Behind the dialogs.

The structure of a command

This is the political-interest example from the manual:

using(
  ess,
  wtable(B1_polintr, vlabel = TRUE)
)

admisc’s using() evaluates the analysis within ess, so the function can refer to columns by their names. declared’s wtable() uses the variable’s labels and missing-value metadata. The vlabel argument requests its descriptive label.

Named arguments control options. TRUE and FALSE are logical values; quoted text is a string. A formula such as F3_agea ~ F2_gndr puts the response on the left and the grouping variable on the right. Commands generally omit options that match a function’s defaults.

Packages and functions

Desktop DialogR is a graphical layer on top of R: install R first, then install the packages through the Packages menu. The application uses CRAN and development repositories. Packages must be available in the R installation selected by DialogR. WebR supplies its own browser-compatible R runtime and library.

The desktop setup also needs httpgd, jsonlite, askpass, later, and digest. Excel import uses readxl. If it is missing, install it in the active R library:

install.packages("readxl")

When reusing dialog commands in a fresh R session, load the required packages and dataset first:

library(admisc)
library(declared)
library(DDIwR)
library(statistics)
ess <- readRDS(file.choose())

For individual function documentation, use the R help system, for example help("wtable", package = "declared").

Why the declared class matters

The teaching file already stores the analysis variables as declared. Their labels and na_values definitions remain attached to the variables. Do not coerce them to ordinary factors or numeric vectors merely to reproduce the screenshots: that changes the data contract the examples demonstrate.

For B1_polintr, values 1–4 are substantive responses and 7, 8, 9 are declared missing. The missing categories keep distinct labels even though valid numerical calculations exclude them. B7_trstlgl instead has valid values 0–10 and declared missing codes 77, 88, 99.

Selection expressions

The subset dialog accepts a condition. Declared variables support comparisons using their value labels:

F2_gndr == "Female"
F3_agea >= 45
F2_gndr == "Female" & F3_agea > 45

Use == for equality, != for inequality, & for “and”, and | for “or”. Conditions evaluating to missing do not select a case. The dialog builds the surrounding subset call and assignment:

essf <- subset(ess, F2_gndr == "Female")

Filters, grouping, and weights

using(
  subset(ess, F2_gndr == "Female"),
  wtable(B1_polintr, wt = fweight, vlabel = TRUE)
)

using(ess, wtable(B1_polintr), split.by = F2_gndr)

The support table in the manual states which dialogs use each setting. Dataset state is not a global promise that every R function will be weighted or split.

Assignments and recoding

inside(
  ess,
  agerec <- recode(F3_agea, rules = "lo:44=1; 45:hi=2")
)

The assignment arrow names a result; inside() updates the named dataset. Semicolons separate recoding rules. Check the result’s labels and missing definitions after a transformation. Recoding and sorting use the underlying dataset rather than the filtered analysis view.

Summary commands

using(ess, wsummary(B7_trstlgl))
using(ess, wmeasures(B7_trstlgl, what = c("mean", "sd")))

A single measure uses its own function; several use wmeasures(). With multiple selected variables the dialog subsets the columns and uses a dot to represent that selection. If this excludes a grouping or weight column needed by the analysis, use individual-variable analyses or adapt a script to retain those columns. The standalone range() call is not weighted.

Tests and multiple results

using(ess, t.test(F3_agea, alternative = "two.sided", mu = 50, conf.level = 0.95))

using(ess, t.testhv(F3_agea ~ F2_gndr))

using(ess, {
  print(anovahv(F3_agea ~ F14_domicil, numsum = TRUE))
  pairwise.t.test(F3_agea, F14_domicil, p.adjust.method = "bonferroni")
})

Braces group statements. print() displays an intermediate result before the next statement runs. The pairwise call uses its own defaults; it does not inherit the ANOVA variance or confidence settings. These unweighted teaching examples do not establish a complete complex-survey analysis.

Current exact-test limitation

The contingency dialog currently emits fischer.exact(), which is not the standard R function. The ordinary R alternative is stats::fisher.test() applied to a suitable count table:

using(ess, {
  .table <- wtable(B1_polintr, F2_gndr)
  stats::fisher.test(.table)
})

Check the table, test assumptions, and computation requirements before using this alternative.

Optional Excel metadata sheets

DDIwR reads the first sheet as data. Supply both a variables sheet and a values sheet (also accepted as codes) to use the metadata route. The variables sheet matches name to label. The values sheet has variable, value (or code), label, and missing columns; y marks a declared missing value. Names must match the data sheet. The import dialog does not expose a sheet selector.