97 lines
2.4 KiB
R
97 lines
2.4 KiB
R
rm(list = ls(all.names = TRUE)) # Clear the memory of variables from previous run.
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cat("\014") # Clear the console
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# load packages -----------------------------------------------------------
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box::use(
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magrittr[`%>%`]
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,RSQLite
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,DBI[dbConnect,dbDisconnect]
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,here[here]
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,dplyr
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,dbplyr
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,tidyr
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)
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# globals -----------------------------------------------------------------
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db <- dbConnect(
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RSQLite$SQLite()
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,here("ML","data-unshared","mimicDB.sqlite")
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)
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#item list shows two different numbers for a few tests, second set of items do not have
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# any results that are on the same samples as TSH and Free T4
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test_list <- c(
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50862 #Albumin
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,50863 #Alkaline Phosphatase
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,50861 #Alanine Aminotransferase (ALT)
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,50878 #Asparate Aminotransferase (AST)
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,51006 #Urea Nitrogen
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,50893 #Calcium, Total
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,50882 #Bicarbonate
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,50902 #Chloride
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,50912 #Creatinine
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,50931 #Glucose
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,50971 #Potassium
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,50983 #Sodium
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,50885 #Bilirubin, Total
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,50976 #Protein, Total
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,50993 #Thyroid Stimulating Hormone
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,50995 #Thyroxine (T4), Free
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)
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# load data ---------------------------------------------------------------
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ds <- dplyr$tbl(db, "labevents") %>%
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dplyr$filter(itemid %in% test_list) %>%
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dplyr$select(-charttime,-storetime) %>%
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tidyr$pivot_wider(
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id_cols = c(subject_id,specimen_id)
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,names_from = itemid
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,values_from = valuenum
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) %>%
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dplyr$filter(!is.na(`50993`) & !is.na(`50995`)) %>%
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dplyr$collect()
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ds %>% dplyr$filter(dplyr$across(where(is.numeric), ~!is.na(.x)))
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count <- data.frame(colSums(is.na(ds))) %>% tibble::rownames_to_column()
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testds <- readr::read_csv(
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here("ML","data-unshared", "labevents.csv")
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,col_types = "_d_ddTT_d______"
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,n_max = 100
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)
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#using chart time instead of spceimen id results in less NA values.
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# total protien still have very low resulting
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ds1 <- dplyr$tbl(db, "labevents") %>%
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dplyr$filter(itemid %in% test_list) %>%
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dplyr$select(-storetime) %>%
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tidyr$pivot_wider(
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id_cols = c(subject_id,charttime)
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,names_from = itemid
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,values_from = valuenum
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) %>%
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dplyr$filter(!is.na(`50993`) & !is.na(`50995`)) %>%
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dplyr$collect()
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count2 <- data.frame(colSums(is.na(ds1))) %>% tibble::rownames_to_column()
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counts <- count %>%
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dplyr$left_join(count2)
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# using charttime, total of 5,424 rows with all values filled in
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ds1 %>% dplyr$filter(dplyr$across(where(is.numeric), ~!is.na(.x)))
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# close database ----------------------------------------------------------
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dbDisconnect(db)
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