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app.R
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pacman::p_load(shiny,shinydashboard,
bs4Dash,bs4cards,waiter,fresh,
thematic,shinythemes,DT,zoo,plotly, # Making dynamic graphs
modeltime, # Times model classics
tidyverse, # All tidy for manipulation
timetk, # Making a tables and graph's of timeseries
lubridate, # Working if date
easystats, # making a spells on the datas.
imputeTS,
readxl,
recipes,
workflows,
parsnip,
Metrics)
thematic_shiny()
########################################## trocar infos -- toast options ############################
toastOpts <- list(
autohide = TRUE,
icon = "fas fa-home",
close = FALSE,
position = "bottomRight"
)
######### color statuses #######
statusColors <- c(
"gray-dark",
"gray",
"secondary",
"navy",
"indigo",
"purple",
"primary",
"lightblue",
"info",
"success",
"olive",
"teal",
"lime",
"warning",
"orange",
"danger",
"fuchsia",
"maroon",
"pink",
"white"
)
##################### my theme ###########
mytheme <- create_theme(
bs4dash_vars(
navbar_light_hover_color = "#3BA368"
),
bs4dash_sidebar_light(
hover_color = "#3BA368"
),
bs4dash_status(
primary = "#3BA368", danger = "#D10003"
),
bs4dash_vars()
)
# actual_price_tab ###################
actual_price_tab <- tabItem(
tabName = "tab_prices",
fluidRow(
box(width = 4,
status = "primary",
title = "Filtro de data para os itens",
solidHeader = TRUE,
dateRangeInput('dateRange',
label = '',
language = "pt",
min = min(data$date),
max = max(data$date),
start = max(data$date) ,
end = "2023-01-01 UTC" ,
format = "dd/mm/yyyy" ,
separator = "Até" )),
tabBox(
title = "Tabela de Preços da Ceasa",
closable = FALSE,
width = 12,
status = "primary",
solidHeader = TRUE,
collapsible = FALSE,
type = "tabs",
selected = "Preço diário",
tabPanel(
"Média Mensal",
DT::dataTableOutput("tbl",width = '1200px')
) ,
tabPanel(
"Preço diário",
DT::dataTableOutput("tbl_1",width = '1200px')
)))
)
# analises_tab ####################
analises_tab <- tabItem(
tabName = "analises_tab",
fluidRow(
box(
closable = FALSE,
width = 3,
solidHeader = TRUE,
status = "primary",
collapsible = TRUE,
selectInput(
inputId = "item",
label = "Escolha o item:",
c(
"ABACATE"=1,
"ABACAXI"=2,
"ABOBORA"=3,
"ABOBRINHA"=4,
"ALFACE"=5,
"ALHO"=6,
"BANANA NANICA"=7,
"BANANA PRATA"=8,
"BATATA"=9,
"BATATA DOCE"=10,
"BERINJELA"=11,
"BETERRABA"=12,
"BROCOLO"=13,
"CARA"=14,
"CEBOLA"=15,
"CENOURA"=16,
"CHUCHU"=17,
"COCO VERDE"=18,
"COUVE"=19,
"COUVE-FLOR"=20,
"GOIABA"=21,
"INHAME"=22,
"JILO"=23,
"LARANJA PERA"=24,
"LIMAO TAHITI"=25,
"MACA"=26,
"MAMAO FORMOSA"=27,
"MAMAO HAWAY"=28,
"MANDIOCA"=29,
"MANDIOQUINHA"=30,
"MANGA"=31,
"MARACUJA AZEDO"=32,
"MELANCIA"=33,
"MELAO AMARELO"=34,
"MILHO VERDE"=35,
"MORANGO"=36,
"OVOS"=37,
"PEPINO"=38,
"PERA IMPORTADA"=39,
"PIMENTAO VERDE"=40,
"QUIABO"=41,
"REPOLHO"=42,
"TANGERINA"=43,
"TOMATE"=44,
"UVA ITALIA"=45,
"UVA NIAGARA"=46,
"VAGEM"=47)),
selectInput(
multiple = TRUE,
inputId = "year_violin",
label = "Escolha o ano:",
choices = c(2015,2016,2017,2018,2019,2020,2021,2022,2023,2024),
