Escovid19data: COVID19 por provincias en España
Incidencia acumulada
Row
Casos por 100.000 hab en 14 días
p <- data_cases_sp_provinces %>% mutate(
cases_PCR_14days = ifelse( ccaa=="Galicia" | ccaa=="Andalucía", cases_14days, cases_PCR_14days)
) %>% filter( !(is.na(ia14) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
# geom_line(aes(date, daily_cases,group=province, color=ccaa), size= 0.8 ) +
geom_line( aes(date, ia14,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(ia14, digits = 1),
big.mark=".", decimal.mark = ","), "" ,"<br>",date )), size= 0.4
) +
# geom_point(aes(date, daily_cases, color=ccaa), size= 1 ) +
# geom_point(aes(date, cases_PCR_14days/poblacion*100000, color=ccaa), size= 0.4 ) +
# geom_text_repel(
# data = data_cases_sp_provinces %>% group_by(province) %>% filter( !is.na(cases_PCR_14days) ) %>% top_n(1, date) %>%
# filter ( cases_PCR_14days/poblacion*100000 > 15 & date > filter_date-4),
# # data=filter( data_cases_sp_provinces, date==max(data_cases_sp_provinces$date) & cases_14days/poblacion*100000 > 40),
# aes(date, cases_PCR_14days/poblacion*100000, color=ccaa,
# label=paste(format( round(cases_PCR_14days/poblacion*100000, digits = 1), nsmall=1, big.mark=".", decimal.mark = ","),province)),
# nudge_x = 2, # adjust the starting y position of the text label
# size=5,
# hjust=0,
# family = "Roboto Condensed",
# direction="y",
# segment.size = 0.1,
# segment.color="#777777"
# ) +
scale_color_manual(values = colors_prov) +
coord_cartesian(
# ylim = c( 0,max(data_cases_sp_provinces[!is.na(data_cases_sp_provinces$daily_cases),]$daily_cases) )
) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE)
) +
# scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
# expand = c(0,0.2) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provinces$date)+15, max(data_cases_sp_provinces$date)),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = "right"
) +
labs(title = paste0("Incidencia acumulada 14 días COVID-19 en España ", updated),
subtitle = paste0("Casos por 100.000 habitantes en últimos 14 días por provincia ",period),
y = "Incidencia acumulada 14 días",
x = "2020-2021",
color = "CCAA",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p <- ggplotly( p, tooltip = "text") %>%
layout(title = list(text = paste0('Incidencia acumulada 14 días COVID-19 en España',
'<br>',
'<sup>',
'Casos por 100.000 habitantes en últimos 14 días por provincia.',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingpNota: Galicia, Andalucía, Aragón y C. Valenciana no diferencian entre PCR+ y otro tipo de positivos.
Casos por 100.000 hab en 14 días (log)
p <- data_cases_sp_provinces %>% mutate(
cases_PCR_14days = ifelse( ccaa=="Galicia" | ccaa=="Andalucía", cases_14days, cases_PCR_14days)
) %>% filter( !(is.na(ia14) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
# geom_line(aes(date, daily_cases,group=province, color=ccaa), size= 0.8 ) +
geom_line( aes(date, ia14,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(ia14, digits = 1),
big.mark=".", decimal.mark = ","), "" ,"<br>",date )), size= 0.4
) +
# geom_point(aes(date, daily_cases, color=ccaa), size= 1 ) +
# geom_point(aes(date, cases_PCR_14days/poblacion*100000, color=ccaa), size= 0.4 ) +
# geom_text_repel(
# data = data_cases_sp_provinces %>% group_by(province) %>% filter( !is.na(cases_PCR_14days) ) %>% top_n(1, date) %>%
# filter ( cases_PCR_14days/poblacion*100000 > 15 & date > filter_date-4),
# # data=filter( data_cases_sp_provinces, date==max(data_cases_sp_provinces$date) & cases_14days/poblacion*100000 > 40),
# aes(date, cases_PCR_14days/poblacion*100000, color=ccaa,
# label=paste(format( round(cases_PCR_14days/poblacion*100000, digits = 1), nsmall=1, big.mark=".", decimal.mark = ","),province)),
# nudge_x = 2, # adjust the starting y position of the text label
# size=5,
# hjust=0,
# family = "Roboto Condensed",
# direction="y",
# segment.size = 0.1,
# segment.color="#777777"
# ) +
scale_color_manual(values = colors_prov) +
