El objetivo de esta tarea es familiarizar al estudiante con el manejo de gráficos dinámicos, específicamente usaremos visualizaciones creadas con el paquete rCharts. Como se discutió en la presentación rCharts permite la creación de gráficos dinámicos basado en diferentes librerías de Javascript, pero para el presente trabajo nos centraremos solo en NVD3. NVD3 es una librería basada en Javascript D3 que permite la creación de distintos tipos de visualizaciones.
Considere el archivo sobre los efectos del virus COVID-19 en el mundo . La data puede ser provista por el instructor o bajada de algunos de los repositorios a nivel mundial de acuerdo a enlace provisto al inicio de este documento. El dataset contiene varias columnas y sus filas corresponden a los valores de cada país día a día desde el inicio de la pandemia. Nos interesa únicamente los totales, por lo que el estudiante deberá realizar los filtros a la última fecha y agregaciones que crea conveniente.
La data puede ser descargada de:
https://covid19.who.int/WHO-COVID-19-global-data.csv
De todas la columnas presentes en el archivo nos interesa únicamente las siguientes:
WHO_region, Country_code Cumulative_cases Cumulative_deaths
Realice una selección de dichas columnas
Realice un filtro de aquellos países que no tiene casos o muertos registrados
Realice un filtro de los países que pertenecen a la región “Other”
Asegúrese de que la data está completa , es decir , no existen valores en blanco
Realizar el siguiente trabajo :
1.- Desarrolle las siguientes visualizaciones utilizando rCharts:
a) Presente el mejor diagrama basado en rCharts usando la librería NVD3 para graficar la posición de cada país con respecto a los casos y muertes agrupando por región. Se requiere 1 solo diagrama . Revise la documentación de rCharts provista en el aula vitual. Note la funcionalidades dinámicas que se presenta. Realice el gráfico primero en R-studio y cuando este satisfecho de su presentación , salve el código javascript generado e inclúyalo en el archivo rmd mediante una etiqueta iframe.. Para esto tome como plantilla el laboratorio:covid_data <- read.csv("https://covid19.who.int/WHO-COVID-19-global-data.csv",
header=TRUE, sep=",")Analizamos que columnas disponemos:
## [1] "Date_reported" "Country_code" "Country"
## [4] "WHO_region" "New_cases" "Cumulative_cases"
## [7] "New_deaths" "Cumulative_deaths"dim(covid_data)## [1] 57120 8head(covid_data)## Date_reported Country_code Country WHO_region New_cases
## 1 2020-01-05 AF Afghanistan EMRO NA
## 2 2020-01-12 AF Afghanistan EMRO NA
## 3 2020-01-19 AF Afghanistan EMRO NA
## 4 2020-01-26 AF Afghanistan EMRO NA
## 5 2020-02-02 AF Afghanistan EMRO NA
## 6 2020-02-09 AF Afghanistan EMRO NA
## Cumulative_cases New_deaths Cumulative_deaths
## 1 0 NA 0
## 2 0 NA 0
## 3 0 NA 0
## 4 0 NA 0
## 5 0 NA 0
## 6 0 NA 0summary(covid_data)## Date_reported Country_code Country
## Length:57120 Length:57120 Length:57120
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## WHO_region New_cases Cumulative_cases
## Length:57120 Min. : -65079 Min. : 0
## Class :character 1st Qu.: 43 1st Qu.: 3946
## Mode :character Median : 399 Median : 45562
## Mean : 20021 Mean : 1774440
## 3rd Qu.: 4031 3rd Qu.: 516023
## Max. :40475477 Max. :103436829
## NA's :18374
## New_deaths Cumulative_deaths
## Min. :-3432.0 Min. : 0.0
## 1st Qu.: 4.0 1st Qu.: 28.0
## Median : 20.0 Median : 544.5
## Mean : 283.1 Mean : 19891.8
## 3rd Qu.: 105.0 3rd Qu.: 6881.0
## Max. :47687.0 Max. :1191632.0
## NA's :32201str(covid_data)## 'data.frame': 57120 obs. of 8 variables:
## $ Date_reported : chr "2020-01-05" "2020-01-12" "2020-01-19" "2020-01-26" ...
## $ Country_code : chr "AF" "AF" "AF" "AF" ...
## $ Country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ WHO_region : chr "EMRO" "EMRO" "EMRO" "EMRO" ...
## $ New_cases : int NA NA NA NA NA NA NA NA 1 NA ...
## $ Cumulative_cases : int 0 0 0 0 0 0 0 0 1 1 ...
## $ New_deaths : int NA NA NA NA NA NA NA NA NA NA ...
## $ Cumulative_deaths: int 0 0 0 0 0 0 0 0 0 0 ...require(dplyr)covid19 <- max(covid_data$Date_reported, na.rm = TRUE)covid19 <- covid_data %>%
filter(Date_reported == covid19)WHO_region, Country_code, Cumulative_cases, Cumulative_deaths.