selected = 2024)
),
box(
title = "Tabela com medidas de escala e dispersão",
closable = FALSE,
width = 9,
solidHeader = TRUE,
status = "primary",
collapsible = TRUE,
tableOutput("tbl_eda_1")
),
tabBox(
title = "Gráfico da distribuição de preços (Boxplot)",
closable = FALSE,
width = 12,
solidHeader = TRUE,
status = "primary",
collapsible = TRUE,
type = "tabs",
selected = "Ano",
tabPanel("Ano",plotly::plotlyOutput("plot_violin")),
tabPanel("Mês",plotly::plotlyOutput("plot_violin_month")),
tabPanel("Dia da Semana",plotly::plotlyOutput("plot_violin_week")),
tabPanel("Semanas",plotly::plotlyOutput("plot_violin_weeks")),
tabPanel("Quarto de ano",plotly::plotlyOutput("plot_violin_quarter"))
)
),
fluidRow(
tabBox(
title = "Gráfico de Serie temporal",
width = 12,
status = "primary",
closable = FALSE,
maximizable = TRUE,
solidHeader = TRUE,
collapsible = TRUE,
type = "tabs",
selected = "Gráfico de Tendência",
tabPanel("Gráfico de Tendência",plotly::plotlyOutput("plot_eda")),
tabPanel("Grafico de Anomalia",plotly::plotlyOutput("plot_an")),
tabPanel("Gráfico do Restante",plotly::plotlyOutput("plot_stl"))
)
),
fluidRow(
tabBox(
title = "Grafico de diagnósticos de Lags",
width = 12,
status = "primary",
closable = FALSE,
maximizable = TRUE,
solidHeader = TRUE,
collapsible = TRUE,
type = "tabs",
selected = "Gráfico de Correlação ACF",
tabPanel("Gráfico de Correlação ACF", plotly::plotlyOutput("plot_acf")),
tabPanel("Gráfico de Correlação PACF", plotly::plotlyOutput("plot_pacf"))
)
)
)
# forcast_model_tab ---- #########################
forcast_model_tab <- tabItem(
tabName = "forecast_diag_tab",
fluidRow(
box(
closable = FALSE,
width = 3,
solidHeader = TRUE,
status = "navy",
collapsible = TRUE,
selectInput(
inputId = "item_2",
label = "Escolha o item:",
c(
"ABACATE"=1,
"ABACAXI"=2,
"ABOBORA"=3,
"ABOBRINHA"=4,
"ALFACE"=5,
"ALHO"=6,
"BANANA NANICA"=7,
"BANANA PRATA"=8,
"BATATA"=9,
"BATATA DOCE"=10,
"BERINJELA"=11,
"BETERRABA"=12,
"BROCOLO"=13,
"CARA"=14,
"CEBOLA"=15,
"CENOURA"=16,
"CHUCHU"=17,
"COCO VERDE"=18,
"COUVE"=19,
"COUVE-FLOR"=20,
"GOIABA"=21,
"INHAME"=22,
"JILO"=23,
"LARANJA PERA"=24,
"LIMAO TAHITI"=25,
"MACA"=26,
"MAMAO FORMOSA"=27,
"MAMAO HAWAY"=28,
"MANDIOCA"=29,
"MANDIOQUINHA"=30,
"MANGA"=31,
"MARACUJA AZEDO"=32,
"MELANCIA"=33,
"MELAO AMARELO"=34,
"MILHO VERDE"=35,
"MORANGO"=36,
"OVOS"=37,
"PEPINO"=38,
"PERA IMPORTADA"=39,
"PIMENTAO VERDE"=40,
"QUIABO"=41,
"REPOLHO"=42,
"TANGERINA"=43,
"TOMATE"=44,
"UVA ITALIA"=45,
"UVA NIAGARA"=46,
"VAGEM"=47))),
box(
title = "Tabela com as metricas dos modelos",
closable = FALSE,
width = 9,
solidHeader = TRUE,
status = "navy",
collapsible = TRUE,
tableOutput("metrics_models")
),
box(
title = "Gráfico de Treino do modelo",
width = 12,
status = "navy",
closable = FALSE,
maximizable = TRUE,
solidHeader = TRUE,
collapsible = TRUE,
tabPanel("Gráfico de Treino",plotly::plotlyOutput("plot_training_model"))
),
tabBox(
title = "Graficos Ruído Branco",
closable = FALSE,
width = 12,
status = "navy",
solidHeader = TRUE,
collapsible = FALSE,
