Descripción

Este es un análisis exploratorio de datos con fines didácticos. El análisis es realizado con datos relacionados a COVID-19 reportados por diversos países. Los datos son obtenidos del sitio: https://ourworldindata.org/coronavirus-source-data. El conjunto de datos del sitio es actualizado constantemente. El resultado del análisis cambia de acuerdo al día en que se ejecuta el código.

Lectura de datos

Se cargan los datos directamente del sitio para obtener la información actualizada al día de ejecución del código.

covid.df <- read.csv("https://covid.ourworldindata.org/data/owid-covid-data.csv")

Se cargan los paquetes necesarios:

library(ggplot2)
library(knitr)
library(reshape2)
library(kableExtra)
#library(dplyr)
library(tidyverse)
library(lubridate)
library(gmodels)
plotscaption <- "oscarcastrolopez.github.io"

La estructura del dataframe con las variables y su tipo:

str(covid.df)
## 'data.frame':    39482 obs. of  40 variables:
##  $ iso_code                       : Factor w/ 212 levels "","ABW","AFG",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ continent                      : Factor w/ 7 levels "","Africa","Asia",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ location                       : Factor w/ 212 levels "Afghanistan",..: 10 10 10 10 10 10 10 10 10 10 ...
##  $ date                           : Factor w/ 239 levels "2019-12-31","2020-01-01",..: 74 80 81 82 83 84 85 86 87 88 ...
##  $ total_cases                    : num  2 NA 4 NA NA NA 12 17 19 28 ...
##  $ new_cases                      : num  2 NA 2 NA NA NA 8 5 2 9 ...
##  $ new_cases_smoothed             : num  NA 0.286 0.286 0.286 0.286 ...
##  $ total_deaths                   : num  0 NA 0 NA NA NA 0 0 0 0 ...
##  $ new_deaths                     : num  0 NA 0 NA NA NA 0 0 0 0 ...
##  $ new_deaths_smoothed            : num  NA 0 0 0 0 0 0 0 0 0 ...
##  $ total_cases_per_million        : num  18.7 NA 37.5 NA NA ...
##  $ new_cases_per_million          : num  18.7 NA 18.7 NA NA ...
##  $ new_cases_smoothed_per_million : num  NA 2.68 2.68 2.68 2.68 ...
##  $ total_deaths_per_million       : num  0 NA 0 NA NA NA 0 0 0 0 ...
##  $ new_deaths_per_million         : num  0 NA 0 NA NA NA 0 0 0 0 ...
##  $ new_deaths_smoothed_per_million: num  NA 0 0 0 0 0 0 0 0 0 ...
##  $ new_tests                      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests_per_thousand       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_per_thousand         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed_per_thousand: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_per_case                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ positive_rate                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_units                    : Factor w/ 7 levels "","people tested",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ stringency_index               : num  0 33.3 33.3 44.4 44.4 ...
##  $ population                     : num  106766 106766 106766 106766 106766 ...
##  $ population_density             : num  585 585 585 585 585 ...
##  $ median_age                     : num  41.2 41.2 41.2 41.2 41.2 41.2 41.2 41.2 41.2 41.2 ...
##  $ aged_65_older                  : num  13.1 13.1 13.1 13.1 13.1 ...
##  $ aged_70_older                  : num  7.45 7.45 7.45 7.45 7.45 ...
##  $ gdp_per_capita                 : num  35974 35974 35974 35974 35974 ...
##  $ extreme_poverty                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ cardiovasc_death_rate          : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ diabetes_prevalence            : num  11.6 11.6 11.6 11.6 11.6 ...
##  $ female_smokers                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ male_smokers                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ handwashing_facilities         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ hospital_beds_per_thousand     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ life_expectancy                : num  76.3 76.3 76.3 76.3 76.3 ...

La descripción de cada una de las variables de este conjunto de datos se encuentra en el sitio: https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-codebook.csv.

Ajuste de la columna date que es tipo factor a tipo date:

covid.df$date <- as.Date(covid.df$date, format("%Y-%m-%d"))

