131 lines
3.1 KiB
R
131 lines
3.1 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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,here[here]
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,dplyr
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,readr
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,tidyr
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,gp2 = ggplot2[ggplot, aes]
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,gtsummary
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)
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# globals -----------------------------------------------------------------
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# load data ---------------------------------------------------------------
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ds0 <- readr$read_rds(here("ML","data-unshared","ds_final.RDS"))
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# data manipulation -------------------------------------------------------
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#here I am adding a column to determine if the Free T4 Value is diagnostic or not
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# using the FT4 Referance range low as the cut off (0.93)
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ds1 <- ds0 %>%
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dplyr$select(-FT4, -subject_id, -charttime) %>%
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dplyr$mutate(dplyr$across(
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ft4_dia
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, ~factor(., levels = c("Hypo", "Non-Hypo", "Normal TSH", "Hyper", "Non-Hyper")
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)
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)
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)
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ds_recode <- ds1 %>%
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dplyr$mutate(
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dplyr$across(
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gender
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,~dplyr$recode(.,"M" = 1, "F" = 2)
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)
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,dplyr$across(
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ft4_dia
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,~dplyr$recode(.
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,"Hypo" = 1
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,"Non-Hypo" = 2
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,"Normal TSH" = 3
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,"Hyper" = 4
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,"Non-Hyper" = 5
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)
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)
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)
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# basic visualization -----------------------------------------------------
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#summary Table
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summary_tbl <- ds1 %>%
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gtsummary$tbl_summary(
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by = ft4_dia
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,missing = "no"
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,type = gtsummary$all_continuous() ~ "continuous2"
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,label = list(
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gender ~ "Gender"
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,anchor_age ~ "Age"
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)
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,statistic = gtsummary$all_continuous() ~ c(
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"{N_miss}"
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,"{median} ({p25}, {p75})"
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,"{min}, {max}"
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)
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) %>%
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gtsummary$bold_labels() %>%
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gtsummary$add_stat_label(
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label = gtsummary$all_continuous() ~ c("Missing", "Median (IQR)", "Range")
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) %>%
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gtsummary$modify_header(label = "**Variable**") %>%
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gtsummary$modify_spanning_header(gtsummary$all_stat_cols() ~ "**Free T4 Diagnostic**")
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# summary_tbl
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# correlation plot
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ds_corr <- cor(ds_recode,use = "complete.obs")
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#code for saving corr plot
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png(here("figures","corrplot.png"), type = 'cairo')
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corrplot::corrplot(ds_corr, method = "number")
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dev.off()
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#quick recode of gender, will still do recoding during feature engineering
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g1 <- ds1 %>%
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dplyr$select(-gender, -ft4_dia) %>%
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tidyr$pivot_longer(cols = dplyr$everything()) %>%
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ggplot(aes(x = value)) +
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gp2$geom_histogram(na.rm = TRUE) +
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gp2$facet_wrap(~name, scales = "free") +
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gp2$theme_bw() +
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gp2$labs(
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x = NULL
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,y = NULL
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)
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g1
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# this takes a bit to load. No discernable paterns in the data
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g2 <- ds_recode %>%
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dplyr$select(-gender) %>%
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tidyr$pivot_longer(cols = !ft4_dia) %>%
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ggplot(aes(x = factor(ft4_dia), y = value, fill = factor(ft4_dia))) +
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gp2$geom_boxplot(outlier.shape = NA, na.rm = TRUE) +
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gp2$geom_jitter(size=.7, width=.1, alpha=.5, na.rm = TRUE) +
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gp2$facet_wrap(~name, scales = "free") +
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gp2$theme_bw() +
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gp2$scale_fill_brewer(palette = "Greys")
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g2
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