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index.qmd
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---
title: "Programa de Prevención y Control del Dengue | 2025"
format:
dashboard:
nav-buttons: [twitter, github, facebook]
theme: custom.scss
---
```{r setup}
library(sf)
mapview::mapviewOptions(default = TRUE,
basemaps.color.shuffle = FALSE)
```
# [**Hotspots de Dengue**]{style="font-size:2pt"}
::: panel-tabset
### [**Ciudad Victoria**]{style="color:#990000"}
<html>
<head>
```{=html}
<style>
body {
margin: 0px;
width: auto;
height: auto;
}
.wrapper {
display: grid;
grid-gap: 1px;
grid-template-columns: 225px 225px 225px 410px;
grid-template-rows: 95px 95px 95px 130px;
background-color: #772953;
color: "#772953";
}
.box {
background-color: #772953;
color: "#772953";
border-radius: 1px;
padding: 2px;
font-size: 100%;
}
.a {
grid-column: 1 / 4;
grid-row: 1 / 4;
}
.b {
grid-column: 4 ;
grid-row: 1 / 3;
}
.c {
grid-column: 4 ;
grid-row: 3 / 5 ;
}
.d {
grid-column: 3;
grid-row: 4;
}
.e {
grid-column: 2;
grid-row: 4;
}
.f {
grid-column: 1;
grid-row: 4;
}
</style>
```
</head>
<body>
::: wrapper
::: {.box .a}
```{r hotspots_dengue_ciudad_victoria}
# Step 1. extract the locality ####
loc <- rgeomex::extract_locality(cve_edo = "28",
locality = "Ciudad Victoria")
# Step 2. Extract the hotspots ####
hotspots <- hotspotsdata::hotspots_hex[loc, ]
mapview::mapview(hotspots,
zcol = "intensity_gi",
layer.name = "Intensidad",
label = FALSE,
color = "white",
lwd = 0.5,
col.regions = rcartocolor::carto_pal(n = max(hotspots$intensity_gi),
name = "OrYel"))
```
:::
::: {.box .b}
```{r forest_plot_ciudad_victoria, dpi = 300, fig.height= 5,fig.width = 7,fig.align = "center", out.height='100%', out.width = '100%',fig.show = "hold"}
# Logistic Regression
hotspots <- hotspots |>
dplyr::mutate(DENV_2008_2010 = DENV_2008 + DENV_2009 + DENV_2010) |>
dplyr::mutate(DENV_2011_2014 = DENV_2011 + DENV_2012 + DENV_2013 + DENV_2014) |>
dplyr::mutate(DENV_2015_2017 = DENV_2015 + DENV_2016 + DENV_2017) |>
dplyr::mutate(DENV_2018_2019 = DENV_2018 + DENV_2019) |>
dplyr::mutate(DENV_2020_2022 = DENV_2020 + DENV_2021 + DENV_2022) |>
dplyr::mutate(DENV_2023_2024 = DENV_2023 + DENV_2024)
# Logistic Regression
glm.fit <- glm(hotspots_gi~DENV_2008_2010 + DENV_2011_2014 +
DENV_2015_2017 + DENV_2018_2019 +
DENV_2020_2022 + DENV_2023_2024,
data = hotspots,
family = binomial)
result <- parameters::model_parameters(glm.fit,
exponentiate = FALSE)
plotly::ggplotly(plot(result) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8),
axis.text.x = ggplot2::element_text(size = 8),
axis.title = ggplot2::element_text(size = 8),
plot.margin = ggplot2::unit(c(0,0,0,0), "cm")) +
ggplot2::scale_y_discrete(labels = c("2008-2010",
"2011-2014",
"2015-2017",
"2018-2019",
"2020-2022",
"2023-2024")))
```
:::
::: {.box .c}
```{r power_law_plot_ciudad_victoria,dpi = 300, warning=FALSE, fig.align = "center", out.width = '100%',out.height='100%',fig.show = "hold"}
# Step 7. make the function por calculate the cumulative ####
funcumsum <- function(x){
x$cumulative_n <- cumsum(x$n_cases)
x$perc_cumsum_n <- round((cumsum(x$n_cases)/sum(x$n_cases))*100, digits = 1)
#x$cumulative_ha <- cumsum(x$ha)
#x$perc_cumsum_ha <- round((cumsum(x$ha)/sum(x$ha))*100, digits = 1)
x$id <- 1
x$id_cumsum <- cumsum(x$id)
x$id_perc_cum <- round((cumsum(x$id)/nrow(x))*100, digits = 1)
