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updated translation strings
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Andreas Gammelgaard Damsbo 2025-09-11 15:21:04 +02:00
parent 013fc19d04
commit 136480ca3d
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7 changed files with 184 additions and 80 deletions
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19
R/correlations-module.R
View file

@ -16,9 +16,13 @@ data_correlations_ui <- function(id, ...) {
#'
#' @param id id
#' @param data data
#' @param color.main main color
#' @param color.sec secondary color
#' @param include.class character vector of classes to include. Default is NULL
#' @param cutoff numeric
#' @param warning_str Character string. Exposed to allow dynamic translations
#' @param warning_no_str Character string. Exposed to allow dynamic translations
#' @param and_strCharacter string. Exposed to allow dynamic translations
#' @param ... arguments passed to toastui::datagrid
#'
#' @name data-correlations
@ -28,6 +32,9 @@ data_correlations_server <- function(id,
data,
include.class = NULL,
cutoff = .7,
warning_str = i18n$t("The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent."),
warning_no_str = i18n$t("No variables have a correlation measure above the threshold."),
and_str = i18n$t("and"),
...) {
shiny::moduleServer(
id = id,
@ -59,17 +66,17 @@ data_correlations_server <- function(id,
shiny::req(cutoff)
pairs <- correlation_pairs(rv$data(), threshold = cutoff())
more <- ifelse(nrow(pairs) > 1, "from each pair ", "")
more <- ifelse(nrow(pairs) > 1, i18n$t("from each pair"), "")
if (nrow(pairs) == 0) {
out <- glue::glue("No variables have a correlation measure above the threshold.")
out <- glue::glue(warning_no_str)
} else {
out <- pairs |>
apply(1, \(.x){
glue::glue("'{.x[1]}'x'{.x[2]}'({round(as.numeric(.x[3]),2)})")
glue::glue("'{.x[1]}'x'{.x[2]}' ({round(as.numeric(.x[3]),2)})")
}) |>
(\(.x){
glue::glue("The following variable pairs are highly correlated: {sentence_paste(.x)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.")
glue::glue(warning_str)
})()
}
out