coord_cartesian(
# ylim = c( 0,max(data_cases_sp_provinces[!is.na(data_cases_sp_provinces$daily_cases),]$daily_cases) )
) +
# scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE)
# ) +
scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.2) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provinces$date)+15, max(data_cases_sp_provinces$date)),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = "right"
) +
labs(title = paste0("Incidencia acumulada 14 días COVID-19 en España ", updated),
subtitle = paste0("Casos por 100.000 habitantes en últimos 14 días por provincia. Escala logarítmica ",period),
y = "Incidencia acumulada 14 días (log)",
x = "2020-2021",
color = "CCAA",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p <- ggplotly( p, tooltip = "text") %>%
layout(title = list(text = paste0('Incidencia acumulada 14 días COVID-19 en España',
'<br>',
'<sup>',
'Casos por 100.000 habitantes en últimos 14 días por provincia. Escala logarítmica ',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in self$trans$transform(x): Se han producido NaNs## Warning: Transformation introduced infinite values in continuous y-axis## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingpNota: Galicia, Andalucía, Aragón y C. Valenciana no diferencian entre PCR+ y otro tipo de positivos.
Casos por día
Row
Casos / día
interactive_dp <- data_cases_sp_provinces %>%
mutate(
daily_cases_PCR_avg7 = ifelse( date > as.Date("2020-10-02") & is.na(daily_cases_PCR_avg7), daily_cases_avg7, daily_cases_PCR_avg7 )
) %>% filter( !(is.na(daily_cases_PCR_avg7) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, daily_cases_PCR_avg7, group = province, color=ccaa,
text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_cases_PCR_avg7, digits = 1),
big.mark=".", decimal.mark = ","), " media casos diaria (ventana 7 días)" ,"<br>",date )),
size= 0.4, se = FALSE, span = 0.6 ) +
# geom_point(aes(date, daily_cases_PCR, color=ccaa,
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_cases_PCR, digits = 1),
# big.mark=".", decimal.mark = ","), " casos el día " ,"<br>",date )),
# size= 0.3
# ) +
scale_color_manual(values = colors_prov) +
coord_cartesian(
# ylim = c(1, max(data_cases_sp_provinces[!is.na(data_cases_sp_provinces$daily_cases_PCR_avg7) & (date > data_cases_sp_provinces$filter_date - 50),]$daily_cases_PCR_avg7) )
ylim = c(0, 5200 )
) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.2) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provinces$date)+13, max(data_cases_sp_provinces$date + 1)),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.3,0.9)
) +
labs(title = paste0("Media de casos por día (media 7 días) por COVID-19. España ", updated ),
subtitle = paste0("Por provincia. Escala logarítmica ",period),
y = "casos por día (media 7 días)",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown parameters: se, span## Warning: Ignoring unknown aesthetics: text# save interactvive
interactive_dp_p <- ggplotly(interactive_dp, tooltip = "text") %>%
layout(title = list(text = paste0('Media de casos por día (ventana 7 días) por COVID-19.',
'<br>',
'<sup>',
' Últimos 50 días. Por provincia. Escala lineal',
'</sup>')),
legend = list(font = list(size = 10))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
)
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusinginteractive_dp_p Se usan casos PCR+, menos las CCAA que empezaron a usar test de antígenos a partir de octubre de 2020.
