PROJECT_AKINKUNMI 1
Hungarian physician Dr. Ignaz Semmelweis worked at the Vienna General Hospital with childbed fever patients. Childbed fever is a deadly disease affecting women who have just given birth, and in the early 1840s, as many as 10% of the women giving birth died from it at the Vienna General Hospital. Dr.Semmelweis discovered that it was the contaminated hands of the doctors delivering the babies, and on June 1st, 1847, he decreed that everyone should wash their hands, an unorthodox and controversial request; nobody in Vienna knew about bacteria.
You will reanalyze the data that made Semmelweis discover the importance of handwashing and its impact on the hospital.
The data is stored as two CSV files within the data folder.
yearly_deaths_by_clinic.csv contains the number of women giving birth at the two clinics at the Vienna General Hospital between the years 1841 and 1846.
| Column | Description |
|---|---|
year | Years (1841-1846) |
births | Number of births |
deaths | Number of deaths |
clinic | Clinic 1 or clinic 2 |
monthly_deaths.csv contains data from 'Clinic 1' of the hospital where most deaths occurred.
| Column | Description |
|---|---|
date | Date (YYYY-MM-DD) |
births | Number of births |
deaths | Number of deaths |
# Imported libraries
library(tidyverse)
# Start coding here..
# Load necessary libraries
library(dplyr)
library(readr)
# Load the CSV files into data frames
yearly <- read_csv("data/yearly_deaths_by_clinic.csv")
monthly <- read_csv("data/monthly_deaths.csv")
# Check the data
head(yearly)
head(monthly)
# Add proportion_deaths column to each dataframe
yearly <- yearly %>%
mutate(proportion_deaths = deaths / births)
monthly <- monthly %>%
mutate(proportion_deaths = deaths / births)
# Check the data
head(yearly)
head(monthly)
# Load necessary libraries
library(dplyr)
library(readr)
library(ggplot2)
# Create the yearly proportion of deaths plot
yearly_plot <- ggplot(yearly, aes(x = year, y = proportion_deaths, color = clinic)) +
geom_line() +
labs(title = "Yearly Proportion of Deaths by Clinic",
x = "Year",
y = "Proportion of Deaths") +
theme_minimal()
# Create the monthly proportion of deaths plot
monthly_plot <- ggplot(monthly, aes(x = date, y = proportion_deaths)) +
geom_line() +
labs(title = "Monthly Proportion of Deaths",
x = "Date",
y = "Proportion of Deaths") +
theme_minimal()
# Display the plots
yearly_plot
monthly_plot
# Add handwashing_started column to monthly dataframe
monthly <- monthly %>%
mutate(handwashing_started = date >= as.Date("1847-06-01"))
# Create the yearly proportion of deaths plot
yearly_plot <- ggplot(yearly, aes(x = year, y = proportion_deaths, color = clinic)) +
geom_line() +
labs(title = "Yearly Proportion of Deaths by Clinic",
x = "Year",
y = "Proportion of Deaths") +
theme_minimal()
# Create the monthly proportion of deaths plot with handwashing_started
monthly_plot <- ggplot(monthly, aes(x = date, y = proportion_deaths, color = handwashing_started)) +
geom_line() +
labs(title = "Monthly Proportion of Deaths",
x = "Date",
y = "Proportion of Deaths") +
theme_minimal()
# Display the plots
yearly_plot
monthly_plot
# Calculate the mean proportion of deaths before and after handwashing
monthly_summary <- monthly %>%
group_by(handwashing_started) %>%
summarize(mean_proportion_deaths = mean(proportion_deaths))
# Convert the logical column to a more descriptive factor
monthly_summary$handwashing_started <- factor(monthly_summary$handwashing_started,
levels = c(FALSE, TRUE),
labels = c("Before Handwashing", "After Handwashing"))
# Display the summary
monthly_summary