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toPutInDashboard.R
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toPutInDashboard.R
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library(tidyverse)
library(tidycovid19)
library(zoo)
theCountry='Mexico'
# Compare Mexico against the world
merged <- download_merged_data(cached = TRUE, silent = TRUE)
## New cases
### The world
w1 = merged %>%
select(date,confirmed) %>%
group_by(date) %>%
summarise(confirmed=sum(confirmed, na.rm = TRUE)) %>%
mutate(
new_cases = confirmed - lag(confirmed),
ave_new_cases = rollmean(new_cases, 7, na.pad=TRUE, align="right"),
region="World"
) %>%
filter(!is.na(new_cases), !is.na(ave_new_cases))
### Mexico
m1 = merged %>%
filter(iso3c == "MEX") %>%
select(date,confirmed) %>%
mutate(
new_cases = confirmed - lag(confirmed),
ave_new_cases = rollmean(new_cases, 7, na.pad=TRUE, align="right"),
region="Mexico"
) %>%
filter(!is.na(new_cases), !is.na(ave_new_cases))
a1 = merge(x = w1, y = m1, by = 'date', all.x = TRUE)
a1 = a1 %>% select(date, new_cases.x,new_cases.y,ave_new_cases.x,ave_new_cases.y)
#a1 = mutate_all(a1, ~if_else(is.na(.), 0, .))
a1$new_cases.x = a1$new_cases.x/(max(a1$new_cases.x, na.rm = T) - min(a1$new_cases.x, na.rm = T))
a1$new_cases.y = a1$new_cases.y/(max(a1$new_cases.y, na.rm = T) - min(a1$new_cases.y, na.rm = T))
a1$ave_new_cases.x = a1$ave_new_cases.x/(max(a1$ave_new_cases.x, na.rm = T) - min(a1$ave_new_cases.x, na.rm = T))
a1$ave_new_cases.y = a1$ave_new_cases.y/(max(a1$ave_new_cases.y, na.rm = T) - min(a1$ave_new_cases.y, na.rm = T))
thisColours = c("Mundo"="#f04546","Mexico"="blue")
ggplot(data = a1, aes(x = date)) +
#geom_bar(aes(y = new_cases.x, fill = "Mundo"), stat = "identity") +
geom_line(aes(y = ave_new_cases.x, colour="Mundo")) +
#geom_bar(aes(y = new_cases.y, fill="Mexico"), stat = "identity") +
geom_line(aes(y = ave_new_cases.y,colour ="Mexico")) +
ggtitle("New cases") +
scale_colour_manual(name="Region", values = thisColours) +
#scale_fill_manual(name="Region", values=alpha(thisColours,0.5)) +
theme_minimal() +
theme(
plot.caption = element_text(hjust = 0.5)
) +
labs(caption = "En esta sobre nuevos casos solo importa la forma,
\nestá normalizada.
\nSon los nuevos casos del mundo contra los de México para ver
\nqué tan en sincronía estamos con el mundo.")
## Daily deaths
### The world
w1 = merged %>%
select(date,deaths) %>%
group_by(date) %>%
summarise(confirmed=sum(deaths, na.rm = TRUE)) %>%
mutate(
new_cases = confirmed - lag(confirmed),
ave_new_cases = rollmean(new_cases, 7, na.pad=TRUE, align="right"),
region="World"
) %>%
filter(!is.na(new_cases), !is.na(ave_new_cases))
### Mexico
m1 = merged %>%
filter(iso3c == "MEX") %>%
select(date,deaths) %>%
mutate(
new_cases = deaths - lag(deaths),
ave_new_cases = rollmean(new_cases, 7, na.pad=TRUE, align="right"),
region="Mexico"
) %>%
filter(!is.na(new_cases), !is.na(ave_new_cases))
a1 = merge(x = w1, y = m1, by = 'date', all.x = TRUE)
a1 = a1 %>% select(date, new_cases.x,new_cases.y,ave_new_cases.x,ave_new_cases.y)
#a1 = mutate_all(a1, ~if_else(is.na(.), 0, .))
