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

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

# Select name and gender
name <- "Garrett"    # Always capitalize
sex <- "M"           # or "M"
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.

knitr::kable(data.frame(
  method = trim_whitespace(pred$method), 
  predicted_2025 = round(xts::last(pred$mean))), "markdown")

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