As previously mentioned, the visual analysis will focus on the following provinces:
Asturias
Barcelona
Madrid
Málaga
Sevilla
First, we need to load packages and clean data generated during previous tasks:
pacman::p_load( here, # file locator tidyverse, # data management and ggplot2 graphics skimr, # get overview of data janitor, # produce and adorn tabulations and cross-tabulations lubridate, # manage dates PerformanceAnalytics, factoextra, tsibble, ggfortify)hosp_data <-readRDS(here("data", "clean", "final_hosp_data.rds"))hosp_data
# A tibble: 122,850 × 8
provincia sexo grupo_edad fecha num_casos num_hosp num_uci num_def
<chr> <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl>
1 Barcelona H 0-9 2020-01-01 0 0 0 0
2 Barcelona H 10-19 2020-01-01 0 0 0 0
3 Barcelona H 20-29 2020-01-01 0 0 0 0
4 Barcelona H 30-39 2020-01-01 0 0 0 0
5 Barcelona H 40-49 2020-01-01 0 0 0 0
6 Barcelona H 50-59 2020-01-01 0 0 0 0
7 Barcelona H 60-69 2020-01-01 0 0 0 0
8 Barcelona H 70-79 2020-01-01 0 0 0 0
9 Barcelona H 80+ 2020-01-01 0 0 0 0
10 Barcelona H NC 2020-01-01 0 0 0 0
# … with 122,840 more rows
Asturias
data_asturias <- hosp_data %>%filter(provincia =="Asturias") %>%select(-provincia) %>%as_tsibble(index = fecha, key =c(sexo, grupo_edad)) %>%mutate(ola =case_when( fecha <as.Date("2020-06-21", format ="%Y-%m-%d") ~"1_ola", fecha <as.Date("2020-12-06", format ="%Y-%m-%d") ~"2_ola", fecha <as.Date("2021-03-14", format ="%Y-%m-%d") ~"3_ola", fecha <as.Date("2021-06-19", format ="%Y-%m-%d") ~"4_ola", fecha <as.Date("2021-10-13", format ="%Y-%m-%d") ~"5_ola",TRUE~"6_ola", ))data_asturias
# A tsibble: 24,570 x 8 [1D]
# Key: sexo, grupo_edad [30]
sexo grupo_edad fecha num_casos num_hosp num_uci num_def ola
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr>
1 H 0-9 2020-01-01 0 0 0 0 1_ola
2 H 0-9 2020-01-02 0 0 0 0 1_ola
3 H 0-9 2020-01-03 0 0 0 0 1_ola
4 H 0-9 2020-01-04 0 0 0 0 1_ola
5 H 0-9 2020-01-05 0 0 0 0 1_ola
6 H 0-9 2020-01-06 0 0 0 0 1_ola
7 H 0-9 2020-01-07 0 0 0 0 1_ola
8 H 0-9 2020-01-08 0 0 0 0 1_ola
9 H 0-9 2020-01-09 0 0 0 0 1_ola
10 H 0-9 2020-01-10 0 0 0 0 1_ola
# … with 24,560 more rows
by wave
data_asturias %>%ggplot(aes(x=grupo_edad, y=num_casos)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Asturias - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_asturias %>%ggplot(aes(x=grupo_edad, y=num_def)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Asturias - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
by age group
data_asturias %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Asturias - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_asturias %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Asturias - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_asturias %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=grupo_edad)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~sexo, ncol=1) +theme(legend.position ="top") +labs(title="Asturias - Cases reported by age group and wave",x ="Date", y ="Nº of cases") +theme_bw()
by sex
data_asturias %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Asturias - Cases reported by sex and wave",x ="Age group", y ="Nº of cases") +theme_bw()
data_asturias %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Asturias - Deaths reported by sex and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_asturias %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=sexo)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~grupo_edad, scales ="free_y", ncol=1) +theme(legend.position ="top") +labs(title="Asturias - Cases reported by sex and age group (free y axis)",x ="Date", y ="Nº of cases") +theme_bw()
