library("tidyverse")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM",
#                        stringsAsFactors = FALSE)

# Datos para reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.mundial.Rda","rb"))

last_date <- max(as.Date(covid.data$dateRep,"%d/%m/%Y"))
covid.data %>% 
  filter(countriesAndTerritories %in% c('Argentina','Brazil', 'Chile', 'Bolivia', 'Paraguay', 'Uruguay')) %>% 
  group_by(countriesAndTerritories) %>% 
  summarize(casos = sum(cases), fallecidos = sum(deaths)) %>% 
  ungroup() %>% 
  select(pais = countriesAndTerritories, casos, fallecidos) %>% 
  gather(referencia, cantidad, -pais) -> plot_data 

kable(plot_data)

plot_data %>% 
  ggplot(aes(x=pais, fill=referencia, y=cantidad)) +
    geom_col(position=position_dodge(width=1)) +
    geom_text(aes(label = format(cantidad, digits=0, big.mark = ',')),  vjust = .6, hjust=1.1,
              position = position_dodge(width=1)) +
    coord_flip() +
    scale_y_log10(
      breaks = scales::trans_breaks("log10", function(x) 10^x),
      labels = scales::trans_format("log10", scales::math_format(10^.x))
    ) +
    labs(title = paste("COVID-19"), 
       subtitle = paste("Relación Casos / fallecidos Argentina y vecinos al: ", last_date) , 
       caption = "Fuente: https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", 
       y = "log10(Cantidad)", 
       x = ""
    ) +
    scale_fill_discrete(palette = function(x) c("#67a9cf", "#ef8a62")) +
    theme_elegante_std(base_family = "Assistant") +
    theme(plot.caption=element_text( margin=margin(1, 0, -.1, 0, "cm")),
          plot.subtitle = element_text(margin=margin(0, 0, .8, 0, "cm")))

library("tidyverse")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))
last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))

covid.data %>% 
  filter(osm_admin_level_4 %in% c('CABA', 'Buenos Aires')) %>% 
  mutate(fecha = as.Date(fecha, "%d/%m/%Y")) %>% 
  select(dia=dia_inicio, distrito=osm_admin_level_4, cantidad=nue_casosconf_diff) -> plot_data

kable(head(plot_data))

plot_data %>% 
  ggplot(mapping=aes(x=dia, color=distrito, y=cantidad)) +
    geom_line() +
    geom_point() +
    geom_smooth(method = 'loess',
                formula = 'y ~ x', alpha = 0.2, size = 1, span = .3, se=FALSE) + 
    labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Variación de los casos diarios en CABA y Buenos Aires por día (al: ", last_date, ")") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData", 
       y = "Casos", 
       x = "Número de días desde el 1er caso"
  ) +
  scale_color_discrete(palette = function(x) c("#67a9cf", "#ef8a62")) +
  theme_elegante_std(base_family = "Assistant") 

library("tidyverse")
library("ggrepel")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Para descarga de los datos actualizados
# covid.data <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM", stringsAsFactors = FALSE)
# hdi <- read.csv("https://data.humdata.org/dataset/05b5d8f1-9e7f-4379-9958-125c203d12ac/resource/4a7fd374-7e35-4c04-b7c8-25e5943aa476/downlo

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.mundial.Rda","rb"))
hdi <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/hdi.Rda","rb"))

hdi %>% 
  group_by(country_code) %>% 
  arrange(year) %>% 
  slice(n()) %>% 
  select(country_code, country, year, value) -> last_hdi

last_date <- max(as.Date(covid.data$dateRep,"%d/%m/%Y"))
paises_de_interes <- c( 'Argentina',
                       "Niger", "Norway", "EEUU", "Mauritania")
covid.data %>% 
  group_by(countriesAndTerritories, countryterritoryCode) %>% 
  summarize(casos = sum(cases), fallecidos = sum(deaths)) %>% 
  ungroup() %>% 
  inner_join(last_hdi,
            by = c("countryterritoryCode" = "country_code")
            ) %>% 
  mutate(pais = ifelse(countriesAndTerritories == 'United_States_of_America', 'EEUU', countriesAndTerritories)) %>% 
  select(pais, casos, HDI = value) %>% 
  mutate(pais_etiquetado = ifelse(pais %in% paises_de_interes, paste0(pais, " (casos: ", format(casos, digits=0, big.mark = ',', trim=TRUE), " hdi: ", HDI, ")"), NA)) -> plot_data

