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Drop Labels from a Table

library(tidyREDCap)

The Problem

The tidyREDCap package creates data sets with labelled columns.

tidyREDCap::import_instruments(
  url = "https://bbmc.ouhsc.edu/redcap/api/",
  token = Sys.getenv("REDCapR_test")
)

If you would like to see the labels on the data set demographics, you can use the RStudio function View(), as shown below.

View(demographics)

However, some functions do not work well with labeled variables.

library(skimr)  # for the skim() function
demographics |> skim()
Data summary
Name demographics
Number of rows 5
Number of columns 10
_______________________
Column type frequency:
Date 1
character 7
numeric 2
________________________
Group variables None

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
dob 0 1 1934-04-09 2003-08-30 1955-04-15 5

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
name_first 0 1 5 8 0 5 0
name_last 0 1 3 8 0 4 0
address 0 1 29 38 0 5 0
telephone 0 1 14 14 0 5 0
email 0 1 12 19 0 5 0
sex 0 1 4 6 0 2 0
demographics_complete 0 1 8 8 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
record_id 0 1 3.0 1.58 1 2 3 4 5 ▇▇▇▇▇
age 0 1 44.4 31.57 11 11 59 61 80 ▇▁▁▇▃

So you need a way to drop the label off of a variable or to drop all the labels from all the variables in a dataset.

The Solution

You can drop the label from a single variable with the drop_label() function. For example:

demographics_changed <- drop_label(demographics, "first_name")

You can drop all the labels using the drop_labels() function. For example:

demographics_without_labels <- drop_labels(demographics)

demographics_without_labels |> 
  skim()
Data summary
Name demographics_without_labe…
Number of rows 5
Number of columns 10
_______________________
Column type frequency:
character 7
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
name_first 0 1 5 8 0 5 0
name_last 0 1 3 8 0 4 0
address 0 1 29 38 0 5 0
telephone 0 1 14 14 0 5 0
email 0 1 12 19 0 5 0
sex 0 1 4 6 0 2 0
demographics_complete 0 1 8 8 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
record_id 0 1 3.0 1.58 1 2 3 4 5 ▇▇▇▇▇
dob 0 1 -56.0 11581.94 -13051 -6269 -5375 12121 12294 ▃▇▁▁▇
age 0 1 44.4 31.57 11 11 59 61 80 ▇▁▁▇▃

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