# interactive_dp <- data_cases_sp_provinces %>%
# filter ( date > filter_date - 50 ) %>%
# mutate(
# daily_cases_PCR_avg7 = ifelse( date > as.Date("2020-10-02") & is.na(daily_cases_PCR_avg7), daily_cases_avg7, daily_cases_PCR_avg7 )
# ) %>% filter( !(is.na(daily_cases_PCR_avg7) & date > as.Date("2020-08-10") ) ) %>%
# ggplot() +
# geom_line(aes(date, daily_cases_PCR_avg7, group = province, color=ccaa,
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_cases_PCR_avg7, digits = 1),
# big.mark=".", decimal.mark = ","), " media casos diaria (ventana 7 días)" ,"<br>",date )),
# size= 0.4, se = FALSE, span = 0.6 ) +
# # geom_point(aes(date, daily_cases_PCR, color=ccaa,
# # text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_cases_PCR, digits = 1),
# # big.mark=".", decimal.mark = ","), " casos el día " ,"<br>",date )),
# # size= 0.3
# # ) +
# scale_color_manual(values = colors_prov) +
# coord_cartesian(
# # ylim = c(1, max(data_cases_sp_provinces[!is.na(data_cases_sp_provinces$daily_cases_PCR_avg7) & (date > data_cases_sp_provinces$filter_date - 50),]$daily_cases_PCR_avg7) )
# ylim = c(0, 5200 )
# ) +
# scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# # minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
# expand = c(0,0.2) ) +
# scale_x_date(date_breaks = "1 week",
# date_labels = "%d/%m",
# limits=c( filter_date - 50, max(data_cases_sp_provinces$date + 1)),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# # panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000"),
# legend.position = c(0.3,0.9)
# ) +
# labs(title = paste0("Media de casos por día (media 7 días) por COVID-19. España ", updated ),
# subtitle = paste0("Por provincia. Escala logarítmica ",period),
# y = "casos por día (media 7 días)",
# x = "2020-2021",
# caption = caption_provincia)
#
#
# # save interactvive
# interactive_dp_p <- ggplotly(interactive_dp, tooltip = "text") %>%
# layout(title = list(text = paste0('Media de casos por día (ventana 7 días) por COVID-19.',
# '<br>',
# '<sup>',
# ' Últimos 50 días. Por provincia. Escala lineal',
# '</sup>')),
# legend = list(font = list(size = 10))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# )
# )
#
# interactive_dp_p Se usan casos PCR+, menos las CCAA que empezaron a usar test de antígenos a partir de octubre de 2020.
Casos / día Log
interactive_dp <- data_cases_sp_provinces %>% mutate(
daily_cases_PCR_avg7 = ifelse( date > as.Date("2020-10-02") & is.na(daily_cases_PCR_avg7), daily_cases_avg7, daily_cases_PCR_avg7 )
) %>% filter( !(is.na(daily_cases_PCR_avg7) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, daily_cases_PCR_avg7, group = province, color=ccaa,
text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_cases_PCR_avg7, digits = 1),
big.mark=".", decimal.mark = ","), " media casos diaria (ventana 7 días)" ,"<br>",date )),
size= 0.4, se = FALSE, span = 0.6 ) +
# geom_point(aes(date, daily_cases_PCR, color=ccaa,
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_cases_PCR, digits = 1),
# big.mark=".", decimal.mark = ","), " casos el día " ,"<br>",date )),
# size= 0.3
# ) +
scale_color_manual(values = colors_prov) +
coord_cartesian(
ylim = c(1,3500)
) +
scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.2) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provinces$date)+13, max(data_cases_sp_provinces$date + 1)),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.3,0.9)
) +
labs(title = paste0("Media de casos por día (media 7 días) por COVID-19. España ", updated ),
subtitle = paste0("Por provincia. Escala logarítmica ",period),
y = "casos por día (media 7 días)",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown parameters: se, span## Warning: Ignoring unknown aesthetics: text# save interactvive
interactive_dp_p <- ggplotly(interactive_dp, tooltip = "text") %>%
layout(title = list(text = paste0('Media de casos por día (ventana 7 días) por COVID-19',
'<br>',
'<sup>',
'Por provincia. Escala logarítmica.',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in self$trans$transform(x): Se han producido NaNs## Warning: Transformation introduced infinite values in continuous y-axis## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusinginteractive_dp_p Se usan casos PCR+, menos las CCAA que empezaron a usar test de antígenos a partir de octubre de 2020.
