#Script para analizar datos acerca de la pandemia Covid-19
#Utilizando datos de 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE
#Bibliotecas
library(readr)
## Warning: package 'readr' was built under R version 3.6.3
library(tidyverse)
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## v tibble 2.1.3 v stringr 1.4.0
## v tidyr 1.0.2 v forcats 0.5.0
## v purrr 0.3.3
## Warning: package 'ggplot2' was built under R version 3.6.3
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library(plotly)
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library(gganimate)
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library(gifski)
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#Declaracion de URL
url_conf <-"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
url_decesos <-"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
url_recuperados <-"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
#Leer los archivos .csv de las variables
datos_conf <- read.csv(url_conf)
datos_decesos <- read.csv(url_decesos)
datos_recuperados <- read.csv(url_recuperados)
#Definir las variables
confirmados_russia <- t(datos_conf[datos_conf$Country.Region=="Russia" ,])
decesos_russia <- t(datos_decesos[datos_decesos$Country.Region=="Russia" ,])
recuperados_russia <- t(datos_recuperados[datos_recuperados$Country.Region=="Russia" ,])
#Figura 1: grafica de confirmados simple
plot(confirmados_russia)
## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introducidos por coerción
#Vector de fecha
Fecha = seq(from = as.Date("2020-01-22"), to = as.Date("2020-04-23"), by = 'day')
#Casos confirmados en Russia
vec1 <- as.vector(confirmados_russia)
vec2 <- vec1[5:97]
num1 <- as.numeric(vec2)
Confirmados <- as.vector(num1)
#Decesos en Russia
vec1 <- as.vector(decesos_russia)
vec2 <- vec1[5:97]
num1 <- as.numeric(vec2)
Decesos <- as.vector(num1)
#Recuperados en Russia
vec1 <- as.vector(recuperados_russia)
vec2 <- vec1[5:97]
num1 <- as.numeric(vec2)
Recuperados <- as.vector(num1)
#Generacion de un data frame
datos1 <- data.frame(Fecha, Confirmados, Decesos, Recuperados)
#Figura 2. Confirmados ggplot
ggplot(data = datos1) +
geom_line(mapping = aes(x = Fecha, y = Confirmados))
#Figura 3. Decesos ggplot
ggplot(data = datos1) +
geom_line(mapping = aes(x = Fecha, y = Decesos))
#Figura 4. Recuperados ggplot
ggplot(data = datos1) +
geom_line(mapping = aes(x = Fecha, y = Recuperados))
#Animacion 1 simple
ggplot(data = datos1) +
ggtitle("Casos confirmados COVID-19 en Russia (Fuente: JHU CSSE), Brayan A.")+
geom_line(mapping = aes(x = Fecha, y = Confirmados))+
transition_reveal(Fecha)
#Figura 5: Multi ejes ggplot
ggplot(data = datos1) +
ggtitle("Casos confirmados COVID-19 en Russia (Fuente: JHU CSSE), Brayan A.")+
geom_line(aes(x=Fecha,y=Confirmados), color = "red")+
geom_line(aes(x=Fecha,y=Decesos), color = "black")+
geom_line(aes(x=Fecha,y=Recuperados), color = "green")+
ylab('COVID-19 Russia') + xlab('Fecha')
theme_bw()
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#Animacion 2
ggplot(data=datos1) +
ggtitle("Casos confirmados COVID-19 en Russia (Fuente: JHU CSSE), Brayan A.")+
geom_line(aes(x=Fecha,y=Confirmados),color='red') +
geom_line(aes(x=Fecha,y=Decesos),color='black') +
geom_line(aes(x=Fecha,y=Recuperados),color="green")+
ylab('COVID-19 Russia') + xlab('Fecha') +
transition_reveal(Fecha)
#Grafica interactiva compuesta
gcov <- ggplot(data=datos1) +
ggtitle("Casos confirmados COVID-19 en Russia (Fuente: JHU CSSE), Brayan A.")+
geom_line(aes(x=Fecha,y=Confirmados),color='red') +
geom_line(aes(x=Fecha,y=Decesos),color='black') +
geom_line(aes(x=Fecha,y=Recuperados),color="green")+
ylab('COVID-19 Russia') + xlab('Fecha') +
transition_reveal(Fecha)
ggplotly(gcov)
#Conclusion.
# Despues de procesar los datos podemos observar en las graficas que el incremento
#de casos confirmados de COVID 19 en Rusia se incremento exponencialmente en el mes de
#abril, superando los 60000 infectados. El numero de decesos y personas recuperdas
#es muy bajo debido a el reciente incremento de contagios en el pais.