REAT (version 3.0.2)

Automotive: Automotive industry data

Description

Top 20 automotive industry companies, including their manufacturing quantity and turnovers (Table from wikipedia)

Usage

data("Automotive")

Format

A data frame with 20 observations on the following 8 variables.

Rank

Rank of the company

Company

Name of the company (German)

Country

Origin county of the company (German)

Quantity2014

Quantity of produced vehicles in 2014

Quantity2014_car

Quantity of produced cars in 2014

Turnover2008

Annual turnover 2008 (in billion dollars)

Turnover2012

Annual turnover 2012 (in billion dollars)

Turnover2013

Annual turnover 2013 (in billion dollars)

References

Wikipedia (2018): “Automobilindustrie --- Wikipedia, Die freie Enzyklopaedie”. https://de.wikipedia.org/wiki/Automobilindustrie (accessed October 14, 2018).

Examples

# NOT RUN {
# Market concentration in automotive industry

data(Automotive)

gini(Automotive$Turnover2008, lsize=1, lc=TRUE, le.col = "black", 
lc.col = "orange", lcx = "Shares of companies", lcy = "Shares of turnover / cars", 
lctitle = "Automotive industry: market concentration", 
lcg = TRUE, lcgn = TRUE, lcg.caption = "Turnover 2008:", lcg.lab.x = 0, lcg.lab.y = 1)
# Gini coefficient and Lorenz curve for turnover 2008

gini(Automotive$Turnover2013, lsize=1, lc = TRUE, add.lc = TRUE, lc.col = "red", 
lcg = TRUE, lcgn = TRUE, lcg.caption = "Turnover 2013:", lcg.lab.x = 0, lcg.lab.y = 0.85)
# Adding Gini coefficient and Lorenz curve for turnover 2013

gini(Automotive$Quantity2014_car, lsize=1, lc = TRUE, add.lc = TRUE, lc.col = "blue", 
lcg = TRUE, lcgn = TRUE, lcg.caption = "Cars 2014:", lcg.lab.x = 0, lcg.lab.y = 0.7)
# Adding Gini coefficient and Lorenz curve for cars 2014
# }