Lista de países del conjunto de datos

levels(covid.df$location)
##   [1] "Afghanistan"                      "Albania"                         
##   [3] "Algeria"                          "Andorra"                         
##   [5] "Angola"                           "Anguilla"                        
##   [7] "Antigua and Barbuda"              "Argentina"                       
##   [9] "Armenia"                          "Aruba"                           
##  [11] "Australia"                        "Austria"                         
##  [13] "Azerbaijan"                       "Bahamas"                         
##  [15] "Bahrain"                          "Bangladesh"                      
##  [17] "Barbados"                         "Belarus"                         
##  [19] "Belgium"                          "Belize"                          
##  [21] "Benin"                            "Bermuda"                         
##  [23] "Bhutan"                           "Bolivia"                         
##  [25] "Bonaire Sint Eustatius and Saba"  "Bosnia and Herzegovina"          
##  [27] "Botswana"                         "Brazil"                          
##  [29] "British Virgin Islands"           "Brunei"                          
##  [31] "Bulgaria"                         "Burkina Faso"                    
##  [33] "Burundi"                          "Cambodia"                        
##  [35] "Cameroon"                         "Canada"                          
##  [37] "Cape Verde"                       "Cayman Islands"                  
##  [39] "Central African Republic"         "Chad"                            
##  [41] "Chile"                            "China"                           
##  [43] "Colombia"                         "Comoros"                         
##  [45] "Congo"                            "Costa Rica"                      
##  [47] "Cote d'Ivoire"                    "Croatia"                         
##  [49] "Cuba"                             "Curacao"                         
##  [51] "Cyprus"                           "Czech Republic"                  
##  [53] "Democratic Republic of Congo"     "Denmark"                         
##  [55] "Djibouti"                         "Dominica"                        
##  [57] "Dominican Republic"               "Ecuador"                         
##  [59] "Egypt"                            "El Salvador"                     
##  [61] "Equatorial Guinea"                "Eritrea"                         
##  [63] "Estonia"                          "Ethiopia"                        
##  [65] "Faeroe Islands"                   "Falkland Islands"                
##  [67] "Fiji"                             "Finland"                         
##  [69] "France"                           "French Polynesia"                
##  [71] "Gabon"                            "Gambia"                          
##  [73] "Georgia"                          "Germany"                         
##  [75] "Ghana"                            "Gibraltar"                       
##  [77] "Greece"                           "Greenland"                       
##  [79] "Grenada"                          "Guam"                            
##  [81] "Guatemala"                        "Guernsey"                        
##  [83] "Guinea"                           "Guinea-Bissau"                   
##  [85] "Guyana"                           "Haiti"                           
##  [87] "Honduras"                         "Hong Kong"                       
##  [89] "Hungary"                          "Iceland"                         
##  [91] "India"                            "Indonesia"                       
##  [93] "International"                    "Iran"                            
##  [95] "Iraq"                             "Ireland"                         
##  [97] "Isle of Man"                      "Israel"                          
##  [99] "Italy"                            "Jamaica"                         
## [101] "Japan"                            "Jersey"                          
## [103] "Jordan"                           "Kazakhstan"                      
## [105] "Kenya"                            "Kosovo"                          
## [107] "Kuwait"                           "Kyrgyzstan"                      
## [109] "Laos"                             "Latvia"                          
## [111] "Lebanon"                          "Lesotho"                         
## [113] "Liberia"                          "Libya"                           
## [115] "Liechtenstein"                    "Lithuania"                       
## [117] "Luxembourg"                       "Macedonia"                       
## [119] "Madagascar"                       "Malawi"                          
## [121] "Malaysia"                         "Maldives"                        
## [123] "Mali"                             "Malta"                           
## [125] "Mauritania"                       "Mauritius"                       
## [127] "Mexico"                           "Moldova"                         
## [129] "Monaco"                           "Mongolia"                        
## [131] "Montenegro"                       "Montserrat"                      
## [133] "Morocco"                          "Mozambique"                      
## [135] "Myanmar"                          "Namibia"                         
## [137] "Nepal"                            "Netherlands"                     
## [139] "New Caledonia"                    "New Zealand"                     
## [141] "Nicaragua"                        "Niger"                           
## [143] "Nigeria"                          "Northern Mariana Islands"        
## [145] "Norway"                           "Oman"                            
## [147] "Pakistan"                         "Palestine"                       
## [149] "Panama"                           "Papua New Guinea"                
## [151] "Paraguay"                         "Peru"                            
## [153] "Philippines"                      "Poland"                          
## [155] "Portugal"                         "Puerto Rico"                     
## [157] "Qatar"                            "Romania"                         
## [159] "Russia"                           "Rwanda"                          
## [161] "Saint Kitts and Nevis"            "Saint Lucia"                     
## [163] "Saint Vincent and the Grenadines" "San Marino"                      
## [165] "Sao Tome and Principe"            "Saudi Arabia"                    
## [167] "Senegal"                          "Serbia"                          
## [169] "Seychelles"                       "Sierra Leone"                    
## [171] "Singapore"                        "Sint Maarten (Dutch part)"       
## [173] "Slovakia"                         "Slovenia"                        
## [175] "Somalia"                          "South Africa"                    
## [177] "South Korea"                      "South Sudan"                     
## [179] "Spain"                            "Sri Lanka"                       
## [181] "Sudan"                            "Suriname"                        
## [183] "Swaziland"                        "Sweden"                          
## [185] "Switzerland"                      "Syria"                           
## [187] "Taiwan"                           "Tajikistan"                      
## [189] "Tanzania"                         "Thailand"                        
## [191] "Timor"                            "Togo"                            
## [193] "Trinidad and Tobago"              "Tunisia"                         
## [195] "Turkey"                           "Turks and Caicos Islands"        
## [197] "Uganda"                           "Ukraine"                         
## [199] "United Arab Emirates"             "United Kingdom"                  
## [201] "United States"                    "United States Virgin Islands"    
## [203] "Uruguay"                          "Uzbekistan"                      
## [205] "Vatican"                          "Venezuela"                       
## [207] "Vietnam"                          "Western Sahara"                  
## [209] "World"                            "Yemen"                           
## [211] "Zambia"                           "Zimbabwe"

Continentes del conjunto de datos

levels(covid.df$continent)
## [1] ""              "Africa"        "Asia"          "Europe"       
## [5] "North America" "Oceania"       "South America"

¿Cuál es la cantidad de días máximo que ha sido reportado por los países?

maxdays <- max(table(covid.df$location))
print(paste("Días máximos reportados:",maxdays))
## [1] "Días máximos reportados: 239"