62
R/data_plots.R
View file

@ -20,7 +20,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
title = "Creating plot",
icon = bsicons::bs_icon("graph-up"),
shiny::uiOutput(outputId = ns("primary")),
shiny::helpText('Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.'),
shiny::helpText(i18n$t('Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.')),
shiny::tags$br(),
shiny::uiOutput(outputId = ns("type")),
shiny::uiOutput(outputId = ns("secondary")),
@ -28,19 +28,19 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
shiny::br(),
shiny::actionButton(
inputId = ns("act_plot"),
label = "Plot",
label = i18n$t("Plot"),
width = "100%",
icon = shiny::icon("palette"),
disabled = FALSE
),
shiny::helpText('Adjust settings, then press "Plot".')
shiny::helpText(i18n$t('Adjust settings, then press "Plot".'))
),
bslib::accordion_panel(
title = "Download",
icon = bsicons::bs_icon("download"),
shinyWidgets::noUiSliderInput(
inputId = ns("height_slide"),
label = "Plot height (mm)",
label = i18n$t("Plot height (mm)"),
min = 50,
max = 300,
value = 100,
@ -58,7 +58,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
# ),
shinyWidgets::noUiSliderInput(
inputId = ns("width"),
label = "Plot width (mm)",
label = i18n$t("Plot width (mm)"),
min = 50,
max = 300,
value = 100,
@ -68,7 +68,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
),
shiny::selectInput(
inputId = ns("plot_type"),
label = "File format",
label = i18n$t("File format"),
choices = list(
"png",
"tiff",
@ -82,7 +82,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
# Button
shiny::downloadButton(
outputId = ns("download_plot"),
label = "Download plot",
label = i18n$t("Download plot"),
icon = shiny::icon("download")
)
)
@ -225,8 +225,8 @@ data_visuals_server <- function(id,
inputId = ns("primary"),
col_subset = names(data())[sapply(data(), data_type) != "text"],
data = data,
placeholder = "Select variable",
label = "Response variable",
placeholder = i18n$t("Select variable"),
label = i18n$t("Response variable"),
multiple = FALSE
)
})
@ -263,7 +263,7 @@ data_visuals_server <- function(id,
vectorSelectInput(
inputId = ns("type"),
selected = NULL,
label = shiny::h4("Plot type"),
label = shiny::h4(i18n$t("Plot type")),
choices = Reduce(c, plots_named),
multiple = FALSE
)
@ -291,12 +291,12 @@ data_visuals_server <- function(id,
inputId = ns("secondary"),
data = data,
selected = cols[1],
placeholder = "Please select",
label = if (isTRUE(rv$plot.params()[["secondary.multi"]])) "Additional variables" else "Secondary variable",
placeholder = i18n$t("Please select"),
label = if (isTRUE(rv$plot.params()[["secondary.multi"]])) i18n$t("Additional variables") else i18n$t("Secondary variable"),
multiple = rv$plot.params()[["secondary.multi"]],
maxItems = rv$plot.params()[["secondary.max"]],
col_subset = cols,
none_label = "No variable"
none_label = i18n$t("No variable")
)
})
@ -305,8 +305,8 @@ data_visuals_server <- function(id,
columnSelectInput(
inputId = ns("tertiary"),
data = data,
placeholder = "Please select",
label = "Grouping variable",
placeholder = i18n$t("Please select"),
label = i18n$t("Grouping variable"),
multiple = FALSE,
col_subset = c(
"none",
@ -319,7 +319,7 @@ data_visuals_server <- function(id,
input$secondary
)
),
none_label = "No stratification"
none_label = i18n$t("No stratification")
)
})
@ -335,7 +335,7 @@ data_visuals_server <- function(id,
ter = input$tertiary
)
shiny::withProgress(message = "Drawing the plot. Hold tight for a moment..", {
shiny::withProgress(message = i18n$t("Drawing the plot. Hold tight for a moment.."), {
rv$plot <- rlang::exec(
create_plot,
!!!append_list(
@ -362,7 +362,7 @@ data_visuals_server <- function(id,
output$code_plot <- shiny::renderUI({
shiny::req(rv$code)
prismCodeBlock(paste0("#Plotting\n", rv$code))
prismCodeBlock(paste0(i18n$t("#Plotting\n"), rv$code))
})
shiny::observeEvent(
@ -407,7 +407,7 @@ data_visuals_server <- function(id,
plot <- rv$plot[[1]]
}
# browser()
shiny::withProgress(message = "Drawing the plot. Hold on for a moment..", {
shiny::withProgress(message = i18n$t("Drawing the plot. Hold on for a moment.."), {
ggplot2::ggsave(
filename = file,
plot = plot,
@ -486,8 +486,8 @@ supported_plots <- function() {
list(
plot_hbars = list(
fun = "plot_hbars",
descr = "Stacked horizontal bars",
note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
descr = i18n$t("Stacked horizontal bars"),
note = i18n$t("A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars"),
primary.type = c("dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -496,8 +496,8 @@ supported_plots <- function() {
),
plot_violin = list(
fun = "plot_violin",
descr = "Violin plot",
note = "A modern alternative to the classic boxplot to visualise data distribution",
descr = i18n$t("Violin plot"),
note = i18n$t("A modern alternative to the classic boxplot to visualise data distribution"),
primary.type = c("datatime", "continuous", "dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -514,8 +514,8 @@ supported_plots <- function() {
# ),
plot_sankey = list(
fun = "plot_sankey",
descr = "Sankey plot",
note = "A way of visualising change between groups",
descr = i18n$t("Sankey plot"),
note = i18n$t("A way of visualising change between groups"),
primary.type = c("dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -524,8 +524,8 @@ supported_plots <- function() {
),
plot_scatter = list(
fun = "plot_scatter",
descr = "Scatter plot",
note = "A classic way of showing the association between to variables",
descr = i18n$t("Scatter plot"),
note = i18n$t("A classic way of showing the association between to variables"),
primary.type = c("datatime", "continuous"),
secondary.type = c("datatime", "continuous", "categorical"),
secondary.multi = FALSE,
@ -534,8 +534,8 @@ supported_plots <- function() {
),
plot_box = list(
fun = "plot_box",
descr = "Box plot",
note = "A classic way to plot data distribution by groups",
descr = i18n$t("Box plot"),
note = i18n$t("A classic way to plot data distribution by groups"),
primary.type = c("datatime", "continuous", "dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -544,8 +544,8 @@ supported_plots <- function() {
),
plot_euler = list(
fun = "plot_euler",
descr = "Euler diagram",
note = "Generate area-proportional Euler diagrams to display set relationships",
descr = i18n$t("Euler diagram"),
note = i18n$t("Generate area-proportional Euler diagrams to display set relationships"),
primary.type = c("dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = TRUE,

