---
title: "Biostat 203B Homework 2"
subtitle: Due Feb 7 @ 11:59PM
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

Use tidyverse (ggpot2, dplyr) to explore the [MIMIC-III](https://mimic.physionet.org) data introduced in [homework 1](https://ucla-biostat203b-2020winter.github.io/hw/hw1/hw1.html).

## Q1

Demographic information of the patients admitted into hospital is available in `ADMISSION.csv`. See <https://mimic.physionet.org/mimictables/admissions/> for details of each field in this file. Summarize following variables using appropriate graphs:   

- admission year  
- admission month  
- admission week day  
- admission hour  
- length of hospital stay  
- admission type  
- number of admissions per patient  
- admission location  
- insurance  
- language  
- religion  
- martial status  
- ethnicity  
- death 

Note it is possible that one patient (uniquely identified by the `SUBJECT_ID`) is admitted into hospital multiple times. When summarizing some demographic information, it makes sense to summarize based on only unique patients. 

## Q2

Link the data in `ADMISSION.csv` and `PATIENTS.csv` (<https://mimic.physionet.org/mimictables/patients/>) and summarize following variables using appropriate graphs:  

- gender  
- age at admission 

## Q3

`ICUSTAYS.csv` (<https://mimic.physionet.org/mimictables/icustays/>) contains data about Intensive Care Units (ICU) stays. Summarize following variables using appropriate graphs:  

- length of ICU stay  
- first ICU unit  
- gender  
- age  

## Q4 

`CHARTEVENTS.csv` (<https://mimic.physionet.org/mimictables/chartevents/>) contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The `ITEMID` variable indicates a single measurement type in the database. The `VALUE` variable is the value measured for `ITEMID`. 

`D_ITEMS.csv` (<https://mimic.physionet.org/mimictables/d_items/>) is the dictionary for the `ITEMID` in `CHARTEVENTS.csv`. Find potential values of `ITEMID` that correspond to systolic blood pressure, i.e., `LABEL` contains the string `systolic`. 

Compile a tibble that contains the first ICU stay of unique patients, with the patient's demographic information, the first systolic blood pressure measurement during ICU stay, and whether the patient died within 30 days of hospitcal admission.