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