COVID19AR

A package for analysing COVID-19 Argentina’s outbreak

Package

Release	Usage	Development

Argentina COVID19 open data

How to get started (Development version)

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

# install.packages("devtools")
devtools::install_github("rOpenStats/COVID19AR")

How to use it

First add variable with your preferred configurations in ~/.Renviron. COVID19AR_data_dir is mandatory while COVID19AR_credits can be configured if you want to publish your own research.

COVID19AR_data_dir = "~/.R/COVID19AR"
COVID19AR_credits = "@youralias"

library(COVID19AR)
#> 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: knitr
#> Loading required package: magrittr
#> Loading required package: lgr
#> Warning: replacing previous import 'ggplot2::Layout' by 'lgr::Layout' when
#> loading 'COVID19AR'
#> Warning: replacing previous import 'readr::col_factor' by 'scales::col_factor'
#> when loading 'COVID19AR'
#> Warning: replacing previous import 'magrittr::not' by 'testthat::not' when
#> loading 'COVID19AR'
#> Warning: replacing previous import 'dplyr::matches' by 'testthat::matches' when
#> loading 'COVID19AR'
#> Warning: replacing previous import 'magrittr::equals' by 'testthat::equals' when
#> loading 'COVID19AR'
#> Warning: replacing previous import 'magrittr::is_less_than' by
#> 'testthat::is_less_than' when loading 'COVID19AR'
library(ggplot2)
#> 
#> Attaching package: 'ggplot2'
#> The following object is masked from 'package:lgr':
#> 
#>     Layout

COVID19AR datos abiertos del Ministerio de Salud de la Nación

opendata From Ministerio de Salud de la Nación Argentina

log.dir <- file.path(getEnv("data_dir"), "logs")
dir.create(log.dir, recursive = TRUE, showWarnings = FALSE)
log.file <- file.path(log.dir, "covid19ar.log")
lgr::get_logger("root")$add_appender(AppenderFile$new(log.file))
lgr::threshold("info", lgr::get_logger("root"))
lgr::threshold("info", lgr::get_logger("COVID19ARCurator"))

# Data from
# http://datos.salud.gob.ar/dataset/covid-19-casos-registrados-en-la-republica-argentina
covid19.curator <- COVID19ARCurator$new(report.date = Sys.Date() -1 , 
                                        download.new.data = FALSE)

dummy <- covid19.curator$loadData()
#> INFO  [09:16:18.046] Exists dest path? {dest.path: ~/.R/COVID19AR/Covid19Casos.csv, exists.dest.path: TRUE}
dummy <- covid19.curator$curateData()
#> INFO  [09:16:33.751] Normalize 
#> INFO  [09:16:39.024] checkSoundness 
#> INFO  [09:16:41.136] Mutating data
# Dates of current processed file
max(covid19.curator$data$fecha_apertura, na.rm = TRUE)
#> [1] "2020-11-11"
# Inicio de síntomas

max(covid19.curator$data$fecha_inicio_sintomas,  na.rm = TRUE)
#> [1] "2020-11-11"

# Ultima muerte
max(covid19.curator$data$fecha_fallecimiento,  na.rm = TRUE)
#> [1] "2020-11-11"

report.date <- max(covid19.curator$data$fecha_inicio_sintomas,  na.rm = TRUE)
covid19.ar.summary <- covid19.curator$makeSummary(group.vars = NULL)

kable(covid19.ar.summary %>% select(max_fecha_diagnostico, confirmados, fallecidos, letalidad.min.porc, letalidad.max.porc, count_fecha_diagnostico, tests, positividad.porc))

max_fecha_diagnostico	confirmados	fallecidos	letalidad.min.porc	letalidad.max.porc	count_fecha_diagnostico	tests	positividad.porc
2020-11-11	1273347	34531	0.023	0.027	267	2599339	0.49

covid19.ar.provincia.summary <- covid19.curator$makeSummary(group.vars = c("residencia_provincia_nombre"))
covid19.ar.provincia.summary.100.confirmed <- covid19.ar.provincia.summary %>% 
  filter(confirmados >= 100) %>%
  arrange(desc(confirmados))
# Provinces with > 100 confirmed cases
kable(covid19.ar.provincia.summary.100.confirmed %>% select(residencia_provincia_nombre, confirmados, fallecidos, confirmados, fallecidos, letalidad.min.porc, letalidad.max.porc, count_fecha_diagnostico, tests, positividad.porc))