Países que han reportado datos la mayor cantidad de días

names(table(covid.df$location)[table(covid.df$location)==maxdays])
##  [1] "Afghanistan"          "Algeria"              "Armenia"             
##  [4] "Australia"            "Austria"              "Azerbaijan"          
##  [7] "Bahrain"              "Belarus"              "Belgium"             
## [10] "Brazil"               "Cambodia"             "Canada"              
## [13] "China"                "Croatia"              "Czech Republic"      
## [16] "Denmark"              "Dominican Republic"   "Ecuador"             
## [19] "Egypt"                "Estonia"              "Finland"             
## [22] "France"               "Georgia"              "Germany"             
## [25] "Greece"               "Iceland"              "India"               
## [28] "Indonesia"            "International"        "Iran"                
## [31] "Iraq"                 "Ireland"              "Israel"              
## [34] "Italy"                "Japan"                "Kuwait"              
## [37] "Lebanon"              "Lithuania"            "Luxembourg"          
## [40] "Macedonia"            "Malaysia"             "Mexico"              
## [43] "Monaco"               "Nepal"                "Netherlands"         
## [46] "New Zealand"          "Nigeria"              "Norway"              
## [49] "Oman"                 "Pakistan"             "Philippines"         
## [52] "Qatar"                "Romania"              "Russia"              
## [55] "San Marino"           "Singapore"            "South Korea"         
## [58] "Sri Lanka"            "Sweden"               "Switzerland"         
## [61] "Taiwan"               "Thailand"             "United Arab Emirates"
## [64] "United Kingdom"       "United States"        "Vietnam"             
## [67] "World"

¿En qué fechas inician y terminan los datos reportados?

startdate <- min(covid.df$date)
enddate <- max(covid.df$date)
todayformatted <- format(Sys.Date(), "%A, %d de %B de %Y")
todayformatted <- paste(toupper(substr(todayformatted, 1, 1)), 
                        substr(todayformatted, 2, nchar(todayformatted)), 
                        sep="")
print(paste("Inicia el", format(startdate, "%d de %B de %Y"), 
            "y termina el",  format(enddate, "%d de %B de %Y")))
## [1] "Inicia el 31 de diciembre de 2019 y termina el 25 de agosto de 2020"

Rankings de países con las variables de totales o acumulados

Del dataframe original, se obtienen sólo las observaciones en donde la columna date sea igual a la fecha máxima. De esta manera se obtienen los totales o acumulados actualizados de cada país. Adicionalmente, se eliminan los países con menos de 1 millón de habitantes. Algunos países con poca población tienen estadísticas por miilón de habitantes muy altas.

Con las siguientes variables se crean los rankings:

# Get one row of each country with the updated totals
covid.total.df <- covid.df[covid.df$date== enddate,]
# Filter countries with less than 1 millón population
covid.total.df <- covid.total.df[covid.total.df$population >= 1000000,]
covid.total.df$location <- droplevels.factor(covid.total.df$location)
print(names(covid.total.df)[c(5,7,9,11)])
## [1] "total_cases"             "new_cases_smoothed"     
## [3] "new_deaths"              "total_cases_per_million"

Al descartar los países que tienen al menos 1 millón de habitantes, el conjunto de datos ahora tiene: 156 Países.

Para cada una de las variables con totales, se obtienen los 20 países con el valor más alto y los 20 países con el valor más bajo. En caso de que México no se encuentre en los primeros 20 países, se agrega en la lista indicando la posición que le toca tomando en cuenta todos los países. Para cada variable a analizar se hace lo siguiente en R:

  1. Se ordenan los datos con la columna correspondiente y se guarda en un nuevo dataframe.
  2. Se guardan sólo los 20 países con los valores más altos y los 20 países con valores más bajos.
  3. Se filtran las cólumnas de interés, el resto de columnas se descarta. Adicionalmente, se agrega una nueva columna indicando con un valor numérico la posición en la que se encuentra el país en el ranking.
  4. Se muestran los datos en una tabla y una gráfica de barras horizontales.

Ranking de países: Total de casos reportados de COVID-19 con la columna total_cases

# Data is ordered according to the total_cases column
ranking.total_cases <- covid.total.df[order(-covid.total.df$total_cases),]
# The row corresponding to World is removed
ranking.total_cases <- ranking.total_cases[ranking.total_cases$location != "World", ]
# A new column indicating the positionin the rank is added
ranking.total_cases$position <- 1:nrow(ranking.total_cases)
# Only columns of interest are kept
columnfilter <- c("position", "location", "total_cases")
bottom20.total_cases <- tail(ranking.total_cases[, columnfilter],20)
top20.total_cases <- head(ranking.total_cases[, columnfilter],20)
rm(ranking.total_cases)
rownames(top20.total_cases) <- c()
rownames(bottom20.total_cases) <- c()

mexrow <- which(top20.total_cases$location=='Mexico')
top20.total_cases$total_cases_formated <- formatC(top20.total_cases$total_cases, 
                                                  format="f", big.mark=",", digits=0)
bottom20.total_cases$total_cases_formated <- formatC(bottom20.total_cases$total_cases, 
                                                     format="f", big.mark=",", digits=0)

