,
  digits.ql = 1L,
  normality = "normal",
  parallel = FALSE,
  mc.cores = 0
)

Value

A S4 objects parseClass() containing the main table accessible by ["table"] subscript.

Arguments

data

A datasaset. Needs to be a data.frame/tibble object

group

Optional. The name of the variable to make sub-groups comparisons.

quanti, quali, na.print, pvalue

Logical. If false, won't display quantitative/qualitative/Missing values/pvalues variable results

digits.p

Integer. Significant digits for p value

digits.qt

Integer. Significant digits for mean/median, SD/IQR

digits.ql

Integer. Significant digits for proportions

normality

One of "assess", "normal", "manual", "non normal". See details

parallel

Logical. Make analysis using parallel from parallel::mclapply().

mc.cores

If parallel is TRUE, how many Cores to used.

Examples

data(iris)
library(stringi)
iris$fact_1<-as.factor(as.character(sample(1:5, 150, replace = TRUE)))
n_na<-sample(1:150, 30)
iris[n_na, "fact_1"]<-NA
iris$fact_2<-as.factor(as.character(sample(1:2, 150, replace = TRUE)))
n_na<-sample(1:150, 10)
iris[n_na, "fact_2"]<-NA
iris$fact_3<-as.factor(as.character(stri_rand_strings(150, 1, '[A-B]')))
iris$num<-runif(150, min = 0, max = 100)
n_na<-sample(1:150, 5)
iris[n_na, "num"]<-NA
iris_test<-descTab(iris, group = "Species", na.print = TRUE)