Contexto

Los datos

Primero carga el paquete o libreria tidyverse que contiene una serie de comandos que facilitan el manejo de los datos.

En caso que no lo tengas instalado, debes instalarlo previamente con el comando

install.packages(“tidyverse”)

y luego ejecutar

devtools::install_github("ThinkR-open/remedy")
Downloading GitHub repo ThinkR-open/remedy@master
from URL https://api.github.com/repos/ThinkR-open/remedy/zipball/master
Installing remedy
'/usr/lib/R/bin/R' --no-site-file  \
  --no-environ --no-save --no-restore  \
  --quiet CMD INSTALL  \
  '/tmp/Rtmp2X5Xux/devtools390d9285d52/ThinkR-open-remedy-e5dcb85'  \
  --library='/home/suribe/R/x86_64-pc-linux-gnu-library/3.4'  \
  --install-tests 

* installing *source* package ‘remedy’ ...
** R
** inst
** tests
** preparing package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded
* DONE (remedy)
devtools::install_github("ThinkR-open/remedy")
Skipping install of 'remedy' from a github remote, the SHA1 (e5dcb85f) has not changed since last install.
  Use `force = TRUE` to force installation
install.package
Error: objeto 'install.package' no encontrado

A continuación, carga los datos, que están en formato comma separated values (csv), mediante el comando read_csv del paquete dplyr dentro de tidyverse

df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vRigP41ihMCW41ecYxTvFtvLKqG86OoURsbwuB0okH-INY_wrM0mbbf-yANf2o6CTGWtX3EPQCnzhVJ/pub?gid=0&single=true&output=csv")
Parsed with column specification:
cols(
  year = col_integer(),
  births = col_integer(),
  deaths = col_integer(),
  clinic = col_character()
)

EDA

summary(df)
      year          births    
 Min.   :1841   Min.   :2442  
 1st Qu.:1842   1st Qu.:2902  
 Median :1844   Median :3108  
 Mean   :1844   Mean   :3153  
 3rd Qu.:1845   3rd Qu.:3338  
 Max.   :1846   Max.   :4010  
     deaths         clinic         
 Min.   : 66.0   Length:12         
 1st Qu.:100.2   Class :character  
 Median :219.5   Mode  :character  
 Mean   :223.3                     
 3rd Qu.:263.5                     
 Max.   :518.0                     

mutate

Calcular la proporción x 100 de muertos por parto

Diferencias y tendencias

Graficar la proporción de muertes por año por clínica

Porqué las diferencias? En la clínica 1 atendían médicos y estudiantes de medicina, mientras que en la 2 matronas y estudiantes de obstetricia

Vamos a comparar el promedio de muertes por clínica con un t-test

library(broom)

La intervención

Entonces, Semmelweiz decide hacer una prueba, y manda: “Wash your hands!”

df_2 <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vRigP41ihMCW41ecYxTvFtvLKqG86OoURsbwuB0okH-INY_wrM0mbbf-yANf2o6CTGWtX3EPQCnzhVJ/pub?gid=994982495&single=true&output=csv")
Parsed with column specification:
cols(
  date = col_date(format = ""),
  births = col_integer(),
  deaths = col_integer()
)

Veamos…en el verano de 1847 les dijo a todos en el hospital: “Lávense las manos!”

Visualización de la intervención

Veamos que pasó antes y después

Divido antes y después del lavado de manos

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CiAgZ2VvbV9ib3hwbG90KCkgKyAKICB0aGVtZV9taW5pbWFsKCkgKyAKICBsYWJzKHRpdGxlID0gIlByb21lZGlvIGRlIHByb3AuIG11ZXJ0ZXMgYW50ZXMgeSBkZXNwdcOpcyBkZWwgbGF2YWRvIGRlIG1hbm9zIiwgCiAgICAgICB5ID0gIlByb3BvcmNpw7NuIGRlIG11ZXJ0ZXMgeCAxMDAgbmFjaW1pZW50b3MiLCAKICAgICAgIHggPSAiTW9tZW50byIpCmBgYAoK