Tablas

tablecolnames <- c("Posición", "País", "Casos totales")
kable(top20.total_cases[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con más casos totales") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_cases[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con menos casos totales") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")
Países con más casos totales
Posición País Casos totales
1 United States 5,740,909
2 Brazil 3,622,861
3 India 3,167,323
4 Russia 961,493
5 South Africa 611,450
6 Peru 600,438
7 Mexico 563,705
8 Colombia 551,696
9 Chile 399,568
10 Iran 358,905
11 Argentina 350,854
12 United Kingdom 326,614
13 Saudi Arabia 308,654
14 Bangladesh 297,083
15 Pakistan 293,711
16 Italy 260,298
17 Turkey 259,692
18 France 244,854
19 Germany 234,853
20 Iraq 207,985
Países con menos casos totales
Posición País Casos totales
137 Latvia 1,337
138 Togo 1,295
139 Liberia 1,290
140 Niger 1,172
141 Trinidad and Tobago 1,099
142 Vietnam 1,022
143 Lesotho 1,015
144 Chad 987
145 Tanzania 509
146 Taiwan 487
147 Myanmar 474
148 Burundi 430
149 Papua New Guinea 401
150 Mauritius 346
151 Eritrea 306
152 Mongolia 298
153 Cambodia 273
154 Timor 26
155 Laos 22
156 NA NA

Gráfica top 20

ggplot(data=top20.total_cases, aes(x=reorder(paste(position, location),total_cases), 
                                   y=total_cases, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Casos totales de COVID-19") +
  geom_text(aes(y=max(total_cases)+170000, 
                label=total_cases_formated,
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más casos de COVID-19", 
       subtitle = todayformatted,
       caption = plotscaption)+
  scale_y_continuous(breaks=c(100000, 500000, 750000, 1000000, 2000000, 3000000, 4000000, 5000000),
                     label=c("100k", "500k", "750k", "1m", "2m", "3m", "4m", "5m"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de casos reportados de COVID-19 con la columna total_cases - Países de América

Se obtiene el ranking sólo incluyendo países del continente americano. Se obtienen los datos en donde la columna continent sea igual a “North America” o “South America”.

ranking.total_cases.america <- covid.total.df[covid.total.df$continent %in% 
                                                c("North America", "South America") ,]
ranking.total_cases.america <- ranking.total_cases.america[order(-ranking.total_cases.america$total_cases),]
ranking.total_cases.america$position <- 1:nrow(ranking.total_cases.america)
bottom20.total_cases.america <- tail(ranking.total_cases.america[, c("position", "location", "total_cases")],5)
top20.total_cases.america <- head(ranking.total_cases.america[, c("position", "location", "total_cases")],20)
rownames(bottom20.total_cases.america) <- c()
rownames(top20.total_cases.america) <- c()
mexrow <- which(ranking.total_cases.america$location=='Mexico')
rm(ranking.total_cases.america)
top20.total_cases.america$total_cases_formated <- formatC(top20.total_cases.america$total_cases,
                                                          format="f", big.mark=",", digits=0)
bottom20.total_cases.america$total_cases_formated <- formatC(bottom20.total_cases.america$total_cases, 
                                                             format="f", big.mark=",", digits=0)

Tabla

tablecolnames <- c("Posición", "País", "Casos totales")
kable(top20.total_cases.america[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países del continente Americano con más casos totales") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "center") %>%
row_spec(mexrow, bold = T, color = "black", background = "yellow")
Países del continente Americano con más casos totales
Posición País Casos totales
1 United States 5,740,909
2 Brazil 3,622,861
3 Peru 600,438
4 Mexico 563,705
5 Colombia 551,696
6 Chile 399,568
7 Argentina 350,854
8 Canada 125,647
9 Bolivia 110,148
10 Ecuador 108,289
11 Dominican Republic 91,608
12 Panama 87,485
13 Guatemala 68,533
14 Honduras 55,479
15 Venezuela 40,338
16 Costa Rica 34,463
17 Puerto Rico 30,618
18 El Salvador 24,811
19 Paraguay 13,602
20 Haiti 8,110

Gráfica top 20

ggplot(data=top20.total_cases.america, aes(x=reorder(paste(position, location),total_cases),
                                           y=total_cases, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Casos de COVID-19") +
  geom_text(aes(y=max(total_cases)+170000, 
                label=format(total_cases, big.mark=","),
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más casos de COVID-19 de América", 
       subtitle = todayformatted,
       caption = plotscaption)+
  scale_y_continuous(breaks=c(100000, 500000, 750000, 1000000, 2000000, 3000000, 4000000, 5000000),
                     label=c("100k", "500k", "750k", "1m", "2m", "3m", "4m", "5m"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de casos reportados de COVID-19 por millón de habitantes con la columna total_cases_per_million

ranking.total_cases_per_million <- covid.total.df[order(-covid.total.df$total_cases_per_million),]
ranking.total_cases_per_million$position <- 1:nrow(ranking.total_cases_per_million)
columnfilter <- c("position", "location", "total_cases_per_million")
mexico.total_cases_per_million <- ranking.total_cases_per_million[
                                  ranking.total_cases_per_million$location == "Mexico", ]
bottom20.total_cases_per_million <- tail(ranking.total_cases_per_million[, columnfilter],20)
top20.total_cases_per_million <- head(ranking.total_cases_per_million[, columnfilter],20)
mexico.total_cases_per_million <- mexico.total_cases_per_million[, columnfilter]
rm(ranking.total_cases_per_million)
rownames(top20.total_cases_per_million) <- c()
rownames(bottom20.total_cases_per_million) <- c()
rownames(mexico.total_cases_per_million) <- c()

top20.total_cases_per_million <- rbind(top20.total_cases_per_million, mexico.total_cases_per_million)
mexrow <- which(top20.total_cases_per_million$location=='Mexico')

top20.total_cases_per_million$total_cases_per_million_formated <- formatC(
  top20.total_cases_per_million$total_cases_per_million, format="f", big.mark=",", digits=2)
bottom20.total_cases_per_million$total_cases_per_million_formated<- formatC(
  bottom20.total_cases_per_million$total_cases_per_million, format="f", big.mark=",", digits=2)