kable(head(plot_data))

plot_data %>% 
  ggplot(aes(x=HDI, y=casos)) +
    geom_point(color = "#67a9cf", alpha=.5, size=3) +
    geom_smooth(method = 'lm',formula='y ~ x', se=FALSE, color="#ef8a62") +
    geom_label_repel(mapping = aes(label = pais_etiquetado),
                     color="#67a9cf",family = "Assistant", vjust = -1.2, hjust = 1.1) +
                     # nudge_x = 1, nudge_y = 5, color="#67a9cf",
                     # vjust = -2, family = "Assistant",  
                     # direction  = "y",
                     # hjust = 2) +
    scale_y_log10(
      breaks = scales::trans_breaks("log10", function(x) 10^x),
      labels = scales::trans_format("log10", scales::math_format(10^.x))
    ) +
    labs(title = paste("COVID-19"), 
         subtitle = paste0("¿Hay relación entre el desarrollo humano y la cantidad de infecciones?\n (Datos al: ", last_date, ")") , 
         caption = "Fuente: https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", 
         y = "log10(Cantidad de infectados)", 
         x = "Human development Index (2013)"
    ) +
    theme_elegante_std(base_family = "Assistant") 

library("tidyverse")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))

last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))

covid.data %>% 
  mutate(fecha = as.Date(fecha, "%d/%m/%Y")) %>% 
  select(dia=dia_inicio, distrito=osm_admin_level_4, cantidad=nue_casosconf_diff) %>% 
  complete(distrito, dia, fill=list(cantidad=0)) %>% 
  arrange(distrito, dia) -> data

data %>% 
  inner_join(data %>% 
               group_by(distrito) %>% 
               summarize(cantidad = sum(cantidad)) %>% 
               arrange(-cantidad) %>% 
               top_n(9), by = c("distrito"), suffix=c("",".y")) %>% 
  select(dia, distrito, cantidad) %>% 
  arrange(dia, distrito) -> plot_data

kable(head(plot_data))

plot_data %>% 
  ggplot(mapping=aes(x=dia, y=cantidad)) +
  geom_line(color="#67a9cf") +
  geom_point(color="#67a9cf") +
  geom_smooth(method = 'loess',
              formula = 'y ~ x', alpha = 0.2, size = 1, span = .3, se=FALSE, color="#ef8a62") + 
  labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Variación de casos diarios en los distritos con más casos (al: ", last_date, ")") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData", 
       y = "Número de casos", 
       x = "Número de días desde el 1er caso"
  ) +
  facet_wrap(~distrito,scales="free") +
  theme_elegante_std(base_family = "Assistant") 

library("geofaceteAR")

argentina_grid <-  data.frame(
  col = c(1, 3, 5, 1, 2, 1, 3, 4, 2, 2, 4, 1, 3, 3, 4, 1, 2, 2, 1, 1, 2, 1, 1, 1),
  row = c(1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 7, 7, 8, 9, 10),
  code = c("AR-Y", "AR-P", "AR-N", "AR-A", "AR-T", "AR-K", "AR-H", "AR-W", "AR-G", "AR-X", "AR-E", "AR-F", "AR-S", "AR-B", "AR-C", "AR-J", "AR-D", "AR-L", "AR-M", "AR-Q", "AR-R", "AR-U", "AR-Z", "AR-V"),
  name_es = c("Jujuy", "Formosa", "Misiones", "Salta", "Tucumán", "Catamarca", "Chaco", "Corrientes", "Santiago del Estero", "Córdoba", 
              "Entre Ríos", "La Rioja", "Santa Fe", "Buenos Aires", "CABA", "San Juan", "San Luis", "La Pampa", "Mendoza", "Neuquén", "Río Negro", 
              "Chubut", "Santa Cruz", "Tierra del Fuego"),
  stringsAsFactors = FALSE
)