x
}
# step 8.
data_pareto <- hotspots |>
# sf::st_drop_geometry() |>
# dplyr::mutate(n_cases = rowSums(dplyr::select(., dplyr::starts_with("DENV"))),
# loc = "locality")
#dplyr::mutate(n_cases = rowSums(dplyr::across(DENV_2008:DENV_2021)),
# loc = "locality") |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi) |>
dplyr::arrange(loc, desc(hotspots_gi), desc(n_cases)) |>
dplyr::group_by(loc) |>
tidyr::nest() |>
dplyr::mutate(pareto_cases = purrr::map(data,
funcumsum)) |>
dplyr::select(-data) |>
tidyr::unnest(cols = c(pareto_cases))
# step 4. visualization of pareto rules
plotly::ggplotly(denhotspots::power_law_plot(x = data_pareto)) |> plotly::layout(showlegend = FALSE)
```
:::
::: {.box .d}
```{r gauge_blocks_ciudad_victoria,fig.align = "center", out.height='100%', out.width = '100%', fig.width= 1.5, fig.height=1}
y_hot <- hotspots |> dplyr::filter(hotspots_gi == 1)
centroid <- rgeomex::blocks_ine20_mx_centroid |>
sf::st_as_sf(coords = c("x", "y"), crs = 4326)
b_loc <- centroid[hotspots, ]
b_hot <- b_loc[y_hot, ]
flexdashboard::gauge(round((nrow(b_hot)/nrow(b_loc))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Manzanas",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33", #"#63C1A0",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .e}
```{r gauge_AGEB_ciudad_victoria, fig.align = "center", out.height='100%', out.width = '100%'}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((nrow(y_hot)/nrow(y))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "AGEBs",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .f}
```{r gauge_casos_ciudad_victoria, out.height='90%', out.width = '90%', fig.align = "center"}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((sum(y_hot$n_cases)/sum(y$n_cases))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Casos de Dengue",
flexdashboard::gaugeSectors(success = c(11, 100),
warning = c(6, 10),
danger = c(0, 5),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
:::
</body>
</html>
### [**Altamira**]{style="color:#990000"}
<html>
<head>
</head>
<body>
::: wrapper
::: {.box .a}
```{r hotspots_dengue_altamira}
# Step 1. extract the locality ####
loc <- rgeomex::extract_locality(cve_edo = "28",
locality = "Altamira")
# Step 2. Extract the hotspots ####
hotspots <- hotspotsdata::hotspots_hex[loc, ]
mapview::mapview(hotspots,
zcol = "intensity_gi",
layer.name = "Intensidad",
label = FALSE,
color = "white",
lwd = 0.5,
col.regions = rcartocolor::carto_pal(n = max(hotspots$intensity_gi),
name = "OrYel"))
```
:::
::: {.box .b}
```{r forest_plot_altamira, dpi = 300, fig.height= 5,fig.width = 7,fig.align = "center", out.height='100%', out.width = '100%',fig.show = "hold"}
# Logistic Regression
hotspots <- hotspots |>
dplyr::mutate(DENV_2008_2010 = DENV_2008 + DENV_2009 + DENV_2010) |>
dplyr::mutate(DENV_2011_2014 = DENV_2011 + DENV_2012 + DENV_2013 + DENV_2014) |>
dplyr::mutate(DENV_2015_2017 = DENV_2015 + DENV_2016 + DENV_2017) |>
dplyr::mutate(DENV_2018_2019 = DENV_2018 + DENV_2019) |>
dplyr::mutate(DENV_2020_2022 = DENV_2020 + DENV_2021 + DENV_2022) |>
dplyr::mutate(DENV_2023_2024 = DENV_2023 + DENV_2024)
# Logistic Regression
glm.fit <- glm(hotspots_gi~DENV_2008_2010 + DENV_2011_2014 +
DENV_2015_2017 + DENV_2018_2019 +
DENV_2020_2022 + DENV_2023_2024,
data = hotspots,
family = binomial)
result <- parameters::model_parameters(glm.fit,
exponentiate = FALSE)
plotly::ggplotly(plot(result) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8),
axis.text.x = ggplot2::element_text(size = 8),
axis.title = ggplot2::element_text(size = 8),
plot.margin = ggplot2::unit(c(0,0,0,0), "cm")) +
ggplot2::scale_y_discrete(labels = c("2008-2010",
"2011-2014",
"2015-2017",
"2018-2019",
"2020-2022",
"2023-2024")))
```
:::
::: {.box .c}
```{r power_law_plot_altamira,dpi = 300, warning=FALSE, fig.align = "center", out.width = '100%',out.height='100%',fig.show = "hold"}
# Step 7. make the function por calculate the cumulative ####
funcumsum <- function(x){
x$cumulative_n <- cumsum(x$n_cases)
x$perc_cumsum_n <- round((cumsum(x$n_cases)/sum(x$n_cases))*100, digits = 1)
#x$cumulative_ha <- cumsum(x$ha)
#x$perc_cumsum_ha <- round((cumsum(x$ha)/sum(x$ha))*100, digits = 1)
x$id <- 1
x$id_cumsum <- cumsum(x$id)
x$id_perc_cum <- round((cumsum(x$id)/nrow(x))*100, digits = 1)