Tablas

tablecolnames <- c("Posición", "País", "Casos por mdh")
kable(top20.total_cases_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con más casos por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_cases_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con menos casos por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")
Países con más casos por millón de habitantes (mdh)
Posición País Casos por mdh
1 Qatar 40,702.38
2 Bahrain 28,990.65
3 Chile 20,902.05
4 Panama 20,275.71
5 Kuwait 18,957.69
6 Peru 18,210.63
7 United States 17,344.00
8 Brazil 17,043.99
9 Oman 16,548.90
10 Armenia 14,452.11
11 Israel 12,138.24
12 Colombia 10,842.47
13 Puerto Rico 10,702.45
14 South Africa 10,309.62
15 Singapore 9,641.14
16 Bolivia 9,436.11
17 Saudi Arabia 8,865.83
18 Sweden 8,586.86
19 Dominican Republic 8,444.76
20 Moldova 8,385.80
41 Mexico 4,372.09
NA NA NA
Países con menos casos por millón de habitantes (mdh)
Posición País Casos por mdh
138 Mongolia 90.90
139 Eritrea 86.28
140 Angola 67.61
141 Yemen 64.24
142 Burkina Faso 64.01
143 China 62.33
144 Chad 60.09
145 Uganda 51.64
146 Thailand 48.74
147 Niger 48.42
148 Papua New Guinea 44.82
149 Burundi 36.16
150 Taiwan 20.45
151 Timor 19.72
152 Cambodia 16.33
153 Vietnam 10.50
154 Myanmar 8.71
155 Tanzania 8.52
156 Laos 3.02
157 NA NA

Gráfica top 20

ggplot(data=top20.total_cases_per_million, 
       aes(x=reorder(paste(position, location),total_cases_per_million), 
           y=total_cases_per_million, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Casos de COVID-19 por millón de habitantes") +
  geom_text(aes(y=max(total_cases_per_million)+1250, 
                label=total_cases_per_million_formated,
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más casos reportados de COVID-19 por millón de habitantes (+ México)",
       subtitle = todayformatted,
       caption = plotscaption)+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 22 rows containing missing values (geom_text).

Ranking de países: Total de muertes reportadas por COVID-19 con la columna total_deaths

ranking.total_deaths <- covid.total.df[order(-covid.total.df$total_deaths),]
ranking.total_deaths <- ranking.total_deaths[ranking.total_deaths$location != "World", ]
ranking.total_deaths$position <- 1:nrow(ranking.total_deaths)
columnfilter <- c("position", "location", "total_deaths")
bottom20.total_deaths <- tail(ranking.total_deaths[, columnfilter],20)
top20.total_deaths <- head(ranking.total_deaths[, columnfilter],20)
mexrow <- which(ranking.total_deaths$location=='Mexico')
rm(ranking.total_deaths)
rownames(top20.total_deaths) <- c()
rownames(bottom20.total_deaths) <- c()

top20.total_deaths$total_deaths_formated <- formatC(top20.total_deaths$total_deaths, 
                                                  format="f", big.mark=",", digits=0)
bottom20.total_deaths$total_deaths_formated <- formatC(bottom20.total_deaths$total_deaths, 
                                                     format="f", big.mark=",", digits=0)

Tablas

tablecolnames <- c("Posición", "País", "Muertes totales")
kable(top20.total_deaths[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con más muertes por COVID-19") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_deaths[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con menos muertes por COVID-19") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")
Países con más muertes por COVID-19
Posición País Muertes totales
1 United States 177,279
2 Brazil 115,309
3 Mexico 60,800
4 India 58,390
5 United Kingdom 41,433
6 Italy 35,441
7 France 30,528
8 Peru 27,813
9 Iran 20,643
10 Colombia 17,612
11 Russia 16,448
12 South Africa 13,159
13 Chile 10,916
14 Belgium 9,996
15 Germany 9,277
16 Canada 9,083
17 Argentina 7,366
18 Indonesia 6,759
19 Iraq 6,519
20 Ecuador 6,322
Países con menos muertes por COVID-19
Posición País Muertes totales
137 Mozambique 21
138 Tanzania 21
139 Georgia 18
140 Jamaica 16
141 Trinidad and Tobago 15
142 Jordan 14
143 Rwanda 14
144 Sri Lanka 12
145 Mauritius 10
146 Taiwan 7
147 Myanmar 6
148 Papua New Guinea 4
149 Botswana 3
150 Burundi 1
151 Eritrea 0
152 Cambodia 0
153 Laos 0
154 Mongolia 0
155 Timor 0
156 NA NA

