cpsievert

library(dplyr)
library(tidyr)
library(readr)

# For other measures, see http://ghdx.healthdata.org/us-data
# TODO: do this on the county level for more interesting data, like this post - https://fivethirtyeight.com/features/mortality-black-belt/
obesity <- read_csv("http://ghdx.healthdata.org/sites/default/files/record-attached-files/IHME_USA_OBESITY_PHYSICAL_ACTIVITY_2001_2011.csv")

# reshape data so value tracks 
ob <- obesity %>%

cpsievert

library(shiny)
library(plotly)

# I keep a collection of my (plotly and non-plotly) apps here 
# https://github.com/cpsievert/apps
runGitHub("apps", "cpsievert", subdir = "shiny/apps/plotlyAg")
browseURL("https://github.com/cpsievert/apps/blob/master/shiny/apps/plotlyAg/app.R")

# The plotly package ships with some rmarkdown/shiny examples
browseURL("https://github.com/ropensci/plotly/tree/master/inst/examples")

cpsievert

{"x":{"data":[{"orientation":"v","width":[0.310344827586207,0.310344827586207,0.310344827586207,0.310344827586207,0.310344827586206,0.310344827586207,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586207,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206,0.310344827586206],"base":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"x":[0.931034482758621,1.24137931034483,1.55172413793103,1.86206896551724,2.17241379310345,2.48275862068966,2.79310344827586,3.10344827586207,3.41379310344828,3.72413793103448,4.03448275862069,4.3448275862069,4.6551724137931,4.96551724137931,5.27586206896552,5.58620689655172,5.89655172413793,6.20689655172414,6.51724137931035,6.82758620689655,7.13793103448276,7.448275862

cpsievert

library(plotly)

plot_ly(
  type = 'sankey',
  node = list(
    label =  c("a","b", "c", "d", "e", "f", "g", "h"),
    color =  c("black","black","black","black","black","black",
               "black","black"),
    pad = 15,
    thickness = 15,

cpsievert

library(grid)
(a <- arrow())
#> $angle
#> [1] 30
#> 
#> $length
#> [1] 0.25inches
#> 
#> $ends
#> [1] 2

cpsievert

library(plotly)
library(htmlwidgets)

plot_ly(x = c(1, 2), y = c(1, 2)) %>%
  onRender(
    'function(el, x) { 
      setTimeout(function() {
        var gd = document.getElementById(el.id);
        var obj = {"mode": "markers"};
        for (var i = 0; i < 10; i++) {

cpsievert

library(sf)
library(dplyr)
library(albersusa)
library(crosstalk)
library(plotly)

usa <- usa_sf("laea")

# st_centroid gets the center POINT of polygons
uscenter <- usa %>% 

cpsievert

library(tidyverse)
library(plotly)

# I'm being lazy, please don't do this
# setwd("~/Downloads/Version 2_0_1/")

d <- read_csv('GEOSTAT_grid_POP_1K_2011_V2_0_1.csv') %>%
  rbind(read_csv('JRC-GHSL_AIT-grid-POP_1K_2011.csv') %>%
          mutate(TOT_P_CON_DT = '')) %>%
  mutate(

cpsievert

library(crosstalk)
library(htmltools)

m <- SharedData$new(mtcars)

set_values <- function(w, sharedData, values) {
  script <- sprintf(
    "HTMLWidgets.addPostRenderHandler(function() { return crosstalk.group('%s').var('%s').set(%s); })",
    sharedData$groupName(), "filter", jsonlite::toJSON(values)
  )

cpsievert

library(plotly)
library(tidyr)
library(crosstalk)

m <- gather(mtcars, variable, value, -vs)
msd <- SharedData$new(m, ~variable)
gg <- ggplot(msd, aes(factor(vs), value)) + 
    geom_jitter(alpha = 0.3)

bscols(widths = c(11, 6, 6),