x
}
# step 8.
data_pareto <- hotspots |>
# sf::st_drop_geometry() |>
# dplyr::mutate(n_cases = rowSums(dplyr::select(., dplyr::starts_with("DENV"))),
# loc = "locality")
#dplyr::mutate(n_cases = rowSums(dplyr::across(DENV_2008:DENV_2021)),
# loc = "locality") |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi) |>
dplyr::arrange(loc, desc(hotspots_gi), desc(n_cases)) |>
dplyr::group_by(loc) |>
tidyr::nest() |>
dplyr::mutate(pareto_cases = purrr::map(data,
funcumsum)) |>
dplyr::select(-data) |>
tidyr::unnest(cols = c(pareto_cases))
# step 4. visualization of pareto rules
plotly::ggplotly(denhotspots::power_law_plot(x = data_pareto)) |> plotly::layout(showlegend = FALSE)
```
:::
::: {.box .d}
```{r gauge_blocks_altamira,fig.align = "center", out.height='100%', out.width = '100%', fig.width= 1.5, fig.height=1}
y_hot <- hotspots |> dplyr::filter(hotspots_gi == 1)
centroid <- rgeomex::blocks_ine20_mx_centroid |>
sf::st_as_sf(coords = c("x", "y"), crs = 4326)
b_loc <- centroid[hotspots, ]
b_hot <- b_loc[y_hot, ]
flexdashboard::gauge(round((nrow(b_hot)/nrow(b_loc))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Manzanas",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33", #"#63C1A0",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .e}
```{r gauge_AGEB_altamira, fig.align = "center", out.height='100%', out.width = '100%'}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((nrow(y_hot)/nrow(y))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "AGEBs",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .f}
```{r gauge_casos_altamira, out.height='90%', out.width = '90%', fig.align = "center"}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((sum(y_hot$n_cases)/sum(y$n_cases))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Casos de Dengue",
flexdashboard::gaugeSectors(success = c(11, 100),
warning = c(6, 10),
danger = c(0, 5),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
:::
</body>
</html>
### [**Ciudad Mante**]{style="color:#990000"}
<html>
<head>
</head>
<body>
::: wrapper
::: {.box .a}
```{r hotspots_dengue_ciudad_mante}
# Step 1. extract the locality ####
loc <- rgeomex::extract_locality(cve_edo = "28",
locality = "Ciudad Mante")
# Step 2. Extract the hotspots ####
hotspots <- hotspotsdata::hotspots_hex[loc, ]
mapview::mapview(hotspots,
zcol = "intensity_gi",
layer.name = "Intensidad",
label = FALSE,
color = "white",
lwd = 0.5,
col.regions = rcartocolor::carto_pal(n = max(hotspots$intensity_gi),
name = "OrYel"))
```
:::
::: {.box .b}
```{r forest_plot_ciudad_mante, dpi = 300, fig.height= 5,fig.width = 7,fig.align = "center", out.height='100%', out.width = '100%',fig.show = "hold"}
# Logistic Regression
hotspots <- hotspots |>
dplyr::mutate(DENV_2008_2010 = DENV_2008 + DENV_2009 + DENV_2010) |>
dplyr::mutate(DENV_2011_2014 = DENV_2011 + DENV_2012 + DENV_2013 + DENV_2014) |>
dplyr::mutate(DENV_2015_2017 = DENV_2015 + DENV_2016 + DENV_2017) |>
dplyr::mutate(DENV_2018_2019 = DENV_2018 + DENV_2019) |>
dplyr::mutate(DENV_2020_2022 = DENV_2020 + DENV_2021 + DENV_2022) |>
dplyr::mutate(DENV_2023_2024 = DENV_2023 + DENV_2024)
# Logistic Regression
glm.fit <- glm(hotspots_gi~DENV_2008_2010 + DENV_2011_2014 +