Gráfica top 20

ggplot(data=top20.total_deaths, 
       aes(x=reorder(paste(position, location),total_deaths), 
                                   y=total_deaths, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Muertes por COVID-19") +
  geom_text(aes(y=max(total_deaths)+7500, 
                label=total_deaths_formated,
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más muertes por COVID-19",
       subtitle = todayformatted,
       caption = plotscaption)+
  scale_y_continuous(breaks=c(10000, 25000, 50000, 75000, 100000, 150000, 170000), 
                     label=c("10k", "25k", "50k", "75k", "100k", "150k", "170k"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de muertes reportadas por COVID-19 por millón de habitantes con la columna total_deaths_per_million

ranking.total_deaths_per_million <- covid.total.df[order(-covid.total.df$total_deaths_per_million),]
ranking.total_deaths_per_million$position <- 1:nrow(ranking.total_deaths_per_million)
columnfilter <- c("position", "location", "total_deaths_per_million")
bottom20.total_deaths_per_million <- tail(ranking.total_deaths_per_million[, columnfilter],20)
top20.total_deaths_per_million <- head(ranking.total_deaths_per_million[, columnfilter],20)
mexrow <- which(ranking.total_deaths_per_million$location=='Mexico')
rm(ranking.total_deaths_per_million)
rownames(top20.total_deaths_per_million) <- c()
rownames(bottom20.total_deaths_per_million) <- c()

top20.total_deaths_per_million$total_deaths_per_million_formated <- formatC(
  top20.total_deaths_per_million$total_deaths_per_million, format="f", big.mark=",", digits=2)
bottom20.total_deaths_per_million$total_deaths_per_million_formated <- formatC(
  bottom20.total_deaths_per_million$total_deaths_per_million, format="f", big.mark=",", digits=2)

Tabla

tablecolnames <- c("Posición", "País", "Muertes por mdh")

kable(top20.total_deaths_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, 
      caption="Países con más muertes por COVID-19 por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_deaths_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, 
      caption="Países con menos muertes por COVID-19 por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")
Países con más muertes por COVID-19 por millón de habitantes (mdh)
Posición País Muertes por mdh
1 Belgium 862.50
2 Peru 843.54
3 United Kingdom 610.33
4 Italy 586.17
5 Sweden 575.59
6 Chile 571.03
7 Brazil 542.48
8 United States 535.58
9 Mexico 471.56
10 France 467.69
11 Panama 441.74
12 Bolivia 392.19
13 Netherlands 361.43
14 Ireland 359.88
15 Ecuador 358.33
16 Colombia 346.13
17 Armenia 288.20
18 Macedonia 270.71
19 Iran 245.77
20 Kosovo 241.62
Países con menos muertes por COVID-19 por millón de habitantes (mdh)
Posición País Muertes por mdh
138 Burkina Faso 2.63
139 Jordan 1.37
140 Botswana 1.28
141 Rwanda 1.08
142 Thailand 0.83
143 Mozambique 0.67
144 Sri Lanka 0.56
145 Uganda 0.48
146 Papua New Guinea 0.45
147 Tanzania 0.35
148 Taiwan 0.29
149 Vietnam 0.28
150 Myanmar 0.11
151 Burundi 0.08
152 Eritrea 0.00
153 Cambodia 0.00
154 Laos 0.00
155 Mongolia 0.00
156 Timor 0.00
157 NA NA

Gráfica top 20

ggplot(data=top20.total_deaths_per_million, 
       aes(x=reorder(paste(position, location),total_deaths_per_million), 
           y=total_deaths_per_million, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Muertes por COVID-19 por millón de habitantes") +
  geom_text(aes(y=max(total_deaths_per_million)+25, 
                label=total_deaths_per_million_formated,
                fontface="bold"),
            color="black")+
  labs(title="Top 20 de los países con más muertes por COVID-19 por millón de habitantes",
       subtitle = todayformatted,
       caption = plotscaption)+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Tendencias de contagios y muertes utilizando las columnas: new_cases y new_deaths

Nuevos contagios de los 5 países con más casos reportados de COVID-19 + México

Las siguientes funciones son para facilitar la creación de las gráficas de líneas.

##Line plot of new_cases with date breaks by month
plot.trend.new_cases.month <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
##Line plot of new_cases with date breaks by short month and days
plot.trend.new_cases.monthday <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}

Gráficas con rangos de fechas. Las fechas iniciales son distintas, la fecha final siempre es el día Martes, 25 de agosto de 2020.

Todo

top5.total_cases <- c(head(as.character(top20.total_cases$location),5), "Mexico")
sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-1 Mes

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-3 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.monthday(enddate-21, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-2 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.monthday(enddate-14, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

Nuevas muertes de los 5 países con más casos reportados de COVID-19 + México

Funciones para generar las gráficas.

##Line plot of new_deaths with date breaks by month
plot.trend.new_deaths.month <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos muertes atribuibles a COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
##Line plot of new_deaths with date breaks by short month and day
plot.trend.new_deaths.monthdays <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}

Gráficas con rangos de fechas. Las fechas iniciales son distitnas, fecha final siempre es el día Martes, 25 de agosto de 2020.