# p2 <- data_cases_sp_provinces %>% mutate(
# cases_PCR_14days = ifelse( ccaa=="Galicia" | ccaa=="Andalucía", cases_14days, cases_PCR_14days)
# ) %>% mutate(
# cases_PCR_14days = ifelse( date > as.Date("2020-10-02") & is.na(cases_PCR_14days), cases_14days, cases_PCR_14days )
# ) %>% filter( !(is.na(daily_cases_PCR_avg7) & date > as.Date("2020-08-10") ) ) %>%
# ggplot() +
# geom_line(aes(date, cases_PCR_14days,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(cases_PCR_14days, digits = 1),
# big.mark=".", decimal.mark = ","), " casos en 14 días" ,"<br>",date )), size= 0.4 ) +
# # geom_point(aes(date, cases_PCR_14days, color=ccaa), size= 1 ) +
# # geom_text_repel( data = data_cases_sp_provinces %>% group_by(province) %>% filter( !is.na(cases_PCR_14days) ) %>% top_n(1, date) %>%
# # filter ( cases_PCR_14days > 100 & date > filter_date-7),
# # aes(date, cases_PCR_14days, color=ccaa, label=paste(format(cases_PCR_14days, nsmall=1, big.mark=".", decimal.mark = ","),province)),
# # nudge_x = 4, # adjust the starting y position of the text label
# # size=5,
# # hjust=0,
# # family = "Roboto Condensed",
# # direction="y",
# # segment.size = 0.1,
# # segment.color="#777777"
# # ) +
# scale_color_manual(values = colors_prov) +
# coord_cartesian(
# ) +
# scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE)
# ) +
# scale_x_date(date_breaks = "3 week",
# date_labels = "%d/%m",
# limits=c( min(data_cases_sp_provinces$date)+15, max(data_cases_sp_provinces$date)),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# # panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000")
# # legend.position = "none"
# ) +
# labs(title = paste0("Casos últimos 14 días por COVID-19 en España ", updated),
# subtitle = paste0("Por provincia ",period),
# y = "casos",
# x = "2020-2021",
# caption = caption_provincia)
#
# # save interactvive
# p2 <- ggplotly(p2, tooltip = "text") %>%
# layout(title = list(text = paste0('Casos en los últimos 14 días por COVID-19 en España',
# '<br>',
# '<sup>',
# 'Por provincia',
# '</sup>'))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# ),
# legend = list(font = list(size = 10))
# )
#
# p2Hospitalizados
Row
# Remove not prevalent hospitalized data for Murcia and Navarra early days
data_cases_sp_provinces <- readRDS(file = "../data/output/spain/covid19-provincias-spain_consolidated.rds")
data_cases_sp_provinces <- data_cases_sp_provinces %>% filter( (date > as.Date("2020-02-25") ) & ( date < filter_date ) )
# Remove not prevalent hospitalized data early days ---
data_cases_sp_provinces <- data_cases_sp_provinces %>% mutate(
hospitalized = ifelse( (province== "Murcia") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized ),
intensive_care = ifelse( (province== "Murcia") & ( date < as.Date("2020-07-16" ) ), NA, intensive_care ),
hospitalized = ifelse( (province== "Navarra") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized ),
intensive_care = ifelse( (province== "Navarra") & ( date < as.Date("2020-07-16" ) ), NA, intensive_care ),
hospitalized = ifelse( (province== "Ceuta") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized ),
intensive_care = ifelse( (province== "Ceuta") & ( date < as.Date("2020-07-16" ) ), NA, intensive_care ),
hospitalized = ifelse( (province== "Melilla") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized ),
intensive_care = ifelse( (province== "Melilla") & ( date < as.Date("2020-07-16" ) ), NA, intensive_care ),
hospitalized = ifelse( (province== "Rioja, La") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized ),
intensive_care = ifelse( (province== "Rioja, La") & ( date < as.Date("2020-07-16" ) ), NA, intensive_care ),
hospitalized = ifelse( (province== "Rioja, La") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized ),
hospitalized_per_100000 = ifelse( (province== "Murcia") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized_per_100000 ),
hospitalized_per_100000 = ifelse( (province== "Navarra") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized_per_100000 ),
hospitalized_per_100000 = ifelse( (province== "Ceuta") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized_per_100000 ),
hospitalized_per_100000 = ifelse( (province== "Melilla") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized_per_100000 ),
