Shiny app - aggregate data set by selection_filter and create new variables - filter

Question

I am quite new to R and have been trying to find a solution for my problem since weeks. I hope someone can help me.
1.I want to develop a shiny app in a dashboard, where the user can select values via selection_filter (e.g. out of the variable "age group" the value "40-49 years" and from "sex" the value "female"). Based on these selections, columns (e.g. column x,y, and z) from the original dataset will be aggregated. I already wrote a function using aggregate().
2.Based on the aggregated columns, new values shall be calculated (e.g. d=(x-y)/(z/2)).
3.The aggregated columns and the newly calculated values shall be displayed in a table to the user.
The function from 1)
aggreg.function <- function(a,b,c) {
agg.data<- aggregate(cbind(x,y,z), shared_Cervix, sum,
subset=c(!AgeGroup %in% a & !Sex %in% b & !Edition %in% c))
#Calculate new values
agg.data$d<- agg.data$x+agg.data$y
agg.data$f<- (agg.data$x+agg.data$y)/(agg.data$z/2)
View(m.agg.data)
}
user_data<- reactive({
aggreg.function(input$AgeGroup, input$Sex, input$Edition)
})
EDIT
Thanks for the recommendations. I change my code but now I struggle a bit with adding new columns. In total, I want to insert 17 new columns based on the filtered table (data_step2()). Is there a way to insert multiple columns at the same time. In my example: is it possible to combine data_step3 and data_step4?
ui<-fluidPage(
selectInput("Age","Age:",sort(unique(Complete_test$Age))),
selectInput("Raced","Race:",sort(unique(Complete_test$Race))),
selectInput("Stage","Stage:",sort(unique(Complete_test$Stage))),
selectInput("Grade","Grade:",sort(unique(Complete_test$Grade))),
selectInput("Edition","Edition:",sort(unique(Complete$Edition))),
DT::dataTableOutput("filtered.result")
)
server = function(input, output) {
data_step1 <- reactive({
Complete%>% filter(Age %in% input$Age & Stage %in% input$Stage & Grade %in% input$Grade & Race %in% input$Race & Edition%in% input$_Edition)})
data_step2 <- reactive({
data_step1() %>% group_by(Age, Stage, Grade, Race, Edition, Year ) %>% summarise(across(everything(), sum))
})
#is it possible to combine data_step3 and data_step_4 ?
data_step3 <- reactive({
data_step2() %>% mutate(xy=x+y)
})
data_step4 <- reactive({
data_step3() %>% mutate(w=xy/(x2))
})
output$filtered.result <- DT::renderDataTable({
data_step4()
})
}
shinyApp(ui, server)
```

Answer 1

Here's something you might be able to do for step 1 of your question, although it's hard to tell what your data might look like and what you end result should look like. I'm assuming that you want to allow a user of your dashboard to select the AgeGroup and Sex variables to view data, so in the UI of the application, two selectInput functions were used to provide that functionality. For the server side, two reactive statements are used to filter the data as a user changes the inputs. In each one, an input is required and then the dataset is filtered by the input. Notice in the second reactive statement, "data_step1()" is used instead of "data_step1"; this allows the reactivity of these statements to continue to be updated as a user changes inputs.
For step 2 and 3, you'll want to use the "data_step2()" dataset to add the new columns and then you can use a function such as renderDataTable to display the output on your dashboard.
library(shiny)
library(dplyr)
ui <- fluidPage(
selectInput("AgeGroup", "AgeGroup:", sort(unique(Data$AgeGroup))),
selectInput("Sex", "Sex:", sort(unique(Data$Sex)))
)
server <- function(input,output,session){
data_step1 <- reactive({
req(input$AgeGroup) # Require input
data %>% filter(AgeGroup %in% input$AgeGroup)}) # Filter full dataset by AgeGroup input
data_step2 <- reactive({
req(input$Sex) # Require input
data_step1() %>% filter(Sex %in% input$Sex)}) # Filter full dataset by Sex input
# Do next steps with the filtered data_step2() dataset...
}
# Run the application
shinyApp(ui = ui, server = server)