labelr (version 0.1.9)

add_m1_lab: Apply One Label to Multiple Values

Description

Apply a single variable value label to multiple values of a variable ("m1" is shorthand for "many values get one label").

Usage

add_m1_lab(
  data,
  vars,
  vals,
  lab,
  partial = FALSE,
  not.vars = NULL,
  max.unique.vals = 10,
  init = FALSE
)
am1l(
  data,
  vars,
  vals,
  lab,
  partial = FALSE,
  not.vars = NULL,
  max.unique.vals = 10,
  init = FALSE
)

Value

A data.frame, with new variable value labels added (call get_val_labs to see them), other provisional/default labelr label information added, and previous user-added labelr label information preserved.

Arguments

data

a data.frame.

vars

a character vector that corresponds to the name(s) of one or more variables to which value labels will be added.

vals

a vector of distinct values of the actual variable, each of which is to be associated with the single label supplied to the lab argument. Note: NA and other "irregular" (e.g., NaN, Inf) values all are automatically assigned the label "NA", and this cannot be overridden.

lab

a single distinct label that will be associated with all values specified in your vals argument. Note: NA and other "irregular" (e.g., NaN, Inf) values are automatically assigned the label "NA" and may not be assigned another label.

partial

To apply the same value labeling scheme to many variables at once, you can provide those variable names explicitly (e.g., vars = c("x1","x2", "x3") or vars = paste0("x", 1:3), or you can provide a substring only and set partial = TRUE (default is FALSE). For example, to apply the same labeling scheme to vars "x1", "x2" ... sequentially through "x10", you could use vars = c("x"), along with partial = TRUE. Be careful with this, as it also will attempt to apply the scheme to "sex" or "tax.bracket", etc.

not.vars

use of the partial argument can result in situations where you inadvertently attempt to value-label a variable. For example, if vars="x" and partial=TRUE, then add_m1_lab will attempt to label not only "x1", "x2","x3", and "x4", but also "sex", "tax.bracket.", and other "x"-containing variable names. Use of not.vars allows you to indicate variables that match your vars argument that you do not wish to attempt to value-label. Note that not.vars gets priority: setting vars="x", partial=TRUE, and not.vars="x" is tantamount to telling add_m1_lab() that you actually do not wish to label any of the variables that you specified in vars, resulting in no variables receiving value labels.

max.unique.vals

add_m1_lab() will not assign value labels to non- integer (i.e., decimal-having) numeric variables. The max.unique.vals argument further constrains the variables that may receive value labels to those whose total unique values do not exceed the integer value supplied to this argument. Note that labelr sets a hard ceiling of 5000 on the total number of unique value labels that any variable is permitted to have under any circumstance, as labelr is primarily intended for interactive use with moderately-sized (<=~1M-row) data.frames.

init

assign placeholder labels for variables that lack decimals and meet the max.unique.vals threshold.

Details

' add_m1_lab(andadd1m1) allows the user to assign the same value label to multiple distinct values of a variable ("m1" is short for "many-to-one"). This is in contrast to add_val_labsandadd_val1`, which require a strict one-to-one mapping of distinct variable values and distinct value labels.

Note 1: Each call to add_m1_lab accepts only one value label, which may be applied to multiple distinct values of the specified column(s). Additional labels can be applied to other values of the same column(s) by making additional calls to add_m1_lab (see the example).

Note 2: am1l is a compact alias for add_m1_lab: they do the same thing, and the former is easier to type

Examples

df <- mtcars

df <- add_m1_lab(df,
  vars = "carb",
  vals = 1:3,
  lab = "<=3",
  max.unique.vals = 10
)

df <- add_m1_lab(df,
  vars = "carb",
  vals = c(4, 6, 8),
  lab = ">=4",
  max.unique.vals = 10
)

get_val_lab1(df, carb)

head(use_val_labs(df), 8) # they're there