residencia_provincia_nombre	confirmados	fallecidos	letalidad.min.porc	letalidad.max.porc	count_fecha_diagnostico	tests	positividad.porc
Buenos Aires	577838	18862	0.028	0.033	259	1222024	0.473
CABA	151000	5172	0.032	0.034	257	423290	0.357
Santa Fe	122821	1835	0.013	0.015	243	149744	0.820
Córdoba	97413	1580	0.012	0.016	249	172476	0.565
Tucumán	57081	958	0.011	0.017	238	66247	0.862
Mendoza	51879	1004	0.016	0.019	248	93235	0.556
Río Negro	27329	692	0.022	0.025	240	44119	0.619
Neuquén	25878	459	0.013	0.018	242	32942	0.786
Salta	19786	915	0.037	0.046	235	37208	0.532
Entre Ríos	18955	349	0.015	0.018	241	33156	0.572
Jujuy	18070	837	0.038	0.046	237	45000	0.402
Chubut	18047	236	0.010	0.013	228	19408	0.930
Chaco	15511	472	0.022	0.030	245	66230	0.234
Tierra del Fuego	13662	171	0.012	0.013	239	20325	0.672
Santiago del Estero	11910	146	0.010	0.012	229	39414	0.302
Santa Cruz	11777	183	0.012	0.016	234	18448	0.638
San Luis	10180	123	0.008	0.012	224	28773	0.354
La Rioja	7955	294	0.034	0.037	232	21573	0.369
La Pampa	4380	46	0.009	0.011	223	17569	0.249
San Juan	4114	102	0.016	0.025	230	4712	0.873
Corrientes	3244	58	0.012	0.018	238	15056	0.215
SIN ESPECIFICAR	2697	26	0.008	0.010	234	6444	0.419
Catamarca	1301	2	0.001	0.002	218	12126	0.107
Misiones	359	7	0.012	0.019	217	7915	0.045
Formosa	160	2	0.008	0.013	213	1905	0.084

covid19.ar.summary <- covid19.curator$makeSummary(group.vars = c("residencia_provincia_nombre"))
nrow(covid19.ar.summary)
#> [1] 25
porc.cols <- names(covid19.ar.summary)[grep("porc", names(covid19.ar.summary))]
kable((covid19.ar.summary %>% filter(confirmados > 0) %>% arrange(desc(confirmados))) %>% 
        select_at(c("residencia_provincia_nombre", "confirmados", "tests", "fallecidos", "dias.fallecimiento",porc.cols)))

residencia_provincia_nombre	confirmados	tests	fallecidos	dias.fallecimiento	letalidad.min.porc	letalidad.max.porc	positividad.porc	internados.porc	cuidado.intensivo.porc	respirador.porc
Buenos Aires	577838	1222024	18862	16.9	0.028	0.033	0.473	0.064	0.010	0.005
CABA	151000	423290	5172	17.2	0.032	0.034	0.357	0.143	0.018	0.009
Santa Fe	122821	149744	1835	13.1	0.013	0.015	0.820	0.025	0.007	0.004
Córdoba	97413	172476	1580	13.1	0.012	0.016	0.565	0.020	0.009	0.003
Tucumán	57081	66247	958	11.6	0.011	0.017	0.862	0.007	0.002	0.001
Mendoza	51879	93235	1004	12.4	0.016	0.019	0.556	0.061	0.007	0.004
Río Negro	27329	44119	692	15.2	0.022	0.025	0.619	0.141	0.006	0.004
Neuquén	25878	32942	459	18.0	0.013	0.018	0.786	0.346	0.009	0.007
Salta	19786	37208	915	14.2	0.037	0.046	0.532	0.105	0.020	0.011
Entre Ríos	18955	33156	349	15.4	0.015	0.018	0.572	0.067	0.007	0.003
Jujuy	18070	45000	837	19.0	0.038	0.046	0.402	0.023	0.010	0.006
Chubut	18047	19408	236	11.2	0.010	0.013	0.930	0.010	0.003	0.002
Chaco	15511	66230	472	14.4	0.022	0.030	0.234	0.077	0.040	0.019
Tierra del Fuego	13662	20325	171	15.0	0.012	0.013	0.672	0.017	0.006	0.005
Santiago del Estero	11910	39414	146	11.1	0.010	0.012	0.302	0.013	0.002	0.001
Santa Cruz	11777	18448	183	15.5	0.012	0.016	0.638	0.056	0.012	0.009
San Luis	10180	28773	123	12.7	0.008	0.012	0.354	0.023	0.006	0.005
La Rioja	7955	21573	294	16.9	0.034	0.037	0.369	0.007	0.002	0.001
La Pampa	4380	17569	46	14.5	0.009	0.011	0.249	0.021	0.005	0.002
San Juan	4114	4712	102	10.7	0.016	0.025	0.873	0.024	0.010	0.003
Corrientes	3244	15056	58	10.2	0.012	0.018	0.215	0.024	0.016	0.011
SIN ESPECIFICAR	2697	6444	26	23.1	0.008	0.010	0.419	0.064	0.008	0.004
Catamarca	1301	12126	2	16.0	0.001	0.002	0.107	0.013	0.002	0.000
Misiones	359	7915	7	9.5	0.012	0.019	0.045	0.217	0.028	0.014
Formosa	160	1905	2	12.0	0.008	0.013	0.084	0.419	0.000	0.000

