analysis.Rmd

---
title: "R Notebook"
output:
  word_document: default
  html_notebook: default
---

```{r}
library(tidyverse)
library(finalfit)
library(cowplot)
library(ggpubr)

data <- read_csv("data.csv", col_types = cols(
  icustay_id = col_double(),
  hadm_id = col_double(),
  subject_id = col_double(),
  charttime = col_datetime(format = ""),
  vtime = col_datetime(format = ""),
  stime = col_datetime(format = ""),
  vasopressors = col_logical(),
  shock = col_logical(),
  gib_codes = col_logical(),
  gib_drg = col_logical(),
  esld = col_factor(),
  death = col_factor(),
  age = col_double(),
  glucose_max = col_double()
))

data %<>% mutate(
  death = fct_recode(death, "Survivors" = "FALSE", "Non-Survivors" = "TRUE"),
  esld = fct_recode(esld, "Yes" = "TRUE", "No" = "FALSE")
) %>% filter(!is.na(glucose_max))
```

## Tables
### All Patients
```{r}
explanatory = c("age", "esld", "glucose_max")
dependent = 'death'

data %>%
  mutate(esld = ff_label(esld, "Cirrhosis"),
         glucose_max = ff_label(glucose_max, "Maximum 24h Glucose (mg/dL)"),
         age = ff_label(age, "Age (y)")) %>%
  summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE) %>%
  knitr::kable(row.names = FALSE)
```

### ESLD Only
```{r}
explanatory = c("age", "glucose_max")
dependent = 'death'

data %>%
  filter(esld == "Yes") %>%
  mutate(esld = ff_label(esld, "Cirrhosis"),
         glucose_max = ff_label(glucose_max, "Maximum 24h Glucose (mg/dL)"),
         age = ff_label(age, "Age (y)")) %>%
  summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE) %>%
  knitr::kable(row.names = FALSE)
```
### No ESLD Only
```{r}
explanatory = c("age", "glucose_max")
dependent = 'death'

data %>%
  filter(esld == "No") %>%
  mutate(esld = ff_label(esld, "Cirrhosis"),
         glucose_max = ff_label(glucose_max, "Maximum 24h Glucose (mg/dL)"),
         age = ff_label(age, "Age (y)")) %>%
  summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE) %>%
  knitr::kable(row.names = FALSE)
```

## Figures
### Figure 1
```{r fig.width=8, fig.height=6}

fig1a <- ggplot(data, aes(death, glucose_max)) + 
  geom_violin() + 
  geom_boxplot(width = 0.1) + 
  geom_signif(comparisons = list(c("Survivors", "Non-Survivors")), map_signif_level = TRUE) + 
  theme_pubr() + 
  labs(y = "Maximum 24h Glucose (mg/dL)", 
       x = "In-Hospital Mortality",
       title = "All Patients")

fig1b <- ggplot(data %>% filter(esld == "Yes"), aes(death, glucose_max)) + 
  geom_violin() + 
  geom_boxplot(width = 0.1) + 
  geom_signif(comparisons = list(c("Survivors", "Non-Survivors")), map_signif_level = TRUE) + 
  theme_pubr() + 
  labs(y = "Maximum 24h Glucose (mg/dL)", 
       x = "In-Hospital Mortality",
       title = "ESLD")

fig1c <- ggplot(data %>% filter(esld != "Yes"), aes(death, glucose_max)) + 
  geom_violin() +
  geom_boxplot(width = 0.1) + 
  geom_signif(comparisons = list(c("Survivors", "Non-Survivors")), map_signif_level = TRUE) + 
  theme_pubr() + 
  labs(y = "Maximum 24h Glucose (mg/dL)", 
       x = "In-Hospital Mortality", 
       title = "No ESLD")

fig1 <- plot_grid(fig1a, fig1b, fig1c, nrow = 1, labels = "AUTO")
save_plot("figure1.pdf", fig1, base_height = 6, base_aspect_ratio = 2)
fig1
```

## Statistics
```{r}
library(pROC)
roc(death ~ glucose_max, data)
roc(death ~ glucose_max, data %>% filter(esld != "Yes"))
roc(death ~ glucose_max, data %>% filter(esld == "Yes"))
```