DENV_2015_2017 + DENV_2018_2019 +
DENV_2020_2022 + DENV_2023_2024,
data = hotspots,
family = binomial)
result <- parameters::model_parameters(glm.fit,
exponentiate = FALSE)
plotly::ggplotly(plot(result) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8),
axis.text.x = ggplot2::element_text(size = 8),
axis.title = ggplot2::element_text(size = 8),
plot.margin = ggplot2::unit(c(0,0,0,0), "cm")) +
ggplot2::scale_y_discrete(labels = c("2008-2010",
"2011-2014",
"2015-2017",
"2018-2019",
"2020-2022",
"2023-2024")))
```
:::
::: {.box .c}
```{r power_law_plot_ciudad_mante,dpi = 300, warning=FALSE, fig.align = "center", out.width = '100%',out.height='100%',fig.show = "hold"}
# Step 7. make the function por calculate the cumulative ####
funcumsum <- function(x){
x$cumulative_n <- cumsum(x$n_cases)
x$perc_cumsum_n <- round((cumsum(x$n_cases)/sum(x$n_cases))*100, digits = 1)
#x$cumulative_ha <- cumsum(x$ha)
#x$perc_cumsum_ha <- round((cumsum(x$ha)/sum(x$ha))*100, digits = 1)
x$id <- 1
x$id_cumsum <- cumsum(x$id)
x$id_perc_cum <- round((cumsum(x$id)/nrow(x))*100, digits = 1)
x
}
# step 8.
data_pareto <- hotspots |>
# sf::st_drop_geometry() |>
# dplyr::mutate(n_cases = rowSums(dplyr::select(., dplyr::starts_with("DENV"))),
# loc = "locality")
#dplyr::mutate(n_cases = rowSums(dplyr::across(DENV_2008:DENV_2021)),
# loc = "locality") |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi) |>
dplyr::arrange(loc, desc(hotspots_gi), desc(n_cases)) |>
dplyr::group_by(loc) |>
tidyr::nest() |>
dplyr::mutate(pareto_cases = purrr::map(data,
funcumsum)) |>
dplyr::select(-data) |>
tidyr::unnest(cols = c(pareto_cases))
# step 4. visualization of pareto rules
plotly::ggplotly(denhotspots::power_law_plot(x = data_pareto)) |> plotly::layout(showlegend = FALSE)
```
:::
::: {.box .d}
```{r gauge_blocks_ciudad_mante,fig.align = "center", out.height='100%', out.width = '100%', fig.width= 1.5, fig.height=1}
y_hot <- hotspots |> dplyr::filter(hotspots_gi == 1)
centroid <- rgeomex::blocks_ine20_mx_centroid |>
sf::st_as_sf(coords = c("x", "y"), crs = 4326)
b_loc <- centroid[hotspots, ]
b_hot <- b_loc[y_hot, ]
flexdashboard::gauge(round((nrow(b_hot)/nrow(b_loc))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Manzanas",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33", #"#63C1A0",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .e}
```{r gauge_AGEB_ciudad_mante, fig.align = "center", out.height='100%', out.width = '100%'}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((nrow(y_hot)/nrow(y))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "AGEBs",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .f}
```{r gauge_casos_ciudad_mante, out.height='90%', out.width = '90%', fig.align = "center"}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((sum(y_hot$n_cases)/sum(y$n_cases))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Casos de Dengue",
flexdashboard::gaugeSectors(success = c(11, 100),
warning = c(6, 10),
danger = c(0, 5),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
:::
</body>
</html>
### [**Nuevo Laredo**]{style="color:#990000"}
<html>
<head>
</head>
<body>
::: wrapper
::: {.box .a}
```{r hotspots_dengue_laredo}
