#' Heatmap of dengue cases.
#'
#' @param dataset is the dengue cases dataset.
#' @param year is the target year.
#' @param size_text is the font size of the text.
#' @param state is the target state.
#' @param EDO is a logical value, if EDO TRUE the heatmap is by state else by Jurisdiction or Municipality.
#' @param JS is a logical value, if JS TRUE the heatmap is by jurisdiction, else by municipality.
#' @param MPO is a logical value, if EDO TRUE the heatmap is by municipality, else the heatmap y by state_municipality.
#' @author Felipe Antonio Dzul Manzanilla \email{felipe.dzul.m@gmail.com}
#' @return
#' @export
#'
#' @examples
heatmap_confirmados <- function(dataset, year, size_text,
state, EDO = NULL, JS = NULL, MPO = NULL){
if(EDO == TRUE){
x <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(!DES_EDO_RES %in% c("OTROS PAISES",
"OTROS PAISES DE LATINOAMERICA",
"ESTADOS UNIDOS DE NORTEAMERICA")) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::group_by(DES_EDO_RES, SEM) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
tidyr::pivot_wider(id_cols = c(DES_EDO_RES),
names_from = SEM,
values_from = n,
values_fill = 0) %>%
tidyr::pivot_longer(cols = -DES_EDO_RES,
names_to = "week",
values_to = "n") %>%
dplyr::mutate(edo = stringr::str_to_title(DES_EDO_RES),
week = as.numeric(week))
z <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(!DES_EDO_RES %in% c("OTROS PAISES",
"OTROS PAISES DE LATINOAMERICA",
"ESTADOS UNIDOS DE NORTEAMERICA")) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::group_by(DES_EDO_RES) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::arrange(dplyr::desc(-n)) %>%
dplyr::mutate(edo = stringr::str_to_title(DES_EDO_RES))
x$edo <- factor(x$edo, levels = z$edo)
plotly::ggplotly(ggplot2::ggplot(data = x,
ggplot2::aes(y = edo,
x = week,
fill = n,
label = n)) +
ggplot2::geom_raster(alpha = 1)+
ggplot2::scale_fill_viridis_c() +
ggplot2::geom_text(col = ifelse(x$n >= as.integer(quantile(x$n, probs = .75)), "black","gray"),
size = size_text,
alpha = 0.4) +
#theme_minimal()+
cowplot::theme_cowplot() +
ggplot2::ylab("") +
ggplot2::xlab("") +
ggplot2::labs(fill = "")+
ggplot2::theme(axis.text.y = ggplot2::element_text(face = "bold", size = 9),
axis.text.x = ggplot2::element_text(size = 6)) +
ggplot2::scale_x_continuous(breaks = c(1:max(unique(x$week)))) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::theme(legend.key.size = unit(.4, "cm"),
legend.key.width = unit(.5,"cm"),
legend.margin = ggplot2::margin(0,0,0,0),
legend.box.margin = ggplot2::margin(-20,0,0,0)))
} else {
if(JS == TRUE){
x <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(DES_EDO_RES %in% c(state)) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA",
"DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::group_by(SEM, DES_JUR_RES) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
tidyr::pivot_wider(id_cols = c(DES_JUR_RES),
names_from = SEM,
values_from = n,
values_fill = 0) %>%
tidyr::pivot_longer(cols = -DES_JUR_RES,
names_to = "week",
values_to = "n") %>%
dplyr::mutate(DES_JUR_RES = stringr::str_to_title(DES_JUR_RES),
week = as.numeric(week))
z <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(DES_EDO_RES %in% c(state)) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::group_by(DES_JUR_RES) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::arrange(dplyr::desc(-n)) %>%
dplyr::mutate(DES_JUR_RES = stringr::str_to_title(DES_JUR_RES))
x$DES_JUR_RES <- factor(x$DES_JUR_RES, levels = z$DES_JUR_RES)
plotly::ggplotly(ggplot2::ggplot(data = x,
ggplot2::aes(y = DES_JUR_RES,
x = week,
fill = n,
label = n)) +
ggplot2::geom_tile(alpha = 1)+
ggplot2::scale_fill_viridis_c() +
ggplot2::geom_text(col = ifelse(x$n >= quantile(x$n, probs = .75),
"black","gray"),
size = size_text,
alpha = 0.4) +
cowplot::theme_cowplot() +
ggplot2::ylab("") +
ggplot2::xlab("") +
ggplot2::labs(fill = "")+
ggplot2::theme(axis.text.y = ggplot2::element_text(face = "bold",
size = 9),
axis.text.x = ggplot2::element_text(size = 6)) +
ggplot2::scale_x_continuous(breaks = c(1:max(unique(x$week)))) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::theme(legend.key.size = unit(.1, "cm"),