rg <- ReportGeneratorCOVID19AR$new(covid19ar.curator = covid19.curator)
rg$preprocess()
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   .default = col_double(),
#>   residencia_provincia_nombre = col_character(),
#>   residencia_departamento_nombre = col_character(),
#>   fecha_apertura = col_date(format = ""),
#>   max_fecha_diagnostico = col_date(format = ""),
#>   max_fecha_inicio_sintomas = col_date(format = ""),
#>   confirmados.inc = col_logical(),
#>   confirmados.rate = col_logical(),
#>   fallecidos.inc = col_logical(),
#>   tests.inc = col_logical(),
#>   tests.rate = col_logical(),
#>   sospechosos.inc = col_logical()
#> )
#> ℹ Use `spec()` for the full column specifications.
rg$getDepartamentosExponentialGrowthPlot()
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.

rg$getDepartamentosCrossSectionConfirmedPostivityPlot()

covid19.ar.summary <- covid19.curator$makeSummary(group.vars = c("sepi_apertura"))
#> INFO  [09:25:18.568] Processing {current.group: }
nrow(covid19.ar.summary)
#> [1] 37
porc.cols <- names(covid19.ar.summary)[grep("porc", names(covid19.ar.summary))]
kable(covid19.ar.summary %>% 
        filter(confirmados > 0) %>% 
        arrange(sepi_apertura, desc(confirmados)) %>% 
        select_at(c("sepi_apertura", "max_fecha_diagnostico", "count_fecha_diagnostico", "confirmados", "tests", "internados", "fallecidos",  porc.cols)))