# Step 1. extract the locality ####
loc <- rgeomex::extract_locality(cve_edo = "28",
locality = "Nuevo Laredo")
# Step 2. Extract the hotspots ####
hotspots <- hotspotsdata::hotspots_hex[loc, ]
mapview::mapview(hotspots,
zcol = "intensity_gi",
layer.name = "Intensidad",
label = FALSE,
color = "white",
lwd = 0.5,
col.regions = rcartocolor::carto_pal(n = max(hotspots$intensity_gi),
name = "OrYel"))
```
:::
::: {.box .b}
```{r forest_plot_laredo, dpi = 300, fig.height= 5,fig.width = 7,fig.align = "center", out.height='100%', out.width = '100%',fig.show = "hold"}
# Logistic Regression
hotspots <- hotspots |>
dplyr::mutate(DENV_2008_2010 = DENV_2008 + DENV_2009 + DENV_2010) |>
dplyr::mutate(DENV_2011_2014 = DENV_2011 + DENV_2012 + DENV_2013 + DENV_2014) |>
dplyr::mutate(DENV_2015_2017 = DENV_2015 + DENV_2016 + DENV_2017) |>
dplyr::mutate(DENV_2018_2019 = DENV_2018 + DENV_2019) |>
dplyr::mutate(DENV_2020_2022 = DENV_2020 + DENV_2021 + DENV_2022) |>
dplyr::mutate(DENV_2023_2024 = DENV_2023 + DENV_2024)
# Logistic Regression
glm.fit <- glm(hotspots_gi~DENV_2008_2010 + DENV_2011_2014 +
DENV_2015_2017 + DENV_2018_2019 +
DENV_2020_2022 + DENV_2023_2024,
data = hotspots,
family = binomial)
result <- parameters::model_parameters(glm.fit,
exponentiate = FALSE)
plotly::ggplotly(plot(result) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8),
axis.text.x = ggplot2::element_text(size = 8),
axis.title = ggplot2::element_text(size = 8),
plot.margin = ggplot2::unit(c(0,0,0,0), "cm")) +
ggplot2::scale_y_discrete(labels = c("2008-2010",
"2011-2014",
"2015-2017",
"2018-2019",
"2020-2022",
"2023-2024")))
```
:::
::: {.box .c}
```{r power_law_plot_laredo,dpi = 300, warning=FALSE, fig.align = "center", out.width = '100%',out.height='100%',fig.show = "hold"}
# Step 7. make the function por calculate the cumulative ####
funcumsum <- function(x){
x$cumulative_n <- cumsum(x$n_cases)
x$perc_cumsum_n <- round((cumsum(x$n_cases)/sum(x$n_cases))*100, digits = 1)
#x$cumulative_ha <- cumsum(x$ha)
#x$perc_cumsum_ha <- round((cumsum(x$ha)/sum(x$ha))*100, digits = 1)
x$id <- 1
x$id_cumsum <- cumsum(x$id)
x$id_perc_cum <- round((cumsum(x$id)/nrow(x))*100, digits = 1)
x
}
# step 8.
data_pareto <- hotspots |>
# sf::st_drop_geometry() |>
# dplyr::mutate(n_cases = rowSums(dplyr::select(., dplyr::starts_with("DENV"))),
# loc = "locality")
#dplyr::mutate(n_cases = rowSums(dplyr::across(DENV_2008:DENV_2021)),
# loc = "locality") |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi) |>
dplyr::arrange(loc, desc(hotspots_gi), desc(n_cases)) |>
dplyr::group_by(loc) |>
tidyr::nest() |>
dplyr::mutate(pareto_cases = purrr::map(data,
funcumsum)) |>
dplyr::select(-data) |>
tidyr::unnest(cols = c(pareto_cases))
# step 4. visualization of pareto rules
plotly::ggplotly(denhotspots::power_law_plot(x = data_pareto)) |> plotly::layout(showlegend = FALSE)
```
:::
::: {.box .d}
```{r gauge_blocks_laredo,fig.align = "center", out.height='100%', out.width = '100%', fig.width= 1.5, fig.height=1}
y_hot <- hotspots |> dplyr::filter(hotspots_gi == 1)
centroid <- rgeomex::blocks_ine20_mx_centroid |>
sf::st_as_sf(coords = c("x", "y"), crs = 4326)
b_loc <- centroid[hotspots, ]
b_hot <- b_loc[y_hot, ]