data %>% 
  filter(distrito!='Indeterminado') %>% 
  ggplot(mapping=aes(x=dia, y=cantidad)) +
  geom_line(color="#67a9cf") +
  geom_point(color="#67a9cf") +
  geom_smooth(method = 'loess',
              formula = 'y ~ x', alpha = 0.2, size = 1, span = .3, se=FALSE, color="#ef8a62") + 
  labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Variación de casos diarios en los distritos con más casos (al: ", last_date, ")") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData", 
       y = "Número de casos", 
       x = "Número de días desde el 1er caso"
  ) +
  facet_geo(~ distrito, grid = argentina_grid, scales = "free_y") +
  theme_elegante_std(base_family = "Assistant") 

library("tidyverse")
library("tidygraph")
library("ggraph")
library("igraph")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

data <- structure(list(persona = structure(c(10L, 10L, 10L, 10L, 10L, 
                                             10L, 6L, 6L, 12L, 12L, 12L, 12L, 8L, 9L, 2L, 7L, 7L, 3L, 3L, 
                                             1L, 11L, 5L, 4L, 4L, 13L), .Label = c("Angel del Guercio", "Carlos Xartuch", 
                                                                                   "Daniel Albertelli", "Edelmiro Molinari", "Eduardo Miró", "Emilio del Guercio", 
                                                                                   "Guido Meda", "Hector Nuñez", "Horacio Soria", "Luis Alberto Spinetta", 
                                                                                   "Ricardo Miró", "Rodolfo Garcia", "Santiago Novoa"), class = "factor"), 
                       grupo = c(" Bundleman", " Los Larkings", " Los Masters", 
                                 " Los Mods", " Los Sbirros", " Almendra (1967-1969)", " Bundleman", 
                                 " Almendra (1967-1969)", " Los Larkings", " Los Masters", 
                                 " Los Mods", " Almendra (1967-1969)", " Los Larkings", " Los Larkings", 
                                 " Los Larkings", " Los Masters", " Los Mods", " Los Masters", 
                                 " Los Mods", " Los Sbirros", " Los Sbirros", " Los Sbirros", 
                                 " Los Sbirros", " Almendra (1967-1969)", " Los Sbirros")), class = "data.frame", row.names = c(NA, 
                                                                                                                                -25L))

data %>% 
  select(from = persona, to = grupo, grupo=grupo, name=persona) -> prepared.data

kable(head(prepared.data))

tbl_graph(edges=prepared.data, directed = TRUE) %>% 
  ggraph(layout = "linear") +
  geom_edge_arc(aes(color=grupo),  edge_width=1.5, edge_alpha = 0.5, fold = TRUE,) +
  geom_node_point(size = 2, color="#67a9cf") +
  geom_node_text(aes(label = str_wrap(name,13)), size = 3, nudge_y =-.7, angle = 90, fontface = "bold",  hjust=.5) +
  coord_cartesian(clip = "off") + 
  theme_elegante_std(base_family = "Assistant") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        legend.position = "none") +
  labs(title = "Historia musical de Luis Albero Spinetta", 
       subtitle = "Los comienzos"
  ) 

library("tidyverse")
library("ggrepel")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))

last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))
break_porc <- .95
covid.data %>% 
  select(distrito=osm_admin_level_4, casos=nue_casosconf_diff) %>% 
  group_by(distrito) %>% 
  summarise(casos=sum(casos)) %>% 
  mutate(porc = casos / sum(casos)) %>%
  ungroup() %>% 
  arrange(-porc) %>% 
  mutate(cporc = cumsum(porc),
         distrito = ifelse(cporc < break_porc, distrito, 'Resto')) %>% 
  group_by(distrito) %>% 
  summarise(casos = sum(casos),
            porc = sum(round(porc*100,2))) -> data