type = "tabs",
selected = "Ruído Branco serie",
tabPanel(
"Ruído Branco serie",plotly::plotlyOutput("plot_rbs")
) ,
tabPanel(
"Ruído Branco boxplot",
plotly::plotlyOutput("plot_rbb")
))
)
)
# prediction_model_tab ############
forecast_tab <- tabItem(
tabName = "4cast_tab",
fluidRow(
box(
closable = FALSE,
width = 3,
solidHeader = TRUE,
status = "primary",
collapsible = TRUE,
selectInput(
inputId = "item_3",
label = "Escolha o item:",
c(
"ABACATE"=1,
"ABACAXI"=2,
"ABOBORA"=3,
"ABOBRINHA"=4,
"ALFACE"=5,
"ALHO"=6,
"BANANA NANICA"=7,
"BANANA PRATA"=8,
"BATATA"=9,
"BATATA DOCE"=10,
"BERINJELA"=11,
"BETERRABA"=12,
"BROCOLO"=13,
"CARA"=14,
"CEBOLA"=15,
"CENOURA"=16,
"CHUCHU"=17,
"COCO VERDE"=18,
"COUVE"=19,
"COUVE-FLOR"=20,
"GOIABA"=21,
"INHAME"=22,
"JILO"=23,
"LARANJA PERA"=24,
"LIMAO TAHITI"=25,
"MACA"=26,
"MAMAO FORMOSA"=27,
"MAMAO HAWAY"=28,
"MANDIOCA"=29,
"MANDIOQUINHA"=30,
"MANGA"=31,
"MARACUJA AZEDO"=32,
"MELANCIA"=33,
"MELAO AMARELO"=34,
"MILHO VERDE"=35,
"MORANGO"=36,
"OVOS"=37,
"PEPINO"=38,
"PERA IMPORTADA"=39,
"PIMENTAO VERDE"=40,
"QUIABO"=41,
"REPOLHO"=42,
"TANGERINA"=43,
"TOMATE"=44,
"UVA ITALIA"=45,
"UVA NIAGARA"=46,
"VAGEM"=47))),
box(
title = "Gráfico de Predição",
width = 12,
status = "primary",
closable = FALSE,
maximizable = TRUE,
solidHeader = TRUE,
collapsible = TRUE,
tabPanel("Gráfico de Predição",plotly::plotlyOutput("plot_prediction_model"))
),
box(
title = "Tabela com as medidas explorátorias",
closable = FALSE,
width = 12,
solidHeader = TRUE,
status = "primary",
collapsible = TRUE,
tableOutput("tbl_eda_pred"))
)
)
# theory_tab ----
theory_tab <- tabItem(
tabName = "theory_tab",
"https://thalisreboucas.github.io/Estatistica_Resumida_Book/"
)
########################################
shinyApp(
ui = dashboardPage(
preloader = list(html = tagList(spin_1(), "Thalis.R: Carregando"), color = "#F14902"),
help = TRUE,
scrollToTop = TRUE,
header = dashboardHeader(
title = dashboardBrand(
title = sprintf("Ceasa - ce", as.character(utils::packageVersion("bs4Dash"))),
color = "primary",
href = "",
image = "https://play-lh.googleusercontent.com/kFwgSRrveDZRgkGWmItfpADxlPoQeRCnhSLqHBE8sT71UoMOqdzchTcF-dHzsOwnzK0",
opacity = 0.95
),
fixed = T,
rightUi = tagList(
dropdownMenu(
badgeStatus = "danger",
type = "messages",
######## Messagem item ##############
messageItem(
inputId = "triggerAction1",
message = "Dados atualizados até fevereiro de 2024",
from = "Thalis Rebouças",
image = "https://thalisreboucas.com.br/images/all/1.jpg",
time = "13/02/2024",
color = "orange"
)
###################################
),
userOutput("user")
),
leftUi = tagList(
dropdownMenu(
badgeStatus = "info",
type = "notifications",
###### Notification item ##########
notificationItem(
inputId = "triggerAction2",
text = "Dados de 13/02/2024 ",
status = "success"
)
################################
),
)
),
sidebar = dashboardSidebar(
fixed = T,
collapsed = T,
expandOnHover = T,
skin = "light",
status = "primary",
id = "sidebar",
customArea = fluidRow(
),
sidebarUserPanel(
image = "logo.png",
name = "Thalis.R!"
