Skip to content

Mellora estadisticas #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
inesortega merged 1 commit into from
Jun 28, 2026
Merged

Mellora estadisticas #3

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
215 changes: 100 additions & 115 deletions app.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -83,11 +83,12 @@ filters <- sidebar(
)

# ---- Tarxeta KPI auxiliar ---------------------------------------------------
kpi_box <- function(outputId, title, subtitle = NULL, accent = "primary") {
kpi_box <- function(outputId, title, subtitle = NULL, accent = "primary", trendId = NULL) {
value_box(
title = tags$span(class = "kpi-title", title,
if (!is.null(subtitle)) tags$span(class = "kpi-sub", subtitle)),
value = textOutput(outputId, inline = TRUE),
if (!is.null(trendId)) tags$span(class = "kpi-trend", textOutput(trendId, inline = TRUE)),
class = paste0("kpi-card kpi-", accent)
)
}
Expand All @@ -104,7 +105,6 @@ ui <- page_navbar(
id = "sidebarID", # conserva input$sidebarID (== "map") que usa o server
theme = app_theme,
fillable = FALSE,
sidebar = filters,
header = tagList(
useShinyjs(),
tags$head(tags$link(rel = "stylesheet", type = "text/css", href = "styles.css"))
Expand All @@ -116,10 +116,13 @@ ui <- page_navbar(
"Mapa en tempo real"
),
value = "map",
card(
full_screen = TRUE,
class = "map-card",
leafletOutput("mymap", height = "calc(100vh - 140px)")
layout_sidebar(
sidebar = filters,
card(
full_screen = TRUE,
class = "map-card",
leafletOutput("mymap", height = "calc(100vh - 140px)")
)
)
),

Expand All @@ -132,30 +135,29 @@ ui <- page_navbar(
div(class = "lead",
h4("Datos e estatísticas"),
p(HTML(paste0(
"Os datos recóllense de xeito colaborativo a través deste ",
"<a href='https://docs.google.com/forms/u/1/d/e/1FAIpQLScHqNH3yxk5yBKhOMZ0mVk0Wl-bNLCowqW9UFr0mo2Hj7klGA/formResponse' target='_blank'>formulario</a>. ",
"As estatísticas calcúlanse automaticamente para os filtros seleccionados e poden ",
"conter erros polo reporte non estandarizado do nome da praia."))
"Resumo sobre todos os datos rexistrados. Recóllense de xeito colaborativo a través deste ",
"<a href='https://docs.google.com/forms/u/1/d/e/1FAIpQLScHqNH3yxk5yBKhOMZ0mVk0Wl-bNLCowqW9UFr0mo2Hj7klGA/formResponse' target='_blank'>formulario</a> ",
"e poden conter erros polo reporte non estandarizado do nome da praia."))
)
),
layout_columns(
col_widths = c(3, 3, 3, 3),
kpi_box("n_concellos", "Concellos", "con actualizacións"),
kpi_box("n_praias", "Praias", "con actualizacións"),
kpi_box("n_update", "Actualizacións", "recibidas no formulario"),
kpi_box("n_112", "Notificacións 112", "total recibidas", accent = "danger")
kpi_box("n_concellos", "Concellos", "con actualizacións", trendId = "n_concellos_trend"),
kpi_box("n_praias", "Praias", "con actualizacións", trendId = "n_praias_trend"),
kpi_box("n_update", "Actualizacións", "recibidas no formulario", trendId = "n_update_trend"),
kpi_box("n_112", "Notificacións 112", "total recibidas", accent = "danger", trendId = "n_112_trend")
),
layout_columns(
col_widths = c(4, 4, 4),
kpi_box("n_pellets", "Praias con residuos", accent = "danger"),
kpi_box("n_limpas", "Praia limpa", accent = "success"),
kpi_box("n_limpas", "Praias limpas", accent = "success"),
kpi_box("n_limpezas", "Outros eventos", accent = "warning")
),
layout_columns(
col_widths = c(6, 6),
card(card_header("Evolución diaria — por tipo de residuo"),
card(card_header("Evolución por tipo de residuo (últimos 3 meses)"),
withSpinner(plotOutput("cum_residuos"))),
card(card_header("Evolución diaria — actualizacións recibidas"),
card(card_header("Actualizacións recibidas por día (histórico)"),
withSpinner(plotOutput("cum_praias")))
)
)
Expand Down Expand Up @@ -184,8 +186,11 @@ server <- function(input, output, session) {

isFirstRun <- reactiveVal(TRUE)

data <- reactive({
# Datos base (lectura + transformacións), SEN filtros. Compártense entre o
# mapa (filtrado polo panel) e as estatísticas (datos completos).
all_data_r <- reactive({
invalidateLater(350000)
all_data <- NULL
tryCatch({
all_data <- read_csv("./data/praias.csv", show_col_types = FALSE)
all_data <- all_data %>%
Expand All @@ -212,6 +217,12 @@ server <- function(input, output, session) {
}, error = function(e){
message("Error loading data...")
})
all_data
})

