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script.R
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script.R
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require(pdftools)
require(tidyverse)
require(ggplot2)
require(unam.theme)
require(stringr)
require(leaflet)
strsplit2 <- function(x,
split,
type = "remove",
perl = FALSE,
...) {
if (type == "remove") {
# use base::strsplit
out <- base::strsplit(x = x, split = split, perl = perl, ...)
} else if (type == "before") {
# split before the delimiter and keep it
out <- base::strsplit(x = x,
split = paste0("(?<=.)(?=", split, ")"),
perl = TRUE,
...)
} else if (type == "after") {
# split after the delimiter and keep it
out <- base::strsplit(x = x,
split = paste0("(?<=", split, ")"),
perl = TRUE,
...)
} else {
# wrong type input
stop("type must be remove, after or before!")
}
return(out)
}
url_sospechosos <- "https://www.gob.mx/cms/uploads/attachment/file/544539/Tabla_casos_sospechosos_COVID-19_2020.03.31.pdf"
download.file(url_sospechosos, 'casos_sospechosos.pdf', mode="wb")
url_positivos <- "https://www.gob.mx/cms/uploads/attachment/file/544538/Tabla_casos_positivos_COVID-19_resultado_InDRE_2020.03.31.pdf"
download.file(url_positivos, "casos_positivos.pdf", mode = "wb")
doc <- pdf_text("casos_positivos.pdf") %>%
readr::read_lines(skip_empty_rows = TRUE, skip = 6) %>%
trimws() %>%
strsplit2(., "\\s[FM]\\s", type = "before")
estados <- lapply(doc,
function(x){
trimws(str_remove(x[1], "\\d{1,}"))
}) %>% unlist()
tmp <- lapply(doc,
function(x){
x[2]
})
tmp <- unlist(tmp)
tmp <- trimws(gsub("\\s+", " ", tmp))
generos <- lapply(strsplit2(tmp, "^[MF]", type = "after"),
function(x){
trimws(x[1])
}) %>% unlist()
tmp <- lapply(strsplit2(tmp, "^[MF]", type = "after"),
function(x){
trimws(x[2])
}) %>% unlist()
edades <- lapply(strsplit2(tmp, " ", type = "after"),
function(x){
trimws(x[1])
}) %>% unlist()
fechas <- str_extract(doc, pattern = "\\d{1,2}\\/\\d{1,2}\\/\\d{4}")
positivos_df <- data.frame(caso = 1:length(doc), estado = estados, genero = generos, edad = edades, fecha = fechas) %>%
na.omit()
casos_fecha <- table(fechas) %>%
as.data.frame()
casos_fecha <- janitor::clean_names(casos_fecha)
casos_fecha <- casos_fecha %>%
mutate(fechas = lubridate::dmy(fechas))
casos_fecha <- arrange(casos_fecha, fechas)
casos_fecha <- casos_fecha %>%
mutate(cum_freq = cumsum(freq))
plotly::ggplotly(ggplot(casos_fecha)+
geom_line(aes(fechas, cum_freq))+
geom_point(aes(fechas, cum_freq))+
scale_x_date(breaks = "day")+
theme_unam()+
theme(axis.text.x = element_text(angle = 90)))
plotly::ggplotly(ggplot(casos_fecha)+
geom_line(aes(fechas, freq))+
geom_point(aes(fechas, freq))+
scale_x_date(breaks = "day")+
theme_unam()+
theme(axis.text.x = element_text(angle = 90)))
plotly::ggplotly(ggplot(casos_positivos)+
geom_bar(aes("genero", fill = genero), position = "fill")+
coord_flip()+
labs(x = "", y = "")+
ggtitle("Casos por género")+
theme_unam()+
scale_fill_discrete("Género")+
theme(axis.text = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank()))
plotly::ggplotly(ggplot(casos_positivos)+
geom_bar(aes(as.numeric(edad)))+
labs(x = "Edad", y = "Casos")+
theme_unam())
ggplot(casos_positivos)+
geom_density(aes(as.numeric(edad)))
casos_positivos <- fastDummies::dummy_cols(casos_positivos, select_columns = c("genero"))
mapa_data <- casos_positivos %>%
group_by(estado) %>%
summarise(casos = n(), edad_prom = mean(edad, na.rm = TRUE), edad_med = median(edad, na.rm = TRUE),
n_M = sum(genero_M, na.rm = TRUE), n_F = sum(genero_F, na.rm = TRUE))
mapa_data <- right_join(mapa_data, estados_coords)
mapa_data$casos_clase <- cut(mapa_data$casos,
c(1,50,100,250,500,1000), include.lowest = T,
labels = c('1-50', '51-100', '101-250', '251-500', '501-1000'))
casosCol <- colorFactor(palette = 'RdYlGn', mapa_data$casos_clase, reverse = TRUE)
leaflet(mapa_data) %>%
addTiles() %>%
addCircleMarkers(lat = ~lat, lng = ~lon, color = ~casosCol(casos_clase), fillOpacity = 0.5,
popup = ~paste(sep = " ",
"<b>Estado:</b>:", estado, "<br/>",
"<b>Casos:</b>", casos, "<br/>",
"<b>Edad promedio:</b>", round(edad_prom,1), "<br/>",
"<b>Edad mediana:</b>", round(edad_med,1), "<br/>",
"<b>Casos masculinos:</b>", n_M, "<br/>",
"<b>Casos femeninos:</b>", n_F
),
label = ~htmltools::htmlEscape(paste(casos, "casos"))) %>%
addLegend('topright', pal = casosCol, values = mapa_data$casos_clase,
title = 'Número de casos',
opacity = 1)
paste(sep = " ",
"<b>Estado:</b>:", estado, "<br/>",
"<b>Casos:</b>", casos, "<br/>",
"<b>Edad promedio:</b>", round(edad_prom,1), "<br/>",
"<b>Edad mediana:</b>", round(edad_med,1), "<br/>",
"<b>Casos masculinos:</b>", n_M, "<br/>",
"<b>Casos femeninos:</b>", n_F
)