),
sidebarMenu(
id = "current_tab",
flat = FALSE,
compact = FALSE,
childIndent = TRUE,
sidebarHeader("Tabelas"),
menuItem(
"Tabela de Preço Atual",
tabName = "tab_prices",
icon = icon("th")
),
menuItem(
"Análise de dados",
tabName = "analises_tab",
icon = icon("chart-bar")
),
sidebarHeader("Modelos"),
menuItem(
"Diagnósticos dos Modelos",
tabName = "forecast_diag_tab",
icon = icon("stethoscope")
),
menuItem(
"Modelos de Previsão",
tabName = "4cast_tab",
icon = icon("chart-line")
),
sidebarHeader("Teoria"),
menuItem(
"Teoria dos Modelos",
tabName = "theory_tab",
icon = icon("layer-group")
)
)
),
body = dashboardBody(
use_theme(mytheme),
tabItems(
actual_price_tab,
analises_tab,
forcast_model_tab,
forecast_tab,
theory_tab
)
),
footer = dashboardFooter(
fixed = FALSE,
right = "© THALIS REBOUÇAS 2024 / TODOS OS DIREITOS RESERVADOS."
),
title = "Ceasa dash"
),
################################ server ##################
server = function(input, output, session) {
useAutoColor(T)
item <- reactive(as.numeric({input$item}))
item_2 <- reactive(as.numeric({input$item_2}))
item_3 <- reactive(as.numeric({input$item_3}))
year_violin <- reactive(as.numeric({input$year_violin}))
# plots -------------------------------------------------------------------
output$plot_eda <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_anomaly_diagnostics(.value = value,.date_var = date) |> plot_ly() |>
add_lines(x = ~date,
y = ~observed,
line = list(color = "rgb(105, 175, 94)"),
name = "Dados") |>
add_lines(x = ~date , y = ~trend , name ="tendência" ,
line = list(color = "rgb(4, 86, 74)")) |>
layout(showlegend = T,
title='',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = "Dia",
gridcolor = 'ffff',
rangeslider = list(visible = T)))
})
output$plot_an <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_anomaly_diagnostics(.value = value,.date_var = date) |> plot_ly() |>
add_lines(x = ~date,
y = ~observed,
line = list(color = "rgb(105, 175, 94)"),
name = "Dados") |>
add_ribbons(x = ~date,
ymin = ~recomposed_l1,
ymax = ~recomposed_l2,
line = list(color = "rgba(105, 172, 135 ,0.5)"),
fillcolor = "rgba(105, 172, 135, 0.3)",
name = "Fator IQR") |>
add_markers(x = ~date,
y = ~observed,
marker = list(color = "#ff0000"),
data = data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_anomaly_diagnostics(.value = value,.date_var = date) |>
dplyr::filter(anomaly == "Yes") ,
name = 'Anomalia')|>
layout(showlegend = T,
title='',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = "Dia",
gridcolor = 'ffff',
rangeslider = list(visible = T)))
})
output$plot_violin <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_seasonal_diagnostics(.date_var = date,.value = value) |>
dplyr::filter(year == year_violin()) |>
plot_ly(
y = ~.value,
x= ~year,
type = 'violin',
color = I("rgba(105, 172, 135 ,0.8)"),
box = list(
visible = T
),
meanline = list(
visible = T
)
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_violin_month <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_seasonal_diagnostics(.date_var = date,.value = value) |>
dplyr::filter(year == year_violin()) |>
plot_ly(
y = ~.value,
x= ~month.lbl,
type = 'violin',
color = I("rgba(105, 172, 135 ,0.8)"),
box = list(
visible = T
),
meanline = list(
visible = T
)
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_violin_week <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_seasonal_diagnostics(.date_var = date,.value = value) |>
dplyr::filter(year == year_violin()) |>