sepi_apertura	max_fecha_diagnostico	count_fecha_diagnostico	confirmados	tests	internados	fallecidos	letalidad.min.porc	letalidad.max.porc	positividad.porc	internados.porc	cuidado.intensivo.porc	respirador.porc
10	2020-11-03	22	16	87	9	1	0.053	0.062	0.184	0.562	0.125	0.125
11	2020-11-03	48	103	671	69	9	0.069	0.087	0.154	0.670	0.117	0.058
12	2020-11-03	84	430	2059	265	17	0.034	0.040	0.209	0.616	0.088	0.051
13	2020-11-07	141	1140	5539	625	66	0.051	0.058	0.206	0.548	0.090	0.054
14	2020-11-07	195	1894	11580	1024	121	0.056	0.064	0.164	0.541	0.090	0.053
15	2020-11-10	232	2663	20319	1401	190	0.062	0.071	0.131	0.526	0.084	0.047
16	2020-11-11	251	3620	31956	1791	258	0.061	0.071	0.113	0.495	0.074	0.040
17	2020-11-11	256	4910	46042	2361	385	0.068	0.078	0.107	0.481	0.067	0.035
18	2020-11-11	256	6069	59270	2804	494	0.071	0.081	0.102	0.462	0.060	0.031
19	2020-11-11	257	7711	73440	3442	609	0.070	0.079	0.105	0.446	0.056	0.029
20	2020-11-11	258	10292	90917	4341	736	0.064	0.072	0.113	0.422	0.052	0.027
21	2020-11-11	258	14981	114448	5748	954	0.057	0.064	0.131	0.384	0.047	0.023
22	2020-11-11	258	20511	139964	7284	1227	0.054	0.060	0.147	0.355	0.043	0.021
23	2020-11-11	258	27338	168361	8903	1561	0.052	0.057	0.162	0.326	0.040	0.019
24	2020-11-11	259	37402	203669	11150	1992	0.048	0.053	0.184	0.298	0.036	0.017
25	2020-11-11	259	50658	245314	13637	2557	0.046	0.050	0.207	0.269	0.031	0.014
26	2020-11-11	259	69073	297885	16869	3328	0.044	0.048	0.232	0.244	0.028	0.013
27	2020-11-11	259	88435	349534	19806	4178	0.043	0.047	0.253	0.224	0.026	0.011
28	2020-11-11	260	112533	409145	23284	5290	0.042	0.047	0.275	0.207	0.024	0.011
29	2020-11-11	262	142354	481908	27103	6619	0.042	0.046	0.295	0.190	0.023	0.010
30	2020-11-11	262	181015	568816	30965	8115	0.040	0.045	0.318	0.171	0.021	0.010
31	2020-11-11	262	221415	660050	34409	9531	0.039	0.043	0.335	0.155	0.019	0.009
32	2020-11-11	262	271709	769564	38405	11257	0.037	0.041	0.353	0.141	0.018	0.008
33	2020-11-11	262	319515	884864	42279	12858	0.036	0.040	0.361	0.132	0.017	0.008
34	2020-11-11	262	369247	996669	46184	14660	0.035	0.040	0.370	0.125	0.016	0.008
35	2020-11-11	262	435262	1133509	50911	16775	0.034	0.039	0.384	0.117	0.016	0.007
36	2020-11-11	262	505848	1276668	55401	18984	0.033	0.038	0.396	0.110	0.015	0.007
37	2020-11-11	262	581896	1430449	60184	21259	0.033	0.037	0.407	0.103	0.014	0.007
38	2020-11-11	262	656077	1575809	64553	23394	0.032	0.036	0.416	0.098	0.014	0.007
39	2020-11-11	263	734813	1717120	69087	25572	0.031	0.035	0.428	0.094	0.013	0.007
40	2020-11-11	265	820362	1857306	73365	27706	0.030	0.034	0.442	0.089	0.013	0.006
41	2020-11-11	266	911659	1997663	77543	29819	0.029	0.033	0.456	0.085	0.012	0.006
42	2020-11-11	266	1006567	2131484	81102	31734	0.028	0.032	0.472	0.081	0.012	0.006
43	2020-11-11	266	1101223	2269990	83748	33166	0.026	0.030	0.485	0.076	0.011	0.006
44	2020-11-11	267	1179513	2394707	85679	34088	0.025	0.029	0.493	0.073	0.011	0.005
45	2020-11-11	267	1245616	2527240	86929	34476	0.024	0.028	0.493	0.070	0.010	0.005
46	2020-11-11	267	1273347	2599339	87260	34531	0.023	0.027	0.490	0.069	0.010	0.005

```r
covid19.ar.summary <- covid19.curator$makeSummary(group.vars = c("residencia_provincia_nombre", "sepi_apertura"))
#> INFO  [09:30:43.778] Processing {current.group: residencia_provincia_nombre = Buenos Aires}
#> INFO  [09:33:36.621] Processing {current.group: residencia_provincia_nombre = CABA}
#> INFO  [09:35:04.643] Processing {current.group: residencia_provincia_nombre = Catamarca}
#> INFO  [09:35:09.849] Processing {current.group: residencia_provincia_nombre = Chaco}
#> INFO  [09:35:23.570] Processing {current.group: residencia_provincia_nombre = Chubut}
#> INFO  [09:35:30.657] Processing {current.group: residencia_provincia_nombre = Córdoba}
#> INFO  [09:35:58.139] Processing {current.group: residencia_provincia_nombre = Corrientes}