data$porc[data$distrito == 'Resto'] <- 100 - sum(data$porc[data$distrito != 'Resto'])
data %>% 
  mutate( ymax = cumsum(porc),
          ymin = lag(ymax, default=0)
  ) -> data

mac_perc <- sum(data$porc[data$distrito != 'Resto'])
data$distrito <- with(data, reorder(distrito, porc))

kable(head(data))

data %>% 
  ggplot(aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=distrito)) +
  geom_rect(color="gray90", size=1.2) +
  geom_label_repel(mapping = aes(x=3.5, y=ymin + (ymax - ymin)/2,
                                 colour =  ifelse(porc > 10, 2, 0),
                                 label = paste0(distrito, ": ", format(porc, digits=2, trim=FALSE), "%\nCasos:", 
                                                format(casos, big.mark = ",", trim=FALSE))),
                   family = "Assistant", 
                   nudge_y = 1,
                   nudge_x = 1) +
  coord_polar(theta="y") + # Try to remove that to understand how the chart is built initially
  xlim(c(2, 4)) +
  labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Distribución del ", mac_perc , "% de los casos por distrito\n (Datos al: ", last_date, ")") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData"
  ) +
  theme_elegante_std(base_family = "Assistant") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        legend.position = "none") +
  scale_fill_brewer(palette = "Blues")

library("tidyverse")
library("ggridges")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))


last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))
break_porc <- .95

covid.data %>% 
  mutate(distrito = osm_admin_level_4, 
         casos = nue_casosconf_diff) %>% 
  select(distrito, casos) -> data

data %>% 
  group_by(distrito) %>% 
  summarise(casos=sum(casos)) %>% 
  mutate(porc = casos / sum(casos)) %>%
  arrange(-porc) %>% 
  mutate(cporc = cumsum(porc),
         distrito = ifelse(cporc < break_porc, distrito, 'Resto')) %>% 
  group_by(distrito) %>% 
  summarise(casos = sum(casos),
            porc = sum(round(porc*100,2))) -> principales

perc <- sum(principales$porc[principales$distrito != 'Resto'])

data %>% 
  left_join(principales, by="distrito") %>% 
  mutate(distrito = ifelse(is.na(casos.y), 'Resto', distrito)) %>% 
  select(distrito, casos=casos.x) -> plot_data
  
kable(head(plot_data))

plot_data %>% 
  ggplot(aes(x = casos, y = distrito, fill = distrito)) +
    geom_density_ridges() +
    theme(legend.position = "none") +
    labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Distribución de casos diarios (al: ", last_date, ")\nDetalle en los distritos que suman el ", perc, "% de los casos totales del país") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData", 
       y = "Distritos", 
       x = "Cantidades de casos diarios"
    ) +
    scale_x_continuous(breaks = c(c(0,5, 10, 20, 50), seq(from=75, to=max(data$casos)+25, by = 25))) +
    theme_elegante_std(base_family = "Assistant") +
    theme(legend.position = "none")  

library("tidyverse")
library("ggrepel")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Para descarga de los datos actualizados
# covid.data <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM", stringsAsFactors = FALSE)
# hdi <- read.csv("https://data.humdata.org/dataset/05b5d8f1-9e7f-4379-9958-125c203d12ac/resource/4a7fd374-7e35-4c04-b7c8-25e5943aa476/downlo

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.mundial.Rda","rb"))
hdi <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/hdi.Rda","rb"))

hdi %>% 
  group_by(country_code) %>% 
  arrange(year) %>% 
  slice(n()) %>% 
  select(country_code, country, year, value) -> last_hdi


last_date <- max(as.Date(covid.data$dateRep,"%d/%m/%Y"))
paises_de_interes <- c( 'Argentina',
                        "Niger", "Norway", "EEUU", "Mauritania")
covid.data %>% 
  group_by(countriesAndTerritories, countryterritoryCode) %>% 
  summarize(casos = sum(cases), fallecidos = sum(deaths)) %>% 
  ungroup() %>% 
  inner_join(last_hdi,
             by = c("countryterritoryCode" = "country_code")
  ) %>% 
  mutate(pais = ifelse(countriesAndTerritories == 'United_States_of_America', 'EEUU', countriesAndTerritories)) %>% 
  select(pais, casos, fallecidos, HDI = value) %>% 
  mutate(pais_etiquetado = ifelse(pais %in% paises_de_interes, paste0(pais, " (casos: ", format(casos, digits=0, big.mark = ',', trim=TRUE), " hdi: ", HDI, ")"), NA)) -> plot_data
  
