49 lines
1.6 KiB
R
49 lines
1.6 KiB
R
# Specifying needed data collection files
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data_source <- c(
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"PASE_rev_v13.dta",
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"MFI_rev_v13.dta",
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"mdi_rev_v13.dta",
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"mmse_rev_v13.dta",
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"mrs_rev_v13.dta",
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"SDMT_rev_v13.dta",
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"who_rev_v13.dta"
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)
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## Cumulated data
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dta<-read.csv("/Volumes/Data/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown"))
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# Getting full filenames
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file_nms <- list.files("/Volumes/Data/STATA13",full.names = TRUE)[match(data_source,list.files("/Volumes/Data/STATA13"))]
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# Loading datafiles
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dta<-read.csv("/Volumes/Data/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown"))
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ls <- lapply(file_nms,function(i){
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d <- read_dta(i)
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colnames(d) <- tolower(gsub("instance","INSTANCE",colnames(d))) #in the sdmt dataset, instance column is lower case
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d
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})
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# Selecting desired variables
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ls_sel <- lapply(seq_along(ls), function(i) {
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ls[[i]] |> select(cpr,
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SYS_SITE,
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INSTANCE,
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starts_with("TALOS_")) |> as_factor() |>
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full_join(select(dta,cpr, rnumb)) |> select(rnumb,everything())
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})
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# Naming lists according to file names
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names(ls_sel) <- tolower(unlist(lapply(data_source,function(x){strsplit(x,"_")[[1]][1]})))
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## Screening list and EOS data
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subjects <- read_dta("/Volumes/Data/STATA13/inkl_rev_v13.dta") |>
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select(c("cpr", "rnumb", "rdate", "rtreat")) |>
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filter(rnumb != 999) |>
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left_join(read_dta("/Volumes/Data/STATA13/end_rev_v13.dta") |>
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select(c("cpr", "TALOS_end00", "TALOS_end01"))
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) |>
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rename(enddate = TALOS_end00,
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eos_early = TALOS_end01) |>
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as_factor()
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