In databrew/covid19: COVID-19 App

# Basic knitr options
library(knitr)
opts_chunk$set(comment = NA, 
               # echo = FALSE, 
               warning = FALSE, 
               message = FALSE, 
               error = TRUE, 
               cache = FALSE,
               fig.width = 9.64,
               fig.height = 5.9,
               fig.path = 'figures/')

## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(ggplot2)
library(sp)
library(raster)
library(viridis)
library(ggthemes)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(RColorBrewer)
library(readr)
library(zoo)
options(scipen = '999')

library(gtrendsR)
if(!'gt.RData' %in% dir()){
  gt <- gtrends(keyword = 'gusto olfato',
              geo = 'ES',
              time = 'today 3-m',
              low_search_volume = T)
  save(gt, file = 'gt.RData')
} else {
  load('gt.RData')
}

# Get the regional ts

by_time <- gt$interest_over_time
by_region <- gt$interest_by_region
by_city <- gt$interest_by_city

# Join regions with covid data
covid <- esp_df %>% filter(date == max(date))

by_region <- by_region %>%
  mutate(ccaa = location) %>%
  mutate(ccaa = ifelse(ccaa == 'Castile-La Mancha', 'CLM',
                       ifelse(ccaa == 'Community of Madrid' , 'Madrid',
                              ifelse(ccaa == 'Navarre', 'Navarra',
                                     ifelse(ccaa == 'Castile and León', 'CyL', 
                                            ifelse(ccaa == 'Catalonia', 'Cataluña', 
                                                   ifelse(ccaa == 'Basque Country', 'País Vasco', 
                                                          ifelse(ccaa == 'Valencian Community', 'C. Valenciana', 
                                                                 ifelse(ccaa == 'Andalusia', 'Andalucía', 
                                                                        ifelse(ccaa == 'Canary Islands', 'Canarias', 
                                                                               ifelse(ccaa == 'Aragon', 'Aragón',
                                                                                      ifelse(ccaa == 'Balearic Islands', 'Baleares', 
                                                                                             ifelse(ccaa == 'Region of Murcia', 'Murcia', ccaa)))))))))))))

pd <- left_join(by_region, covid) %>%
  left_join(esp_pop) %>%
  mutate(y = cases / pop * 1000000)

library(ggrepel)
ggplot(data = pd,
       aes(x = y,
           y = hits)) +
  geom_point(size = 4, alpha = 0.6) +
  geom_text_repel(aes(label = ccaa), size = 6) +
  # scale_y_log10() +
  geom_smooth(method='lm', formula= y~x) +
  theme_simple() +
  labs(y = 'Frecuencia relativa de búsquedas',
       x = 'Muertes acumuladas por 1.000.000',
       title = 'Correlación entre búsquedas en Google con las palabras "gusto"/"olfato" y muertes por 1.000.000')

ggplot(data = by_time,
       aes(x = date,
           y = hits)) +
  geom_area(fill = 'darkorange',
            color = 'black') +
  geom_vline(xintercept = as.POSIXct(as.Date('2020-03-20')),
             lwd = 3,
             alpha = 0.7,
             color = 'blue') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Porcentaje relativo de búsquedas',
       title = 'Búsquedas en Google con las palabras "gusto" y "olfato"')