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COVID19AR

A package for analysing COVID-19 Argentina’s outbreak

Package

Release Usage Development
minimal R version Travis
CRAN codecov
Project Status: Active – The project has reached a stable, usable state and is being actively developed.

How to get started (Development version)

Install the R package using the following commands on the R console:

The repository is private so it have to be cloned first for installation

# install.packages("devtools")
devtools::install()

How to use it

First add variable with data dir in ~/.Renviron. You will recieve a message if you didn’t do it.

COVID19AR_data_dir = "~/.R/COVID19AR"

Example script for calculating proportion of influenza/Neumonia deaths in total deaths by year

library(COVID19AR)
#> Loading required package: readr
#> Loading required package: readxl
#> Loading required package: R6
#> Loading required package: checkmate
#> Loading required package: rvest
#> Loading required package: xml2
#> 
#> Attaching package: 'rvest'
#> The following object is masked from 'package:readr':
#> 
#>     guess_encoding
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> Loading required package: lgr
#> Loading required package: reshape2
#> Loading required package: knitr
#> Loading required package: COVID19analytics
#> Warning: replacing previous import 'ggplot2::Layout' by 'lgr::Layout' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'dplyr::intersect' by 'lubridate::intersect'
#> when loading 'COVID19analytics'
#> Warning: replacing previous import 'dplyr::union' by 'lubridate::union' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'dplyr::setdiff' by 'lubridate::setdiff' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::equals' by 'testthat::equals' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::not' by 'testthat::not' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::is_less_than' by
#> 'testthat::is_less_than' when loading 'COVID19analytics'
#> Warning: replacing previous import 'dplyr::matches' by 'testthat::matches' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'testthat::matches' by 'tidyr::matches' when
#> loading 'COVID19analytics'
#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
#> loading 'COVID19analytics'
#> Loading required package: httr
#> Loading required package: jsonlite
#> Loading required package: testthat
#> 
#> Attaching package: 'testthat'
#> The following object is masked from 'package:dplyr':
#> 
#>     matches
# Downloads csv from official source at:
# http://www.deis.msal.gov.ar/index.php/base-de-datos/
retrieveArgentinasDeathsStatistics()
#> [1] FALSE


consolidated.deaths.stats <- ConsolidatedDeathsData.class$new()
# Consolidates all years and includes the different codes as factor in the data frame
data.deaths <- consolidated.deaths.stats$consolidate()
#> INFO  [16:08:47.235] Reading {file: DefWeb12.csv}
#> WARN  [16:08:47.654] 2012 
#> WARN  [16:08:47.654] CAUSA 
#> WARN  [16:08:47.654] Codes I84 in field CAUSA not coded 
#> WARN  [16:08:47.654] I84 
#> WARN  [16:08:47.654] 1 
#> INFO  [16:08:47.788] NA values {count: 47155, field: MAT}
#> INFO  [16:08:47.817] Reading {file: DefWeb13.csv}
#> WARN  [16:08:48.147] 2013 
#> WARN  [16:08:48.147] CAUSA 
#> WARN  [16:08:48.147] Codes I84 in field CAUSA not coded 
#> WARN  [16:08:48.147] I84 
#> WARN  [16:08:48.147] 1 
#> INFO  [16:08:48.174] NA values {count: 48249, field: MAT}
#> INFO  [16:08:48.244] Reading {file: DefWeb14.csv}
#> WARN  [16:08:48.626] 2014 
#> WARN  [16:08:48.626] CAUSA 
#> WARN  [16:08:48.626] Codes I84 in field CAUSA not coded 
#> WARN  [16:08:48.626] I84 
#> WARN  [16:08:48.626] 2 
#> INFO  [16:08:48.651] NA values {count: 48105, field: MAT}
#> INFO  [16:08:48.822] Reading {file: DefWeb15.csv}
#> WARN  [16:08:49.124] 2015 
#> WARN  [16:08:49.124] CAUSA 
#> WARN  [16:08:49.124] Codes I84 in field CAUSA not coded 
#> WARN  [16:08:49.124] I84 
#> WARN  [16:08:49.124] 3 
#> INFO  [16:08:49.154] NA values {count: 52250, field: MAT}
#> INFO  [16:08:49.286] Reading {file: DefWeb16.csv}
#> WARN  [16:08:49.679] 2016 
#> WARN  [16:08:49.679] CAUSA 
#> WARN  [16:08:49.679] Codes R97 in field CAUSA not coded 
#> WARN  [16:08:49.679] R97 
#> WARN  [16:08:49.679] 10 
#> INFO  [16:08:49.713] NA values {count: 50951, field: MAT}
#> INFO  [16:08:49.955] Reading {file: DefWeb17.csv}
#> INFO  [16:08:50.286] NA values {count: 49630, field: MAT}
#> INFO  [16:08:50.557] Reading {file: DefWeb18.csv}
#> INFO  [16:08:50.885] NA values {count: 49602, field: MAT}