#> INFO  [09:36:06.147] Processing {current.group: residencia_provincia_nombre = Entre Ríos}
#> INFO  [09:36:16.205] Processing {current.group: residencia_provincia_nombre = Formosa}
#> INFO  [09:36:21.339] Processing {current.group: residencia_provincia_nombre = Jujuy}
#> INFO  [09:36:31.495] Processing {current.group: residencia_provincia_nombre = La Pampa}
#> INFO  [09:36:36.401] Processing {current.group: residencia_provincia_nombre = La Rioja}
#> INFO  [09:36:43.305] Processing {current.group: residencia_provincia_nombre = Mendoza}
#> INFO  [09:36:56.125] Processing {current.group: residencia_provincia_nombre = Misiones}
#> INFO  [09:37:01.038] Processing {current.group: residencia_provincia_nombre = Neuquén}
#> INFO  [09:37:09.288] Processing {current.group: residencia_provincia_nombre = Río Negro}
#> INFO  [09:37:18.764] Processing {current.group: residencia_provincia_nombre = Salta}
#> INFO  [09:37:26.661] Processing {current.group: residencia_provincia_nombre = San Juan}
#> INFO  [09:37:30.934] Processing {current.group: residencia_provincia_nombre = San Luis}
#> INFO  [09:37:36.372] Processing {current.group: residencia_provincia_nombre = Santa Cruz}
#> INFO  [09:37:41.907] Processing {current.group: residencia_provincia_nombre = Santa Fe}
#> INFO  [09:38:03.763] Processing {current.group: residencia_provincia_nombre = Santiago del Estero}
#> INFO  [09:38:11.776] Processing {current.group: residencia_provincia_nombre = SIN ESPECIFICAR}
#> INFO  [09:38:17.153] Processing {current.group: residencia_provincia_nombre = Tierra del Fuego}
#> INFO  [09:38:23.531] Processing {current.group: residencia_provincia_nombre = Tucumán}
nrow(covid19.ar.summary)
#> [1] 868
porc.cols <- names(covid19.ar.summary)[grep("porc", names(covid19.ar.summary))]
sepi.fechas <- covid19.curator$data %>% 
  group_by(sepi_apertura) %>% 
  summarize(ultima_fecha_sepi = max(fecha_apertura), .groups = "keep")


data2plot <- covid19.ar.summary %>%
                filter(residencia_provincia_nombre %in% covid19.ar.provincia.summary.100.confirmed$residencia_provincia_nombre) %>%
                filter(confirmados > 0 ) %>%
                filter(positividad.porc <=0.6 | confirmados >= 20)

                
data2plot %<>% inner_join(sepi.fechas, by = "sepi_apertura")
dates <- sort(unique(data2plot$ultima_fecha_sepi))

covplot <- data2plot %>%
 ggplot(aes(x = ultima_fecha_sepi, y = confirmados, color = "confirmados")) +
 geom_line() +
 facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
 labs(title = "Evolución de casos confirmados y tests\n en provincias > 100 confirmados")
covplot <- covplot +
 geom_line(aes(x = ultima_fecha_sepi, y = tests, color = "tests")) +
 facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y")
covplot <- setupTheme(covplot, report.date = report.date, x.values = dates, x.type = "dates",
                     total.colors = 2,
                     data.provider.abv = "@msalnacion", base.size = 6)
covplot <- covplot + scale_y_log10()
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.
covplot

covplot <- data2plot %>%
 ggplot(aes(x = ultima_fecha_sepi, y = positividad.porc, color = "positividad.porc")) +
 geom_line() +
 facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
 labs(title = "Porcentajes de positividad, uso de UCI, respirador y letalidad\n en provincias > 100 confirmados")
covplot <- covplot +
 geom_line(aes(x = ultima_fecha_sepi, y = cuidado.intensivo.porc, color = "cuidado.intensivo.porc")) +
 facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y")
covplot <- covplot  +
 geom_line(aes(x = ultima_fecha_sepi, y = respirador.porc, color = "respirador.porc"))+
 facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y")
covplot <- covplot +
 geom_line(aes(x = ultima_fecha_sepi, y = letalidad.min.porc, color = "letalidad.min.porc")) +
 facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y")