Barcelona
data_Barcelona <- hosp_data %>%filter(provincia =="Barcelona") %>%select(-provincia) %>%as_tsibble(index = fecha, key =c(sexo, grupo_edad)) %>%mutate(ola =case_when( fecha <as.Date("2020-06-21", format ="%Y-%m-%d") ~"1_ola", fecha <as.Date("2020-12-06", format ="%Y-%m-%d") ~"2_ola", fecha <as.Date("2021-03-14", format ="%Y-%m-%d") ~"3_ola", fecha <as.Date("2021-06-19", format ="%Y-%m-%d") ~"4_ola", fecha <as.Date("2021-10-13", format ="%Y-%m-%d") ~"5_ola",TRUE~"6_ola", ))data_Barcelona
# A tsibble: 24,570 x 8 [1D]
# Key: sexo, grupo_edad [30]
sexo grupo_edad fecha num_casos num_hosp num_uci num_def ola
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr>
1 H 0-9 2020-01-01 0 0 0 0 1_ola
2 H 0-9 2020-01-02 0 0 0 0 1_ola
3 H 0-9 2020-01-03 0 0 0 0 1_ola
4 H 0-9 2020-01-04 0 0 0 0 1_ola
5 H 0-9 2020-01-05 0 0 0 0 1_ola
6 H 0-9 2020-01-06 0 0 0 0 1_ola
7 H 0-9 2020-01-07 0 0 0 0 1_ola
8 H 0-9 2020-01-08 0 0 0 0 1_ola
9 H 0-9 2020-01-09 0 0 0 0 1_ola
10 H 0-9 2020-01-10 1 0 0 0 1_ola
# … with 24,560 more rows
by wave
data_Barcelona %>%ggplot(aes(x=grupo_edad, y=num_casos)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Barcelona - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Barcelona %>%ggplot(aes(x=grupo_edad, y=num_def)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Barcelona - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
by age group
data_Barcelona %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Barcelona - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Barcelona %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Barcelona - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Barcelona %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=grupo_edad)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~sexo, ncol=1) +theme(legend.position ="top") +labs(title="Barcelona - Cases reported by age group and wave",x ="Date", y ="Nº of cases") +theme_bw()
by sex
data_Barcelona %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Barcelona - Cases reported by sex and wave",x ="Age group", y ="Nº of cases") +theme_bw()
data_Barcelona %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Barcelona - Deaths reported by sex and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Barcelona %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=sexo)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~grupo_edad, scales ="free_y", ncol=1) +theme(legend.position ="top") +labs(title="Barcelona - Cases reported by sex and age group (free y axis)",x ="Date", y ="Nº of cases") +theme_bw()
Madrid
data_Madrid <- hosp_data %>%filter(provincia =="Madrid") %>%select(-provincia) %>%as_tsibble(index = fecha, key =c(sexo, grupo_edad)) %>%mutate(ola =case_when( fecha <as.Date("2020-06-21", format ="%Y-%m-%d") ~"1_ola", fecha <as.Date("2020-12-06", format ="%Y-%m-%d") ~"2_ola", fecha <as.Date("2021-03-14", format ="%Y-%m-%d") ~"3_ola", fecha <as.Date("2021-06-19", format ="%Y-%m-%d") ~"4_ola", fecha <as.Date("2021-10-13", format ="%Y-%m-%d") ~"5_ola",TRUE~"6_ola", ))data_Madrid
# A tsibble: 24,570 x 8 [1D]
# Key: sexo, grupo_edad [30]
sexo grupo_edad fecha num_casos num_hosp num_uci num_def ola
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr>
1 H 0-9 2020-01-01 0 0 0 0 1_ola
2 H 0-9 2020-01-02 0 1 1 0 1_ola
3 H 0-9 2020-01-03 0 0 0 0 1_ola
4 H 0-9 2020-01-04 0 0 0 0 1_ola
5 H 0-9 2020-01-05 0 0 0 0 1_ola
6 H 0-9 2020-01-06 0 0 0 0 1_ola
7 H 0-9 2020-01-07 0 0 0 0 1_ola
8 H 0-9 2020-01-08 0 0 0 0 1_ola
9 H 0-9 2020-01-09 0 0 0 0 1_ola
