# c. Weighting (declared) values

For the examples in this vignette, the following data frame is created:

    library(declared)

    n <- 1234
    set.seed(n)
    dfm <- data.frame(
      Area = declared(
        sample(1:2, n, replace = TRUE, prob = c(0.45, 0.55)),
        labels = c("Rural" = 1, "Urban" = 2)
      ),
      Gender = declared(
        sample(1:2, n, replace = TRUE, prob = c(0.55, 0.45)),
        labels = c("Males" = 1, "Females" = 2)
      ),
      Opinion = declared(
        sample(c(1:5, NA, -91), n, replace = TRUE),
        labels = c(
          "Very bad" = 1, "Bad" = 2, "Neither" = 3,
          "Good" = 4, "Very good" = 5, "Don't know" = -91
        ),
        na_values = -91
      ),
      Age = sample(18:90, n, replace = TRUE),
      Children = sample(0:5, n, replace = TRUE)
    )

One of the most interesting applications to make use of the declared missing values are the tables of frequencies. The base function `table()` ignores missing values by default, but they can be revealed by using the `useNA` argument:

    table(dfm$Opinion, useNA = "ifany")
    #>
    #>  Very bad       Bad   Neither      Good Very good      <NA>
    #>       180       170       188       171       162       363

However, it does not differentiate between empty and declared missing values. Since “Opinion” is the equivalent of a categorical variable, this can be improved through a custom built coercion to the base `factor` class:

    table(as.factor(undeclare(dfm$Opinion)), useNA = "ifany")
    #>
    #> Don't know   Very bad        Bad    Neither       Good  Very good       <NA>
    #>        180        180        170        188        171        162        183

The dedicated function `w_table()` does the same thing by automatically recognizing objects of class `"declared"`, additionally printing more detailed information:

    w_table(dfm$Opinion, values = TRUE)
    #>
    #>                 fre    rel   per   vld   cpd
    #>                -----------------------------
    #>   Very bad   1  180  0.146  14.6  20.7  20.7
    #>        Bad   2  170  0.138  13.8  19.5  40.2
    #>    Neither   3  188  0.152  15.2  21.6  61.8
    #>       Good   4  171  0.139  13.9  19.6  81.4
    #>  Very good   5  162  0.131  13.1  18.6 100.0
    #>          ------
    #> Don't know -91  180  0.146  14.6
    #>             NA  183  0.148  14.8
    #>                -----------------------------
    #>                1234  1.000 100.0

The prefix `w_` from the function name stands for “weighted”, this being another example of functionality where the declared missing values play a different role than the empty, base NA missing values.

It is important to differentiate between frequency weights, on one hand, and other probability based, post-stratification weights on one other, the later being thoroughly treated by the specialized package survey. The `w_` family of functions are solely dealing with frequency weights, to allow corrections in descriptive statistics, such as the tables of frequencies and other similar descriptive measures for both categorical and numeric variables.

To exemplify, a frequency weights variable is constructed, to correct for the distributions of gender by males and females, as well as the theoretical distribution by residential areas differentiating between urban and rural settlements.

    # Observed proportions
    op <- with(dfm, proportions(table(Gender, Area)))

    # Theoretical / population proportions:
    # 53% Rural, and 50% Females
    weights <- rep(c(0.53, 0.47), each = 2) * rep(0.5, 4) / op

    dfm$fweight <- weights[
      match(10 * dfm$Area + dfm$Gender, c(11, 12, 21, 22))
    ]

The updated frequency table, this time using the frequency weights, can be constructed by passing the weights to the argument `wt`:

    with(dfm, w_table(Opinion, wt = fweight, values = TRUE))
    #>
    #>                 fre    rel   per   vld   cpd
    #>                -----------------------------
    #>   Very bad   1  179  0.145  14.5  20.5  20.5
    #>        Bad   2  167  0.135  13.5  19.2  39.7
    #>    Neither   3  187  0.152  15.2  21.4  61.1
    #>       Good   4  171  0.139  13.9  19.6  80.7
    #>  Very good   5  168  0.136  13.6  19.3 100.0
    #>          ------
    #> Don't know -91  179  0.145  14.5
    #>             NA  183  0.148  14.8
    #>                -----------------------------
    #>                1234  1.000 100.0

Except for the empty NA values, for which the weights cannot be applied, almost all other frequencies (including the one for the declared missing value -91) are now updated by applying the weights. This shows that, despite being interpreted as “missing” values, the declared ones can and should also be weighted, with a very useful result. Other versions of weighted frequencies do exist in R, but a custom one was needed to identify (and weight) the declared missing values.

In the same spirit, many other similar functions are provided such as `w_mean()`, `w_var()`, `w_sd()` etc., and the list will likely grow in the future. They are similar to the base package counterparts, with a single difference: the argument `na.rm` is activated by default, with or without weighting. This is an informed decision about which users are alerted in the functions’ respective help pages.

The package declared was built with the specific intention to provide a lightweight, zero dependency resource in the R ecosystem. It contains an already extensive, robust and ready to use functionality that duly takes into account the difference between empty and declared missing values.

It extends base R and opens up data analysis possibilities without precedent. By providing generic classes for all its objects and functions, package declared is easily extensible to any type of object, for both creation and coercion to class `"declared"`.