a1$new_cases.x = a1$new_cases.x/(max(a1$new_cases.x, na.rm = T) - min(a1$new_cases.x, na.rm = T))
a1$new_cases.y = a1$new_cases.y/(max(a1$new_cases.y, na.rm = T) - min(a1$new_cases.y, na.rm = T))
a1$ave_new_cases.x = a1$ave_new_cases.x/(max(a1$ave_new_cases.x, na.rm = T) - min(a1$ave_new_cases.x, na.rm = T))
a1$ave_new_cases.y = a1$ave_new_cases.y/(max(a1$ave_new_cases.y, na.rm = T) - min(a1$ave_new_cases.y, na.rm = T))
thisColours = c("Mundo"="#f04546","Mexico"="blue")
ggplot(data = a1, aes(x = date)) +
#geom_bar(aes(y = new_cases.x, fill = "Mundo"), stat = "identity") +
geom_line(aes(y = ave_new_cases.x, colour="Mundo")) +
#geom_bar(aes(y = new_cases.y, fill="Mexico"), stat = "identity") +
geom_line(aes(y = ave_new_cases.y,colour ="Mexico")) +
ggtitle(paste("Muertes en el mundo y en", theCountry)) +
scale_colour_manual(name="Region", values = thisColours) +
#scale_fill_manual(name="Region", values=alpha(thisColours,0.5)) +
theme_minimal() +
theme(
plot.caption = element_text(hjust = 0.5)
) +
labs(caption = "Son números normalizados para ver el parecido (o no)\n
de las gráficas.")
## Daily recovers
### The world
w1 = merged %>%
select(date,recovered) %>%
group_by(date) %>%
summarise(confirmed=sum(recovered, na.rm = TRUE)) %>%
mutate(
new_cases = confirmed - lag(confirmed),
ave_new_cases = rollmean(new_cases, 7, na.pad=TRUE, align="right"),
region="World"
) %>%
filter(!is.na(new_cases), !is.na(ave_new_cases))
### Mexico
m1 = merged %>%
filter(iso3c == "MEX") %>%
select(date,recovered) %>%
mutate(
new_cases = recovered - lag(recovered),
ave_new_cases = rollmean(new_cases, 7, na.pad=TRUE, align="right"),
region="Mexico"
) %>%
filter(!is.na(new_cases), !is.na(ave_new_cases))
a1 = merge(x = w1, y = m1, by = 'date', all.x = TRUE)
a1 = a1 %>% select(date, new_cases.x,new_cases.y,ave_new_cases.x,ave_new_cases.y)
#a1 = mutate_all(a1, ~if_else(is.na(.), 0, .))
a1$new_cases.x = a1$new_cases.x/(max(a1$new_cases.x, na.rm = T) - min(a1$new_cases.x, na.rm = T))
a1$new_cases.y = a1$new_cases.y/(max(a1$new_cases.y, na.rm = T) - min(a1$new_cases.y, na.rm = T))
a1$ave_new_cases.x = a1$ave_new_cases.x/(max(a1$ave_new_cases.x, na.rm = T) - min(a1$ave_new_cases.x, na.rm = T))
a1$ave_new_cases.y = a1$ave_new_cases.y/(max(a1$ave_new_cases.y, na.rm = T) - min(a1$ave_new_cases.y, na.rm = T))
thisColours = c("Mundo"="#f04546","Mexico"="blue")
ggplot(data = a1, aes(x = date)) +
#geom_bar(aes(y = new_cases.x, fill = "Mundo"), stat = "identity") +
geom_line(aes(y = ave_new_cases.x, colour="Mundo")) +
#geom_bar(aes(y = new_cases.y, fill="Mexico"), stat = "identity") +
geom_line(aes(y = ave_new_cases.y,colour ="Mexico")) +
ggtitle(paste("Muertes en el mundo y en", theCountry)) +
scale_colour_manual(name="Region", values = thisColours) +
#scale_fill_manual(name="Region", values=alpha(thisColours,0.5)) +
theme_minimal() +
theme(
plot.caption = element_text(hjust = 0.5)
) +
labs(caption = "Son números normalizados para ver el parecido (o no)\n
de las gráficas.")
