UK income and tax data. Copy of https://twitter.com/DLeonhardt/status/1180990667700477952 but for the UKData from https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax

UkIncome.r

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
money <- tibble::rowid_to_column(money, "ID")

#now the after tax part of that spreadsheet
tax <- read.csv("NS_Table_3_1a_1617.csv",skip = 106, header = F, nrows = 100, dec=",")
#, nrows = 1,
tax <- tax[-1,]
tax<-select (tax,-c(V2,V3,V13))
tax <- tibble::rowid_to_column(tax, "ID")
head(tax,20)

#Graph raw earnings
library(ggplot2)
# Basic line plot with points
p<-ggplot(data=money, aes(x=X2016.17, y=ID)) +#, group=1 X1999.00
  geom_line()+
  geom_point()


p<-p + ggtitle("UK Earnings 2017") +
  xlab("Earnings 1000's") + ylab("Percentile")

#p<-p + scale_y_discrete(breaks=c(0,10,20,30,40,50,60,70,80,90,100),limits=c("0","10","20","30","40","50","60","70","80","90","100")
#p<-p +  scale_y_discrete(name="Percentiles", limits=c(0, 100))  
p<-p+ theme_bw()
p<-p+theme(plot.title = element_text(hjust = 0.5))
ggsave("earnings.png")

#Graph raw afeter tax
library(ggplot2)
# Basic line plot with points
p<-ggplot(data=tax, aes(x=V21, y=ID)) +#, group=1 X1999.00
  geom_line()+
  geom_point()


p<-p + ggtitle("UK after tax Earnings 2017") +
  xlab("Earnings 1000's") + ylab("Percentile")

#p<-p + scale_y_discrete(breaks=c(0,10,20,30,40,50,60,70,80,90,100),limits=c("0","10","20","30","40","50","60","70","80","90","100")
#p<-p +  scale_y_discrete(name="Percentiles", limits=c(0, 100))  
p<-p+ theme_bw()
p<-p+theme(plot.title = element_text(hjust = 0.5))
ggsave("afterTax.png")

#make a new dataframe to do NYT calculations
after_tax <- select(money,X2016.17, ID)
after_tax["left"] <-tax$V21

after_tax["after"] <-100-(((tax$V21)/money$X2016.17)*100)


#Now graph using analysed data
p<-ggplot(data=after_tax, aes(y=after, x=ID)) +#, group=1 X1999.00
  geom_line()+
  geom_point()


p<-p + ggtitle("UK Income Tax Rate") +
  xlab("<- Less income               Income Percentile               More income ->") + ylab("Percentage Tax")

#p<-p + scale_y_discrete(breaks=c(0,10,20,30,40,50,60,70,80,90,100),limits=c("0","10","20","30","40","50","60","70","80","90","100")
#p<-p +  scale_y_discrete(name="Percentiles", limits=c(0, 100))  
p<-p+ theme_bw()
p<-p+theme(plot.title = element_text(hjust = 0.5))
#<-p + annotate("text", x = 2:5, y = 0, label = "<- Less income")
ggsave("taxrate.png")

#comparison not in percentiles

p<-ggplot(data=after_tax, aes(y=left, x=X2016.17)) +#, group=1 X1999.00
  geom_line()+
  geom_point()


p<-p + ggtitle("UK Income Before and After Tax") +
  xlab("Income") + ylab("After Tax")

#p<-p + scale_y_discrete(breaks=c(0,10,20,30,40,50,60,70,80,90,100),limits=c("0","10","20","30","40","50","60","70","80","90","100")
#p<-p +  scale_y_discrete(name="Percentiles", limits=c(0, 100))  
p<-p+ theme_bw()
p<-p+theme(plot.title = element_text(hjust = 0.5))
#<-p + annotate("text", x = 2:5, y = 0, label = "<- Less income")
ggsave("taxEarned.png")