jamesdunham

library(rvest)
library(data.table)
library(lubridate)

doc = read_html("http://history.house.gov/Institution/Session-Dates/All/")

sessions = doc %>% 
  html_nodes(xpath = "//div[contains(@class, 'manual-table')]/table") %>%
  html_table() %>%
  as.data.frame()

jamesdunham

plot_rhats <- function(dgirt_out) {
  # Save to disk plots of split R-hats for theta-bars (from a particular model)
  #
  # This function produces two plots showing the average R-hat in each year by
  # 1) state and 2) party, each averaging over the other. It assumes that
  # particular variable names exist in dgirt_out as the grouping variable (D_pid3), 
  # time variable (D_year), and geographic variable (D_abb).

  # From the stanfit object extract R-hats and effective sample sizes
  tb_sum <- summary(dgirt_out, par = "theta_bar", verbose = TRUE)$summary

jamesdunham

smooth_years <- function(d, varnames = NULL) {
  # Smooth variables over years using a loess interpolation
  #
  # param d: a dataframe
  # param varnames: the names of the variables to smooth over; 
  #   if NULL names will be guessed.
  #   
  # Returns a data.frame 2.5x larger than the input, 
  # containing the original values and predicted values from a
  # loess fit. Assumes that a variable called 'year' exists.