legend.key.width = unit(.5,"cm"),
legend.margin = ggplot2::margin(0,0,0,0),
legend.box.margin = ggplot2::margin(-20,0,0,0)))
} else {
if(MPO == TRUE){
x <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(DES_EDO_RES %in% c(state)) %>%
dplyr::filter(!DES_EDO_RES %in% c("OTROS PAISES",
"OTROS PAISES DE LATINOAMERICA",
"ESTADOS UNIDOS DE NORTEAMERICA")) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::group_by(DES_MPO_RES, SEM) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
tidyr::pivot_wider(id_cols = c(DES_MPO_RES),
names_from = SEM,
values_from = n,
values_fill = 0) %>%
tidyr::pivot_longer(cols = -DES_MPO_RES,
names_to = "week",
values_to = "n") %>%
dplyr::arrange(dplyr::desc(-n)) %>%
dplyr::mutate(mpo = stringr::str_to_title(DES_MPO_RES),
week = as.numeric(week))
z <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(DES_EDO_RES %in% c(state)) %>%
dplyr::filter(!DES_EDO_RES %in% c("OTROS PAISES",
"OTROS PAISES DE LATINOAMERICA",
"ESTADOS UNIDOS DE NORTEAMERICA")) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::group_by(DES_MPO_RES) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::arrange(dplyr::desc(-n)) %>%
dplyr::mutate(mpo = stringr::str_to_title(DES_MPO_RES))
x$mpo <- factor(x$mpo, levels = z$mpo)
plotly::ggplotly(ggplot2::ggplot(data = x,
ggplot2::aes(y = mpo,
x = week,
fill = n,
label = n)) +
ggplot2::geom_tile(alpha = 1)+
ggplot2::scale_fill_viridis_c() +
ggplot2::geom_text(col = ifelse(x$n >= as.integer(quantile(x$n,
probs = .75)),
"black","gray"),
size = size_text,
alpha = 0.4) +
cowplot::theme_cowplot() +
ggplot2::ylab("") +
ggplot2::xlab("") +
ggplot2::labs(fill = "")+
ggplot2::theme(axis.text.y = ggplot2::element_text(face = "bold", size = 9),
axis.text.x = ggplot2::element_text(size = 6)) +
ggplot2::scale_x_continuous(breaks = c(1:max(unique(x$week)))) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::theme(legend.key.size = unit(.4, "cm"),
legend.key.width = unit(.5,"cm"),
legend.margin = ggplot2::margin(0,0,0,0),
legend.box.margin = ggplot2::margin(-20,0,0,0)))
} else{
x <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(!DES_EDO_RES %in% c("OTROS PAISES",
"OTROS PAISES DE LATINOAMERICA",
"ESTADOS UNIDOS DE NORTEAMERICA")) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::mutate(DES_MPO_RES = paste(CVE_EDO_RES, DES_MPO_RES, sep = " ")) %>%
dplyr::group_by(DES_MPO_RES, SEM) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
tidyr::pivot_wider(id_cols = c(DES_MPO_RES),
names_from = SEM,
values_from = n,
values_fill = 0) %>%
tidyr::pivot_longer(cols = -DES_MPO_RES,
names_to = "week",
values_to = "n") %>%
dplyr::arrange(dplyr::desc(-n)) %>%
dplyr::mutate(mpo = stringr::str_to_title(DES_MPO_RES),
week = as.numeric(week))
z <- dataset %>%
dplyr::filter(ANO == year) %>%
dplyr::filter(!DES_EDO_RES %in% c("OTROS PAISES",
"OTROS PAISES DE LATINOAMERICA",
"ESTADOS UNIDOS DE NORTEAMERICA")) %>%
dplyr::filter(DES_DIAG_FINAL %in%
c("DENGUE CON SIGNOS DE ALARMA", "DENGUE NO GRAVE",
"DENGUE GRAVE")) %>%
dplyr::mutate(DES_MPO_RES = paste(CVE_EDO_RES, DES_MPO_RES, sep = " ")) %>%
dplyr::group_by(DES_MPO_RES) %>%
dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
dplyr::arrange(dplyr::desc(-n)) %>%
dplyr::mutate(mpo = stringr::str_to_title(DES_MPO_RES))
x$mpo <- factor(x$mpo, levels = z$mpo)
plotly::ggplotly(ggplot2::ggplot(data = x,
ggplot2::aes(y = mpo,
x = week,
fill = n,
label = n)) +
ggplot2::geom_tile(alpha = 1)+
ggplot2::scale_fill_viridis_c() +
ggplot2::geom_text(col = ifelse(x$n >= as.integer(quantile(x$n,
probs = .75)),
"black","gray"),
size = size_text,
alpha = 0.4) +
cowplot::theme_cowplot() +
ggplot2::ylab("") +
ggplot2::xlab("") +
ggplot2::labs(fill = "")+
ggplot2::theme(axis.text.y = ggplot2::element_text(face = "bold",
size = 6),
axis.text.x = ggplot2::element_text(size = 6)) +
ggplot2::scale_x_continuous(breaks = c(1:max(unique(x$week)))) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::theme(legend.key.size = unit(.4, "cm"),
legend.key.width = unit(.5,"cm"),
legend.margin = ggplot2::margin(0,0,0,0),
legend.box.margin = ggplot2::margin(-20,0,0,0)))
}
}
}
}
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