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feat: the missingness module was overhauled to include two different analysis methods and a better, standalone module
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Andreas Gammelgaard Damsbo 2025-12-11 09:34:40 +01:00
commit af523edc00
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25 changed files with 1049 additions and 720 deletions
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27
R/ui_elements.R
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@ -452,26 +452,15 @@ ui_elements <- function(selection) {
data_correlations_ui(id = "correlations", height = 600)
)
),
bslib::nav_panel(
title = i18n$t("Missings"),
icon = bsicons::bs_icon("x-circle"),
bslib::layout_sidebar(
sidebar = bslib::sidebar(
bslib::accordion(
id = "acc_mis",
open = "acc_chars",
multiple = FALSE,
bslib::accordion_panel(
value = "acc_pan_mis",
title = "Settings",
icon = bsicons::bs_icon("x-circle"),
shiny::uiOutput("missings_var"),
shiny::helpText(i18n$t("To consider if data is missing by random, choose the outcome/dependent variable (only variables with any missings are available). If there is a significant difference across other variables depending on missing observations, it may not be missing at random."))
)
)
do.call(
bslib::nav_panel,
c(
list(
title = i18n$t("Missings"),
icon = bsicons::bs_icon("x-circle")
),
validation_ui("validation_mcar"),
data_missings_ui(id = "missingness")
data_missings_ui(id = "missingness",
validation_ui("validation_mcar"))
)
)
),