flexdashboard::gauge(round((nrow(b_hot)/nrow(b_loc))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Manzanas",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33", #"#63C1A0",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .e}
```{r gauge_AGEB_laredo, fig.align = "center", out.height='100%', out.width = '100%'}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((nrow(y_hot)/nrow(y))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "AGEBs",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .f}
```{r gauge_casos_laredo, out.height='90%', out.width = '90%', fig.align = "center"}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((sum(y_hot$n_cases)/sum(y$n_cases))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Casos de Dengue",
flexdashboard::gaugeSectors(success = c(11, 100),
warning = c(6, 10),
danger = c(0, 5),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
:::
</body>
</html>
### [**Heroíca Matamoros**]{style="color:#990000"}
<html>
<head>
</head>
<body>
::: wrapper
::: {.box .a}
```{r hotspots_dengue_matamoros}
# Step 1. extract the locality ####
loc <- rgeomex::extract_locality(cve_edo = "28",
locality = "Heroica Matamoros")
# Step 2. Extract the hotspots ####
hotspots <- hotspotsdata::hotspots_hex[loc, ]
mapview::mapview(hotspots,
zcol = "intensity_gi",
layer.name = "Intensidad",
label = FALSE,
color = "white",
lwd = 0.5,
col.regions = rcartocolor::carto_pal(n = max(hotspots$intensity_gi),
name = "OrYel"))
```
:::
::: {.box .b}
```{r forest_plot_matamoros, dpi = 300, fig.height= 5,fig.width = 7,fig.align = "center", out.height='100%', out.width = '100%',fig.show = "hold"}
# Logistic Regression
hotspots <- hotspots |>
dplyr::mutate(DENV_2008_2010 = DENV_2008 + DENV_2009 + DENV_2010) |>
dplyr::mutate(DENV_2011_2014 = DENV_2011 + DENV_2012 + DENV_2013 + DENV_2014) |>
dplyr::mutate(DENV_2015_2017 = DENV_2015 + DENV_2016 + DENV_2017) |>
dplyr::mutate(DENV_2018_2019 = DENV_2018 + DENV_2019) |>
dplyr::mutate(DENV_2020_2022 = DENV_2020 + DENV_2021 + DENV_2022) |>
dplyr::mutate(DENV_2023_2024 = DENV_2023 + DENV_2024)
# Logistic Regression
glm.fit <- glm(hotspots_gi~DENV_2008_2010 + DENV_2011_2014 +
DENV_2015_2017 + DENV_2018_2019 +
DENV_2020_2022 + DENV_2023_2024,
data = hotspots,
family = binomial)
result <- parameters::model_parameters(glm.fit,
exponentiate = FALSE)
plotly::ggplotly(plot(result) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8),
axis.text.x = ggplot2::element_text(size = 8),
axis.title = ggplot2::element_text(size = 8),
plot.margin = ggplot2::unit(c(0,0,0,0), "cm")) +
ggplot2::scale_y_discrete(labels = c("2008-2010",
"2011-2014",
"2015-2017",
"2018-2019",
"2020-2022",
"2023-2024")))
```
:::
::: {.box .c}
```{r power_law_plot_matamoros,dpi = 300, warning=FALSE, fig.align = "center", out.width = '100%',out.height='100%',fig.show = "hold"}
# Step 7. make the function por calculate the cumulative ####
funcumsum <- function(x){
x$cumulative_n <- cumsum(x$n_cases)
x$perc_cumsum_n <- round((cumsum(x$n_cases)/sum(x$n_cases))*100, digits = 1)
#x$cumulative_ha <- cumsum(x$ha)
#x$perc_cumsum_ha <- round((cumsum(x$ha)/sum(x$ha))*100, digits = 1)
x$id <- 1
x$id_cumsum <- cumsum(x$id)
x$id_perc_cum <- round((cumsum(x$id)/nrow(x))*100, digits = 1)