Todo

top5.total_cases <- c(head(as.character(top20.total_cases$location),5), "Mexico")
sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- "Tendencia de nuevas muertes por COVID-19"
plot.trend.new_deaths.month(startdate, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-1 Mes

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-3 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.monthdays(enddate-21, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-2 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.monthdays(enddate-14, enddate, top5.total_cases, title.tmp, sublabel.tmp)

Acumulados de casos y muertes COVID-19

Para las siguientes gráficas se utilizan las columnas total_cases y total_deaths.

Curva de contagios acumulados de los 5 países con más casos reportados (+ México)

Gráficas con rangos de fechas. Las fechas iniciales son distitnas, fecha final siempre es el día Martes, 25 de agosto de 2020.

Funciones para generar las gráficas.

##Line plot of new_total_cases with date breaks by month
plot.trend.total_cases.month <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Casos acumulados de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
##Line plot of new_deaths with date breaks by short month and day
plot.trend.total_cases.monthdays <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Casos acumulados de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}

Todo

sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- "Contagios acumulados de COVID-19"
plot.trend.total_cases.month(startdate, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-1 Mes

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-1 Mes TOP[3:7]

La cantidad de contagios que tienen Estados Unidos y Brasil no permiten apreciar las curvas del resto de los países. Se eliminan esos 2 países y se agregan los 2 países que se encuentran en la posición 6 y 7 del ranking de los países con más contagios reportados.

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
total_cases.others <- as.character(top20.total_cases$location)[3:7]
plot.trend.total_cases.month(startdate.tmp, enddate, total_cases.others, title.tmp, sublabel.tmp)

-3 Semanas TOP[3:7]

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.monthdays(enddate-21, enddate, total_cases.others, title.tmp, sublabel.tmp)

-2 Semanas TOP[3:7]

sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.monthdays(enddate-14, enddate, total_cases.others, title.tmp, sublabel.tmp)

-2 Semanas MX, PE y SA

total_cases.others <- as.character(top20.total_cases$location)[3:7]
total_cases.others <- total_cases.others[c(-1,-2)]
countrieslabels <- paste(total_cases.others, collapse = ', ')
sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- paste(title.tmp, "de", countrieslabels)
plot.trend.total_cases.monthdays(enddate-14, enddate, total_cases.others, title.tmp, sublabel.tmp)

Curvas de muertes acumuladas de los 5 países con más muertes reportadas (+ México)

Gráficas con rangos de fechas. Las fechas iniciales son distitnas, fecha final siempre es el día Martes, 25 de agosto de 2020.

Funciones para generar las gráficas.

# Generate total_deaths line plot with month breaks
plot.trend.total_deaths.month <- function(startdate, enddate, countries, graphtitle, subgraphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = subgraphtitle)+
    labs(caption = plotscaption)+
    ylab("Muertes totales por COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
# Generate total_deaths line plot with month and day breaks
plot.trend.total_deaths.monthdays <- function(startdate, enddate, countries, graphtitle, subgraphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = subgraphtitle)+
    labs(caption = plotscaption)+
    ylab("Muertes totales por COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}

Todo

top5.total_deaths <-  head(as.character(top20.total_deaths$location),5)
sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- "Muertes acumuladas por COVID-19"
plot.trend.total_deaths.month(startdate, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-1 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-1 Mes TOP[3:7]

La cantidad de muertes que tienen Estados Unidos y Brasil no permiten apreciar las curvas del resto de los países. Se quita a esos 2 países y se agrega a los 2 países que sigan en el ranking de países con más muertes totales. 
mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
total_deaths.others <- as.character(top20.total_deaths$location)[3:7]
plot.trend.total_deaths.month(startdate.tmp, enddate, total_deaths.others, title.tmp, sublabel.tmp)

-3 Semanas TOP[3:7]

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.monthdays(enddate-21, enddate, total_deaths.others, title.tmp, sublabel.tmp)

-3 Semanas IN, IR, MX, PE, RU

Las curvas de Francia, Italia y Reino Unido están planas, se ha estancado la cantidad de muertes atribuidas a COVID-19. Una posible razón es que la pandemia inició primero en Europa y la cantidad de muertes reportadas recientemente es nula o muy baja. Las curvas de México e India siguen en aumento. Se eliminan los países de Francia, Italia y Reino Unido y se agregan otros países en donde al parecer la cantidad de muertes atribuidas a COVID-19 aún sigue en aumento. Se genera la gráfica con las curvas de muertes acumuladas de las últimas 3 semanas de India, Iran, México, Perú y Rusia:

total_deaths.others <- as.character(top20.total_deaths$location)[c(3,5,8,9,10)]
countrieslabels <- paste(total_deaths.others, collapse = ', ')
title.tmp <- paste(title.tmp, "de", countrieslabels)
sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.monthdays(enddate-21, enddate, total_deaths.others, title.tmp, sublabel.tmp)

-2 Semanas IN, IR, MX, PE, RU

total_deaths.others <- as.character(top20.total_deaths$location)[c(3,5,8,9,10)]
countrieslabels <- paste(total_deaths.others, collapse = ', ')
title.tmp <- paste(title.tmp, "de", countrieslabels)
sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.monthdays(enddate-14, enddate, total_deaths.others, title.tmp, sublabel.tmp)