covid <- covid19[, c("WHO_region", "Country_code", "Cumulative_cases", "Cumulative_deaths" )]#View(covid)
dim(covid)## [1] 240 4colSums(covid == 0)## WHO_region Country_code Cumulative_cases Cumulative_deaths
## 0 NA 2 13covid <- covid[covid$Cumulative_cases != 0, ]
covid <- covid[covid$Cumulative_deaths != 0, ]
colSums(covid == 0)## WHO_region Country_code Cumulative_cases Cumulative_deaths
## 0 NA 0 0dim(covid)## [1] 227 4covid <- covid[covid$WHO_region != "OTHER", ]
#View(covid)
unique(covid$WHO_region)## [1] "EMRO" "EURO" "AFRO" "WPRO" "AMRO" "SEARO" ""sum(covid$WHO_region == "")## [1] 14covid<- covid[covid$WHO_region != "", ]colSums(covid == 0)## WHO_region Country_code Cumulative_cases Cumulative_deaths
## 0 NA 0 0covid <- covid[!is.na(covid$Country_code), ]
colSums(covid == 0)## WHO_region Country_code Cumulative_cases Cumulative_deaths
## 0 0 0 0head(covid)## WHO_region Country_code Cumulative_cases Cumulative_deaths
## 1 EMRO AF 235214 7998
## 2 EURO AL 335047 3605
## 3 AFRO DZ 272083 6881
## 4 WPRO AS 8359 34
## 5 EURO AD 48015 159
## 6 AFRO AO 107481 1937dim(covid)## [1] 211 4require(devtools)
install_github('ramnathv/rCharts' ,type="source", force=TRUE)## Downloading GitHub repo ramnathv/rCharts@HEAD## ── R CMD build ──────────────────────────────────────────────────────────
## checking for file ‘/tmp/RtmpXVb533/remotes14957e84865/ramnathv-rCharts-479a4f9/DESCRIPTION’ ... ✔ checking for file ‘/tmp/RtmpXVb533/remotes14957e84865/ramnathv-rCharts-479a4f9/DESCRIPTION’ (547ms)
## ─ preparing ‘rCharts’:
## checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
## ─ checking for LF line-endings in source and make files and shell scripts (509ms)
## ─ checking for empty or unneeded directories
## ─ building ‘rCharts_0.4.5.tar.gz’
## Warning: invalid uid value replaced by that for user 'nobody'
##
## ## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)require(rCharts)require(rCharts)
require(rCharts)
require(dplyr)
require(reshape2)
require(tidyr)
require(knitr)
library(rCharts)
names(covid)## [1] "WHO_region" "Country_code" "Cumulative_cases"
## [4] "Cumulative_deaths"covidm <- covid %>%
group_by(Country_code, WHO_region) %>%
dplyr::summarise(across(c(Cumulative_deaths, Cumulative_cases), sum, na.rm = TRUE))## `summarise()` has grouped output by 'Country_code'. You can override
## using the `.groups` argument.covidr <- covid %>%
group_by(Country_code, WHO_region) %>%
summarise(
Cumulative_cases = sum(Cumulative_cases, na.rm = TRUE),
Cumulative_deaths = sum(Cumulative_deaths, na.rm = TRUE),
.groups = 'drop'
)print(dim(covidr))## [1] 211 4covidm_l <- covidr %>%
pivot_longer(cols = c(Cumulative_cases, Cumulative_deaths), names_to = "Metric", values_to = "Count")head(covidm_l)## # A tibble: 6 × 4
## Country_code WHO_region Metric Count
## <chr> <chr> <chr> <int>
## 1 AD EURO Cumulative_cases 48015
## 2 AD EURO Cumulative_deaths 159
## 3 AE EMRO Cumulative_cases 1067030
## 4 AE EMRO Cumulative_deaths 2349
## 5 AF EMRO Cumulative_cases 235214
## 6 AF EMRO Cumulative_deaths 7998covidm_long <- covidm %>%
pivot_longer(cols = c(Cumulative_deaths, Cumulative_cases),
names_to = "Metric",
values_to = "Count")chart <- nPlot(Count ~ Country_code, group = 'Metric', data = covidm_long, type = 'multiBarChart')chart$xAxis(axisLabel = 'Country Code')
chart$yAxis(axisLabel = 'Count')chart$save('D:\\NO ELIMINAR\\Desktop\\PUCE\\VISUALIZACIÓN\\covid_chart.html', standalone = TRUE)En el mismo archivo vamos a incluir un gráfico D3 relacionado con la misma data Covid El instructor pondrá los siguientes ejemplos como plantillas para la resolución de esta tarea. Index44.html Index42.html Index31.html El archivo index44.html servirá como plantilla base sobre el cual el alumno deberá realizar los cambios siguientes,
Necesitamos extraer la data covid en 3 sets (listas) que pasaremos al código D3.
