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The story for this visualisation is taken from the website theconversation.com. The story says about “Vital Signs: economics can’t explain why unemployment and inflation are both low”.

Data

library(readr)
library(zoo)
library(dplyr)
library(plotly)
library(ggplot2)
Inflation_Employment <- read_csv("~/Documents/Inflation_Employment.csv", 
    col_types = cols(Date = col_date(format = "%m/%Y")))
PartTimeEmployment <- read_csv("~/Documents/PartTimeEmployment.csv", 
    col_types = cols(Date = col_date(format = "%m/%Y")))

Plot one

The Inflation Data and the Underemployment Data is obtained from the Australian Bureau of Statistics. This plot confirms the fact mentioned in the artilcle about the relation ship between Unemployment and Inflation. For many years the trend was like, when unemployment was high, wages increased slowly; when unemployment was low, wages rose rather quickly and hence incresed inflation. It is according to basic economic theory, that when workers are so scarce one has to pay them more to work, so inflation goes up.

#p1 <- ggplot(data=Inflation_Employment,aes(x=Date,y=Value, group=Group,colour=Group)) + geom_line() +  xlab("Date") + ylab("Rate") +
    #ggtitle("Employment Rate and Inflation rate from June 1972 till June 2017")
#p1
#ggplotly(p1)


p1 <- plot_ly(data = Inflation_Employment, x = ~Date, y = ~Value, color = ~Group, type = 'scatter', mode = 'lines', hoverinfo = "text",
              text = paste("<b>Date</b> = ", as.yearmon(Inflation_Employment$Date,"%y%m"), 
                           "<br><b>Rate</b> = ", Inflation_Employment$Value,
                           "<br><b>Type</b> = ", Inflation_Employment$Group)) %>% 

  layout(title ="Employment Rate and Inflation rate from June 1972 till June 2017",
         yaxis = list(zeroline = FALSE, title = "Rate"),
         xaxis = list(zeroline = FALSE, title = "Date",rangeslider = list(type = "Date")))

p1
#api_create(p1, filename = "Employment Rate and Inflation rate from June 1972 till June 2017")

But according to the recent trend it is seen from the data that both inflation and unemployment are so low. The reason for this may be that we are not measuring things correctly. For example, there is a lot of “underemployment” which is not counted in umemployment. Some people aren’t working all the hours they want. This is exlpored in the next graph.

Plot two

There has been a dramatic shift in the proportion of those underemployed in the last few years.This is evident from the following graph.

q1 <- plot_ly(data = PartTimeEmployment, x = ~Date, y = ~Employed_Part_Time_to_population_Ratio, type = 'scatter', mode = 'lines', hoverinfo = "text",
              text = paste("<b>Date</b> = ",as.yearmon(PartTimeEmployment$Date,"%y%m")  , 
                           "<br><b>Part-time Employment Rate</b> = ", PartTimeEmployment$Employed_Part_Time_to_population_Ratio)) %>% 

  layout(title ="Part-Time Employment Rate from June 1972 till June 2017",
         yaxis = list(zeroline = FALSE, title = "Ratio of People Employed Part Time to population"),
         xaxis = list(zeroline = FALSE, title = "Date",rangeslider = list(type = "Date")))

q1
#api_create(q1, filename = "Part-Time Employment Rate from June 1972 till June 2017")

In August 2017, the ratio of people employed part time to population has increased to 19.42. So the increase in people with part time jobs may have an effect in current statistics of low inflation and low unemployment.