In baseverse: Modern Base-R Functions

Overview

baseverse is intended to be a relatively minimal suite of packages, supporting the use of base R with native piping.

Several functions are wrapper functions for existing base-R functions, adding support for native piping:

• p_cor(): a wrapper for cor()
• p_glm(): a wrapper for glm()
• p_lm(): a wrapper for lm()
• p_t.test(): a wrapper for t.test()
• p_table(): a wrapper for table()
• p_wilcox.test(): a wrapper for wilcox.test()

Other functions are wrapper functions for existing base-R features:

• bang(): is a wrapper for !, and is similar to not() from magrittr
• bracket(): is a wrapper for []
• dollar(): is a wrapper for $, and is similar to pull() from dplyr

Other functions mimic tidyverse functions:

• base_match(): mimics case_match(), but returns a factor and respects the user's desired order of groups
• base_when(): mimics case_when(), but returns a factor and respects the user's desired order of groups
• et(): mimics count()

Loading the package

Load the package:

library(baseverse)

Load the data

This vignette will draw from the built-in nhanes data:

data(nhanes)

Country of birth

Table the dmdborn4 variable:

nhanes |> p_table(dmdborn4)

Create a new, labelled version of dmdborn4:

nhanes<-nhanes |> transform(
  country=base_match(dmdborn4,'USA'=1,'Other'=2)
)

Table the new variable using p_table():

nhanes |> p_table(country)

Or, table the new variable using et():

nhanes |> et(country)

Notice that the USA group is listed first. This is, deliberately, hugely different behavior from case_match().

Total cholesterol

Summarize the lbxtc variable:

nhanes$lbxtc |> summary()

Or, using dollar():

nhanes |> dollar(lbxtc) |> summary()

Create a categorical variable for total cholesterol:

nhanes<-nhanes |>
  transform(
    cholesterol=base_when(
      'Desirable' = (lbxtc<200),
      'Borderline high' = (lbxtc>=200)&(lbxtc<240),
      'High' = (lbxtc>=240)
    )
  )

Table the new variable using p_table():

nhanes |> p_table(cholesterol)

Or, table the new variable using et():

nhanes |> et(cholesterol)

Notice that the Desirable group is listed first. This is, deliberately, hugely different behavior from case_when().

Linear regression

Fit a linear model for systolic blood pressure (bpxosy1):

model_1<-nhanes |> 
  p_lm(bpxosy1~ridageyr+country+lbxtc) 

Summarize the model:

model_1 |>
  summary()

Obtain 95% confidence intervals for the coefficients:

model_1 |>
  confint()

baseverse documentation built on April 29, 2026, 1:08 a.m.