La primera lista es de las regiones
La segunda lista es de lista es de los paises
La tercera lista es de los casos acumulados.
listareg <- as.list(covid$WHO_region)
for (r in 1:length(listareg))
{
if (r < length(listareg)) {
cat('"',as.character(listareg[r]),'"', ",", sep="")}
else{
cat('"',as.character(listareg[r]),'"', sep="")
}
}## "EMRO","EURO","AFRO","WPRO","EURO","AFRO","AMRO","AMRO","AMRO","EURO","AMRO","WPRO","EURO","EURO","AMRO","EMRO","SEARO","AMRO","EURO","EURO","AMRO","AFRO","AMRO","SEARO","AMRO","AMRO","EURO","AFRO","AMRO","AMRO","WPRO","EURO","AFRO","AFRO","AFRO","WPRO","AFRO","AMRO","AMRO","AFRO","AFRO","AMRO","WPRO","AMRO","AFRO","AFRO","WPRO","AMRO","AFRO","EURO","AMRO","AMRO","EURO","EURO","AFRO","EURO","EMRO","AMRO","AMRO","AMRO","EMRO","AMRO","AFRO","AFRO","EURO","AFRO","AFRO","WPRO","EURO","EURO","WPRO","AFRO","AFRO","EURO","EURO","AFRO","EURO","EURO","AMRO","WPRO","AMRO","AFRO","AFRO","AMRO","AMRO","AMRO","EURO","EURO","SEARO","SEARO","EMRO","EMRO","EURO","EURO","EURO","AMRO","WPRO","EMRO","EURO","AFRO","WPRO","EURO","EMRO","EURO","WPRO","EURO","EMRO","AFRO","AFRO","EMRO","EURO","EURO","AFRO","AFRO","WPRO","SEARO","AFRO","EURO","WPRO","AFRO","AFRO","AMRO","WPRO","EURO","WPRO","EURO","AMRO","EMRO","AFRO","SEARO","WPRO","SEARO","EURO","WPRO","WPRO","AMRO","AFRO","AFRO","EURO","WPRO","EURO","EMRO","EMRO","EMRO","WPRO","AMRO","WPRO","AMRO","AMRO","WPRO","EURO","EURO","AMRO","EMRO","WPRO","EURO","EURO","EURO","AFRO","AMRO","AMRO","AMRO","WPRO","EURO","AFRO","EMRO","AFRO","EURO","AFRO","AFRO","WPRO","AMRO","EURO","EURO","WPRO","EMRO","AFRO","AFRO","EURO","SEARO","EMRO","AMRO","EURO","EURO","EMRO","EURO","SEARO","EURO","SEARO","AFRO","WPRO","AMRO","EMRO","EURO","AMRO","WPRO","AFRO","EURO","EMRO","AFRO","AMRO","AMRO","AMRO","EURO","WPRO","AMRO","WPRO","WPRO","EMRO","AFRO","AFRO"listareg <- as.list(covid$Country_code)
for (r in 1:length(listareg))
{
if (r < length(listareg)) {
cat('"',as.character(listareg[r]),'"', ",", sep="")}
else{
cat('"',as.character(listareg[r]),'"', sep="")
}
}## "AF","AL","DZ","AS","AD","AO","AI","AG","AR","AM","AW","AU","AT","AZ","BS","BH","BD","BB","BY","BE","BZ","BJ","BM","BT","BO","BQ","BA","BW","BR","VG","BN","BG","BF","BI","CV","KH","CM","CA","KY","CF","TD","CL","CN","CO","KM","CG","CK","CR","CI","HR","CU","CW","CY","CZ","CD","DK","DJ","DM","DO","EC","EG","SV","GQ","ER","EE","SZ","ET","FJ","FI","FR","PF","GA","GM","GE","DE","GH","GR","GL","GD","GU","GT","GN","GW","GY","HT","HN","HU","IS","IN","ID","IR","IQ","IE","IL","IT","JM","JP","JO","KZ","KE","KI","XK","KW","KG","LA","LV","LB","LS","LR","LY","LT","LU","MG","MW","MY","MV","ML","MT","MH","MR","MU","MX","FM","MC","MN","ME","MS","MA","MZ","MM","NR","NP","NL","NC","NZ","NI","NE","NG","MK","MP","NO","PS","OM","PK","PW","PA","PG","PY","PE","PH","PL","PT","PR","QA","KR","MD","RO","RU","RW","KN","LC","VC","WS","SM","ST","SA","SN","RS","SC","SL","SG","SX","SK","SI","SB","SO","ZA","SS","ES","LK","SD","SR","SE","CH","SY","TJ","TH","GB","TL","TG","TO","TT","TN","TR","TC","TV","UG","UA","AE","TZ","US","VI","UY","UZ","VU","VE","VN","WF","YE","ZM","ZW"for (r in 1:length(covid$Cumulative_cases))
{
if (r < length(covid$Cumulative_cases)) {
cat('[',as.character(covid$Cumulative_cases[r]),',' ,as.character(covid$Cumulative_deaths[r]), "]", ",", sep="") }
