In rstudio/reportsWS: Interactive Reports with R Markdown and Shiny Workshop

# Exercise 14: Use components of this interactive document to recreate the Shiny wep app in the slides. To do this, you will need to 
# 1. Open a new file and save it as `app.R`. In the file,
# 2. Call each library used in this app, including `shiny`
# 3. define a server object 
#    a. add the minimum necessary code for a server object, then
#    b. add the reactive expressions used in this file
#    c. add the outputs used in the new app. These include:
#        i. the dygraphs plot
#            + the dygraphs libary provides two useful functions 
#              for working with dygraphs: `renderDygraph` and 
#              `dygraphOutput`. you should use each in the course 
#              of this assignment.
#        ii. The sum of children with the name
#        iii. The predicted number of children in 2025
#        iv. A rendered character string that says "children have had NAME since 1880."
#        v. A rendered character string that says "children will have the name in 2025 according to an MODEL"
#        vi. A data table created with `renderDataTable(names())`
#    d. save each of the above outputs to the output object
# 4. define a ui object
#    a. for this app, create a navbarPage with two tab panels
#       i. title the navbar page "What's in a name?"
#       ii. title the first panel "Home"
#           + use shiny's fluidRow() and column() functions to create the layout in the slide
#           + place well panels and inputs into the layout
#           + place each of the rendered outputs above, except the data dable, into the layout with *Output functions
#       i. title the second panel "Raw Data"
#           + place the data table output into this tab panel
# 4. call shinyApp() at the very end of the file

library(reportsWS)
library(forecast)
library(dygraphs)
library(shiny)

# Select name and gender
textInput(inputId = "name", label = "Type a name", value = "Garrett")
radioButtons(inputId = "sex", label = "Choose gender",
             choices = c("M", "F"), inline = TRUE)
names <- reactive(get_babyname(input$name, input$sex))

# Create time series and forecast
nbirths <- reactive(ts(names()$n, start = 1880))

pred <- reactive({
  mod <- auto.arima(nbirths())
  forecast(mod, h = 12) # 12 for 2025
})

Since 1880, r renderText(sum(nbirths(), na.rm = TRUE)) children have been named r renderText(input$name). The graph below breaks this number down by year showing the number of children named r renderText(input$name) as a time series.

renderDygraph({
  title <- paste0("Number of children named ", input$name)
  all <- bind_as_xts(nbirths(), pred()) 

  dygraph(all, main = title) %>%
    dySeries("x", label = "n children") %>% 
    dySeries(c("lower", "fitted", "upper"), label = "Predicted") %>% 
    dyRangeSelector()
})

We modeled this time series with an r renderText(trim_whitespace(pred()$method)) model to predict the number of children that will be given the name r renderText(input$name) in 2025.

renderTable({
  data.frame(
    method = trim_whitespace(pred()$method), 
    predicted_2025 = round(xts::last(pred()$mean)))
})

rstudio/reportsWS documentation built on May 28, 2019, 5:42 a.m.