# Datos filtrados polo panel lateral (só para o mapa).
data <- reactive({
all_data <- all_data_r()
# Filter data based on selected date range
max <- max(as.Date(all_data$Data.Norm))
min <- min(as.Date(all_data$Data.Norm))
Expand Down Expand Up @@ -252,116 +263,90 @@ server <- function(input, output, session) {
filtered_data
})

###### RENDER Statisticsc ################
output$n_concellos <- renderText({
distinct_counts <- data() %>%
count(Concello)
n_concellos <- length(unique(distinct_counts$Concello))
box_value(n_concellos)
})

output$n_praias <- renderText({
praias_tipos <- c("Hai pellets na praia", "Hai chapapote", "Hai biosoportes", "Non hai pellets na praia", "A praia está limpa", "Xa non hai (a praia quedaba limpa cando se encheu o formulario)")
distinct_counts <- data() %>%
count(Nome.da.praia..Concello, Concello, Tipo.de.actualización.que.nos.queres.facer.chegar) %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% praias_tipos)
box_value(length(unique(distinct_counts$Nome.da.praia..Concello)))

})

output$n_update <- renderText({
box_value(nrow(data()))
})

output$n_112 <- renderText({
distinct_counts <- data() %>%
count(Está.avisado.o.112.) %>% filter(Está.avisado.o.112. == "Si")
box_value(distinct_counts)
})

reconto <- reactive({
distinct_counts <- data() %>% count(Nome.da.praia..Concello, Concello, Tipo.de.actualización.que.nos.queres.facer.chegar)
###### Estatísticas (datos completos, sen filtros) ################
fulldata <- reactive(all_data_r())

residuo_tipos <- c("Hai pellets na praia", "Hai chapapote", "Hai biosoportes")
praia_tipos <- c("Hai pellets na praia", "Hai chapapote", "Hai biosoportes",
"Non hai pellets na praia", "A praia está limpa",
"Xa non hai (a praia quedaba limpa cando se encheu o formulario)")
limpa_tipos <- c("A praia está limpa", "Non hai pellets na praia",
"Xa non hai (a praia quedaba limpa cando se encheu o formulario)")
evento_tipos <- c("Convocatoria de xornada de limpeza", "Outras Convocatorias")

# Data de referencia = última con datos (en produción ≈ hoxe). As xanelas
# (semana, 3 meses) áncoranse a ela para que sempre amosen actividade recente.
ref_date <- reactive({
d <- fulldata()
if (is.null(d) || !nrow(d)) Sys.Date() else max(d$Data.Norm, na.rm = TRUE)
})
last_week <- reactive(ref_date() - 7)

output$n_pellets <- renderText({
count <- reconto() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% c("Hai pellets na praia", "Hai chapapote", "Hai biosoportes"))
box_value(sum(count$n))
})

output$n_limpas <- renderText({
count <- reconto() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% c("A praia está limpa", "Non hai pellets na praia", "Xa non hai (a praia quedaba limpa cando se encheu o formulario)"))
box_value(sum(count$n))
})

output$n_limpezas <- renderText({
count <- reconto() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% c("Convocatoria de xornada de limpeza", "Outras Convocatorias"))
box_value(sum(count$n))
})
# Tendencia: actividade na última semana con datos
trend_chip <- function(n) {
if (is.na(n) || n == 0) "sen novidades esta semana"
else paste0("▲ ", format(n, big.mark = " ", trim = TRUE), " esta semana")
}

output$top5 <- renderPlot({
top5 <- data() %>%
count(Concello) %>%
arrange(desc(n)) %>%
head(5)
ggplot(top5, aes(x = factor(Concello), y = n, fill = n)) +
geom_col(stat = "n", show.legend = FALSE) +
geom_text(aes(label = Concello, y = n), position = position_stack(vjust = 0.5), color = "white") +
scale_fill_gradient(low = "#136f6f", high = "#136f6f") +
labs(x = NULL, y = NULL) + coord_flip() +
theme(
panel.background = element_rect(fill = "transparent", colour = NA),
plot.background = element_rect(fill = "transparent", colour = NA),
panel.border = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "null"),
axis.text = element_blank(),
axis.line = element_blank(),
axis.line.x = element_line(color = "black", size = 1),
axis.text.x = element_text(color = "black", size = 10),
legend.position = "none"
)
output$n_concellos <- renderText(box_value(length(unique(fulldata()$Concello))))
output$n_concellos_trend <- renderText(
trend_chip(length(unique((fulldata() %>% filter(Data.Norm >= last_week()))$Concello))))

output$n_praias <- renderText(
box_value(length(unique((fulldata() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% praia_tipos))$Nome.da.praia..Concello))))
output$n_praias_trend <- renderText(
trend_chip(length(unique((fulldata() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% praia_tipos,
Data.Norm >= last_week()))$Nome.da.praia..Concello))))