kable(head(plot_data))

plot_data %>% 
  ggplot(aes(x=HDI, y=casos)) +
  geom_point(color = "#67a9cf", alpha=.5, size=3) +
  geom_smooth(method = 'lm',formula='y ~ x', se=FALSE, color="#ef8a62") +
  geom_density2d(contour = TRUE, n = 1000) +
  geom_label_repel(mapping = aes(label = pais_etiquetado),
                   color="#67a9cf",family = "Assistant", vjust = -1.2, hjust = 1.1, fontface="bold") +

  scale_y_log10(
    breaks = scales::trans_breaks("log10", function(x) 10^x),
    labels = scales::trans_format("log10", scales::math_format(10^.x))
  ) +
  labs(title = paste("COVID-19"), 
       subtitle = paste0("¿Hay relación entre el desarrollo humano y la cantidad de infecciones?\n (Datos al: ", last_date, ")") , 
       caption = "Fuente: https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", 
       y = "log10(Cantidad de infectados)", 
       x = "Human development Index (2013)"
  ) +
  theme_elegante_std(base_family = "Assistant")

library("tidyverse")
library("ggrepel")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))

covid.data %>% 
  mutate(fecha=as.Date(fecha,"%d/%m/%Y")) %>% 
  group_by(dia_inicio,fecha) %>% 
  summarize(casos = sum(nue_casosconf_diff),
         fallecidos = sum(nue_fallecidos_diff)) %>% 
  select(dia = dia_inicio, fecha, casos, fallecidos) %>% 
  pivot_longer(-c("dia", "fecha"), names_to = 'metrica', values_to='cantidades') -> data

data %>% 
  left_join(data %>% 
              group_by(metrica) %>%
              summarise(cantidades = max(cantidades), maximo = TRUE),
            by = c("metrica", "cantidades")
  ) %>% 
  mutate(maximo = ifelse(maximo, paste0("Pico de ", metrica, " de ", cantidades, "\nel ", fecha), NA)) -> plot_data

last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))

kable(head(plot_data))

plot_data %>% 
  ggplot(aes(x=dia, y=cantidades, fill=metrica, color=metrica)) + 
  geom_area(alpha=0.6 , size=1) +
  labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Variación de casos y fallecimientos por día (al: ", last_date, ")") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData", 
       y = "Cantidades diarias", 
       x = "Número de días desde el 1er caso"
  ) +
  geom_label_repel(mapping = aes(label = maximo),
                   color = "white",
                   segment.color="gray90",
                   family = "Assistant", 
                   vjust = -1,
                   hjust = 1.5,
                   box.padding = 1,
                   show.legend = FALSE) +
  scale_fill_discrete(palette = function(x) c("#67a9cf", "#ef8a62")) +
  scale_color_discrete(palette = function(x) c("#67a9cf", "#ef8a62")) +
  guides(color = FALSE, label=FALSE) +
  theme_elegante_std(base_family = "Assistant")

library("tidyverse")
library("wesanderson")
library("gridExtra")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

colores <- 20
paletas <- names(wes_palettes)

data.frame(x=factor(1:colores), y=1) %>% 
  ggplot(aes(x = x, y = y, fill = x)) + 
  geom_col() +
  theme_elegante_std(base_family = "Assistant") +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        legend.position = "none")  -> g

plots <- list()
for (p in paletas) {
  plots[[p]] <- g + scale_fill_manual(values = wes_palette(colores, name = p, type = "continuous"), name = "") +
    labs(x = p)
}
grid.arrange(grobs = plots, ncol = 2)

library("tidyverse")
library("zoo")
library("forcats")
library("scales")
  if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))

