cavedave

#load the libraries
library(ggplot2)
library(grid)
library(dplyr)

#load the data
#https://public.opendatasoft.com/explore/dataset/us-zip-code-latitude-and-longitude/export/ 
zips <-
    read.csv("us-zip-code-latitude-and-longitude.csv",
             header = TRUE, stringsAsFactors = TRUE,sep=";")

cavedave

```{r}
library(dplyr)
library(readr)
library(ggplot2)  # devtools::install_github("hadley/ggplot2")
#library(hrbrmisc) # devtools::install_github("hrbrmstr/hrbrmisc")
library(grid)
library(scales)

library(grid)

cavedave

Now start with maps and locations

See if i can get the center of constituencies
data from https://geoportal.statistics.gov.uk/datasets/westminster-parliamentary-constituencies-december-2017-super-generalised-clipped-boundaries-in-the-uk/data

```{r}

locat<-read_csv("Westminster_Parliamentary_Constituencies_December_2017_Super_Generalised_Clipped_Boundaries_in_the_UK.csv")#, sheet = 2, skip=6 , header=TRUE
head(locat)

cavedave

---
title: "uk election"
output: html_notebook
---
Inspired by this great graph by Alasdair Rae
https://twitter.com/undertheraedar/status/1190192038274310144 i want to make an election visualisation

data from https://commonslibrary.parliament.uk/parliament-and-elections/elections-elections/constituency-data-election-results/

```{r}

cavedave

cavedave

library(tidyverse)
money <- read.csv("NS_Table_3_1a_1617.csv",skip = 5, header = T, nrows = 100, dec=",")
#, nrows = 1,
money <- money[-1,]
money<-select (money,-c(X,X.1,X.2,X.3,X.4,X.5,X.6,X.7,X.8,X2008.09..a.))
#money<-as.numeric(sub(",", ".", money))
money$Percentile.point <- as.integer(money$Percentile.point) 
#money <- money[order(money$Percentile.point), ]
head(money)
#make an id row

cavedave

elect <- read.csv("ElectionsUk.csv", header = TRUE)

#turn this format into a date
library(lubridate)
elect$date2 <- dmy(elect$date)

p <-  ggplot(elect,aes(x = date2, y = Millions.of.Votes)) +#, colour = elect$Type
     geom_point(aes(colour = Type),
                    shape=21, 
                    fill= "White",

cavedave

library(eurostat)      # eurostat data
library(rnaturalearth) # worldwide map data
library(tidyverse)     # tidy data transformation
library(lubridate)     # date and time support
library(sf)            # simple features GIS
library(RColorBrewer)

euro_pop <-
  get_eurostat('demo_r_pjanaggr3', stringsAsFactors = FALSE) %>%

cavedave

#The start of this file is from https://www.reddit.com/r/CodingHelp/comments/9jguwj/help_with_python_data_analsyis/
#Specifically David Kopp http://www.cis.umassd.edu/~dkoop/ where he ingets the noaa data
import os
from urllib.request import urlretrieve

# download the data if we don't have it locally
url = "https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2017-050118.txt"
local_fname = "hurdat2.txt"
if not os.path.exists("hurdat2.txt"):
    urlretrieve(url, local_fname)

cavedave

eur<-read_csv('GEOSTAT_grid_POP_1K_2011_V2_0_1.csv')

#two ways to extract east and north
eur$North <-str_match(eur$GRD_ID, "1kmN(\\d\\d\\d\\d)E(\\d\\d\\d\\d)")[,2] 
eur$East <-str_match(eur$GRD_ID, "1kmN(\\d\\d\\d\\d)E(\\d\\d\\d\\d)")[,3] 
eur<-eur%>%
mutate(
    lat = as.numeric(gsub('.*N([0-9]+)[EW].*', '\\1', GRD_ID))/100,
    lng = as.numeric(gsub('.*[EW]([0-9]+)', '\\1', GRD_ID)) * ifelse(gsub('.*([EW]).*', '\\1', GRD_ID) == 'W', -1, 1) / 100
  )