73 lines
No EOL
1.7 KiB
R
73 lines
No EOL
1.7 KiB
R
# Sample data set is generated with rownames and colnames
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ds <- do.call(cbind,lapply(1:133,function(i){
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sample(c(1,2,3,4,5,rep(NA,12)),size=17)
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}))
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rownames(ds) <- letters[seq_len(nrow(ds))]
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colnames(ds) <- paste0("sub",seq_len(ncol(ds)))
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# df[as.character(as.matrix(ds))==0] <- 17
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# Clearing NAs and applying the max cost instead
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# ds[is.na(ds)] <- 17
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# I believe this would actually be the organic data set
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df <- data.frame("ID"=colnames(ds),t(ds))
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openxlsx::write.xlsx(df,"assign_sample.xlsx")
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write.csv(df,"assign_sample.csv",na = "",row.names = FALSE)
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df[as.matrix(df)==0] <- 17
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assigned <- df |>
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group_assignment(cap_classes = rep(8, 17),excess_space = 1)
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df |> group_assignment()
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assigned$`Group assignment`
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assigned$`Cost evaluation` |> assignment_plot(1:5)
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pre_grouped <- data.frame("ID"=sample(df$ID,10),"group"=sample(1:17,10))
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ds <- df
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assigned <- df |>
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group_assignment(excess_space = 1.05,
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pre_assign = pre_grouped)
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lengths(assigned[[1]])
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#
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# ds <- read.csv("assign_sample.csv")
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#
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# ls <- read.csv("assign_sample.csv") |> group_assignment(cap_classes = 8, excess_space = 1)
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#
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# ls |> assignment_plot()
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#
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# lst <- ls
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#
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# "[["(ls,4) |> head(10)
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# View(ls$export)
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#
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# pre_grouped <- data.frame("ID"=sample(ds$ID,10),"group"=sample(1:17,10))
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# write.csv(pre_grouped,"pre_grouped.csv",row.names = FALSE)
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#
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# assigned <- ds |>
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# group_assignment(excess_space = 1.05,
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# pre_assign = pre_grouped)
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# ls <-
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# read.csv("assign_sample.csv") |> group_assignment(
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# cap_classes = 8,
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# excess_space = 1,
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# pre_assign = read.csv("pre_grouped.csv")
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# ) |