jimcrozier

library(tidyverse)
library(tidycensus)
#install your acs api key 
#census_api_key(YOUR_KEY, install=T)
pops <- get_acs(geography = "county", 
                variables = c("B19013_001",
                              "B01002_001",
                              "B01003_001","B01001_002", #sex, male
                              "B01001A_001", #swhite
                              "B15003_001","B15003_017",#education, HS

jimcrozier

CREATE TEMP FUNCTION
  loess(x ARRAY<FLOAT64>,
    y ARRAY<FLOAT64>)
  RETURNS STRUCT<x ARRAY<FLOAT64>,
  y ARRAY<FLOAT64>,
  pred ARRAY<FLOAT64>>
  LANGUAGE js AS """
  //adapted from https://gist.github.com/avibryant/1151823
//adapted from the LoessInterpolator in org.apache.commons.math
//turtles on turles. Matches R pretty damn close:

jimcrozier

#if needed, uncomment out the follow lines and install R packages:
# install.packages("dplyr")
# install.packages("poliscidata")
# install.packages("covidregionaldata")
library(dplyr)
library(poliscidata)
library(covidregionaldata)
library(plotly)

#get regional covid stats

jimcrozier

  library(dplyr)
  library(ggplot)
  library(plotly)
  #fake data 
  dat = data.frame(x = c(1,2,3), rev = c(10,20,30), upper = c(15,25,35), lower = c(5,15,25))
  
  #create a plot
  p1 = dat %>% 
    ggplot(aes(x = x,
               y=rev,ymin=lower,ymax=upper)) +

jimcrozier

<div id="app">
  <pre>
  Smoothing  <input type="range" min="0" max="0.25" step="0.01" v-model="options.line.smoothing"> <span v-html="options.line.smoothing"></span><br>
  Flattening <input type="range" min="0" max="1" step="0.01" v-model="options.line.flattening"> <span v-html="options.line.flattening"></span></pre>
  <div class="container" ref="container">
    <svg-chart :datasets="datasets" :options="options" :svg="svg"></svg-chart>
  </div>
</div>

<div>