# How many records do we have?
nrow(data.deaths)
#> [1] 347549
# [1] 347549

# Cases with missing codes in CAUSA
kable(consolidated.deaths.stats$warnings)
year field message missed.codes cases
2012 CAUSA Codes I84 in field CAUSA not coded I84 1
2013 CAUSA Codes I84 in field CAUSA not coded I84 1
2014 CAUSA Codes I84 in field CAUSA not coded I84 2
2015 CAUSA Codes I84 in field CAUSA not coded I84 3
2016 CAUSA Codes R97 in field CAUSA not coded R97 10 
regexp.neumonia.influenza <- "^J(09|1[0-8])"
regexp.otras.respiratorias <- "^J"

# List all Causas related to Influenza|Neumonía considered for classification

causas.descriptions <- sort(unique(data.deaths$codigo.causas))
causas.descriptions[grep(regexp.neumonia.influenza, causas.descriptions, ignore.case = TRUE)]
#> [1] "J09|Influenza debida a ciertos virus de la influenza identificados"                   
#> [2] "J10|Influenza debida a otro virus de la influenza identificado"                       
#> [3] "J11|Influenza debida a virus no identificado"                                         
#> [4] "J12|Neumonía viral no clasificada en otra parte"                                      
#> [5] "J13|Neumonía debida a Streptococcus pneumoniae"                                       
#> [6] "J14|Neumonía debida a Haemophilus influenzae"                                         
#> [7] "J15|Neumonía bacteriana no clasificada en otra parte"                                 
#> [8] "J16|Neumonía debida a otros microorganismos infecciosos no clasificados en otra parte"
#> [9] "J18|Neumonía organismo no especificado"

data.deaths$causa_agg <- "Otra"
data.deaths[grep(regexp.neumonia.influenza, data.deaths$codigo.causa, ignore.case = TRUE),]$causa_agg <- "Influenza_Neumonia"
data.deaths[which(grepl(regexp.otras.respiratorias, data.deaths$codigo.causa, ignore.case = TRUE) & data.deaths$causa_agg == "Otra"),]$causa_agg <- "Otras_respiratorias"

influenza.deaths <- data.deaths %>%
                      group_by(year, causa_agg) %>%
                      summarize (total = sum(CUENTA),
                                 edad.media = mean(EDAD_MEDIA, na.rm = TRUE))
influenza.deaths %>% filter(year == 2018)
#> # A tibble: 3 x 4
#> # Groups:   year [1]
#>    year causa_agg            total edad.media
#>   <dbl> <chr>                <dbl>      <dbl>
#> 1  2018 Influenza_Neumonia   31916       46.8
#> 2  2018 Otra                275155       50.9
#> 3  2018 Otras_respiratorias  29752       54.5

influenza.deaths.tab <- dcast(influenza.deaths, formula = year~causa_agg, value.var = "total")
influenza.deaths.tab$total <- apply(influenza.deaths.tab[,2:4], MARGIN = 1, FUN = sum)
influenza.deaths.tab$Influenza_Neumonia.perc <- round(influenza.deaths.tab[,"Influenza_Neumonia"]/influenza.deaths.tab$total, 2)
influenza.deaths.tab$Otra.perc <- round(influenza.deaths.tab[,"Otra"]/influenza.deaths.tab$total, 2)
influenza.deaths.tab$Otras_respiratorias.perc <- round(influenza.deaths.tab[,"Otras_respiratorias"]/influenza.deaths.tab$total, 2)
kable(influenza.deaths.tab)
year Influenza_Neumonia Otra Otras_respiratorias total Influenza_Neumonia.perc Otra.perc Otras_respiratorias.perc
2012 20009 270283 29247 319539 0.06 0.85 0.09
2013 23389 273675 29133 326197 0.07 0.84 0.09
2014 24583 271289 29667 325539 0.08 0.83 0.09
2015 27804 276506 29097 333407 0.08 0.83 0.09
2016 33632 287807 31553 352992 0.10 0.82 0.09
2017 33504 276819 31365 341688 0.10 0.81 0.09
2018 31916 275155 29752 336823 0.09 0.82 0.09 
influenza.deaths.edad.tab <- dcast(influenza.deaths, formula = year~causa_agg, value.var = "edad.media")
# Edad media is aproximated by the average of the mean of age ranges
kable(influenza.deaths.edad.tab)
year Influenza_Neumonia Otra Otras_respiratorias
2012 46.15945 50.16380 53.24300
2013 46.59759 50.37501 54.32957
2014 47.00361 50.50093 53.47351
2015 47.91418 50.75700 54.46687
2016 46.37225 50.67127 53.76223
2017 47.20525 50.68764 54.23077
2018 46.79927 50.86007 54.53614