else{
cat('[',as.character(covid$Cumulative_cases[r]),',' ,as.character(covid$Cumulative_deaths[r]), "]", sep="") }
}## [235214,7998],[335047,3605],[272083,6881],[8359,34],[48015,159],[107481,1937],[3904,12],[9106,146],[10113624,130685],[451944,8777],[44224,292],[11861161,25236],[6082414,22534],[835636,10353],[39064,849],[696614,1536],[2051272,29498],[107794,593],[994037,7118],[4870157,34339],[71412,688],[28036,163],[18860,165],[62697,21],[1212145,22387],[11922,41],[403662,16391],[330696,2801],[37511921,702116],[7557,64],[347069,178],[1329588,38700],[22139,400],[54569,15],[64474,417],[139310,3056],[125241,1974],[4818690,55280],[31472,37],[15441,113],[7702,194],[5400928,62727],[99369029,122280],[6389838,142727],[9109,160],[25227,389],[7344,2],[1230653,9368],[88433,835],[1317144,18752],[1113662,8530],[45883,305],[696031,1451],[4760891,43507],[100933,1471],[3435679,9693],[15690,189],[16047,74],[661103,4384],[1077122,36050],[516023,24830],[201912,4230],[17130,183],[10189,103],[610471,2998],[75356,1427],[501185,7574],[69047,885],[1499712,11466],[38997490,168091],[79340,650],[49051,307],[12627,372],[1863615,17150],[38437756,174979],[172057,1462],[5661735,39167],[11971,21],[19693,238],[52287,419],[1250369,20203],[38572,468],[9614,177],[74357,1302],[34298,860],[472888,11114],[2230591,49053],[210315,186],[45041192,533623],[6829353,162059],[7627863,146837],[2465545,25375],[1743422,9699],[4841558,12707],[26727644,197081],[157137,3604],[33803572,74694],[1746997,14122],[1504370,19072],[344105,5689],[5085,24],[274279,3212],[667290,2570],[88953,1024],[219060,671],[977765,7475],[1239904,10947],[36138,709],[7930,294],[507269,6437],[1367900,9809],[393140,1000],[68564,1427],[89168,2686],[5306834,37351],[186694,316],[33166,743],[122577,918],[16252,17],[63869,997],[328167,1073],[7617685,334524],[26460,65],[17181,67],[1011489,2136],[251280,2654],[1403,8],[1279115,16305],[233841,2252],[642839,19494],[5393,1],[1003450,12031],[8639162,22986],[80163,314],[2635247,4246],[16179,245],[9518,315],[267188,3155],[350731,9977],[14837,41],[1511220,5732],[703228,5708],[399449,4628],[1580631,30656],[6372,10],[1044783,8738],[46864,670],[735759,19880],[4526977,220975],[4140383,66864],[6666637,120718],[5661625,28695],[1252713,5938],[514524,690],[34571873,35934],[636266,12241],[3533987,68815],[24254803,403155],[133264,1468],[6607,46],[30271,410],[9674,124],[17057,31],[25292,126],[6771,80],[841469,9646],[89409,1971],[2583470,18057],[51790,172],[7979,125],[3006155,2024],[11051,92],[1877895,21226],[1356318,10073],[25954,199],[27334,1361],[4072759,102595],[18823,147],[13980340,121852],[672797,16907],[63993,5046],[82501,1406],[2753981,27339],[4456903,14170],[57423,3163],[17786,125],[4797692,34712],[24964791,232112],[23460,138],[39530,290],[16976,12],[191496,4390],[1153361,29423],[17004718,101419],[6805,40],[2943,1],[172154,3632],[5532518,109920],[1067030,2349],[43230,846],[103436829,1191632],[25389,132],[1041317,7682],[175081,1016],[12019,14],[552695,5856],[11624000,43206],[3760,9],[11945,2159],[349749,4069],[266385,5740]min(covid$Cumulative_cases)## [1] 1403max(covid$Cumulative_cases)## [1] 103436829min(covid$Cumulative_deaths)## [1] 1max(covid$Cumulative_deaths)## [1] 1191632quantile(covid$Cumulative_cases)## 0% 25% 50% 75% 100%
## 1403 32319 267188 1502041 103436829