2
R/hosted_version.R
View file

@ -1 +1 @@
hosted_version <- function()'v25.8.3-250910'
hosted_version <- function()'v25.8.3-250911'

87
inst/apps/FreesearchR/app.R
View file

@ -228,9 +228,13 @@ data_correlations_ui <- function(id, ...) {
#'
#' @param id id
#' @param data data
#' @param color.main main color
#' @param color.sec secondary color
#' @param include.class character vector of classes to include. Default is NULL
#' @param cutoff numeric
#' @param warning_str Character string. Exposed to allow dynamic translations
#' @param warning_no_str Character string. Exposed to allow dynamic translations
#' @param and_strCharacter string. Exposed to allow dynamic translations
#' @param ... arguments passed to toastui::datagrid
#'
#' @name data-correlations
@ -240,6 +244,9 @@ data_correlations_server <- function(id,
data,
include.class = NULL,
cutoff = .7,
warning_str = i18n$t("The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent."),
warning_no_str = i18n$t("No variables have a correlation measure above the threshold."),
and_str = i18n$t("and"),
...) {
shiny::moduleServer(
id = id,
@ -271,17 +278,17 @@ data_correlations_server <- function(id,
shiny::req(cutoff)
pairs <- correlation_pairs(rv$data(), threshold = cutoff())
more <- ifelse(nrow(pairs) > 1, "from each pair ", "")
more <- ifelse(nrow(pairs) > 1, i18n$t("from each pair"), "")
if (nrow(pairs) == 0) {
out <- glue::glue("No variables have a correlation measure above the threshold.")
out <- glue::glue(warning_no_str)
} else {
out <- pairs |>
apply(1, \(.x){
glue::glue("'{.x[1]}'x'{.x[2]}'({round(as.numeric(.x[3]),2)})")
glue::glue("'{.x[1]}'x'{.x[2]}' ({round(as.numeric(.x[3]),2)})")
}) |>
(\(.x){
glue::glue("The following variable pairs are highly correlated: {sentence_paste(.x)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.")
glue::glue(warning_str)
})()
}
out
@ -1633,7 +1640,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
title = "Creating plot",
icon = bsicons::bs_icon("graph-up"),
shiny::uiOutput(outputId = ns("primary")),
shiny::helpText('Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.'),
shiny::helpText(i18n$t('Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.')),
shiny::tags$br(),
shiny::uiOutput(outputId = ns("type")),
shiny::uiOutput(outputId = ns("secondary")),
@ -1641,19 +1648,19 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
shiny::br(),
shiny::actionButton(
inputId = ns("act_plot"),
label = "Plot",
label = i18n$t("Plot"),
width = "100%",
icon = shiny::icon("palette"),
disabled = FALSE
),
shiny::helpText('Adjust settings, then press "Plot".')
shiny::helpText(i18n$t('Adjust settings, then press "Plot".'))
),
bslib::accordion_panel(
title = "Download",
icon = bsicons::bs_icon("download"),
shinyWidgets::noUiSliderInput(
inputId = ns("height_slide"),
label = "Plot height (mm)",
label = i18n$t("Plot height (mm)"),
min = 50,
max = 300,
value = 100,
@ -1671,7 +1678,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
# ),
shinyWidgets::noUiSliderInput(
inputId = ns("width"),
label = "Plot width (mm)",
label = i18n$t("Plot width (mm)"),
min = 50,
max = 300,
value = 100,
@ -1681,7 +1688,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
),
shiny::selectInput(
inputId = ns("plot_type"),
label = "File format",
label = i18n$t("File format"),
choices = list(
"png",
"tiff",
@ -1695,7 +1702,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
# Button
shiny::downloadButton(
outputId = ns("download_plot"),
label = "Download plot",
label = i18n$t("Download plot"),
icon = shiny::icon("download")
)
)
@ -1838,8 +1845,8 @@ data_visuals_server <- function(id,
inputId = ns("primary"),
col_subset = names(data())[sapply(data(), data_type) != "text"],
data = data,
placeholder = "Select variable",
label = "Response variable",
placeholder = i18n$t("Select variable"),
label = i18n$t("Response variable"),
multiple = FALSE
)
})
@ -1876,7 +1883,7 @@ data_visuals_server <- function(id,
vectorSelectInput(
inputId = ns("type"),
selected = NULL,
label = shiny::h4("Plot type"),
label = shiny::h4(i18n$t("Plot type")),
choices = Reduce(c, plots_named),
multiple = FALSE
)
@ -1904,12 +1911,12 @@ data_visuals_server <- function(id,
inputId = ns("secondary"),
data = data,
selected = cols[1],
placeholder = "Please select",
label = if (isTRUE(rv$plot.params()[["secondary.multi"]])) "Additional variables" else "Secondary variable",
placeholder = i18n$t("Please select"),