10 H 0-9 2020-01-10 0 0 0 0 1_ola
# … with 24,560 more rows
by wave
data_Madrid %>%ggplot(aes(x=grupo_edad, y=num_casos)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Madrid - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Madrid %>%ggplot(aes(x=grupo_edad, y=num_def)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Madrid - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
by age group
data_Madrid %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Madrid - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Madrid %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Madrid - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Madrid %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=grupo_edad)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~sexo, ncol=1) +theme(legend.position ="top") +labs(title="Madrid - Cases reported by age group and wave",x ="Date", y ="Nº of cases") +theme_bw()
by sex
data_Madrid %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Madrid - Cases reported by sex and wave",x ="Age group", y ="Nº of cases") +theme_bw()
data_Madrid %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Madrid - Deaths reported by sex and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Madrid %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=sexo)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~grupo_edad, scales ="free_y", ncol=1) +theme(legend.position ="top") +labs(title="Madrid - Cases reported by sex and age group (free y axis)",x ="Date", y ="Nº of cases") +theme_bw()
Malaga
data_Malaga <- hosp_data %>%filter(provincia =="Málaga") %>%select(-provincia) %>%as_tsibble(index = fecha, key =c(sexo, grupo_edad)) %>%mutate(ola =case_when( fecha <as.Date("2020-06-21", format ="%Y-%m-%d") ~"1_ola", fecha <as.Date("2020-12-06", format ="%Y-%m-%d") ~"2_ola", fecha <as.Date("2021-03-14", format ="%Y-%m-%d") ~"3_ola", fecha <as.Date("2021-06-19", format ="%Y-%m-%d") ~"4_ola", fecha <as.Date("2021-10-13", format ="%Y-%m-%d") ~"5_ola",TRUE~"6_ola", ))data_Malaga
# A tsibble: 24,570 x 8 [1D]
# Key: sexo, grupo_edad [30]
sexo grupo_edad fecha num_casos num_hosp num_uci num_def ola
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr>
1 H 0-9 2020-01-01 0 0 0 0 1_ola
2 H 0-9 2020-01-02 0 0 0 0 1_ola
3 H 0-9 2020-01-03 0 0 0 0 1_ola
4 H 0-9 2020-01-04 0 0 0 0 1_ola
5 H 0-9 2020-01-05 0 0 0 0 1_ola
6 H 0-9 2020-01-06 0 0 0 0 1_ola
7 H 0-9 2020-01-07 0 0 0 0 1_ola
8 H 0-9 2020-01-08 0 0 0 0 1_ola
9 H 0-9 2020-01-09 0 0 0 0 1_ola
10 H 0-9 2020-01-10 0 0 0 0 1_ola
# … with 24,560 more rows
by wave
data_Malaga %>%ggplot(aes(x=grupo_edad, y=num_casos)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Malaga - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Malaga %>%ggplot(aes(x=grupo_edad, y=num_def)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Malaga - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
by age group
data_Malaga %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Malaga - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Malaga %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Malaga - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Malaga %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=grupo_edad)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~sexo, ncol=1) +theme(legend.position ="top") +labs(title="Malaga - Cases reported by age group and wave",x ="Date", y ="Nº of cases") +theme_bw()
by sex
data_Malaga %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Malaga - Cases reported by sex and wave",x ="Age group", y ="Nº of cases") +theme_bw()
