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col widths on delivs and tasks
1 parent 2bff775 commit 1d0ef95

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chapters/deliverables_tasks.qmd

Lines changed: 30 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -14,10 +14,25 @@ deliv$Workpackage <- as.character(deliv$Workpackage )
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deliv$Workpackage <- factor(deliv$Workpackage, levels = as.character(1:13))
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deliv$Organisation <- factor(deliv$Organisation)
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names(deliv)[names(deliv) == 'Workpackage'] <- 'WP'
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names(deliv)[names(deliv) == 'Organisation'] <- 'Org.'
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DT::datatable(deliv ,
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filter = 'top',
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rownames = F,
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options = list(pageLength = 5))
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options = list(
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pageLength = 5,
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autoWidth = TRUE,
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responsive = TRUE,
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columnDefs = list(
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list(width = "35%", targets = 0),
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list(width = "10%", targets = 1),
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list(width = "10%", targets = 2),
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list(width = "10%", targets = 3),
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list(width = "20%", targets = 4),
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list(width = "15%", targets = 5)
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)
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))
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```
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## Tasks
@@ -30,10 +45,23 @@ tasks <- tasks[ , c('Description','Workpackage', 'ID' , 'Start Date' , 'End Date
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tasks$Workpackage <- as.character(tasks$Workpackage)
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tasks$Workpackage <- factor(tasks$Workpackage, levels = as.character(1:13))
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names(tasks)[names(tasks) == 'Workpackage'] <- 'WP'
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DT::datatable(tasks ,
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filter = 'top',
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rownames = F,
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options = list(pageLength = 5))
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options = list(
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pageLength = 5,
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autoWidth = TRUE,
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responsive = TRUE,
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columnDefs = list(
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list(width = "50%", targets = 0),
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list(width = "10%", targets = 1),
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list(width = "10%", targets = 2),
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list(width = "15%", targets = 3),
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list(width = "15%", targets = 4)
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)
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))
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```
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<br>

index.qmd

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@@ -117,7 +117,7 @@ gsbpm: "5.1" # See complete taxonomy: https://www.unescap.org/sites/default/d8fi
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# Project Structure
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The AIML4OS project is divided into 13 workpackages. Six of these are 'supporting' workpackages that provide cross-cutting insight and resources. The remaining seven are 'use case' workpackages, each of which explore a specific domain within AI/ML and its relevance to official statistics.
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The AIML4OS project is divided into 13 workpackages. Six of these are 'supporting' workpackages that provide cross-cutting insight and resources. The remaining seven are 'use case' workpackages, each of which explores a specific domain within AI/ML and its relevance to official statistics.
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