label = if (isTRUE(rv$plot.params()[["secondary.multi"]])) i18n$t("Additional variables") else i18n$t("Secondary variable"),
multiple = rv$plot.params()[["secondary.multi"]],
maxItems = rv$plot.params()[["secondary.max"]],
col_subset = cols,
none_label = "No variable"
none_label = i18n$t("No variable")
)
})
@ -1918,8 +1925,8 @@ data_visuals_server <- function(id,
columnSelectInput(
inputId = ns("tertiary"),
data = data,
placeholder = "Please select",
label = "Grouping variable",
placeholder = i18n$t("Please select"),
label = i18n$t("Grouping variable"),
multiple = FALSE,
col_subset = c(
"none",
@ -1932,7 +1939,7 @@ data_visuals_server <- function(id,
input$secondary
)
),
none_label = "No stratification"
none_label = i18n$t("No stratification")
)
})
@ -1948,7 +1955,7 @@ data_visuals_server <- function(id,
ter = input$tertiary
)
shiny::withProgress(message = "Drawing the plot. Hold tight for a moment..", {
shiny::withProgress(message = i18n$t("Drawing the plot. Hold tight for a moment.."), {
rv$plot <- rlang::exec(
create_plot,
!!!append_list(
@ -1975,7 +1982,7 @@ data_visuals_server <- function(id,
output$code_plot <- shiny::renderUI({
shiny::req(rv$code)
prismCodeBlock(paste0("#Plotting\n", rv$code))
prismCodeBlock(paste0(i18n$t("#Plotting\n"), rv$code))
})
shiny::observeEvent(
@ -2020,7 +2027,7 @@ data_visuals_server <- function(id,
plot <- rv$plot[[1]]
}
# browser()
shiny::withProgress(message = "Drawing the plot. Hold on for a moment..", {
shiny::withProgress(message = i18n$t("Drawing the plot. Hold on for a moment.."), {
ggplot2::ggsave(
filename = file,
plot = plot,
@ -2099,8 +2106,8 @@ supported_plots <- function() {
list(
plot_hbars = list(
fun = "plot_hbars",
descr = "Stacked horizontal bars",
note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
descr = i18n$t("Stacked horizontal bars"),
note = i18n$t("A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars"),
primary.type = c("dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -2109,8 +2116,8 @@ supported_plots <- function() {
),
plot_violin = list(
fun = "plot_violin",
descr = "Violin plot",
note = "A modern alternative to the classic boxplot to visualise data distribution",
descr = i18n$t("Violin plot"),
note = i18n$t("A modern alternative to the classic boxplot to visualise data distribution"),
primary.type = c("datatime", "continuous", "dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -2127,8 +2134,8 @@ supported_plots <- function() {
# ),
plot_sankey = list(
fun = "plot_sankey",
descr = "Sankey plot",
note = "A way of visualising change between groups",
descr = i18n$t("Sankey plot"),
note = i18n$t("A way of visualising change between groups"),
primary.type = c("dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -2137,8 +2144,8 @@ supported_plots <- function() {
),
plot_scatter = list(
fun = "plot_scatter",
descr = "Scatter plot",
note = "A classic way of showing the association between to variables",
descr = i18n$t("Scatter plot"),
note = i18n$t("A classic way of showing the association between to variables"),
primary.type = c("datatime", "continuous"),
secondary.type = c("datatime", "continuous", "categorical"),
secondary.multi = FALSE,
@ -2147,8 +2154,8 @@ supported_plots <- function() {
),
plot_box = list(
fun = "plot_box",
descr = "Box plot",
note = "A classic way to plot data distribution by groups",
descr = i18n$t("Box plot"),
note = i18n$t("A classic way to plot data distribution by groups"),
primary.type = c("datatime", "continuous", "dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = FALSE,
@ -2157,8 +2164,8 @@ supported_plots <- function() {
),
plot_euler = list(
fun = "plot_euler",
descr = "Euler diagram",
note = "Generate area-proportional Euler diagrams to display set relationships",
descr = i18n$t("Euler diagram"),
note = i18n$t("Generate area-proportional Euler diagrams to display set relationships"),
primary.type = c("dichotomous", "categorical"),
secondary.type = c("dichotomous", "categorical"),
secondary.multi = TRUE,
@ -4061,7 +4068,7 @@ simple_snake <- function(data){
#### Current file: /Users/au301842/FreesearchR/R//hosted_version.R
########
hosted_version <- function()'v25.8.3-250910'
hosted_version <- function()'v25.8.3-250911'
########
@ -11583,7 +11590,11 @@ server <- function(input, output, session) {
)
shiny::observeEvent(
list(
rv_validations$var_filter,
rv_validations$obs_filter,
rv_validations$missings
),
{
validation_server(
id = "validation_all",

