The data - chronological sequences (time series) of the price per bushel of wheat and the weekly wage of a skilled laborer in England between 1565 and 1821 - were compiled by William Playfair and published in 1821 in his book Letter on our Agricultural Distresses, Their Causes and Remedies.. This dataset is available “directly” to R users after installing the HistData package; it is also available in csv format from the Rdatasets site on GitHub. We’re going to use the latter. The full URL of the data is: https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv. As it starts with https and not http, we can’t use the read.csv function directly, we need to download the data to our disk first. We can do this with the download.file function, specifying "wget" as the value of the formal method parameter:

download.file(url="https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv",
              destfile="Wheat.csv",
              method="wget")

We can now use read.csv to load the data into our workspace:

wheat = read.csv("Wheat.csv")

We then quickly check that the download followed by the reading of the data has been carried out correctly by looking at the variable names in the data.frame we’ve just created:

names(wheat)
## [1] "rownames" "Year"     "Wheat"    "Wages"

We can also examine the top of the table with :

head(wheat)
##   rownames Year Wheat Wages
## 1        1 1565  41.0  5.00
## 2        2 1570  45.0  5.05
## 3        3 1575  42.0  5.08
## 4        4 1580  49.0  5.12
## 5        5 1585  41.5  5.15
## 6        6 1590  47.0  5.25

We see that the first column (variable X) is useless and we delete it with :

wheat = wheat[,names(wheat)[-1]]
names(wheat) = c("Year", "Wheat", "Salary")

Finally, we look at the bottom of the table:

tail(wheat)
##    Year Wheat Salary
## 48 1800    79   28.5
## 49 1805    81   29.5
## 50 1810    99   30.0
## 51 1815    78     NA
## 52 1820    54     NA
## 53 1821    54     NA

We can “finish” this example by reproducing the graph showing the co-evolution of wheat prices and wages, adapting (and correcting!) the example in the documentation for this dataset in the HistDat package:

with(wheat, 
     {
       known_salary = !is.na(Salary)
       plot(Year, Wheat, type="s", ylim=c(0,105), 
            ylab="Price of a quarter of a wheat bushel (shillings)")
       polygon(c(Year[known_salary],rev(Year[known_salary])), 
               c(Salary[known_salary],rep(0,sum(known_salary))), 
               col="lightskyblue", border=NA)
    lines(Year[known_salary], 
          Salary[known_salary], lwd=3, col="red")
    text(1625,10, "Weekly salary of a skilled worker", 
         cex=0.8, srt=3, col="red")
    })

We can compare our figure to the original one: