#script para analizar datos acerca de la pandemia de COVID-19
#Utilizando los datos de
#2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE
#Bibliotecas
library(readr)
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library(tidyverse)
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## v tidyr 1.0.0 v forcats 0.4.0
## v purrr 0.3.3
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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 de variables
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 URL
datos_conf <- read.csv(url_conf)
datos_decesos <-read.csv(url_decesos)
datos_recuperados <- read.csv(url_recuperados)
#definir variables
conf_mex <- t(datos_conf [datos_conf$Country.Region=="Mexico" ,])
dec_mex <- t(datos_decesos [datos_decesos$Country.Region=="Mexico" ,])
rec_mex <- t(datos_recuperados [datos_recuperados$Country.Region=="Mexico" ,])
#Figura 1 grafica de confirmados simple
plot(conf_mex)
## 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("2020-04-22"), by = 'day')
#Casos confirmados
vec1 <- as.vector(conf_mex)
vec2 <-vec1[5:96]
num1 <- as.numeric(vec2)
Confirmados <- as.vector(num1)
#decesos
vec1 <- as.vector(dec_mex)
vec2 <-vec1[5:96]
num1 <- as.numeric(vec2)
Decesos <- as.vector(num1)
#recuperados
vec1 <- as.vector(rec_mex)
vec2 <-vec1[5:96]
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 México (Fuente: JHU CSSE) @jigbadouin")+
geom_line(mapping = aes(x = Fecha, y = Confirmados))+
transition_reveal(Fecha)
#Figura 5 multi ejes ggplot
ggplot(data=datos1) +
geom_line(aes(x=Fecha,y=Confirmados),color='red') +
geom_line(aes(x=Fecha,y=Decesos),color='blue') +
geom_line(aes(x=Fecha,y=Recuperados),color='green')+
ylab('COVID-19 Mexico') + xlab('Fecha')
theme_bw()
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#animacion 2
ggplot(data=datos1) +
geom_line(aes(x=Fecha,y=Confirmados),colour='red') +
geom_line(aes(x=Fecha,y=Decesos),colour='blue') +
geom_line(aes(x=Fecha,y=Recuperados),colour="green")+
ylab('COVID-19 Mexico') + xlab('Fecha') +
transition_reveal(Fecha)
#Grafica interactiva compuesta
gcov <-ggplot(data=datos1) +
geom_line(aes(x=Fecha,y=Confirmados),colour='red') +
geom_line(aes(x=Fecha,y=Decesos),colour='blue') +
geom_line(aes(x=Fecha,y=Recuperados),colour="green")+
ylab('COVID-19 Mexico') + xlab('Fecha') +
transition_reveal(Fecha)
ggplotly(gcov)