Comparación de curvas de contagios y muertes de México

Nuevos casos y nuevas muertes

covid.mexico <- covid.df[covid.df$location=='Mexico',]
covid.mexico <- covid.mexico[covid.mexico$date > "2020-04-01",]
covid.mexico.new <- covid.mexico[,c("date", "new_cases", "new_deaths")]
covid.mexico.new.lf <-melt(covid.mexico.new, id.vars = c("date"))
ggplot(data=covid.mexico.new.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Nuevos Contagios", "Nuevas Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    ggtitle("Curvas de contagios y muertes diarias por COVID-19 en México")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Nuevos casos y nuevas muertes último mes

covid.mexico.new.lastmonth <- covid.mexico.new[covid.mexico.new$date >= enddate-30, ]
covid.mexico.new.lastmonth.lf <- melt(covid.mexico.new.lastmonth, id.vars = c("date"))
ggplot(data=covid.mexico.new.lastmonth.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Nuevos Contagios", "Nuevas Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    ggtitle("Curvas de contagios y muertes diarias por COVID-19 en México - Último mes")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Contagios y muertes totales

covid.mexico.total <- covid.mexico[,c("date", "total_cases", "total_deaths")]
covid.mexico.total.lf <-melt(covid.mexico.total, id.vars = c("date"))
ggplot(data=covid.mexico.total.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Contagios", "Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    scale_y_continuous(breaks=c(50000, 100000, 200000, 300000, 400000, 500000, 750000),
                     label=c("50k","100k", "200k", "300k", "400k", "500k", "750k"))+
    ggtitle("Curvas de contagios y muertes acumulados por COVID-19 en México")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Contagios y muertes totales último mes

covid.mexico.total.lasttmonth <- covid.mexico.total[covid.mexico.total$date >= enddate-30, ]
covid.mexico.total.lasttmonth.lf <- melt(covid.mexico.total.lasttmonth, id.vars = c("date"))
ggplot(data=covid.mexico.total.lasttmonth.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Contagios", "Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    scale_y_continuous(breaks=c(50000, 100000, 200000, 300000, 400000, 500000, 750000),
                     label=c("50k","100k", "200k", "300k", "400k", "500k", "750k"))+
    ggtitle("Curvas de contagios y muertes acumulados por COVID-19 en México - Último mes")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Casos y muertes COVID-19 semanales en México

covid.mexico <- covid.df[covid.df$location=='Mexico',]
week.agg <- covid.mexico %>%
  group_by(week = week(date)) %>%
  summarise(mean_new_cases = mean(new_cases),
            n = n(), 
            loCI_new_cases = ci(new_cases)[2], 
            hiCI_new_cases = ci(new_cases)[3], 
            mean_new_deaths = mean(new_deaths), 
            loCI_new_deaths = ci(new_deaths)[2], 
            hiCI_new_deaths = ci(new_deaths)[3], 
            total_new_cases = sum(new_cases), 
            total_new_deaths = sum(new_deaths))
# Weeks with zero mean new cases are deleted
week.agg <- week.agg[which(week.agg$mean_new_cases > 0),]
# Finding the date of the first week to be ploted
firstweek <- min(week.agg$week)
rowindex <- match(firstweek, week(covid.mexico[,"date"]))
firstweek.date <- format(covid.mexico[rowindex, ]$date, "%d/%m/%Y")
rm(firstweek, rowindex)
# Finding the date of the last week to be ploted
lastweek <- max(week.agg$week)
rowindex <- match(lastweek, week(covid.mexico[,"date"]))
lastweek.date <- format(covid.mexico[rowindex, ]$date, "%d/%m/%Y")
rm(lastweek, rowindex)
sublabel <- paste("Rango:", firstweek.date, "-", lastweek.date)

Promedio semanal de casos

ggplot(data=week.agg, aes(x=as.character(week),y=mean_new_cases))+
  geom_bar(stat = 'identity', aes(fill = mean_new_cases)) +
  geom_errorbar(aes(ymin=loCI_new_cases, ymax=hiCI_new_cases), width=.2,
                position=position_dodge(.9)) +
  xlab("Semana")+
  ylab("Promedio semanal de casos ± IC 95%")+
  labs(title="Promedio semanal de casos de COVID-19 en México",
       subtitle = sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Casos", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Promedio semanal de muertes

ggplot(data=week.agg, aes(x=as.character(week),y=mean_new_deaths))+
  geom_bar(stat = 'identity', aes(fill = mean_new_deaths)) +
  geom_errorbar(aes(ymin=loCI_new_deaths, ymax=hiCI_new_deaths), width=.2,
                position=position_dodge(.9)) +
  xlab("Semana")+
  ylab("Promedio semanal de muertes ± IC 95%")+
  labs(title="Promedio semanal de muertes por COVID-19 en México",
       subtitle = sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Muertes", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Casos totales por semana

ggplot(data=week.agg, aes(x=as.character(week),y=total_new_cases))+
  geom_bar(stat = 'identity', aes(fill = total_new_cases)) +
  xlab("Semana")+
  ylab("Casos totales por semana")+
  labs(title="Casos totales de COVID-19 por semana en México",
       subtitle = sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Casos totales", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Muertes totales por semana

ggplot(data=week.agg, aes(x=as.character(week),y=total_new_deaths))+
  geom_bar(stat = 'identity', aes(fill = total_new_deaths)) +
  xlab("Semana")+
  ylab("Muertes totales por semana")+
  labs(title="Muertes totales por COVID-19 por semana en México",
       subtitle=sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Muertes totales", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))