covplot <- setupTheme(covplot, report.date = report.date, x.values = dates, x.type = "dates",
                     total.colors = 4,
                     data.provider.abv = "@msalnacion", base.size = 6)
covplot

covid19.ar.summary <- covid19.curator$makeSummary(group.vars = c("residencia_provincia_nombre", "sexo"))
#> Warning in max.default(structure(c(NA_real_, NA_real_, NA_real_, NA_real_, : no
#> non-missing arguments to max; returning -Inf
nrow(covid19.ar.summary)
#> [1] 71
porc.cols <- names(covid19.ar.summary)[grep("porc", names(covid19.ar.summary))]
kable((covid19.ar.summary %>% filter(confirmados >= 10) %>% arrange(desc(confirmados))) %>% select_at(c("residencia_provincia_nombre", "sexo", "confirmados", "internados", "fallecidos",  porc.cols)))

residencia_provincia_nombre	sexo	confirmados	internados	fallecidos	letalidad.min.porc	letalidad.max.porc	positividad.porc	internados.porc	cuidado.intensivo.porc	respirador.porc
Buenos Aires	M	291985	20373	10453	0.031	0.036	0.486	0.070	0.012	0.006
Buenos Aires	F	283847	16692	8286	0.025	0.029	0.460	0.059	0.008	0.003
CABA	F	76190	10317	2414	0.029	0.032	0.336	0.135	0.013	0.006
CABA	M	74198	11112	2705	0.034	0.036	0.381	0.150	0.022	0.012
Santa Fe	F	62802	1344	810	0.012	0.013	0.811	0.021	0.005	0.003
Santa Fe	M	59961	1684	1022	0.015	0.017	0.831	0.028	0.009	0.005
Córdoba	F	49834	916	659	0.010	0.013	0.560	0.018	0.007	0.002
Córdoba	M	47532	1048	918	0.015	0.019	0.570	0.022	0.010	0.003
Tucumán	M	29087	245	599	0.013	0.021	0.815	0.008	0.002	0.001
Tucumán	F	27966	174	359	0.008	0.013	0.916	0.006	0.001	0.001
Mendoza	F	26064	1408	392	0.012	0.015	0.544	0.054	0.004	0.002
Mendoza	M	25610	1730	608	0.019	0.024	0.571	0.068	0.010	0.006
Río Negro	F	14126	1943	281	0.018	0.020	0.603	0.138	0.005	0.003
Río Negro	M	13184	1914	411	0.028	0.031	0.638	0.145	0.008	0.006
Neuquén	F	13016	4513	170	0.010	0.013	0.775	0.347	0.006	0.004
Neuquén	M	12852	4449	288	0.017	0.022	0.796	0.346	0.012	0.010
Salta	M	10934	1238	609	0.045	0.056	0.539	0.113	0.024	0.014
Jujuy	M	10057	276	545	0.044	0.054	0.407	0.027	0.013	0.009
Chubut	M	9653	104	131	0.011	0.014	0.922	0.011	0.004	0.003
Entre Ríos	F	9563	601	129	0.011	0.013	0.551	0.063	0.005	0.002
Entre Ríos	M	9383	661	217	0.019	0.023	0.595	0.070	0.009	0.003
Salta	F	8795	835	303	0.027	0.034	0.524	0.095	0.015	0.007
Chubut	F	8360	83	104	0.010	0.012	0.942	0.010	0.003	0.002
Jujuy	F	7990	138	290	0.030	0.036	0.395	0.017	0.006	0.004
Chaco	M	7752	634	300	0.028	0.039	0.240	0.082	0.047	0.023
Chaco	F	7751	557	172	0.016	0.022	0.229	0.072	0.034	0.015
Tierra del Fuego	M	7003	161	122	0.016	0.017	0.686	0.023	0.008	0.007
Tierra del Fuego	F	6642	76	49	0.007	0.007	0.657	0.011	0.003	0.002
Santiago del Estero	M	6326	101	91	0.012	0.014	0.290	0.016	0.002	0.001
Santa Cruz	M	6143	375	120	0.015	0.020	0.675	0.061	0.017	0.012
Santa Cruz	F	5624	279	63	0.009	0.011	0.602	0.050	0.008	0.006
Santiago del Estero	F	5577	59	55	0.008	0.010	0.329	0.011	0.001	0.001
San Luis	M	5214	141	79	0.010	0.015	0.364	0.027	0.008	0.006
San Luis	F	4956	97	44	0.006	0.009	0.344	0.020	0.003	0.003
La Rioja	M	4124	29	187	0.041	0.045	0.375	0.007	0.002	0.000
La Rioja	F	3797	24	103	0.025	0.027	0.362	0.006	0.002	0.001
La Pampa	F	2296	46	21	0.008	0.009	0.239	0.020	0.004	0.001
San Juan	M	2250	48	56	0.016	0.025	0.858	0.021	0.010	0.004
La Pampa	M	2054	43	25	0.011	0.012	0.261	0.021	0.007	0.002
Buenos Aires	NR	2006	159	123	0.045	0.061	0.480	0.079	0.017	0.007
San Juan	F	1859	49	46	0.015	0.025	0.892	0.026	0.011	0.002
Corrientes	M	1686	54	41	0.016	0.024	0.210	0.032	0.023	0.015
SIN ESPECIFICAR	F	1601	91	11	0.006	0.007	0.412	0.057	0.006	0.002
Corrientes	F	1558	23	17	0.007	0.011	0.222	0.015	0.010	0.006
SIN ESPECIFICAR	M	1089	79	14	0.011	0.013	0.430	0.073	0.009	0.006
Catamarca	M	759	9	2	0.002	0.003	0.107	0.012	0.000	0.000
CABA	NR	612	141	53	0.074	0.087	0.367	0.230	0.039	0.023
Catamarca	F	542	8	0	0.000	0.000	0.108	0.015	0.004	0.000
Mendoza	NR	205	8	4	0.014	0.020	0.415	0.039	0.010	0.010