hospitalized_per_100000 = ifelse( (province== "Rioja, La") & ( date < as.Date("2020-07-16" ) ), NA, hospitalized_per_100000 )
)
# Solamente deja las ccaa prevalentes
noprevalentes <- c("")
data_cases_sp_provincesX <- data_cases_sp_provinces %>% filter( ! ccaa %in% noprevalentes )
data_cases_sp_provincesX_sm <- data_cases_sp_provinces_sm %>% filter( ! ccaa %in% noprevalentes )
# creates extended color palette https://www.r-bloggers.com/how-to-expand-color-palette-with-ggplot-and-rcolorbrewer/
colourCount <- length(unique(data_cases_sp_provincesX$ccaa))
getPalette <- colorRampPalette(brewer.pal(9, "Set1"))
colors_provX <- getPalette(colourCount )
# Change yellow to blue
colors_provX[1] <- "#a60000"
colors_provX[12] <- "#84d3e7"Hospitalizados (lineal)
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(hospitalized) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, hospitalized,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(hospitalized, digits = 1),
big.mark=".", decimal.mark = ","), " hospitalizados" ,"<br>",date )), size= 0.4) +
# geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
scale_color_manual(values = colors_provX) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("Hospitalizados prevalentes por COVID-19 en España ", updated ),
subtitle = paste0("Por provincia. ",period),
y = "hospitalizados",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('Hospitalizados prevalentes por COVID-19 en España',
'<br>',
'<sup>',
'Por provincia ',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2# p2 <- data_cases_sp_provincesX %>% filter( !(is.na(hospitalized) & date > as.Date("2020-08-10") ) ) %>%
# ggplot() +
# geom_line(aes(date, hospitalized,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(hospitalized, digits = 1),
# big.mark=".", decimal.mark = ","), " hospitalizados" ,"<br>",date )), size= 0.4) +
# # geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# # big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
# scale_color_manual(values = colors_provX) +
# scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
# expand = c(0,0.1) ) +
# scale_x_date(date_breaks = "3 week",
# date_labels = "%d/%m",
# limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# # panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000"),
# legend.position = c(0.07,0.7)
# ) +
# labs(title = paste0("Hospitalizados prevalentes por COVID-19 en España ", updated ),
# subtitle = paste0("Por provincia (escala logarítmica). ",period),
# y = "hospitalizados",
# x = "2020-2021",
# caption = caption_provincia)
#
#
# # save interactvive
# p2 <- ggplotly(p2, tooltip = "text") %>%
# layout(title = list(text = paste0('Hospitalizados prevalentes por COVID-19 en España',
# '<br>',
# '<sup>',
# 'Por provincia (escala logarítmica)',
# '</sup>'))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# ),
# legend = list(font = list(size = 10))
# )
#
# p2Hospitalizados por 100.000 hab.
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(hospitalized) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, hospitalized/poblacion*100000,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(hospitalized/poblacion*100000, digits = 1),
big.mark=".", decimal.mark = ","), " hospitalizados" ,"<br>",date )), size= 0.4) +
# geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
scale_color_manual(values = colors_provX) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("Hospitalizados prevalentes por COVID-19 en España por 100.000 habitantes ", updated ),
subtitle = paste0("Por provincia. ",period),
y = "hospitalizados",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('Hospitalizados prevalentes por COVID-19 en España por 100.000 habitantes',
'<br>',
'<sup>',
'Por provincia ',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2# p2 <- data_cases_sp_provincesX %>% filter( !(is.na(hospitalized) & date > as.Date("2020-08-10") ) ) %>%