plot_ly(
y = ~.value,
x= ~wday.lbl,
type = 'violin',
color = I("rgba(105, 172, 135 ,0.8)"),
box = list(
visible = T
),
meanline = list(
visible = T
)
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_violin_weeks <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_seasonal_diagnostics(.date_var = date,.value = value) |>
dplyr::filter(year == year_violin()) |>
plot_ly(
y = ~.value,
x= ~week,
type = 'violin',
color = I("rgba(105, 172, 135 ,0.8)"),
box = list(
visible = T
),
meanline = list(
visible = T
)
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_violin_quarter <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_seasonal_diagnostics(.date_var = date,.value = value) |>
dplyr::filter(year == year_violin()) |>
plot_ly(
y = ~.value,
x= ~quarter,
type = 'violin',
color = I("rgba(105, 172, 135 ,0.8)"),
box = list(
visible = T
),
meanline = list(
visible = T
)
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_stl <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_anomaly_diagnostics(.value = value,.date_var = date) |> plot_ly() |>
add_lines(x = ~date,
y = ~remainder,
line = list(color = "rgb(105, 175, 94)"),
name = "Restante") |>
layout(showlegend = T,
title='',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = "Ano",
gridcolor = 'ffff'))
})
output$plot_acf <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_acf_diagnostics(.date_var = date,.value = value) |>
plot_ly() |>
add_lines(x = ~lag,
y = ~ACF,
line = list(color = "rgb(105, 175, 94)"),
name = "Correlação ACF") |>
add_lines(y = ~.white_noise_lower,
x = ~lag,
line = list(color = "rgb(0, 0, 0)",dash = 'dash'),
name = "Ruído Branco inferior ") |>
add_lines(y = ~.white_noise_upper,
x = ~lag,
line = list(color = "rgba(0, 0, 0 )",dash = 'dash'),
name = "Ruído Branco superior") |>
layout(
yaxis = list(title = "Correlação ACF" ),
xaxis = list(title = "Quantidade de Lags"))
})
output$plot_pacf <- plotly::renderPlotly({
data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
tk_acf_diagnostics(.date_var = date,.value = value) |>
plot_ly() |>
add_lines(x = ~lag,
y = ~PACF,
line = list(color = "rgb(105, 175, 94)"),
name = "Correlação PACF") |>
add_markers(x = ~lag,
y = ~PACF,
marker = list(color = "rgb(105, 175, 94)",
size = 4),
name = "Pontos da Correlação ACF") |>
add_lines(y = ~.white_noise_lower,
x = ~lag,
line = list(color = "rgb(0, 0, 0)",dash = 'dash'),
name = "Ruído Branco inferior ") |>
add_lines(y = ~.white_noise_upper,
x = ~lag,
line = list(color = "rgba(0, 0, 0 )",dash = 'dash'),
name = "Ruído Branco superior") |>
layout(
yaxis = list(title = "Correlação PACF" ),
xaxis = list(title = "Quantidade de Lags"))
})
############################# forecast model ################
output$plot_training_model <- plotly::renderPlotly({
data_traing |>
plot_ly() |>
add_lines( data = data_traing |> filter(id == item_2(),
.key == "actual",
.index > min(data_traing |>
filter(.key == "prediction") |>
pull(.index))),
x = ~.index,
y = ~.value,
line = list(color = "rgb(105, 175, 94)"),
name = "Dados") |>
add_lines( data = data_traing |>
group_by(.model_id) |>
filter(id == item_2(),.key == "prediction"),
x = ~.index,
y = ~.value,
color = ~.model_desc,
name = ~Name_models) |>
layout(showlegend = T,
title='',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = "Dia",
gridcolor = 'ffff',
rangeslider = list(visible = T)))
})
output$plot_rbs <- plotly::renderPlotly({
residual |> filter(id == item_2()) |>
ungroup() |>
