labelr (version 0.1.9)

use_name_labs: Swap Name Labels for Variable Names

Description

Replace data.frame variable names with their corresponding name labels (previously assigned using add_name_labs).

Usage

use_name_labs(data, vars = NULL)
unl(data, vars = NULL)

Value

A data.frame, with (all or the select) name labels swapped in for the variable names.

Arguments

data

the data.frame whose name labels you wish to "use" (aka swap, turn on, activate, etc.).

vars

the names of the columns (variables) to which name labels will be applied. If NULL, all available name labels will be swapped in for the corresponding variable (column) names.

Details

Note: unl is a compact alias for use_name_labs: they do the same thing, and the former is easier to type

use_name_labs works with add_name_labs, get_name_labs, use_var_names, and drop_name_labs, to facilitate the creation, accessing, substitution (swap out, swap back in), and destruction of variable name labels for variable names. Each variable (column) of a data.frame can receive one and only one "name label," which typically is a noun phrase that expounds the meaning of contents of the variable's name (e.g., "Weight in ounces at birth" might be a name label for a column called "wgt"). add_name_labs associates these labels with variables in a data.frame, use_name_labs applies or "turns on" those name labels, i.e., swaps out variable names for corresponding labels, and you can assign the name-label-swapped data.frame to an object, or you may use it strictly for display purposes (e.g., head(use_name_labs(df), 5)). Because they are intended to be more descriptive than column names, they tend to be more verbose -- possibly so verbose as to undermine their value or convenience for anything other than an on-demand "What is this variable again?" cheat sheet via get_name_labs(). That said, this may have some uses (see examples).

Examples

# variable names and their labels
names_labs_vec <- c(
  "mpg" = "Miles/(US) gallon",
  "cyl" = "Number of cylinders",
  "disp" = "Displacement (cu.in.)",
  "hp" = "Gross horsepower",
  "drat" = "Rear axle ratio",
  "wt" = "Weight (1000 lbs)",
  "qsec" = "1/4 mile time",
  "vs" = "Engine (0 = V-shaped, 1 = straight)",
  "am" = "Transmission (0 = automatic, 1 = manual)",
  "gear" = "Number of forward gears",
  "carb" = "Number of carburetors"
)

# add the above name labeling scheme
mt2 <- add_name_labs(mtcars, name.labs = names_labs_vec)

# use the name labeling scheme (i.e., swap out column/variable names for
# ...their name labels)
mt2 <- use_name_labs(mt2)

# compare these two - concision vs. informativeness
as.data.frame(sapply(mtcars, mean))
as.data.frame(sapply(mt2, mean))

# compare the plot labeling we get with mtcars
with(mtcars, hist(mpg))

get_name_labs(mt2) # get the lab of interest, and paste it into `` below
with(mt2, hist(`Miles/(US) gallon`))

# regression - this is easier to type
lm(mpg ~ cyl, data = mtcars)

# regression with name labs - more painful to type/copy-paste, but may be
# ...the more informative labels are worth it (your mileage/mpg may vary)
# let's see the name labels, then copy paste mpg and cyl labs from console to
# ...where we need them in the lm() call
get_name_labs(mt2) # copy from this call's console output
lm(`Miles/(US) gallon` ~ `Number of cylinders`, data = mt2) # paste into `` here

# same results, more informative labels, more steps/hand-jamming pain
# can also turn them on (semi) permanently
# ...then you can use mt2$ syntax in RStudio, and RStudio will autocomplete,
# then you can backspace delete the "mt2$"
# if you like
mt2 <- use_name_labs(mt2)
lm(`Miles/(US) gallon` ~ `Number of cylinders`, data = mt2)
lm(mpg ~ cyl, data = use_var_names(mt2))

# let's turn them back off
mt2 <- use_var_names(mt2) # use_var_names() as "undo" of use_name_labs()

# back to our previous variable names
head(mt2)
# even with name labels "off," mt2 retains labelr attribute meta-data
# ...which we can strip away using strip_labs()
identical(strip_labs(mt2), mtcars) # and we're back