#install.packages("maps")
library(tidyverse)
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## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
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## x dplyr::filter() masks stats::filter()
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library(lubridate)
## Warning: package 'lubridate' was built under R version 3.6.3
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
library(maps)
## Warning: package 'maps' was built under R version 3.6.3
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
confirmados <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
recuperados <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
muertos <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
names(confirmados)
## [1] "Province.State" "Country.Region" "Lat" "Long"
## [5] "X1.22.20" "X1.23.20" "X1.24.20" "X1.25.20"
## [9] "X1.26.20" "X1.27.20" "X1.28.20" "X1.29.20"
## [13] "X1.30.20" "X1.31.20" "X2.1.20" "X2.2.20"
## [17] "X2.3.20" "X2.4.20" "X2.5.20" "X2.6.20"
## [21] "X2.7.20" "X2.8.20" "X2.9.20" "X2.10.20"
## [25] "X2.11.20" "X2.12.20" "X2.13.20" "X2.14.20"
## [29] "X2.15.20" "X2.16.20" "X2.17.20" "X2.18.20"
## [33] "X2.19.20" "X2.20.20" "X2.21.20" "X2.22.20"
## [37] "X2.23.20" "X2.24.20" "X2.25.20" "X2.26.20"
## [41] "X2.27.20" "X2.28.20" "X2.29.20" "X3.1.20"
## [45] "X3.2.20" "X3.3.20" "X3.4.20" "X3.5.20"
## [49] "X3.6.20" "X3.7.20" "X3.8.20" "X3.9.20"
## [53] "X3.10.20" "X3.11.20" "X3.12.20" "X3.13.20"
## [57] "X3.14.20" "X3.15.20" "X3.16.20" "X3.17.20"
## [61] "X3.18.20" "X3.19.20" "X3.20.20" "X3.21.20"
## [65] "X3.22.20" "X3.23.20" "X3.24.20" "X3.25.20"
## [69] "X3.26.20" "X3.27.20" "X3.28.20" "X3.29.20"
## [73] "X3.30.20" "X3.31.20" "X4.1.20" "X4.2.20"
## [77] "X4.3.20" "X4.4.20" "X4.5.20" "X4.6.20"
## [81] "X4.7.20" "X4.8.20" "X4.9.20" "X4.10.20"
## [85] "X4.11.20" "X4.12.20" "X4.13.20" "X4.14.20"
## [89] "X4.15.20" "X4.16.20" "X4.17.20" "X4.18.20"
## [93] "X4.19.20" "X4.20.20" "X4.21.20" "X4.22.20"
## [97] "X4.23.20" "X4.24.20" "X4.25.20" "X4.26.20"
## [101] "X4.27.20" "X4.28.20" "X4.29.20" "X4.30.20"
## [105] "X5.1.20" "X5.2.20" "X5.3.20" "X5.4.20"
## [109] "X5.5.20" "X5.6.20" "X5.7.20" "X5.8.20"
## [113] "X5.9.20" "X5.10.20" "X5.11.20" "X5.12.20"
## [117] "X5.13.20" "X5.14.20" "X5.15.20" "X5.16.20"
## [121] "X5.17.20" "X5.18.20" "X5.19.20" "X5.20.20"
## [125] "X5.21.20" "X5.22.20" "X5.23.20" "X5.24.20"
## [129] "X5.25.20" "X5.26.20" "X5.27.20" "X5.28.20"
## [133] "X5.29.20" "X5.30.20"
head(confirmados$Country.Region)
## [1] Afghanistan Albania Algeria
## [4] Andorra Angola Antigua and Barbuda
## 188 Levels: Afghanistan Albania Algeria Andorra Angola ... Zimbabwe
covid19 <- mutate(confirmados, Category = "Confirmed") %>%
bind_rows(mutate(recuperados, Category = "Recovered")) %>%
bind_rows(mutate(muertos, Category = "Deaths")) %>%
select(Province.State:Long, Category, everything()) %>%
pivot_longer(-(Province.State:Category), names_to = "Date", values_to = "Value") %>%
mutate(Date = mdy(str_remove(Date, "X")))
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
covid19 <- covid19 %>%
filter(!is.na(Value))
covid19 %>%
filter(Date == ymd("2020 03 01")) %>%
head
## # A tibble: 6 x 7
## Province.State Country.Region Lat Long Category Date Value
## <chr> <fct> <dbl> <dbl> <chr> <date> <int>
## 1 "" Afghanistan 33 65 Confirmed 2020-03-01 1
## 2 "" Albania 41.2 20.2 Confirmed 2020-03-01 0
## 3 "" Algeria 28.0 1.66 Confirmed 2020-03-01 1
## 4 "" Andorra 42.5 1.52 Confirmed 2020-03-01 0
## 5 "" Angola -11.2 17.9 Confirmed 2020-03-01 0
## 6 "" Antigua and Barbuda 17.1 -61.8 Confirmed 2020-03-01 0
covid19 %>%
filter(Date == max(Date),
