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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
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inst/translations/translation_de.csv
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@ -105,7 +105,6 @@
"First five rows are shown below:","First five rows are shown below:"
"No variable chosen for analysis","No variable chosen for analysis"
"No missing observations","No missing observations"
"Missing vs non-missing observations in the variable **'{variabler()}'**","Missing vs non-missing observations in the variable **'{variabler()}'**"
"Grouped by {get_label(data,ter)}","Grouped by {get_label(data,ter)}"
"Import data from REDCap","Import data from REDCap"
"REDCap server","REDCap server"
@ -207,7 +206,6 @@
"Correlation cut-off","Correlation cut-off"
"Set the cut-off for considered 'highly correlated'.","Set the cut-off for considered 'highly correlated'."
"Missings","Missings"
"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.","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."
"Visuals","Visuals"
"Analysis validation","Analysis validation"
"Report","Report"
@ -230,7 +228,6 @@
"You removed {p_out} % of observations.","You removed {p_out} % of observations."
"You removed {p_out} % of variables.","You removed {p_out} % of variables."
"There is a total of {p_miss} % missing observations.","There is a total of {p_miss} % missing observations."
"There is a significant correlation between {n_nonmcar} variables and missing observations in the outcome variable {outcome}.","There is a significant correlation between {n_nonmcar} variables and missing observations in the outcome variable {outcome}."
"Data includes {n_pairs} pairs of highly correlated variables.","Data includes {n_pairs} pairs of highly correlated variables."
"Class","Class"
"Observations","Observations"
@ -299,3 +296,11 @@
"Words","Words"
"Shorten to first letters","Shorten to first letters"
"Shorten to first words","Shorten to first words"
"Select missings analysis to apply","Select missings analysis to apply"
"Variables","Variables"
"By outcome","By outcome"
"Missings across variables by the variable **'{input$missings_var}'**","Missings across variables by the variable **'{input$missings_var}'**"
"Missing vs non-missing observations in the variable **'{input$missings_var}'**","Missing vs non-missing observations in the variable **'{input$missings_var}'**"
"Evaluate missingness by either comparing missing values across variables (optionally grouped by af categorical or dichotomous variable) or compare variables grouped by the missing status (missing or not) of an outcome variable. If there is a significant difference i the missingness, this may cause a bias in you data and should be considered carefully interpreting the data and analyses as data may not be missing at random.","Evaluate missingness by either comparing missing values across variables (optionally grouped by af categorical or dichotomous variable) or compare variables grouped by the missing status (missing or not) of an outcome variable. If there is a significant difference i the missingness, this may cause a bias in you data and should be considered carefully interpreting the data and analyses as data may not be missing at random."
"Calculating. Hold tight for a moment..","Calculating. Hold tight for a moment.."
"There is a significant difference in data missingness in {n_nonmcar} {ifelse(n_nnonmcar==1,'variable','variables')} grouped by the selected outcome/grouping variable {outcome}.","There is a significant difference in data missingness in {n_nonmcar} {ifelse(n_nnonmcar==1,'variable','variables')} grouped by the selected outcome/grouping variable {outcome}."

1 en de
105 First five rows are shown below: First five rows are shown below:
106 No variable chosen for analysis No variable chosen for analysis
107 No missing observations No missing observations
Missing vs non-missing observations in the variable **'{variabler()}'** Missing vs non-missing observations in the variable **'{variabler()}'**
108 Grouped by {get_label(data,ter)} Grouped by {get_label(data,ter)}
109 Import data from REDCap Import data from REDCap
110 REDCap server REDCap server
206 Correlation cut-off Correlation cut-off
207 Set the cut-off for considered 'highly correlated'. Set the cut-off for considered 'highly correlated'.
208 Missings Missings
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. 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.
209 Visuals Visuals
210 Analysis validation Analysis validation
211 Report Report
228 You removed {p_out} % of observations. You removed {p_out} % of observations.
229 You removed {p_out} % of variables. You removed {p_out} % of variables.
230 There is a total of {p_miss} % missing observations. There is a total of {p_miss} % missing observations.
There is a significant correlation between {n_nonmcar} variables and missing observations in the outcome variable {outcome}. There is a significant correlation between {n_nonmcar} variables and missing observations in the outcome variable {outcome}.
231 Data includes {n_pairs} pairs of highly correlated variables. Data includes {n_pairs} pairs of highly correlated variables.
232 Class Class
233 Observations Observations
296 Words Words
297 Shorten to first letters Shorten to first letters
298 Shorten to first words Shorten to first words
299 Select missings analysis to apply Select missings analysis to apply
300 Variables Variables
301 By outcome By outcome
302 Missings across variables by the variable **'{input$missings_var}'** Missings across variables by the variable **'{input$missings_var}'**
303 Missing vs non-missing observations in the variable **'{input$missings_var}'** Missing vs non-missing observations in the variable **'{input$missings_var}'**
304 Evaluate missingness by either comparing missing values across variables (optionally grouped by af categorical or dichotomous variable) or compare variables grouped by the missing status (missing or not) of an outcome variable. If there is a significant difference i the missingness, this may cause a bias in you data and should be considered carefully interpreting the data and analyses as data may not be missing at random. Evaluate missingness by either comparing missing values across variables (optionally grouped by af categorical or dichotomous variable) or compare variables grouped by the missing status (missing or not) of an outcome variable. If there is a significant difference i the missingness, this may cause a bias in you data and should be considered carefully interpreting the data and analyses as data may not be missing at random.
305 Calculating. Hold tight for a moment.. Calculating. Hold tight for a moment..
306 There is a significant difference in data missingness in {n_nonmcar} {ifelse(n_nnonmcar==1,'variable','variables')} grouped by the selected outcome/grouping variable {outcome}. There is a significant difference in data missingness in {n_nonmcar} {ifelse(n_nnonmcar==1,'variable','variables')} grouped by the selected outcome/grouping variable {outcome}.