manydata (version 1.0.3)

resolving: Resolving multiple observations of the same variable into one

Description

This family of functions provides row-wise summarization for data frames or tibbles, returning a single value per row based on specified columns. They are useful for tasks like extracting typical or summary values from multiple variables, simplifying wide data structures, and imputing representative values.

Usage

resolve_unite(.data, vars, na.rm = TRUE)
resolve_coalesce(.data, vars)
resolve_min(.data, vars, na.rm = TRUE)
resolve_max(.data, vars, na.rm = TRUE)
resolve_random(.data, vars, na.rm = TRUE)
resolve_precision(.data, vars)
resolve_mean(.data, vars, na.rm = TRUE)
resolve_mode(.data, vars, na.rm = TRUE)
resolve_median(.data, vars, na.rm = TRUE)
resolve_consensus(.data, vars, na.rm = TRUE)

Arguments

.data

A data frame or tibble containing the variables.

vars

A vector of variables from .data to be resolved or converged. If this argument is left unspecified, then all variables will be merged together.

na.rm

Logical whether missing values (NAs) should be removed before operation of the function. Note that unlike how the na.rm argument operates in functions in base R, e.g. max(), here the default is TRUE.

Unite

Uniting returns all the unique values as a set, separated by commas and contained within braces. Note that uniting always returns a character/string vector, which enables it to accommodate different classes of variables. The order of the values reflects their first appearance; that is, they are not ordered by increasing value.

Coalesce

Coalescing returns a vector of the first non-missing values found when reading the variables from left to right. That is, missing values in the first vector may be filled by observations in the second vector, or later vectors if the second vector also misses an observation for that cell. Variables can be reordered manually.

Min and Max

These functions return a vector containing each row's minimum or maximum value. Note that these functions work not only on numeric and date vectors, but also on character string vectors. For character data, these functions will return the shortest or longest strings, respectively, in each row.

Random

This function returns a vector of values selected randomly from among the values contained in each row. Note that by default na.rm = TRUE, which means that missing data will not be selected at random by default, which can also change the probability distribution by each row. Where na.rm = FALSE, the probability of each value being selected is uniform.

Precision

This function returns a vector that maximises the precision of the values in each row. For numeric vectors, precision is expressed in significant digits, such that 1.01 would be more precise than 1. For character vectors, precision is expressed in terms of the character length proportional to the max character length in the row. This applies also to messydates, meaning precision is expressed in the lowest level date component specified, such that 2008-10 would be more precise than 2008, and 2008-10-10 would be more precise still.

Mean and median

These functions return a vector of the means or medians, respectively, of the values in each row.

Consensus

This function returns a vector of consensus values, i.e. where there is no variation in values by each row. If the values (excluding missing values by default) are not equivalent, then an NA is returned for that row.

Examples

test <- data.frame(preferred_dataset = c(1,6,NA), 
                   more_comprehensive = c(1,3,3), 
                   precise_where_available = c(NA,3.3,4.1))
test
resolve_unite(test)
resolve_coalesce(test)
resolve_min(test)
resolve_max(test)
resolve_random(test)
resolve_precision(test)
resolve_mean(test)
resolve_mode(test)
resolve_median(test)
resolve_consensus(test)