35
inst/translations/translation_da.csv
View file

@ -108,3 +108,38 @@
"{data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases.","{data_text} har {n} observationer og {n_var} variabler, med {n_complete} ({p_complete} %) komplette cases."
"Prepare","Forbered"
"At 0, only complete variables are included; at 100, all variables are included.","At 0, only complete variables are included; at 100, all variables are included."
"The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.","The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent."
"No variables have a correlation measure above the threshold.","Ingen variabler er korrelerede over den angivne tærskelværdi."
"and","og"
"from each pair","fra hvert par"
"Only non-text variables are available for plotting. Go the ""Data"" to reclass data to plot.","Only non-text variables are available for plotting. Go the ""Data"" to reclass data to plot."
"Plot","Plot"
"Adjust settings, then press ""Plot"".","Adjust settings, then press ""Plot""."
"Plot height (mm)","Plot height (mm)"
"Plot width (mm)","Plot width (mm)"
"File format","File format"
"Download plot","Download plot"
"Select variable","Select variable"
"Response variable","Response variable"
"Plot type","Plot type"
"Please select","Please select"
"Additional variables","Additional variables"
"Secondary variable","Secondary variable"
"No variable","No variable"
"Grouping variable","Grouping variable"
"No stratification","No stratification"
"Drawing the plot. Hold tight for a moment..","Drawing the plot. Hold tight for a moment.."
"#Plotting\n","#Plotting\n"
"Drawing the plot. Hold on for a moment..","Drawing the plot. Hold on for a moment.."
"Stacked horizontal bars","Stacked horizontal bars"
"A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars","A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars"
"Violin plot","Violin plot"
"A modern alternative to the classic boxplot to visualise data distribution","A modern alternative to the classic boxplot to visualise data distribution"
"Sankey plot","Sankey plot"
"A way of visualising change between groups","A way of visualising change between groups"
"Scatter plot","Scatter plot"
"A classic way of showing the association between to variables","A classic way of showing the association between to variables"
"Box plot","Box plot"
"A classic way to plot data distribution by groups","A classic way to plot data distribution by groups"
"Euler diagram","Euler diagram"
"Generate area-proportional Euler diagrams to display set relationships","Generate area-proportional Euler diagrams to display set relationships"