group_by(Name_models) |>
plot_ly(
x= ~.index,
y = ~residual,
color = ~Name_models,
name = ~Name_models,
type = 'scatter', mode = 'lines'
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Resíduos",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_rbb <- plotly::renderPlotly({
residual |> filter(id == item_2()) |>
ungroup() |>
group_by(Name_models) |>
plot_ly(
y = ~residual,
type = 'violin',
color = ~Name_models,
box = list(
visible = T
),
meanline = list(
visible = T
)
) |>
layout(showlegend = F,
title=' ',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = " "))
})
output$plot_prediction_model <- plotly::renderPlotly({
data_prediction |>
plot_ly() |>
add_lines( data = data_prediction |>
dplyr::filter(id == item_3(),
.key == "actual",
Ano >= 2023),
x = ~.index,
y = ~.value,
line = list(color = "rgb(105, 175, 94)"),
name = "Dados") |>
add_lines( data = data_prediction |>
group_by(.model_id) |>
filter(id == item_3(),.key == "prediction"),
x = ~.index,
y = ~.value,
color = ~.model_desc,
name = ~Name_models) |>
layout(showlegend = T,
title='',
yaxis = list(title = "Preço",
tickprefix = "R$",
gridcolor = 'ffff'),
xaxis = list(title = "Dia",
gridcolor = 'ffff',
rangeslider = list(visible = T)))
})
##### tab 1 ---- actual price ###################
output$tbl <- DT::renderDataTable({
data |> dplyr::mutate(my = zoo::as.yearmon(date)) |>
dplyr::filter( date >= input$dateRange[2] & date <= input$dateRange[1] ) |>
dplyr::mutate(Produto = paste(produte,unit,sep = " ")) |>
dplyr::group_by(my,Produto) |>
dplyr::summarise(value = round(mean(value),2)) |> drop_na() |>
dplyr::arrange(desc(my)) |>
tidyr::pivot_wider(id_cols = "Produto", names_from = my,
values_from = value) |>
DT::datatable(filter = 'top',
options = list(
language = pt,
mark = ",",
pageLength = 10,
autoWidth = TRUE,
scrollX = TRUE))
})
###### tab de explorator data --- ########################
output$tbl_eda_1 <- renderTable({ data |>
dplyr::group_by(id) |>
dplyr::filter(id == item()) |>
timetk::tk_seasonal_diagnostics(.date_var = date,.value = value) |>
dplyr::group_by(year) |>
dplyr::filter(year == year_violin()) |>
dplyr::rename(Ano = year) |>
dplyr::summarize( Mínimo = min(.value),
Média = mean(.value),`Desvio Padrão` = sd(.value) ,
IQR = IQR(.value) ,
Curtose = as.numeric(kurtosis(.value)) ,
Assimetria = as.numeric(skewness(.value)),
Q1 = stats::quantile(.value,probs = 0.25, na.rm = TRUE),
Mediana = median(.value),
Q3 = stats::quantile(.value,probs = 0.75, na.rm = TRUE),
Maxímo = max(.value),
Amplitude = (max(.value) -min(.value)))
})
output$tbl_eda_pred <- renderTable({ data_prediction |>
dplyr::group_by(id,Mes,Name_models) |>
dplyr::filter(id == item_3(),.key=="prediction") |>
dplyr::mutate(Mes = case_when( Mes == 1 ~ "Janeiro",
Mes == 2 ~ "Fevereiro",
Mes == 3 ~ "Março",
Mes == 4 ~ "Abril",
Mes == 5 ~ "Maio",
Mes == 6 ~ "Junho",
Mes == 7 ~ "Julho",
Mes == 8 ~ "Agosto",
Mes == 9 ~ "Setembro",
Mes == 10 ~ "Outubro",
Mes == 11 ~ "Novembro",
Mes == 12 ~ "Dezembro"
)) |>
dplyr::summarize( Mínimo = min(.value),
Média = mean(.value),`Desvio Padrão` = sd(.value) ,
IQR = IQR(.value) ,
Mad = mad(.value),
Curtose = as.numeric(kurtosis(.value)) ,
Assimetria = as.numeric(skewness(.value)),
Q1 = stats::quantile(.value,probs = 0.25, na.rm = TRUE),
Mediana = median(.value),
Q3 = stats::quantile(.value,probs = 0.75, na.rm = TRUE),
Maxímo = max(.value),
Amplitude = (max(.value) -min(.value)),
Dias = length(.value))
})
output$metrics_models <- renderTable({