# ggplot() +
# geom_line(aes(date, hospitalized_new,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round( hospitalized_new, digits = 1),
# big.mark=".", decimal.mark = ","), " ingresos nuevos en planta" ,"<br>",date )), size= 0.4) +
# # geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# # big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
# scale_color_manual(values = colors_provX) +
# scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
# expand = c(0,0.1) ) +
# scale_x_date(date_breaks = "3 week",
# date_labels = "%d/%m",
# limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# # panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000"),
# legend.position = c(0.07,0.7)
# ) +
# labs(title = paste0("Hospitalizados prevalentes por COVID-19 en España ", updated ),
# subtitle = paste0("Por provincia (escala logarítmica). ",period),
# y = "hospitalizados",
# x = "2020-2021",
# caption = caption_provincia)
#
#
# # save interactvive
# p2 <- ggplotly(p2, tooltip = "text") %>%
# layout(title = list(text = paste0('Ingresados nuevos en hospital por COVID-19 en España',
# '<br>',
# '<sup>',
# 'Por provincia (escala logarítmica)',
# '</sup>'))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# ),
# legend = list(font = list(size = 10))
# )
#
# p2UCI
Row
UCI (lineal)
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(intensive_care) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, intensive_care,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.4) +
scale_color_manual(values = colors_provX) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("UCI (hospitalizados) por COVID-19 en España ", updated ),
subtitle = paste0("Por provincia. ",period),
y = "hospitalizados",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('UCI (hospitalizados) por COVID-19 en España',
'<br>',
'<sup>',
'Por provincia',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2# p2 <- data_cases_sp_provincesX %>% filter( !(is.na(intensive_care) & date > as.Date("2020-08-10") ) ) %>%
# ggplot() +
# geom_line(aes(date, intensive_care,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.4) +
# scale_color_manual(values = colors_provX) +
# scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
# expand = c(0,0.1) ) +
# scale_x_date(date_breaks = "3 week",
# date_labels = "%d/%m",
# limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# # panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000"),
# legend.position = c(0.07,0.7)
# ) +
# labs(title = paste0("UCI (hospitalizados) por COVID-19 en España ", updated ),
# subtitle = paste0("Por provincia (escala logarítmica). ",period),
# y = "hospitalizados",
# x = "2020-2021",
# caption = caption_provincia)
#
#
# # save interactvive
# p2 <- ggplotly(p2, tooltip = "text") %>%
# layout(title = list(text = paste0('UCI (hospitalizados) por COVID-19 en España',
# '<br>',
# '<sup>',
# 'Por provincia (escala logarítmica)',
# '</sup>'))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# ),
# legend = list(font = list(size = 10))
# )
#
# p2UCI por 100.000 hab (lineal)
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(intensive_care) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, intensive_care/poblacion*1000000,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care/poblacion*1000000, digits = 1),
big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.4) +
scale_color_manual(values = colors_provX) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("UCI (hospitalizados) por COVID-19 en España por 100.000 habitantes ", updated ),
subtitle = paste0("Por provincia. ",period),
y = "hospitalizados",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('UCI (hospitalizados) por COVID-19 en España por 100.000 habitantes',
'<br>',
'<sup>',
'Por provincia',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2### Periodo completo (rejilla)
# p2 <- data_cases_sp_provincesX %>% filter( ! ccaa %in% noprevalentes ) %>%
# ggplot() +
# geom_line(aes(date, hospitalized,group=province, color="black",
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(hospitalized, digits = 1),
# big.mark=".", decimal.mark = ","), " hospitalizados" ,"<br>",date )
# ), size=0.8 ) +
# # geom_point(aes(date, hospitalized), size= 0.2 ) +
# geom_line(aes(date, intensive_care,group=province, color= "red",
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )
# ), size=0.8 ) +
# scale_color_identity(
# guide = "legend",
# labels = c("Hospitalizados","UCI"),
# ) +
# expand_limits(y = 0) +
# facet_wrap(~province, scales = "free_y") +
# scale_y_continuous(
# # limits = c(0,max(data_cases_sp_provincesX$cases_accumulated) ),
# labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE) ) +
# scale_x_date(date_breaks = "1 month",
# date_labels = "%m",
# limits=c( min(data_cases_sp_provincesX$date), max(data_cases_sp_provincesX$date)),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 8) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000"),
# legend.position = "none",
# axis.text.x = element_text(size = 6)
# ) +
# labs(title = paste0("Hospitalizados y UCI por COVID-19 en España ", updated ),
# subtitle = paste0("Por provincia (escala lineal). ",period),
# y = "hospitalizados | UCI",
# x = "fecha (mes) 2020",
# color = "",
# caption = caption_provincia)
#
# # save interactvive
# p2 <- ggplotly(p2, tooltip = "text") %>%
# layout(title = list(text = paste0('Hospitalizados y UCI por COVID-19 en España',
# '<br>',
# '<sup>',
# 'Por provincia',
# '</sup>'))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# )
# )
#
# p2# ### Últimos 50 días (rejilla)
# p2 <- data_cases_sp_provincesX %>% filter( ! ccaa %in% noprevalentes ) %>% filter( date > filter_date - 50 ) %>%
# ggplot() +
# geom_line(aes(date, hospitalized,group=province, color="black",
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(hospitalized, digits = 1),
# big.mark=".", decimal.mark = ","), " hospitalizados" ,"<br>",date )
# ), size=0.8 ) +
# # geom_point(aes(date, hospitalized), size= 0.2 ) +
# geom_line(aes(date, intensive_care,group=province, color= "red",
# text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )
# ), size=0.8 ) +
# scale_color_identity(
# guide = "legend",
# labels = c("Hospitalizados","UCI"),
# ) +
# expand_limits(y = 0) +
# facet_wrap(~province, scales = "free_y") +
# scale_y_continuous(
# # limits = c(0,max(data_cases_sp_provincesX$cases_accumulated) ),
# labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE) ) +
# scale_x_date(date_breaks = "1 month",
# date_labels = "%m",
# limits=c( filter_date - 50, max(data_cases_sp_provincesX$date)),
# expand = c(0,0)
# ) +
# theme_minimal(base_family = "Roboto Condensed",base_size = 8) +
# theme(
# panel.grid.minor.x = element_blank(),
# panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
# axis.ticks.x = element_line(color = "#000000"),
# legend.position = "none",
# axis.text.x = element_text(size = 6)
# ) +
# labs(title = paste0("Hospitalizados y UCI por COVID-19 en España ", updated ),
# subtitle = paste0("Por provincia (escala lineal). ",period),
# y = "hospitalizados | UCI",
# x = "fecha (mes) 2020",
# color = "",
# caption = caption_provincia)
#
# # save interactvive
# p2 <- ggplotly(p2, tooltip = "text") %>%
# layout(title = list(text = paste0('Hospitalizados y UCI por COVID-19 en España',
# '<br>',
# '<sup>',
# 'Por provincia',
# '</sup>'))
# , annotations =
# list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
# showarrow = F, xref='paper', yref='paper',
# xanchor='right', yanchor='auto', xshift=0, yshift=0,
# font=list(size=15, color="grey")
# )
# )
#
# p2Fallecidos
Row
# Remove not prevalent hospitalized data for Murcia and Navarra early days
data_cases_sp_provinces <- readRDS(file = "../data/output/spain/covid19-provincias-spain_consolidated.rds")
data_cases_sp_provinces <- data_cases_sp_provinces %>% filter( (date > as.Date("2020-02-25") ) & ( date < filter_date ) )
# creates extended color palette https://www.r-bloggers.com/how-to-expand-color-palette-with-ggplot-and-rcolorbrewer/
colourCount <- length(unique(data_cases_sp_provincesX$ccaa))
getPalette <- colorRampPalette(brewer.pal(9, "Set1"))
colors_provX <- getPalette(colourCount )
# Change yellow to blue
colors_provX[1] <- "#a60000"
colors_provX[12] <- "#84d3e7"Fallecidos (lineal)
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(daily_deaths_avg7) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, daily_deaths_avg7,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_deaths_avg7, digits = 1),
big.mark=".", decimal.mark = ","), " fallecidos" ,"<br>",date )), size= 0.4) +
# geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
scale_color_manual(values = colors_provX) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("Fallecidos diarios por COVID-19 en España ", updated ),
subtitle = paste0("Por provincia. Media ventana de 7 días ",period),
y = "fallecidos",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('Fallecidos diarios por COVID-19 en España',
'<br>',
'<sup>',