1 en da
108 {data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases. {data_text} har {n} observationer og {n_var} variabler, med {n_complete} ({p_complete} %) komplette cases.
109 Prepare Forbered
110 At 0, only complete variables are included; at 100, all variables are included. At 0, only complete variables are included; at 100, all variables are included.
111 The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent. The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.
112 No variables have a correlation measure above the threshold. Ingen variabler er korrelerede over den angivne tærskelværdi.
113 and og
114 from each pair fra hvert par
115 Only non-text variables are available for plotting. Go the "Data" to reclass data to plot. Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.
116 Plot Plot
117 Adjust settings, then press "Plot". Adjust settings, then press "Plot".
118 Plot height (mm) Plot height (mm)
119 Plot width (mm) Plot width (mm)
120 File format File format
121 Download plot Download plot
122 Select variable Select variable
123 Response variable Response variable
124 Plot type Plot type
125 Please select Please select
126 Additional variables Additional variables
127 Secondary variable Secondary variable
128 No variable No variable
129 Grouping variable Grouping variable
130 No stratification No stratification
131 Drawing the plot. Hold tight for a moment.. Drawing the plot. Hold tight for a moment..
132 #Plotting\n #Plotting\n
133 Drawing the plot. Hold on for a moment.. Drawing the plot. Hold on for a moment..
134 Stacked horizontal bars Stacked horizontal bars
135 A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars
136 Violin plot Violin plot
137 A modern alternative to the classic boxplot to visualise data distribution A modern alternative to the classic boxplot to visualise data distribution
138 Sankey plot Sankey plot
139 A way of visualising change between groups A way of visualising change between groups
140 Scatter plot Scatter plot
141 A classic way of showing the association between to variables A classic way of showing the association between to variables
142 Box plot Box plot
143 A classic way to plot data distribution by groups A classic way to plot data distribution by groups
144 Euler diagram Euler diagram
145 Generate area-proportional Euler diagrams to display set relationships Generate area-proportional Euler diagrams to display set relationships

35
inst/translations/translation_sw.csv
View file

@ -108,3 +108,38 @@
"{data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases.","{data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases."
"Prepare","Prepare"
"At 0, only complete variables are included; at 100, all variables are included.","At 0, only complete variables are included; at 100, all variables are included."
"The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.","The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent."
"No variables have a correlation measure above the threshold.","No variables have a correlation measure above the threshold."
"and","and"
"from each pair","from each pair"
"Only non-text variables are available for plotting. Go the ""Data"" to reclass data to plot.","Only non-text variables are available for plotting. Go the ""Data"" to reclass data to plot."
"Plot","Plot"
"Adjust settings, then press ""Plot"".","Adjust settings, then press ""Plot""."
"Plot height (mm)","Plot height (mm)"
"Plot width (mm)","Plot width (mm)"
"File format","File format"
"Download plot","Download plot"
"Select variable","Select variable"
"Response variable","Response variable"
"Plot type","Plot type"
"Please select","Please select"
"Additional variables","Additional variables"
"Secondary variable","Secondary variable"
"No variable","No variable"
"Grouping variable","Grouping variable"
"No stratification","No stratification"
"Drawing the plot. Hold tight for a moment..","Drawing the plot. Hold tight for a moment.."
"#Plotting\n","#Plotting\n"
"Drawing the plot. Hold on for a moment..","Drawing the plot. Hold on for a moment.."
"Stacked horizontal bars","Stacked horizontal bars"
"A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars","A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars"
"Violin plot","Violin plot"
"A modern alternative to the classic boxplot to visualise data distribution","A modern alternative to the classic boxplot to visualise data distribution"
"Sankey plot","Sankey plot"
"A way of visualising change between groups","A way of visualising change between groups"
"Scatter plot","Scatter plot"
"A classic way of showing the association between to variables","A classic way of showing the association between to variables"
"Box plot","Box plot"
"A classic way to plot data distribution by groups","A classic way to plot data distribution by groups"
"Euler diagram","Euler diagram"
"Generate area-proportional Euler diagrams to display set relationships","Generate area-proportional Euler diagrams to display set relationships"

