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

Turn this file into an interactive document with Shiny components. To do this:

1. Prepare the document to run shiny and call shiny functions
2. Add an input function to collect a name value
3. Add an input function to collect a sex value
   • The value choices must be "M" and "F"
4. Correctly call each R object in the file so that it reacts when the user changes an input value.
   • consider how to refer to the object (name v. input$name v. names())
   • consider how to treat the object
     • do you need to wrap the code that calls it in one of
       • reactive()
       • render*()
       • observeEvent()
       • eventReactive()
     • does that change how you need to refer to the output of the code?
5. Use renderDygraph from the dygraphs package to render your dygraph plot
6. Re-render the document frequently as you work to catch errors early.
   • to make this easy, I've commented out each R object. Errors will come only from the objects you have worked on (uncomment as you go).

---

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

# SELECT A NAME
# SELECT A SEX ("M" or "F")

# names <- get_babyname(name, sex)

# Create time series and forecast
#nbirths <- ts(#names$n, start = 1880)
#mod <- auto.arima(#nbirths)
#pred <- forecast(#mod, h = 12) # 12 for 2025

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

# title <- paste0("Number of children named ", #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 #trim_whitespace(#pred$method) model to predict the number of children that will be given the name r #name in 2025.

# 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.