Capital in the 21st Century: Chapter 3

Data provenance

The data were downloaded as Excel files from: http://piketty.pse.ens.fr/en/capital21c2.

Loading relevant libraries and data

This document depends on the xlsx, reshape2, scales, and ggplot2 packages.

setwd(“/Users/pacha/Dropbox/R/capitalIn21stCenturyinR/Chapter3”)

library(XLConnect)
library(WriteXLS)

excel.file <- file.path(“../_data/Chapter3TablesFigures.xlsx”)

library(ggplot2)
library(xlsx)
library(reshape2)
### scales for percent of the axes
library(scales)

Figures 3.1 and 3.2

These figures are based on Table TS3.1 and TS3.2. Here we read
data from the excel file and name the data series.

## Table TS3.1
tab = read.xlsx("../_data/Chapter3TablesFigures.xlsx",sheetName="TS3.1",rowIndex=9:19,colIndex=1:10,header=FALSE)
names(tab) =  c("year","nat","nat_land", "nat_house", "nat_dom", "nat_foreign", "public", "public_ass", "public_debt", "private")

## Table TS3.1
tab32 = read.xlsx("../_data/Chapter3TablesFigures.xlsx",sheetName="TS3.2",rowIndex=9:19,colIndex=1:10,header=FALSE)
names(tab32) =  c("year","nat","nat_land", "nat_house", "nat_dom", "nat_foreign", "public", "public_ass", "public_debt", "private")

The caption of the table TS3.1 lists the sources for this table
as

Source: Piketty-Zucman 2013, UK.xls, links frozen on 01-24-2013.

and
> Source: Piketty-Zucman 2013, France.xls, links frozen on 01-24-2013

for table TS3.2
Let’s make a function that will plot our data:

run31_32 = function(tab, num, country){
  fdat <- tab[,c("year","nat_land", "nat_house", "nat_dom", "nat_foreign")]
  names(fdat) <- c("year","Agricultural land","Housing", "Other Domestic Capital", "Net foreign capital")
  
  # Make the data long for stacking
  fdat <- melt(fdat, id.var="year")
  names(fdat) <- c("year","capital_type","percent")
  
  qplot(year, percent, data=fdat, geom="ribbon", position = "stack", 
        fill = capital_type, xlab= "Year", ylab= "Value of National Capital (% National Income)") + scale_y_continuous(labels = percent, limits = c(0, 8)) + 
    ggtitle(paste0("Figure ", num, ". Capital in ", country, ", 1700-2010")) + theme(legend.position="bottom", legend.background = element_rect(fill="transparent")) + scale_x_continuous(breaks = seq(1700, 2020, by=20))
}

Now we make Figure F3.1:

run31_32(tab, num = "3.1", "Britain")

## Warning: Removed 3 rows containing missing values (position_stack).

## ymax not defined: adjusting position using y instead

plot of chunk figF3_1

Figure F3.2:

run31_32(tab32, num = "3.2", "France")

## ymax not defined: adjusting position using y instead

plot of chunk figF3_2

In Figure 3.1 and 3.2 of Piketty, the axes are equally spaced, but correspond to different periods of time such as the first two tickmarks represent 1700-1750, a period of 50 years, whereas the last 2 tickmarks represent 1990-2010, spanning 30 years, and the ones before span 1970-1990, only 20 years.

Figures 3.3 and 3.4

Let’s make another function to create the plot for 3.3 and 3.4

run_33_34 = function(tab, num, country){
fdat <- tab[, c("year","public_ass", "public_debt")]
names(fdat) <- c("year", "Public Assets", "Public Debt")
fdat <- melt(fdat, id.var="year")
names(fdat) <- c("year", "Capital", "percent")
 
qplot(year, percent, data=fdat, geom=c("line", "point"), colour = Capital, xlab= "Year", ylab= "Public assets and debt (% national income)") + scale_y_continuous(labels = percent, limits = c(0, 2.5)) + 
  ggtitle(paste0("Figure ", num, ". Public wealth in ", country, ", 1700-2010")) + theme(legend.position=c(0.5, .8), legend.background = element_rect(fill="transparent")) + scale_x_continuous(breaks = seq(1700, 2020, by=20))
}

Figure 3.3

run_33_34(tab, "3.3", "Britain")

plot of chunk fig3_3

Figure 3.4

run_33_34(tab32, "3.4", "France")

plot of chunk fig3_4

Figures 3.5 and 3.6

Let’s make another function to create the plot for 3.5 and 3.6

run_35_36 = function(tab, num, country){
fdat <- tab[, c("year","nat", "public", "private")]
names(fdat) <- c("year", "National Capital (Public + Private)", "Public", "Private")
fdat <- melt(fdat, id.var="year")
names(fdat) <- c("year", "Capital_Type", "percent")
 
qplot(year, percent, data=fdat, geom=c("line", "point"), colour = Capital_Type, xlab= "Year", ylab= "National, private and public capital (% national income)") + scale_y_continuous(labels = percent, limits = c(-2, 9)) + 
  ggtitle(paste0("Figure ", num, ". Private and public capital in ", country, ", 1700-2010")) + theme(legend.position=c(0.5, .5), legend.background = element_rect(fill="transparent")) + scale_x_continuous(breaks = seq(1700, 2020, by=20))
}

Figure 3.5

run_35_36(tab, "3.5", "Britain")

plot of chunk fig3_5

Figure 3.6

run_35_36(tab32, "3.6", "France")

plot of chunk fig3_6