Misiones	M	196	45	4	0.013	0.020	0.043	0.230	0.031	0.020
Misiones	F	163	33	3	0.011	0.018	0.048	0.202	0.025	0.006
Formosa	M	112	37	1	0.006	0.009	0.101	0.330	0.000	0.000
Santa Fe	NR	58	6	3	0.041	0.052	0.552	0.103	0.000	0.000
Salta	NR	57	5	3	0.042	0.053	0.475	0.088	0.035	0.018
Formosa	F	48	30	1	0.010	0.021	0.061	0.625	0.000	0.000
Córdoba	NR	47	1	3	0.036	0.064	0.653	0.021	0.000	0.000
Chubut	NR	34	1	1	0.021	0.029	0.479	0.029	0.000	0.000
La Rioja	NR	34	0	4	0.114	0.118	0.343	0.000	0.000	0.000
La Pampa	NR	30	1	0	0.000	0.000	0.337	0.033	0.000	0.000
Tucumán	NR	28	0	0	0.000	0.000	0.700	0.000	0.000	0.000
Jujuy	NR	23	1	2	0.041	0.087	0.303	0.043	0.000	0.000
Río Negro	NR	19	4	0	0.000	0.000	0.594	0.211	0.053	0.053
Tierra del Fuego	NR	17	0	0	0.000	0.000	1.545	0.000	0.000	0.000
Neuquén	NR	10	2	1	0.045	0.100	0.667	0.200	0.100	0.100
San Luis	NR	10	0	0	0.000	0.000	0.455	0.000	0.000	0.000
Santa Cruz	NR	10	0	0	0.000	0.000	0.833	0.000	0.000	0.000

covid19.ar.summary <- covid19.curator$makeSummary(group.vars = c("residencia_provincia_nombre", "edad.rango"))
#> Warning in max.default(structure(c(NA_real_, NA_real_, NA_real_, NA_real_, : no
#> non-missing arguments to max; returning -Inf

 # Share per province
  provinces.cases <-covid19.ar.summary %>%
    group_by(residencia_provincia_nombre) %>%
    summarise(fallecidos.total.provincia = sum(fallecidos),
              confirmados.total.provincia = sum(confirmados),
              .groups = "keep")
 covid19.ar.summary %<>% inner_join(provinces.cases, by = "residencia_provincia_nombre")
 covid19.ar.summary %<>% mutate(fallecidos.prop = fallecidos/fallecidos.total.provincia)
 covid19.ar.summary %<>% mutate(confirmados.prop = confirmados/confirmados.total.provincia)

 # Data 2 plot
 data2plot <- covid19.ar.summary %>% filter(residencia_provincia_nombre %in%
 # Proporción de confirmados por rango etario
 covid19.ar.provincia.summary.100.confirmed$residencia_provincia_nombre)

 
 covidplot <-
   data2plot %>%
   ggplot(aes(x = edad.rango, y = confirmados.prop, fill = edad.rango)) +
   geom_bar(stat = "identity") + facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
   labs(title = "Proporción de confirmados por rango etario\n en provincias > 100 confirmados")

 covidplot <- setupTheme(covidplot, report.date = report.date, x.values = NULL, x.type = NULL,
                         total.colors = length(unique(data2plot$edad.rango)),
                         data.provider.abv = "@msalnacion", base.size = 6)
 # Proporción de muertos por rango etario
 covidplot

 #Plot of deaths share
 covidplot <-
    data2plot %>%
    ggplot(aes(x = edad.rango, y = fallecidos.prop, fill = edad.rango)) +
    geom_bar(stat = "identity") + facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
    labs(title = "Proporción de muertos por rango etario\n en provincias > 100 confirmados")
 covidplot <- setupTheme(covidplot, report.date = report.date, x.values = NULL, x.type = NULL,
                      total.colors = length(unique(data2plot$edad.rango)),
                      data.provider.abv = "@msalnacion", base.size = 6)
 # Proporción de muertos por rango etario
 covidplot

 # UCI rate
 covidplot <- data2plot %>%
   ggplot(aes(x = edad.rango, y = cuidado.intensivo.porc, fill = edad.rango)) +
   geom_bar(stat = "identity") + facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
    labs(title = "Porcentaje de pacientes en Unidades de Cuidados Intensivos por rango etario\n en provincias > 100 confirmados")
 covidplot <- setupTheme(covidplot, report.date = report.date, x.values = NULL, x.type = NULL,
                      total.colors = length(unique(data2plot$edad.rango)),
                      data.provider.abv = "@msalnacion", base.size = 6)
 covidplot

 # ventilator rate
 covidplot <- data2plot %>%
   ggplot(aes(x = edad.rango, y = respirador.porc, fill = edad.rango)) +