output$n_update <- renderText(box_value(nrow(fulldata())))
output$n_update_trend <- renderText(
trend_chip(nrow(fulldata() %>% filter(Data.Norm >= last_week()))))

output$n_112 <- renderText(
box_value(sum(fulldata()$Está.avisado.o.112. == "Si", na.rm = TRUE)))
output$n_112_trend <- renderText(
trend_chip(sum((fulldata() %>% filter(Data.Norm >= last_week()))$Está.avisado.o.112. == "Si", na.rm = TRUE)))

output$n_pellets <- renderText(
box_value(sum(fulldata()$Tipo.de.actualización.que.nos.queres.facer.chegar %in% residuo_tipos)))
output$n_limpas <- renderText(
box_value(sum(fulldata()$Tipo.de.actualización.que.nos.queres.facer.chegar %in% limpa_tipos)))
output$n_limpezas <- renderText(
box_value(sum(fulldata()$Tipo.de.actualización.que.nos.queres.facer.chegar %in% evento_tipos)))

# Evolución por tipo de residuo — últimos 3 meses
output$cum_residuos <- renderPlot({
d <- fulldata() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% residuo_tipos,
Data.Norm >= ref_date() - 90) %>%
count(Residuo, Data.Norm) %>%
group_by(Data.Norm, Residuo) %>%
summarize(sum_n = sum(n), .groups = "drop")
ggplot(d, aes(x = Data.Norm, y = sum_n, color = Residuo, group = Residuo)) +
geom_line(linewidth = 1, lineend = "round", linejoin = "mitre") +
scale_color_manual(values = c("Pellets" = "#d6342a", "Biosoportes" = "#e8920c", "Chapapote" = "#6b3fa0")) +
labs(x = NULL, y = "Actualizacións", color = NULL) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom",
plot.background = element_rect(fill = "transparent", colour = NA))
})

# Actualizacións recibidas por día — histórico completo
output$cum_praias <- renderPlot({
distinct_counts <- data() %>%
count(Nome.da.praia..Concello, Concello, Data.Norm)

summarized_data <- distinct_counts %>%
d <- fulldata() %>%
count(Nome.da.praia..Concello, Concello, Data.Norm) %>%
group_by(Data.Norm) %>%
summarize(sum_n = sum(n))

ggplot(summarized_data, aes(x = Data.Norm, y = sum_n)) +
summarize(sum_n = sum(n), .groups = "drop")
ggplot(d, aes(x = Data.Norm, y = sum_n)) +
geom_area(fill = "#136f6f", alpha = 0.15) +
geom_line(linewidth = 1, lineend = "round", linejoin = "mitre", colour = "#136f6f") +
labs(x = "Data", y = "Número de Actualizacións") +
labs(x = NULL, y = "Actualizacións") +
theme_minimal(base_size = 13) +
theme(plot.background = element_rect(fill = "transparent", colour = NA))
})



output$cum_residuos <- renderPlot({
distinct_counts <- data() %>%
filter(Tipo.de.actualización.que.nos.queres.facer.chegar %in% c("Hai pellets na praia", "Hai chapapote", "Hai biosoportes")) %>%
count(Residuo, Data.Norm)

# Group by both Data.Norm and Residuo to calculate sums per Residuo type
summarized_data <- distinct_counts %>%
group_by(Data.Norm, Residuo) %>%
summarize(sum_n = sum(n), .groups = 'drop')

# Plot each Residuo as a separate line
ggplot(summarized_data, aes(x = Data.Norm, y = sum_n, color = Residuo, group = Residuo)) +
geom_line(linewidth = 1, lineend = "round", linejoin = "mitre") +
scale_color_manual(values = c("Pellets" = "#d6342a", "Biosoportes" = "#e8920c", "Chapapote" = "#6b3fa0")) +
labs(x = "Data", y = "Número de Actualizacións", color = "Residuo") +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom",
plot.background = element_rect(fill = "transparent", colour = NA))
})

###### RENDER MAP ################
# Create a reactive object for each type
data_hai_pellets <- reactive({
Expand Down
1 change: 1 addition & 0 deletions www/styles.css
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,7 @@ body{ border-top:3px solid #136f6f; }
.kpi-title{ display:flex; flex-direction:column; gap:1px;
font-size:.72rem; font-weight:700; letter-spacing:.07em; text-transform:uppercase; color:var(--muted); }
.kpi-sub{ font-weight:500; letter-spacing:0; text-transform:none; color:var(--faint); font-size:.7rem; }
.kpi-trend{ display:block; margin-top:7px; font-size:.72rem; font-weight:560; color:var(--accent, #136f6f); }

/* tarxetas de gráficas */
.card-header{ font-weight:620; font-size:.95rem; }
Expand Down
Loading