Country.Region == "Peru" ) %>%
head
## # A tibble: 3 x 7
## Province.State Country.Region Lat Long Category Date Value
## <chr> <fct> <dbl> <dbl> <chr> <date> <int>
## 1 "" Peru -9.19 -75.0 Confirmed 2020-05-30 155671
## 2 "" Peru -9.19 -75.0 Recovered 2020-05-30 66447
## 3 "" Peru -9.19 -75.0 Deaths 2020-05-30 4371
options(scipen = 20)
paises_a_comparar <- c("Argentina", "Brazil", "Chile", "Uruguay", "Peru", "Paraguay", "Bolivia", "Colombia", "Venezuela", "Ecuador" )
covid19 %>%
filter(Country.Region %in% paises_a_comparar) %>%
group_by(Category, Date, Country.Region) %>%
summarise(Value = sum(Value)) %>%
ggplot() +
geom_col(aes(x = Date, y = Value, fill = Category)) +
facet_wrap(~Country.Region, scales = "free") +
scale_fill_brewer(palette = "Set2") +
labs(title = "COVID19: Cantidad de casos", subtitle = paste("al", max(covid19$Date)),
fill = "Situación",
caption = "Fuente: 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE",
y = NULL, x = NULL) +
theme_light()
covid19 %>%
filter(Country.Region %in% paises_a_comparar, Category == "Confirmed") %>%
group_by(Date, Country.Region) %>%
summarise(Value = sum(Value)) %>%
ggplot() +
geom_col(aes(x = Country.Region, y = Value)) +
coord_flip()+
labs(title = "COVID19: Confirmados | América del Sur" , subtitle = paste("al", max(covid19$Date)),
color = "",
x = NULL, y = NULL,
caption = "Fuente: 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE") +
theme_light()
covid19 %>%
filter(Country.Region %in% paises_a_comparar, Category == "Confirmed") %>%
group_by(Date, Country.Region) %>%
summarise(Value = sum(Value)) %>%
ggplot() +
geom_path(aes(x = Date, y = Value, color = Country.Region)) +
scale_y_log10() +
labs(title = "COVID19: Confirmados | América del Sur" , subtitle = paste("al", max(covid19$Date)),
color = "",
x = NULL, y = NULL,
caption = "Fuente: 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE") +
theme_light()
## Warning: Transformation introduced infinite values in continuous y-axis
covid19 %>%
filter(Country.Region %in% paises_a_comparar, Category == "Recovered") %>%
group_by(Date, Country.Region) %>%
summarise(Value = sum(Value)) %>%
ggplot() +
geom_path(aes(x = Date, y = Value, color = Country.Region)) +
scale_y_log10() +
labs(title = "COVID19: Recuperados | América del Sur" , subtitle = paste("al", max(covid19$Date)),
color = "",
x = NULL, y = NULL,
caption = "Fuente: 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE") +
theme_light()
## Warning: Transformation introduced infinite values in continuous y-axis
covid19 %>%
filter(Country.Region %in% paises_a_comparar, Category == "Deaths") %>%
group_by(Date, Country.Region) %>%
summarise(Value = sum(Value)) %>%
ggplot() +
geom_path(aes(x = Date, y = Value, color = Country.Region)) +
scale_y_log10() +
labs(title = "COVID19: Fallecidos | América del Sur", subtitle = paste("al", max(covid19$Date)),
color = "",
x = NULL, y = NULL,
caption = "Fuente: 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE") +
theme_light()
## Warning: Transformation introduced infinite values in continuous y-axis
ggplot() +
geom_polygon(data = map_data("world"),
aes(x = long, y = lat, group = group),
fill="gray90", colour = "white") +
# Retiramos los lugares donde el conteo aún es 0
geom_point(data = filter(covid19, Date == max(Date), Category == "Confirmed", Value > 0),
aes(x = Long, y = Lat, size = Value),
alpha = .5, color = "salmon") +
# Usamos escala logarÃtmica
scale_size(trans = "log10") +
labs(title = "COVID19: Infecciones confirmadas",
subtitle = paste("al", max(covid19$Date)),
caption = "Fuente: 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE", x="Longitud" , y="Latitud") +
theme_light()