   geom_bar(stat = "identity") +
   facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
   labs(title = "Porcentaje de pacientes que utilizaron respirador mecánico por rango etario\n en provincias > 100 confirmados")
 covidplot <- setupTheme(covidplot, report.date = report.date, x.values = NULL, x.type = NULL,
                      total.colors = length(unique(data2plot$edad.rango)),
                      data.provider.abv = "@msalnacion", base.size = 6)
 covidplot

 # fatality rate

 covidplot <- data2plot %>%
  ggplot(aes(x = edad.rango, y = letalidad.min.porc, fill = edad.rango)) +
  geom_bar(stat = "identity") +
  facet_wrap(~residencia_provincia_nombre, ncol = 2, scales = "free_y") +
  labs(title = "Porcentaje de letalidad por rango etario\n en provincias > 100 confirmados")
 covidplot <- setupTheme(covidplot, report.date = report.date, x.values = NULL, x.type = NULL,
                      total.colors = length(unique(data2plot$edad.rango)),
                      data.provider.abv = "@msalnacion", base.size = 6)
 covidplot

Generar diferentes agregaciones y guardar csv / Generate different aggregations

output.dir <- "~/.R/COVID19AR/"
dir.create(output.dir, showWarnings = FALSE, recursive = TRUE)
exportAggregatedTables(covid19.curator, output.dir = output.dir,
                       aggrupation.criteria = list(provincia_residencia = c("residencia_provincia_nombre"),
                                                   provincia_localidad_residencia = c("residencia_provincia_nombre", "residencia_departamento_nombre"),
                                                   provincia_residencia_sexo = c("residencia_provincia_nombre", "sexo"),
                                                   edad_rango_sexo = c("edad.rango", "sexo"),
                                                   provincia_residencia_edad_rango = c("residencia_provincia_nombre", "edad.rango"),
                                                   provincia_residencia_sepi_apertura = c("residencia_provincia_nombre", "sepi_apertura"),
                                                   provincia_residencia = c("residencia_provincia_nombre", "residencia_departamento_nombre", "sepi_apertura"),
                                                   provincia_residencia_fecha_apertura = c("residencia_provincia_nombre", "fecha_apertura")))

All this tables are accesible at COVID19ARdata

How to Cite This Work

Citation

Alejandro Baranek, COVID19AR, 2020. URL: https://github.com/rOpenStats/COVID19AR

BibTex
@techreport{baranek2020Covid19AR,
Author = {Alejandro Baranek},
Institution = {rOpenStats},
Title = {COVID19AR: a package for analysing Argentina COVID-19 outbreak},
Url = {https://github.com/rOpenStats/COVID19AR},
Year = {2020}}

Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
R		R
inst/extdata/testcases		inst/extdata/testcases
man		man
tests		tests
.Rbuildignore		.Rbuildignore
.env.example		.env.example
.gitignore		.gitignore
.lintr		.lintr
.travis.yml		.travis.yml
COVID19AR.Rproj		COVID19AR.Rproj
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
README-deaths.Rmd		README-deaths.Rmd
README-deaths.md		README-deaths.md
README.Rmd		README.Rmd
README.md		README.md

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COVID19AR

Package

Argentina COVID19 open data

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COVID19AR datos abiertos del Ministerio de Salud de la Nación

Generar diferentes agregaciones y guardar csv / Generate different aggregations

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COVID19AR

Package

Argentina COVID19 open data

How to get started (Development version)

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COVID19AR datos abiertos del Ministerio de Salud de la Nación

Generar diferentes agregaciones y guardar csv / Generate different aggregations

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