Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "# >"
)
mtcars <- datasets::mtcars
## ----setup--------------------------------------------------------------------
library(CRUF)
mtcars <- datasets::mtcars
desctable <- tabkris_2(mtcars)
knitr::kable(desctable)
## ----auto_detect--------------------------------------------------------------
# In mtcars, "cyl", "vs", "am", "gear" and "carb" are encoded as numeric but they are factors in reality.
# tabkris_2 changes each variable and display a message for each transformation.
desctable <- tabkris_2(mtcars, auto_detect = T, lev_co = 8)
knitr::kable(desctable)
## ----return_table-------------------------------------------------------------
# desc_prep <- tabkris_2(mtcars, return_table = F, auto_detect = T)
#
# # Change the method for variable "vs" from a binomial to a categorical method
# desc_prep$method["vs"] <- "cate"
#
# desctable <- tabkris_2(desc_prep)
#
# # Variable of interest set to "am", also using the previous changed arguments
# desc_prep$varint <- "am"
#
# desctable_2 <- tabkris_2(desc_prep)
## ----return_method------------------------------------------------------------
desc_prep <- tabkris_2(mtcars, return_table = F)
# Change the method for all binomial variable to categorical
desc_prep$default_method[2] <- "cate"
desctable <- tabkris_2(desc_prep)
# Changing only the method for "vs" to categorical
desc_prep$method["vs"] <- "cate"
desctable_2 <- tabkris_2(desc_prep)
## ----presentations------------------------------------------------------------
# Changing the names
lab <- c("Miles/US gallon", "Number of cylinders", "Displacement", "Horsepower", "Rear axle ratio", "Weight", "1/4 mile time", "Engine", "Transmission", "N Forward gears", "N carburetors")
desctable <- tabkris_2(mtcars, names = lab,
pres_quant = c("mean", "range"),
pres_quali = c("n", "total", "per"),
explicit_na = T,
digits = 1,
lang = "fr",
auto_detect = T)
knitr::kable(desctable)
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