last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))
first_date <- min(as.Date(covid.data$fecha,"%d/%m/%Y"))
ndias <- max(covid.data$dia_inicio)


dias <- expand.grid(distrito = unique(covid.data$osm_admin_level_4),
                    dia = 1:ndias,
                    stringsAsFactors = FALSE)
dias$fecha = first_date + dias$dia - 1
dias$casos = 0

dias_rolling <- 3
dias_ventana <- 14

dias %>% 
  left_join(covid.data, by=c("distrito" = "osm_admin_level_4", "dia" = "dia_inicio")) %>% 
  mutate(casos = replace_na(nue_casosconf_diff, 0)) %>% 
  select(dia, distrito, fecha=fecha.x, casos)  %>% 
  group_by(dia, distrito, fecha) %>% 
  summarise(casos = sum(casos)) %>%
  arrange(distrito, dia) %>% 
  group_by(distrito) %>% 
  filter(dia >= ndias - dias_ventana - dias_rolling,
         distrito != 'Indeterminado') %>% 
  mutate(roll_casos_3 = rollmean(casos, dias_rolling, align='right', fill=0),
         s_roll_casos_3 = replace_na((roll_casos_3-min(roll_casos_3))/(max(roll_casos_3)-min(roll_casos_3)),0),
         label_casos = casos
  ) %>% 
  filter(dia >= ndias - dias_ventana ) -> plot_data

kable(head(plot_data))

plot_data %>% 
  ggplot(aes(x = dia, y =   fct_reorder(distrito, casos), fill = s_roll_casos_3)) + 
    geom_tile(colour="gray80", size=0.2) +
    geom_text(aes(label=label_casos, color = s_roll_casos_3 > .7)) +
    scale_color_manual(guide = FALSE, values = c("gray30", "gray90")) +
    scale_fill_distiller(palette = "YlGnBu", direction = 1, na.value = "white") +
    labs(title = paste("COVID-19 en Argentina"), 
       subtitle = paste0("Evolución diaria de los casos por distrito (últimos ", dias_ventana, " días al: ", last_date, ")\n",
                         "Los números son casos diarios, la escala de color en función del promedio de casos de los últimos ", dias_rolling, " días") , 
       caption = "Fuente: https://github.com/SistemasMapache/Covid19arData", 
       x = "Últimos días"
    ) +
    theme_elegante_std(base_family = "Assistant") + 
    scale_x_continuous(breaks = seq(ndias - dias_ventana, ndias, by = 1)) +
    theme(axis.title.y=element_blank(),
        legend.position = "none") 

library("tidyverse")
library("forcats")
library("countrycode")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Para descarga de los datos actualizados
# covid.data <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM", stringsAsFactors = FALSE)
# hdi <- read.csv("https://data.humdata.org/dataset/05b5d8f1-9e7f-4379-9958-125c203d12ac/resource/4a7fd374-7e35-4c04-b7c8-25e5943aa476/downlo

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.mundial.Rda","rb"))

last_date <- max(as.Date(covid.data$dateRep,"%d/%m/%Y"))
covid.data %>% 
  group_by(countryterritoryCode) %>% 
  summarize(casos = sum(cases), fallecidos = sum(deaths)) %>% 
  arrange(-casos) %>% 
  mutate(nr = row_number()) %>% 
  select(code_pais = countryterritoryCode, casos, fallecidos, nr) -> data

media_casos <- mean(data$casos)
media_fallecidos <- mean(data$fallecidos)

data %>% 
  filter(nr <= 50) %>% 
  mutate(sobre_media = casos > media_casos,
         pais = paste0(countrycode(code_pais, origin = 'iso3c', destination = 'un.name.es'), " (", nr, ")")) -> plot_data

kable(head(plot_data))

