CRAN status R-CMD-check

Vector Look-Ups and Safer Sampling

A collection of utility functions that facilitate looking up vector values from a lookup table, annotate values in at table for clearer viewing, and support a safer approach to vector sampling, sequence generation, and aggregation.


You can install the released version of zmisc from CRAN with:


You can use pak to install the development version of zmisc from GitHub with:



In order to use the package, you generally want to attach it first:


Quick and easy value lookups

The functions lookup() and lookuper() are used to look up values from a lookup table, which can be supplied as a vector, a list, or a data.frame. The functions are in some ways similar to the Excel function VLOOKUP(), but are designed to work smoothly in an R workflow, in particular within pipes.

lookup: Lookup values from a lookup table

The lookup() function implements lookup of certain strings (such as variable names) from a lookup table which maps keys onto values (such as variable labels or descriptions).

The lookup table can be in the form of a two-column data.frame, in the form of a named vector, or in the form of a list. If the table is in the form of a data.frame, the lookup columns should be named name (for the key) and value (for the value). If the lookup table is in the form of a named vector or list, the name is used for the key, and the returned value is taken from the values in the vector or list.

Original values are returned if they are not found in the lookup table. Alternatively, a default can be specified for values that are not found. Note that an NA in x will never be found and will be replaced with the default value. To specify different defaults for values that are not found and for NA values in x, the default must be crafted manually to achieve this.

Any names in x are not included in the result.


fruit_lookup_vector <- c(a="Apple", b="Banana", c="Cherry")
lookup(letters[1:5], fruit_lookup_vector)
lookup(letters[1:5], fruit_lookup_vector, default = NA)

mtcars_lookup_data_frame <- data.frame(
  name = c("mpg", "hp", "wt"),
  value = c("Miles/(US) gallon", "Gross horsepower", "Weight (1000 lbs)"))
lookup(names(mtcars), mtcars_lookup_data_frame)

lookuper: Construct lookup function based on a specific lookup table

The lookuper() function returns a function equivalent to the lookup() function, except that instead of taking a lookup table as an argument, the lookup table is embedded in the function itself.

This can be very useful, in particular when using the lookup function as an argument to other functions that expect a function which maps character->character but do not offer a good way to pass additional arguments to that function.


lookup_fruits <- lookuper(list(a="Apple", b="Banana", c="Cherry"))

Safer sampling, sequencing and aggregation

The functions zample(), zeq(), and zingle() are intended to make your code less likely to break in mysterious ways when you encounter unexpected boundary conditions. The zample() and zeq() are almost identical to the sample() and seq() functions, but a bit safer.

zample: Sample from a vector in a safe way

The zample() function duplicates the functionality of sample(), with the exception that it does not attempt the (sometimes dangerous) user-friendliness of switching the interpretation of the first element to a number if the length of the vector is 1. zample() always treats its first argument as a vector containing elements that should be sampled, so your code won’t break in unexpected ways when the input vector happens to be of length 1.


# For vectors of length 2 or more, zample() and sample() are identical
set.seed(42); zample(7:11)
set.seed(42); sample(7:11)

# For vectors of length 1, zample() will still sample from the vector,
# whereas sample() will "magically" switch to interpreting the input
# as a number n, and sampling from the vector 1:n.
set.seed(42); zample(7)
set.seed(42); sample(7)

# The other arguments work in the same way as for sample()
set.seed(42); zample(7:11, size=13, replace=TRUE, prob=(5:1)^3)
set.seed(42); sample(7:11, size=13, replace=TRUE, prob=(5:1)^3)

# Of course, sampling more than the available elements without
# setting replace=TRUE will result in an error
set.seed(42); tryCatch(zample(7, size=2), error=wrap_error)

zeq: Generate sequence in a safe way

The zeq() function creates an increasing integer sequence, but differs from the standard one in that it will not silently generate a decreasing sequence when the second argument is smaller than the first. If the second argument is one smaller than the first it will generate an empty sequence, if the difference is greater, the function will throw an error.


# For increasing sequences, zeq() and seq() are identical

# If second argument equals first-1, an empty sequence is returned

# If second argument is less than first-1, the function throws an error
tryCatch(zeq(11,9), error=wrap_error)

zingle: Return the single (unique) value found in a vector

The zingle() function returns the first element in a vector, but only if all the other elements are identical to the first one (the vector only has a zingle value). If the elements are not all identical, it throws an error. The vector must contain at least one non-NA value, or the function errors out as well. This is especially useful in aggregations, when all values in a given group should be identical, but you want to make sure.


# If all elements are identical, all is good.
# The value of the element is returned.
zingle(c("Alpha", "Alpha", "Alpha"))

# If any elements differ, an error is thrown
tryCatch(zingle(c("Alpha", "Beta", "Alpha")), error=wrap_error)

if (require("dplyr", quietly=TRUE, warn.conflicts=FALSE)) {
  d <- tibble::tribble(
    ~id, ~name, ~fouls,
    1, "James", 3,
    2, "Jack",  2,
    1, "James", 4

  # If the data is of the correct format, all is good
  d %>%
    dplyr::group_by(id) %>%
    dplyr::summarise(name=zingle(name), total_fouls=sum(fouls))

if (require("dplyr", quietly=TRUE, warn.conflicts=FALSE)) {
  # If a name does not match its ID, we should get an error
  d[1,"name"] <- "Jammes"
    d %>%
      dplyr::group_by(id) %>%
      dplyr::summarise(name=zingle(name), total_fouls=sum(fouls))
  }, error=wrap_error)

Getting a better view on variables

The notate() function adds annotations to factor and labelled variables that make it easier to see both values and labels/levels when using the View() function

notate: Embed factor levels and value labels in values.

This function adds level/label information as an annotation to either factors or labelled variables. This function is called notate() rather than annotate() to avoid conflict with ggplot2::annotate(). It is a generic that can operate either on individual vectors or on a data.frame.

When printing labelled variables from a tibble in a console, both the numeric value and the text label are shown, but no variable labels. When using the View() function, only variable labels are shown but no value labels. For factors, there is no way to view the integer levels and values at the same time.

In order to allow the viewing of both variable and value labels at the same time, this function converts both factor and labelled variables to character, including both numeric levels (labelled values) and character values (labelled labels) in the output.


d <- data.frame(
  chr = letters[1:4],
  fct = factor(c("alpha", "bravo", "chrly", "delta")),
  lbl = ll_labelled(c(1, 2, 3, NA),
                    labels = c(one=1, two=2),
                    label = "A labelled vector")
dn <- notate(d)
# View(dn)