Modal counts and frequencies


In addition to finding a vector’s modes, you might be interested in some metadata about them:

This vignette lays out all the functions for modal metadata. In the end, it talks about a special feature of these functions, the max_unique argument.

Maximal number of unique values

All of moder’s functions for metadata, such as mode_is_trivial() and mode_count_range(), have a max_unique argument. It allows you to state how many unique values your data can have at the maximum. Why is this important? The two functions care about possible modes beyond the known values. In other words, their results might depend on whether or not the NAs can mask modal values that don’t even occur among the known values! If that is possible, it presents an additional source of uncertainty.

Conversely, max_unique limits the possible number of such wildcard modes. Specify it as an integer that is the maximal number of unique values. If there can be no other values than those already known, specify max_unique as "known" instead. Always use "known" if you have factor data or you will get a warning. (The idea behind factors is that all possible values are known at the outset.)

Note that this argument does not represent an analytical decision but simply conveys your knowledge of the data to the computer. There is no meaningful choice to make: If the maximum number of unique values is known, you must specify max_unique; if not, you must not do so. Otherwise, you risk incorrect results if any values are missing. The default is NULL because the baseline assumption is always that nothing is known about missing values except for their number.

Below is an example. If two of the NAs represent 8 and the other three stand for a third value, all values appear with the same frequency. In this case, all values would trivially be modes in the sense of mode_is_trivial(). This scenario is not certain at all, but it can’t be ruled out either, so the function returns NA. As mode_count_range() shows, there could be three modes at most. (The minimum is always one if any values are missing.)

x1 <- c(7, 7, 7, 8, NA, NA, NA, NA, NA)
#> [1] NA
#> [1] 1 3

The picture is different if we know that each missing value must represent a known value, i.e., 7 or 8. Even if two NAs stand for 8, the other three can’t be evenly distributed across 7 and 8, so one of these values must be more frequent than the other one. This makes the mode nontrivial. Also, there can only be one mode, so both the minimal and maximal mode counts are 1.

#> [1]  7  7  7  8 NA NA NA NA NA
mode_is_trivial(x1, max_unique = "known")
#> [1] FALSE
mode_count_range(x1, max_unique = "known")
#> [1] 1 1

Three more functions have a max_unique parameter: mode_count(), mode_frequency(), and mode_frequency_range(). However, this only matters for corner cases. See this Github issue.


Härdle, Wolfgang Karl, Sigbert Klinke, and Bernd Rönz. 2015. Introduction to Statistics: Using Interactive MM*Stat Elements. Cham: Springer International Publishing.
Manikandan, S. 2011. “Measures of Central Tendency: Median and Mode.” Journal of Pharmacology and Pharmacotherapeutics 2 (3): 214–15.