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The Simon–Ehrlich wager was a 1980 scientific wager between business professor Julian L. Simon and biologist Paul Ehrlich, betting on a mutually agreed-upon measure of resource scarcity over the decade leading up to 1990
https://en.wikipedia.org/wiki/Simon%E2%80%93Ehrlich_wager

Roughly it was a bet where Simon thought things would get better as people invented and organised. Eldrich thought we would run out of stuff and overpopulation would result in starvation of hundreds of millions.

Simon won the bet as these metals were cheaper ten years later. But I wondered how ofter Simon would be rigth if the bet happened at different times.
Ten years later metals would be cheaper 55% of the time in this dataset.

Data from https://www.usgs.gov/centers/nmic/historical-statistics-mineral-and-material-commodities-united-states

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library(tidyverse)
library(lubridate)
library(RCurl)
library(reshape2)
#URL <- "https://www.metoffice.gov.uk/hadobs/hadcet/cetmaxdly1878on_urbadj4.dat"

cet2 <- read.table("cetmaxdly1878on_urbadj4.dat.txt", sep = "", header = FALSE, 
                  fill = TRUE)#),na.string = c(-99.99, -99.9, -999))
colnames(cet2) <- c("year","day","Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")

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breaks <- read.csv("breaks.csv", stringsAsFactors=FALSE)
breaks<-select (breaks,-c(X,X.1,X.2))
breaks<-na.omit(breaks)
names(breaks)[names(breaks) == "X."] <- "Centuries"

library(ggplot2)
# Basic scatter plot
g<-ggplot(breaks, aes(x=Year, y=Centuries)) +
    geom_point(shape=1) +    # Use hollow circles
    geom_smooth(method=lm) +

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historic<-read.csv("historic.csv", skip=10)
library(dplyr)

 
# Drop the columns of the dataframe
historic<-select (historic,-c(X.1,X.2,X.3,X.4,X.5,X.6,X.7,X.8,X.9,X.10,X.11))
historic <- rename(historic, number = Level..numbers.of.unemployed.people.) #For renaming dataframe column
historic <- rename(historic, rate = Rate....) #For renaming dataframe column
#start is nas
historic2<-slice(historic, 499:n())

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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))

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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,

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library(dplyr)
library(ggplot2)
library(tidyr)
library(viridis)
library(scales)
library(ggthemes)

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

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

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---
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?

cavedave

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}