plot_data %>% 
  ggplot(aes(x=fct_reorder(pais, -nr), y=casos, color = sobre_media)) +
  geom_segment(aes( y=0 , xend = pais, yend = casos)) + 
  geom_point() +
  coord_flip() +
  geom_text(aes(label = format(abs(casos), digits=0, big.mark = ','),
                vjust = .5,
                hjust = -.1),
            position = position_dodge(width=1)) +
  labs(title = paste("COVID-19 en el mundo"), 
       subtitle = paste("Casos por pais al: ", last_date, '\nLos primeros 50 países') , 
       caption = "Fuente: https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", 
       y = "Casos", 
       x = ""
  ) +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 5), limits = c(0, max(data$casos) * 1.01),
                     labels = scales::comma) +

  scale_color_manual(labels = c("Sobre la media", "Debajo de la media"), values = c("#67a9cf", "#ef8a62")) +
  theme_elegante_std(base_family = "Assistant") 

library("tidyverse")
library("ggrepel")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

presidencias <- data.frame(fecha = as.Date(c('2019-12-10', '2015-12-10')), presidencia=c('Alberto Fernández', 'Mauricio Macri'))


# dolar <- read.csv("https://apis.datos.gob.ar/series/api/series/?ids=168.1_T_CAMBIOR_D_0_0_26&limit=5000&format=csv", na.strings = "", fileEncoding = "UTF-8-BOM",
#                        stringsAsFactors = FALSE)
dolar <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/dolar.Rda","rb"))

dolar$indice_tiempo = as.Date(dolar$indice_tiempo, format = "%Y-%m-%d")

kable(head(dolar))

dolar %>% 
  ggplot(mapping = aes(x=indice_tiempo, y=tipo_cambio_bna_vendedor)) + 
  geom_line(size = 1, color="#67a9cf") +
  geom_vline(data = presidencias,
             mapping = aes(xintercept=fecha),
             color = "#ef8a62",
             linetype="dashed") +
  geom_point(data = presidencias,
             mapping = aes(x=fecha, y=10),
             color = "#ef8a62",
             size = 2) +
  geom_label_repel(data = presidencias,
                   mapping = aes(x=fecha, y=10, label = presidencia), 
                   hjust= -1,
                   color = "#ef8a62",
                   family = "Assistant", fontface = 'bold',
                   arrow = arrow(length = unit(0.03, "npc"), type = "closed", ends = "first")
  ) +      
  
  
  labs(title = paste("Dólar en Argentina"), 
       subtitle = paste("Cotización del Banco nación entre el", min(dolar$indice_tiempo), "y el" , max(dolar$indice_tiempo)),
       caption = "Fuente: https://datos.gob.ar/", 
       y = "Cotización en $", 
       x = ""
  ) +
  scale_x_date(date_breaks = "12 month", date_labels="%Y-%m") +
  theme_elegante_std(base_family = "Assistant") 

library("tidyverse")
library("ggrepel")
library("treemapify")

if ("ggelegant" %in% rownames(installed.packages())) {
  library("ggelegant")
} else {
  # devtools::install_github("pmoracho/ggelegant")
  theme_elegante_std <- function(base_family) {}
}

# Datos originales
# covid.data <- read_csv('https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0')

# Datos reproducir la gráfica
covid.data <- readRDS(url("https://github.com/pmoracho/R/raw/master/data/covid.casos.arg.Rda","rb"))

last_date <- max(as.Date(covid.data$fecha,"%d/%m/%Y"))
first_date <- min(as.Date(covid.data$fecha,"%d/%m/%Y"))
ndias <- max(covid.data$dia_inicio)
dias <- expand.grid(distrito = unique(covid.data$osm_admin_level_4),
                    dia = 1:ndias,
                    stringsAsFactors = FALSE)

dias %>% 
  left_join(covid.data, by=c("distrito" = "osm_admin_level_4", "dia" = "dia_inicio")) %>% 
  mutate(casos = replace_na(nue_casosconf_diff, 0)) %>% 
  select(distrito, casos)  %>% 
  filter(distrito != 'Indeterminado') %>% 
  group_by(distrito) %>% 
  summarise(casos = sum(casos)) %>% 
  arrange(-casos) %>% 
  mutate(porc = cumsum(casos/sum(casos))) -> plot_data

kable(head(plot_data))