In BAREJAA/reactivity: This package provides a way to generate introductory dplyr and ggplot2 presentations using user-supplied tidy datasets

library(tidyverse)
library(tidybiology)
library(viridis)

#clear environment
#rm(list=ls()) 

source(here::here("code", "healthcare.R")) #loads dataset-specific variables

##### START HERE #####

#What percent of patients in this dataset are male v. female?

#What is the median age of patients

#What is the average cholesterol level of patients at the median age?

#Store a new dataframe object that groups patients by sex and age, and then calculates the average cholesterol levels for patients in this group

#store some of these values, so that you can call them inline below

In the heart study data set, the proportion of females is % and males is %. The median age of patients in this data set is median age, which have an average serum cholesterol level of chol level num mg/dl.

heart %>% 
  ggplot(aes(chol, fill = sex)) +
  geom_density(alpha = 0.5) +
  theme_bw() +
  labs(
    x = "Serum Cholesterol (mg/dl)",
    y = "Density",
    fill = "",
    title = "Distribution of Serum Cholesterol in Men and Women"
  ) +
  scale_x_continuous(expand = c(0.005, 0.005)) +
  scale_y_continuous(expand = c(0, 0)) +
  geom_vline(data = filter(heart, sex == "male"), aes(xintercept = median(chol)), colour = "yellow", size = 1, linetype  = 2) +
  geom_vline(data = filter(heart, sex == "female"), aes(xintercept = median(chol)), colour = "navy", size = 1, linetype = 2) +
  scale_fill_viridis(discrete = TRUE, option = "E")

#change code chunks so only the report output and plot are shown on the knitted report

#Session information for provenance and reproducibility
session_provenance()

BAREJAA/reactivity documentation built on April 16, 2020, 6:57 p.m.