x
}
# step 8.
data_pareto <- hotspots |>
# sf::st_drop_geometry() |>
# dplyr::mutate(n_cases = rowSums(dplyr::select(., dplyr::starts_with("DENV"))),
# loc = "locality")
#dplyr::mutate(n_cases = rowSums(dplyr::across(DENV_2008:DENV_2021)),
# loc = "locality") |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi) |>
dplyr::arrange(loc, desc(hotspots_gi), desc(n_cases)) |>
dplyr::group_by(loc) |>
tidyr::nest() |>
dplyr::mutate(pareto_cases = purrr::map(data,
funcumsum)) |>
dplyr::select(-data) |>
tidyr::unnest(cols = c(pareto_cases))
# step 4. visualization of pareto rules
plotly::ggplotly(denhotspots::power_law_plot(x = data_pareto)) |> plotly::layout(showlegend = FALSE)
```
:::
::: {.box .d}
```{r gauge_blocks_matamoros,fig.align = "center", out.height='100%', out.width = '100%', fig.width= 1.5, fig.height=1}
y_hot <- hotspots |> dplyr::filter(hotspots_gi == 1)
centroid <- rgeomex::blocks_ine20_mx_centroid |>
sf::st_as_sf(coords = c("x", "y"), crs = 4326)
b_loc <- centroid[hotspots, ]
b_hot <- b_loc[y_hot, ]
flexdashboard::gauge(round((nrow(b_hot)/nrow(b_loc))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Manzanas",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33", #"#63C1A0",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .e}
```{r gauge_AGEB_matamoros, fig.align = "center", out.height='100%', out.width = '100%'}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((nrow(y_hot)/nrow(y))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "AGEBs",
flexdashboard::gaugeSectors(success = c(0, 30),
warning = c(31, 50),
danger = c(51, 100),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
::: {.box .f}
```{r gauge_casos_matamoros, out.height='90%', out.width = '90%', fig.align = "center"}
y <- hotspots |>
sf::st_drop_geometry() |>
dplyr::mutate(n_cases = rowSums(dplyr::across(dplyr::starts_with("DENV"))),
loc = "locality") |>
dplyr::select(loc, n_cases, hotspots_gi)
y_hot <- y |>dplyr::filter(hotspots_gi == 1)
flexdashboard::gauge(round((sum(y_hot$n_cases)/sum(y$n_cases))*100, digits = 1),
min = 0, max = 100,
symbol = '%',
label = "Casos de Dengue",
flexdashboard::gaugeSectors(success = c(11, 100),
warning = c(6, 10),
danger = c(0, 5),
colors = c("#11BB33",
"#FC8427",
"#E01A59")))
```
:::
:::
</body>
</html>
### [**Reynosa**]{style="color:#990000"}
<html>
<head>
</head>
<body>
::: wrapper
::: {.box .a}
```{r hotspots_dengue_reynosa}
# Step 1. extract the locality ####
loc <- rgeomex::extract_locality(cve_edo = "28",
locality = "Reynosa")
# Step 2. Extract the hotspots ####
hotspots <- hotspotsdata::hotspots_hex[loc, ]
mapview::mapview(hotspots,
zcol = "intensity_gi",
layer.name = "Intensidad",
label = FALSE,
color = "white",
lwd = 0.5,
col.regions = rcartocolor::carto_pal(n = max(hotspots$intensity_gi),
name = "OrYel"))
```
:::
::: {.box .b}
```{r forest_plot_reynosa, dpi = 300, fig.height= 5,fig.width = 7,fig.align = "center", out.height='100%', out.width = '100%',fig.show = "hold"}
# Logistic Regression
hotspots <- hotspots |>
dplyr::mutate(DENV_2008_2010 = DENV_2008 + DENV_2009 + DENV_2010) |>
dplyr::mutate(DENV_2011_2014 = DENV_2011 + DENV_2012 + DENV_2013 + DENV_2014) |>
dplyr::mutate(DENV_2015_2017 = DENV_2015 + DENV_2016 + DENV_2017) |>
dplyr::mutate(DENV_2018_2019 = DENV_2018 + DENV_2019) |>
dplyr::mutate(DENV_2020_2022 = DENV_2020 + DENV_2021 + DENV_2022) |>
dplyr::mutate(DENV_2023_2024 = DENV_2023 + DENV_2024)
# Logistic Regression
glm.fit <- glm(hotspots_gi~DENV_2008_2010 + DENV_2011_2014 +
DENV_2015_2017 + DENV_2018_2019 +
DENV_2020_2022 + DENV_2023_2024,
data = hotspots,
family = binomial)
result <- parameters::model_parameters(glm.fit,
exponentiate = FALSE)
plotly::ggplotly(plot(result) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 8),
axis.text.x = ggplot2::element_text(size = 8),
axis.title = ggplot2::element_text(size = 8),
plot.margin = ggplot2::unit(c(0,0,0,0), "cm")) +
ggplot2::scale_y_discrete(labels = c("2008-2010",
"2011-2014",
"2015-2017",
"2018-2019",
"2020-2022",
"2023-2024")))
```
:::