1 en sw
108 {data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases. {data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases.
109 Prepare Prepare
110 At 0, only complete variables are included; at 100, all variables are included. At 0, only complete variables are included; at 100, all variables are included.
111 The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent. The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.
112 No variables have a correlation measure above the threshold. No variables have a correlation measure above the threshold.
113 and and
114 from each pair from each pair
115 Only non-text variables are available for plotting. Go the "Data" to reclass data to plot. Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.
116 Plot Plot
117 Adjust settings, then press "Plot". Adjust settings, then press "Plot".
118 Plot height (mm) Plot height (mm)
119 Plot width (mm) Plot width (mm)
120 File format File format
121 Download plot Download plot
122 Select variable Select variable
123 Response variable Response variable
124 Plot type Plot type
125 Please select Please select
126 Additional variables Additional variables
127 Secondary variable Secondary variable
128 No variable No variable
129 Grouping variable Grouping variable
130 No stratification No stratification
131 Drawing the plot. Hold tight for a moment.. Drawing the plot. Hold tight for a moment..
132 #Plotting\n #Plotting\n
133 Drawing the plot. Hold on for a moment.. Drawing the plot. Hold on for a moment..
134 Stacked horizontal bars Stacked horizontal bars
135 A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars
136 Violin plot Violin plot
137 A modern alternative to the classic boxplot to visualise data distribution A modern alternative to the classic boxplot to visualise data distribution
138 Sankey plot Sankey plot
139 A way of visualising change between groups A way of visualising change between groups
140 Scatter plot Scatter plot
141 A classic way of showing the association between to variables A classic way of showing the association between to variables
142 Box plot Box plot
143 A classic way to plot data distribution by groups A classic way to plot data distribution by groups
144 Euler diagram Euler diagram
145 Generate area-proportional Euler diagrams to display set relationships Generate area-proportional Euler diagrams to display set relationships

24
man/data-correlations.Rd
View file

@ -8,18 +8,34 @@
\usage{
data_correlations_ui(id, ...)
data_correlations_server(id, data, include.class = NULL, cutoff = 0.7, ...)
data_correlations_server(
id,
data,
include.class = NULL,
cutoff = 0.7,
warning_str =
i18n$t("The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\\nConsider excluding one {more}from the dataset to ensure variables are independent."),
warning_no_str = i18n$t("No variables have a correlation measure above the threshold."),
and_str = i18n$t("and"),
...
)
}
\arguments{
\item{id}{Module id. (Use 'ns("id")')}
\item{id}{id}
\item{...}{arguments passed to toastui::datagrid}
\item{data}{data}
\item{color.main}{main color}
\item{include.class}{character vector of classes to include. Default is NULL}
\item{color.sec}{secondary color}
\item{cutoff}{numeric}
\item{warning_str}{Character string. Exposed to allow dynamic translations}
\item{warning_no_str}{Character string. Exposed to allow dynamic translations}
\item{and_strCharacter}{string. Exposed to allow dynamic translations}
}
\value{
Shiny ui module