#' Place of Mexico by population or pop_density by
#' confirmed, ecdc_cases
#' deaths, ecdc_deaths
#' recovered
#' total_tests
#' positive_rate
#' hosp_patients
#' icu_patients
#' total_vaccinations
#' soc_dist
#' mov_rest
#' pub_health
#' gov_soc_econ
#' lockdown
#'
#' How to use this:
#' "apple_mtr_driving" "apple_mtr_walking"
#' "apple_mtr_transit" "gcmr_retail_recreation" "gcmr_grocery_pharmacy"
#' "gcmr_parks" "gcmr_transit_stations" "gcmr_workplaces"
#' "gcmr_residential" "gtrends_score" "gtrends_country_score"
#'
#' For each variable v1 get
#' i1 = v1 by date by country * 100000 / population
#' Show top 3, bottom 3, Mexico, 3 above Mexico, 3 below Mexico
#' and some distance indicator
m2mex = merged[merged$iso3c=="MEX",]
n = nrow(m2mex)
theNames = names(m2mex)
x3 = sapply(1:ncol(m2mex), function(c1){
ifelse(test = n==sum(is.na(m2mex[,theNames[c1]]))
, yes = print(paste(theNames[c1],"is empty"))
, no = print(paste(theNames[c1],"has", sum(!is.na(m2mex[,theNames[c1]])),"not empty rows from", n)))
})
## Only for max date
m2 = merged %>%
select(date, iso3c, country, population,
confirmed, ecdc_cases,
deaths, ecdc_deaths,
recovered,
total_tests,
positive_rate,
#hosp_patients,
#icu_patients,
total_vaccinations
) %>%
filter(date == max(date)) %>%
mutate(
confirmedX100K = confirmed * 100000 / population,
ecdc_casesX100K = ecdc_cases * 100000 / population,
deathsX100K = deaths * 100000 / population,
ecdc_deathsX100K = ecdc_deaths * 100000 / population,
recoveredX100K = recovered * 100000 / population,
total_testsX100K = total_tests * 100000 / population,
positive_rateX100K = positive_rate * 100000 / population,
#hosp_patientsX100K = hosp_patients * 100000 / population,
#icu_patientsX100K = icu_patients * 100000 / population,
total_vaccinationsX100K = total_vaccinations * 100000 / population
) %>%
select(iso3c,country,confirmedX100K,
ecdc_casesX100K, deathsX100K,
ecdc_deathsX100K, recoveredX100K,
total_testsX100K, positive_rateX100K,
total_vaccinationsX100K)
## Mexican place for each metric
x3 = sapply(names(m2), function(thisMetric){
c1 = m2[,c("iso3c","country",thisMetric)]
c1 = c1[!is.na(c1[,thisMetric]),]
if(nrow(c1) > 0 && nrow(c1[c1$iso3c=="MEX",thisMetric])==1) {
c1 = c1[order(c1[,thisMetric]),]
c1$place = seq(1:nrow(c1))
mex.place = which(c1$iso3c=="MEX")
print(paste("Mexico place:", mex.place,"for",thisMetric,"from",nrow(c1)))
c1
}
})
x3[sapply(x3, is.null)] <- NULL
## testing plots
tv=x3[["total_vaccinationsX100K"]]
tv=x3[["deathsX100K"]]
highlight=which(tv$iso3c=="MEX")
#tv[,"iso3c"] <- as.factor(tv[,"iso3c"])
bar.colors = c(rep(x="blue",highlight-1),"red",rep(x="blue",nrow(tv)-highlight))
ggplot(data = tv) +
geom_bar(aes(x = reorder(country, place), y = tv[[3]], fill = as.factor(place)),
stat = "identity") +
theme(axis.text.x = element_text(angle = 45),
legend.position = "none") +
scale_fill_manual(values = bar.colors)