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Copy pathnew_model_UI_elements_Compute.R
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new_model_UI_elements_Compute.R
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## risk mapping elements
output_dist_new_model<-selectInput(inputId = 'district_new',
label = 'District',
choices = c(3:20),
selected =20,
selectize =T,
multiple =F)
output_dist_seas<-selectInput(inputId = 'district_seas',
label = 'District',
choices = c(3:20),
selected =20,
selectize =T,
multiple =F)
output_dist_validation<-selectInput(inputId = 'district_validation',
label = 'District',
choices = c(3:20),
selected =15,
selectize =T,
multiple =F)
output_dist_pros<-selectInput(inputId = 'district_prospective',
label = 'District',
choices = c(3:20),
selected =15,
selectize =T,
multiple =F)
year_validation<-sliderInput(inputId = "new_model_Year_validation",
label = "Year",
min = 2008,
max=2030,
value =2013,
sep='',
step=1)
N_lags<-sliderInput(inputId = "nlags",
label = "Lag Weeks",
min = 4,
max=16,
value =12,
sep='',
step=1)
z_outbreak_New<-sliderInput(inputId = "z_outbreak_new",
label = "Z outbreak",
min = 1,
max=4,
value =1.2,
sep='',
step=0.1)
output_year_New_model<-inputPanel(column(12,
uiOutput("Year_Input2")),
column(12,offset=6,sliderInput(inputId = "new_model_Week_plot_spat",
label = "Week",
min = 1,
max=52,
step=1,
sep='',
value =35))
)
surv.Input_prospective<-column(12,offset =2,
fileInput('dat_prospective', 'Upload prospective data',
accept=c('.xls','.xlsx','.csv')
))
header_Input_pros<-fluidRow(inputPanel(surv.Input_prospective,
column(12,offset=7,output_dist_pros)))
pros_new_out1<-tabPanel("Uploaded_data",
DT::dataTableOutput("uploaded_pros")
)
pros_new_out2<-tabPanel("Prediction table",
DT::dataTableOutput("prediction_tab_pros")
)
pros_new_out3<-tabPanel("Outbreak",
plotOutput("outbreak_plot_pros",width ="700px",height ="320px")
)
pros_new_out4<- tabPanel("Probability",
#inputPanel(uiOutput("sel_diss1")),
plotOutput("prob_plot_pros",width ="700px",height ="320px")
)
pros_new_out5<- tabPanel("Outbreak and Probability",
#inputPanel(uiOutput("sel_diss1")),
plotOutput("out_break_prob_plot_pros",width ="700px",height ="320px")
)
pros_new_out6<- tabPanel("Response",
#inputPanel(uiOutput("sel_diss1")),
plotOutput("response_plot_pros")
)
row_elements_New_Model_prospective<-fluidRow(
header_Input_pros,
tabsetPanel(pros_new_out1,
pros_new_out2,
#pros_new_out3,
#pros_new_out4,
pros_new_out5,
pros_new_out6
)
)
output_graphs_New_Model<-tabsetPanel(
tabPanel("Descriptive analysis",
fluidPage(inputPanel(column(12,offset=0,
uiOutput("dist_Input1"))),
tabsetPanel(
tabPanel("Tables",
tableOutput("new_model_data_descriptives")
)
,
tabPanel("Plots",
# output_dist_new_model,
uiOutput("new_model_data_descriptive_Plots")
)
)
)
)
,
tabPanel("Time_series",
# output_year_New_model,
uiOutput("new_model_Time_series_Plots")
),
tabPanel("Spatial Plots",
fluidPage(output_year_New_model,
tabsetPanel(
tabPanel("Spatial_Covariate_Plots",
uiOutput("Error"),
uiOutput("Spat_Covariate_Plots_new_Model")
),
tabPanel("DIR",
leafletOutput("spat_DIR_new_Model"),
)
)
)
),
tabPanel("Lag non linear plots",
fluidPage(column(12,
N_lags),
tabsetPanel(
tabPanel("Lag Countour Plots",
uiOutput("lag_contour_Plots")
),
tabPanel("Var Slices",
uiOutput("var_Slices_Plots")
)
)
)
),
# uiOutput("dist_Input2")
tabPanel("Seasonality",
fluidPage(inputPanel(column(12,offset=0,
uiOutput("dist_Input2"))),
plotOutput("Seasonality_plot"))),
tabPanel("Model Validation",
uiOutput("valid_section")
)
)
boundary.Input<-column(12,offset =0,
fileInput('shape_file_new_Model', 'District sub-district boundary file (.shp file)',
accept=c('.shp','.dbf','.shx','.prj'),
multiple =T,
width='70%'))
surv.Input<-column(12,offset =0,
fileInput('dat_new_Model', 'Choose surveillance data with spatial inputs',
accept=c('.xls','.xlsx','.csv'),
width='70%'))
pop.Input<-column(12,offset =0,selectInput("population_New_model",
"Variable for annual total Population",
"population",multiple =F,
width='70%'))
case.Input<-column(12,offset =0,selectInput("number_of_cases_New_model",
"Variable for the weekly number of outbreak",
"weekly_hospitalised_cases",multiple =F,
width='70%'))
alarm.Input<-column(12,offset =0,selectInput("alarm_indicators_New_model", selected =c("rainsum","meantemperature"),
"Alarm indicator(s)",
choices=c("rainsum","meantemperature"),multiple =T,
width='70%'))
alarm_Spline.Input<-column(12,offset =0,selectInput("other_alarm_indicators_New_model", selected =c("rainsum","meantemperature"),
"Other alarm indicator(s)",
choices=c(" "),multiple =T,
width='70%'))
header_Input<-fluidRow(boundary.Input,
surv.Input,
pop.Input,
case.Input,
alarm.Input,
alarm_Spline.Input)
dat_opts_new_Model<- tabPanel("Spatial data Upload",
column(12,
shinydashboardPlus::box(width=12,
##choose point/ lat long data
fileInput('shape_file_new_Model', 'District sub-district boundary file (.shp file)',
accept=c('.shp','.dbf','.shx','.prj'),
multiple =T),
## choose aggregated or point data
fileInput('dat_new_Model', 'Choose surveillance data with spatial inputs',
accept=c('.xls','.xlsx','.csv')),
uiOutput("Spat_error_new_Model"),
selectInput("population_New_model",
"Variable for annual total Population",
"population",multiple =F),
selectInput("number_of_cases_New_model",
"Variable for the weekly number of outbreak",
"weekly_hospitalised_cases",multiple =F),
selectInput("alarm_indicators_New_model", selected =c("rainsum","meantemperature"),
"Alarm indicator(s)",
choices=c("rainsum","meantemperature"),multiple =T)
)
))
row_elements_New_Model.prev<-fluidRow(
column(4,
tabsetPanel(id="input_pan_new_Model",
dat_opts_new_Model
# sel_vars
)),
column(8,
uiOutput("spat_Display_new_Model")
)
)
row_elements_New_Model<-fluidRow(
column(3,
header_Input),
column(9,
uiOutput("spat_Display_new_Model")
)
)
dashboad_elements_New_model<-tabPanel("New Model",
fluidPage(
row_elements_New_Model
)
)
dashboad_elements_New_model_pros<-tabPanel("Prospective analysis (New Model)",
row_elements_New_Model_prospective
)