David Curran cavedave

## ElectionBar

Cand <- c('Donnelly\n SF','Varadkar\n FG','Chambers\n FF','Coppinger\n SOL-PBP','O’Gorman\n Grn','Burton\n Lab')
per <- c(29, 18, 16,11,9,5)
part<-c("#CC0000","#660000","#66BB66","#326760","#99CC33","#009FF3")
df <- data.frame(Cand, per,part)

library(ggplot2)
# Basic barplot
p<-ggplot(data=df, aes(x=Cand, y=per, fill =part)) +
  geom_bar(stat="identity", show.legend = FALSE)+scale_fill_manual(values=c("#CC0000","#660000","#66BB66","#326760","#99CC33","#009FF3"))+theme_minimal()+theme(plot.title = element_text(hjust = 0.5))

## dail.r
require(data.table)
require(tidyverse)
require(ggparliament)

irl<-read.csv("ireland.csv",header=1)
#colour column needs to be chr not factor
irl<-irl %>% mutate_if(is.factor, as.character)

irl_horseshoe <- parliament_data(election_data = irl,
                                       party_seats = irl$seats,

## CountriesGates.csv

          
            Country
            Continent
            random
            randomC

            
              Algeria
              AFRICA
              60.6167300016224
              61

            
              Angola
              AFRICA
              71.5212095913241
              72

            
              Benin
              AFRICA
              51.7624535957612
              52

            
              Botswana
              AFRICA
              15.7730987774009
              16

            
              Burkina
              AFRICA
              43.4650351116097
              44

            
              Burundi
              AFRICA
              65.5623808542023
              66

            
              Cameroon
              AFRICA
              50.6499306806902
              51

            
              Cape Verde
              AFRICA
              13.4829147803843
              14

            
              Central African Republic
              AFRICA
              17.8374229865049
              18

## australiaheatmap.r
library(dplyr)
library(ggplot2)
library(tidyr)
library(viridis)
library(scales)
library(ggthemes)

aus<-read.csv("latest.txt",sep=",",header=FALSE)

names(aus)[1] <- "time"

## genreloud.rmd
---
title: "Loudness by Genre"
output: html_notebook
---

An analysis of music by Genre to see if loudness varies

It was believed that online streaming platform have reduced loudness. But does this have the same effect accross all genres of music?


## percentiles.r
data from https://www.federalreserve.gov/releases/z1/dataviz/dfa/distribute/table/#range:1989.3,2019.2;quarter:119;series:Net%20worth;demographic:networth;population:all;units:shares

```{r}

wealth<-read_csv("dfa-networth-shares.csv")
head(wealth)
```

```{r}

## zipcodes.r
#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=";")

## Martin-Quinn.r

```{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)

## CenterUKVoting.r
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)

## ukelection.Rmd
---
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}

	Cand <- c('Donnelly\n SF','Varadkar\n FG','Chambers\n FF','Coppinger\n SOL-PBP','O’Gorman\n Grn','Burton\n Lab')
	per <- c(29, 18, 16,11,9,5)
	part<-c("#CC0000","#660000","#66BB66","#326760","#99CC33","#009FF3")
	df <- data.frame(Cand, per,part)

	library(ggplot2)
	# Basic barplot
	p<-ggplot(data=df, aes(x=Cand, y=per, fill =part)) +
	geom_bar(stat="identity", show.legend = FALSE)+scale_fill_manual(values=c("#CC0000","#660000","#66BB66","#326760","#99CC33","#009FF3"))+theme_minimal()+theme(plot.title = element_text(hjust = 0.5))
	require(data.table)
	require(tidyverse)
	require(ggparliament)

	irl<-read.csv("ireland.csv",header=1)
	#colour column needs to be chr not factor
	irl<-irl %>% mutate_if(is.factor, as.character)

	irl_horseshoe <- parliament_data(election_data = irl,
	party_seats = irl$seats,
Country	Continent	random	randomC
Algeria	AFRICA	60.6167300016224	61
Angola	AFRICA	71.5212095913241	72
Benin	AFRICA	51.7624535957612	52
Botswana	AFRICA	15.7730987774009	16
Burkina	AFRICA	43.4650351116097	44
Burundi	AFRICA	65.5623808542023	66
Cameroon	AFRICA	50.6499306806902	51
Cape Verde	AFRICA	13.4829147803843	14
Central African Republic	AFRICA	17.8374229865049	18
	library(dplyr)
	library(ggplot2)
	library(tidyr)
	library(viridis)
	library(scales)
	library(ggthemes)

	aus<-read.csv("latest.txt",sep=",",header=FALSE)

	names(aus)[1] <- "time"
	---
	title: "Loudness by Genre"
	output: html_notebook
	---

	An analysis of music by Genre to see if loudness varies

	It was believed that online streaming platform have reduced loudness. But does this have the same effect accross all genres of music?
	data from https://www.federalreserve.gov/releases/z1/dataviz/dfa/distribute/table/#range:1989.3,2019.2;quarter:119;series:Net%20worth;demographic:networth;population:all;units:shares

	```{r}

	wealth<-read_csv("dfa-networth-shares.csv")
	head(wealth)
	```

	```{r}
	#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=";")

	```{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)
	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)
	---
	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}