data_Malaga %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Malaga - Deaths reported by sex and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Malaga %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=sexo)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~grupo_edad, scales ="free_y", ncol=1) +theme(legend.position ="top") +labs(title="Malaga - Cases reported by sex and age group (free y axis)",x ="Date", y ="Nº of cases") +theme_bw()
Sevilla
data_Sevilla <- hosp_data %>%filter(provincia =="Sevilla") %>%select(-provincia) %>%as_tsibble(index = fecha, key =c(sexo, grupo_edad)) %>%mutate(ola =case_when( fecha <as.Date("2020-06-21", format ="%Y-%m-%d") ~"1_ola", fecha <as.Date("2020-12-06", format ="%Y-%m-%d") ~"2_ola", fecha <as.Date("2021-03-14", format ="%Y-%m-%d") ~"3_ola", fecha <as.Date("2021-06-19", format ="%Y-%m-%d") ~"4_ola", fecha <as.Date("2021-10-13", format ="%Y-%m-%d") ~"5_ola",TRUE~"6_ola", ))data_Sevilla
# A tsibble: 24,570 x 8 [1D]
# Key: sexo, grupo_edad [30]
sexo grupo_edad fecha num_casos num_hosp num_uci num_def ola
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <chr>
1 H 0-9 2020-01-01 0 0 0 0 1_ola
2 H 0-9 2020-01-02 0 0 0 0 1_ola
3 H 0-9 2020-01-03 0 0 0 0 1_ola
4 H 0-9 2020-01-04 0 0 0 0 1_ola
5 H 0-9 2020-01-05 0 0 0 0 1_ola
6 H 0-9 2020-01-06 0 0 0 0 1_ola
7 H 0-9 2020-01-07 0 0 0 0 1_ola
8 H 0-9 2020-01-08 0 0 0 0 1_ola
9 H 0-9 2020-01-09 0 0 0 0 1_ola
10 H 0-9 2020-01-10 0 0 0 0 1_ola
# … with 24,560 more rows
by wave
data_Sevilla %>%ggplot(aes(x=grupo_edad, y=num_casos)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Sevilla - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Sevilla %>%ggplot(aes(x=grupo_edad, y=num_def)) +geom_boxplot(aes(fill=grupo_edad)) +facet_grid(. ~ ola) +theme(legend.position ="top") +labs(title="Sevilla - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
by age group
data_Sevilla %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Sevilla - Cases reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Sevilla %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ grupo_edad) +theme(legend.position ="top") +labs(title="Sevilla - Deaths reported by wave and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Sevilla %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=grupo_edad)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~sexo, ncol=1) +theme(legend.position ="top") +labs(title="Sevilla - Cases reported by age group and wave",x ="Date", y ="Nº of cases") +theme_bw()
by sex
data_Sevilla %>%ggplot(aes(x=ola, y=num_casos)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Sevilla - Cases reported by sex and wave",x ="Age group", y ="Nº of cases") +theme_bw()
data_Sevilla %>%ggplot(aes(x=ola, y=num_def)) +geom_boxplot(aes(fill=ola)) +facet_grid(. ~ sexo) +theme(legend.position ="top") +labs(title="Sevilla - Deaths reported by sex and age",x ="Age group", y ="Nº of cases") +theme_bw()
data_Sevilla %>%ggplot(aes(x=fecha, y=num_casos)) +geom_line(aes(color=sexo)) +geom_vline(xintercept =as.Date("2020-06-21", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2020-12-06", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-03-14", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-06-19", format ="%Y-%m-%d"), linetype="dashed") +geom_vline(xintercept =as.Date("2021-10-13", format ="%Y-%m-%d"), linetype="dashed") +facet_wrap(~grupo_edad, scales ="free_y", ncol=1) +theme(legend.position ="top") +labs(title="Sevilla - Cases reported by sex and age group (free y axis)",x ="Date", y ="Nº of cases") +theme_bw()
