conjugateprior

# Download data from here: https://taz.de/Proteste-gegen-Rechtsextremismus/!5988174/

library(tidyverse)

# first version with police data
fl <- "~/Downloads/data-Q5MHN.csv"
dd <- read_csv(fl) |>
  mutate(Ort = trimws(Ort),
         date = as.Date(Datum, format = "%d.%m.%Y"),
         pcount = `Teilnehmende laut Polizei`,

conjugateprior

library(ggplot2)

# the data
dd <- data.frame(x = c(0,3,6,9,10,14), 
                 y = c(7,2,8,6,12,7))

# OLS model

mod <- lm(y ~ x, dd)

conjugateprior

library(sf)
library(rmapshaper)

# Grab shapefiles, here from http://www.diva-gis.org/gdata
shps2 <- read_sf("GBR_adm/GBR_adm2.shp")
shps2s <- rmapshaper::ms_simplify(shps2)
outline <- st_union(shps2s)
outline_flat <- st_transform(outline, crs = 27700)
counties <- st_transform(shps2s, crs = 27700)
outline_5km_in <- st_buffer(outline_flat, dist = -5000) # 5km inland

conjugateprior

# Data from https://yg-infographics-data.s3-eu-west-1.amazonaws.com/ZAfbtHgj42wx4reHnaMtbBamoKdMxkFMpz4gnWMjiZCUAxDX66MsCB38K/2019_data/MRP_Tables_2019_Election_Public_Release.pdf

library(tidyverse)
theme_set(theme_minimal()) # no mouldy waffle

age <- "Age Con Lab LD Brexit Green SNP PC Other Turnout Fraction
18-25 22 53 13 2 5 4 0 1 52 8
25-30 26 48 13 2 5 4 0 1 52 6
30-35 30 43 15 2 4 4 0 1 57 7
35-40 36 38 15 2 4 3 0 1 63 7

conjugateprior

% Syllabus example by Will Lowe, (wlowe@princeton.edu) 2018 
% for the course POL 245 'Visualizing Data' (Part of the Freshman Scholars Institute)
%
% Compile it with xelatex (after you've inserted your own fonts)

\documentclass[11pt,letterpaper]{article}
\usepackage[margin=1in]{geometry}

\usepackage{marginnote}              
\usepackage[dvipsnames]{xcolor}

conjugateprior

# install.packages(c("jsonlite", "dplyr"))
library(jsonlite)
library(dplyr)

json <- "https://www.sutterhealth.org/for-patients/chargemaster-2019.json"
cmr <- read_json(json)
gg <- bind_rows(lapply(cmr$CDM, as.data.frame))

head(gg) # ok, now what...

conjugateprior

library(tidyverse)

page <- "https://www.newsobserver.com/news/politics-government/election/article100235752.html"
decreasers <- read_html(page) %>%  # grab and parse the webpage
  html_table %>%
  pluck(1) %>%  # first (and only) table
  mutate(h2016 = parse_number(`2016 Hours`), # parse ugly number formats
         h2012 = parse_number(`2012 Hours`),
         County = toupper(County)) %>%
  filter((h2016 - h2012) < 0) %>%

conjugateprior

```{r}
library(MonetDBLite)
library(RSQLite)
library(DBI)
library(dplyr)

library(microbenchmark)
library(ggplot2)
```

conjugateprior

# convenience function for logit models
invlogit <- function(x){ 1 / (1 + exp(-x)) }

# some data cribbed from the R help pages
dd <- data.frame(ldose = rep(0:5, 2),
                 dead = c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16),
                 sex = rep(c("M", "F"), each = 6))
# 30 trials in each row of which 'dead' beasties died

# fit a model

conjugateprior

fourfold_box <- function(bl_br_tl_tr,                        # values for bottom left, top right, etc.
                         cols = rep(rgb(0.8, 0.8, 0.8), 4),  # default to grey fill
                         labels = NULL,                      # use these instead of bl, br, tl, and tr values
                         label.cols = rep("black", 4),       # value colors
                         quadrant.labels = NULL,             # default: don't label quadrants
                         quadrant.label.col = NULL,          # default: "black"
                         small.min = 5,                      # center label in box unless it's smaller than this
                         small.label.mult = 1.5,             # label this multiple of box side length further out
                         axis.lwd = 1,                       # crossbar thickness
                         axis.col = NULL,                    # default: "black"