'Por provincia. Media ventana de 7 días ',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2Fallecidos (log)
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(daily_deaths_avg7) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, daily_deaths_avg7,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_deaths_avg7, digits = 1),
big.mark=".", decimal.mark = ","), " fallecidos" ,"<br>",date )), size= 0.4) +
# geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
scale_color_manual(values = colors_provX) +
scale_y_log10( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("Fallecidos diarios por COVID-19 en España ", updated ),
subtitle = paste0("Por provincia (escala logarítmica). Media ventana de 7 días ",period),
y = "fallecidos",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('Fallecidos diarios por COVID-19 en España',
'<br>',
'<sup>',
'Por provincia (escala logarítmica). Media ventana de 7 días ',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in self$trans$transform(x): Se han producido NaNs## Warning: Transformation introduced infinite values in continuous y-axis## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2Fallecidos por 100.000 hab. (lineal)
p2 <- data_cases_sp_provincesX %>% filter( !(is.na(daily_deaths_avg7) & date > as.Date("2020-08-10") ) ) %>%
ggplot() +
geom_line(aes(date, daily_deaths_avg7/poblacion*100000,group=province, color=ccaa, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(daily_deaths_avg7/poblacion*100000, digits = 1),
big.mark=".", decimal.mark = ","), " fallecidos" ,"<br>",date )), size= 0.4) +
# geom_line(aes(date, intensive_care,group=province, text = paste0("<b>", province, " (", ccaa, ")</b><br>", format( round(intensive_care, digits = 1),
# big.mark=".", decimal.mark = ","), " UCI" ,"<br>",date )), size= 0.7, color = "grey") +
scale_color_manual(values = colors_provX) +
scale_y_continuous( labels=function(x) format(round(x, digits = 0), big.mark = ".", scientific = FALSE),
# minor_breaks = c(seq(1 , 10, 1),seq(10 , 100, 10), seq(100 , 1000, 100), seq(1000 , 10000, 1000)),
expand = c(0,0.1) ) +
scale_x_date(date_breaks = "3 week",
date_labels = "%d/%m",
limits=c( min(data_cases_sp_provincesX$date)+7, max(data_cases_sp_provincesX$date )),
expand = c(0,0)
) +
theme_minimal(base_family = "Roboto Condensed",base_size = 16) +
theme(
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
# panel.grid.minor.y = element_blank(),
axis.ticks.x = element_line(color = "#000000"),
legend.position = c(0.07,0.7)
) +
labs(title = paste0("Fallecidos diarios por COVID-19 en España por 100.000 habitantes ", updated ),
subtitle = paste0("Por provincia. Media ventana de 7 días ",period),
y = "fallecidos",
x = "2020-2021",
caption = caption_provincia)## Warning: Ignoring unknown aesthetics: text# save interactvive
p2 <- ggplotly(p2, tooltip = "text") %>%
layout(title = list(text = paste0('Fallecidos diarios por COVID-19 en España por 100.000 habitantes',
'<br>',
'<sup>',
'Por provincia. Media ventana de 7 días ',
'</sup>'))
, annotations =
list(x = 1, y = -0.11, text = "<a style='color:grey;' href='https://lab.montera34.com/covid19'>lab.montera34.com/covid19</a> | Data: <a style='color:grey;'href='https://github.com/montera34/escovid19data9'>esCOVID19data</a>",
showarrow = F, xref='paper', yref='paper',
xanchor='right', yanchor='auto', xshift=0, yshift=0,
font=list(size=15, color="grey")
),
legend = list(font = list(size = 10))
)## Warning in prettyNum(.Internal(format(x, trim, digits, nsmall, width, 3L, :
## 'big.mark' and 'decimal.mark' are both '.', which could be confusingp2Datos y código
Row
Proyecto esCOVID19data
Más información sobre los datos recopilados, en su mayorías de las comunidades autónomas, en esCOVID19data, proyecto colaborativo de recopilación de datos: https://github.com/montera34/escovid19data
Más visualizaciones en https://lab.montera34.com/covid19/provincias.html
Código de las visualizaciones realizadas en R: https://code.montera34.com/numeroteca/covid19/
Código en Rmarkdown de este dashboard en: https://code.montera34.com:4443/numeroteca/covid19/-/blob/master/dashboard/index.Rmd
Visualizaciones realizadas por @numeroteca y alojadas en Montera34.com.
Realizado por Montera34, disponible bajo licencia GNU GPL 3 y CC BY SA | Código para generar los gráficos | Datos | Contacto | @montera34.