Assignment 2
Deconstruct, Reconstruct Web Report
Gregory Baharis (s3545698)
Original
Objective
The objective of the original data visualisation is to determine the relationship between the GDP per capita and Index of Economic Freedom per country.
The targeted audience in this visualisation is not specified by the creator and therefore it can be interpreted to by any individual who is aware or interested in GDP per Capita and the Index of Economic Freedom (IEF). Whilst GDP per Capita is a mathematical ratio (GDP/Number of people per country), the IEF is an average value of 12 different index scores based on the 4 subcategories of Rule of Law, Goverment Size, Regulatory Efficiency and Open Markets. The creator does not specify these sub-scores, however the creator does provide a link to the website/source of these values and their respective calculations.
The visualisation chosen had the following three main issues:
Visual Obscurity of Identified Data Points: Whilst in a large scatterplot it is not recommend or desirable to have labels for each data point, the data points that are identified in this graph seem to have no distinct pattern for identification. Some countries such as Cuba and Venezula are identified (probably due to having the lowest values), whereas others like Rwanda and Georgia have no easily discernible reason to the viewer as to why they are identified. Likewise for the special counties of Tax Havens and Oil/Gas Majors, not all of these points are labelled on the graph making it quite difficult for the reader to ascertain what those countries may be.
Non-intuitive Legend: The Legend is based on Oil/ Gas as a major part of the economy and if a country is a tax haven. The Reader also fails to specify if a country is neither or both of these options. The term “Major” is also not identified, therefore making it subjective to the reader as to what percentage a major part in an economy may be, likewise the conditions for a tax haven is are also not specified.
Absence of Trend: The graph does not display a trend line, failing to provide a conclusion to the research question as to what the relationship between the key variables are.
Reference
- Reddit(Data is Beautiful. (2019). [OC] How Economic Freedom relates to GDP (PPP) per capita. Retrieved September 21, 2019, from Reddit: https://www.reddit.com/r/dataisbeautiful/comments/bwbilc/oc_how_economic_freedom_relates_to_gdp_ppp_per/
Code
library(ggplot2)
library(knitr)
library(readr)
library(dplyr)
library(gridExtra)
#Import and make sure all the data is in the correct Format.
IEF <- read_csv("index2019_data.csv")
str(IEF)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 187 obs. of 34 variables:
## $ CountryID : num 1 2 3 4 5 6 7 8 9 10 ...
## $ Country Name : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ WEBNAME : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ Region : chr "Asia-Pacific" "Europe" "Middle East and North Africa" "Sub-Saharan Africa" ...
## $ World Rank : chr "152" "52" "171" "156" ...
## $ Region Rank : chr "39" "27" "14" "33" ...
## $ 2019 Score : chr "51.5" "66.5" "46.2" "50.6" ...
## $ Property Rights : chr "19.6" "54.8" "31.6" "35.9" ...
## $ Judical Effectiveness : chr "29.6" "30.6" "36.2" "26.6" ...
## $ Government Integrity : chr "25.2" "40.4" "28.9" "20.5" ...
## $ Tax Burden : chr "91.7" "86.3" "76.4" "83.9" ...
## $ Gov't Spending : chr "80.3" "73.9" "48.7" "80.7" ...
## $ Fiscal Health : chr "99.3" "80.6" "18.7" "58.2" ...
## $ Business Freedom : chr "49.2" "69.3" "61.6" "55.7" ...
## $ Labor Freedom : chr "60.4" "52.7" "49.9" "58.8" ...
## $ Monetary Freedom : chr "76.7" "81.5" "74.9" "55.4" ...
## $ Trade Freedom : chr "66.0" "87.8" "67.4" "61.2" ...
## $ Investment Freedom : chr "10" "70" "30" "30" ...
## $ Financial Freedom : chr "10" "70" "30" "40" ...
## $ Tariff Rate (%) : chr "7.0" "1.1" "8.8" "9.4" ...
## $ Income Tax Rate (%) : chr "20.0" "23.0" "35.0" "17.0" ...
## $ Corporate Tax Rate (%) : chr "20.0" "15.0" "23.0" "30.0" ...
## $ Tax Burden % of GDP : chr "5.0" "24.9" "24.5" "20.6" ...
## $ Gov't Expenditure % of GDP: chr "25.6" "29.5" "41.4" "25.3" ...
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ Population (Millions) : chr "35.5" "2.9" "41.5" "28.2" ...
## $ GDP (Billions, PPP) : chr "$69.6" "$36.0" "$632.9" "$190.3" ...
## $ GDP Growth Rate (%) : chr "2.5" "3.9" "2.0" "0.7" ...
## $ 5 Year GDP Growth Rate (%): chr "2.9" "2.5" "3.1" "2.9" ...
## $ GDP per Capita (PPP) : chr "1957.58" "12506.65" "15237.20" "6752.58" ...
## $ Unemployment (%) : chr "8.8" "13.9" "10.0" "8.2" ...
## $ Inflation (%) : chr "5.0" "2.0" "5.6" "31.7" ...
## $ FDI Inflow (Millions) : chr "53.9" "1,119.1" "1,203.0" "-2,254.5" ...
## $ Public Debt (% of GDP) : chr "7.3" "71.2" "25.8" "65.3" ...
## - attr(*, "spec")=
## .. cols(
## .. CountryID = col_double(),
## .. `Country Name` = col_character(),
## .. WEBNAME = col_character(),
## .. Region = col_character(),
## .. `World Rank` = col_character(),
## .. `Region Rank` = col_character(),
## .. `2019 Score` = col_character(),
## .. `Property Rights` = col_character(),
## .. `Judical Effectiveness` = col_character(),
## .. `Government Integrity` = col_character(),
## .. `Tax Burden` = col_character(),
## .. `Gov't Spending` = col_character(),
## .. `Fiscal Health` = col_character(),
## .. `Business Freedom` = col_character(),
## .. `Labor Freedom` = col_character(),
## .. `Monetary Freedom` = col_character(),
## .. `Trade Freedom` = col_character(),
## .. `Investment Freedom` = col_character(),
## .. `Financial Freedom` = col_character(),
## .. `Tariff Rate (%)` = col_character(),
## .. `Income Tax Rate (%)` = col_character(),
## .. `Corporate Tax Rate (%)` = col_character(),
## .. `Tax Burden % of GDP` = col_character(),
## .. `Gov't Expenditure % of GDP` = col_character(),
## .. Country = col_character(),
## .. `Population (Millions)` = col_character(),
## .. `GDP (Billions, PPP)` = col_character(),
## .. `GDP Growth Rate (%)` = col_character(),
## .. `5 Year GDP Growth Rate (%)` = col_character(),
## .. `GDP per Capita (PPP)` = col_character(),
## .. `Unemployment (%)` = col_character(),
## .. `Inflation (%)` = col_character(),
## .. `FDI Inflow (Millions)` = col_character(),
## .. `Public Debt (% of GDP)` = col_character()
## .. )head(IEF)## # A tibble: 6 x 34
## CountryID `Country Name` WEBNAME Region `World Rank` `Region Rank`
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 Afghanistan Afghan~ Asia-~ 152 39
## 2 2 Albania Albania Europe 52 27
## 3 3 Algeria Algeria Middl~ 171 14
## 4 4 Angola Angola Sub-S~ 156 33
## 5 5 Argentina Argent~ Ameri~ 148 26
## 6 6 Armenia Armenia Europe 47 24
## # ... with 28 more variables: `2019 Score` <chr>, `Property Rights` <chr>,
## # `Judical Effectiveness` <chr>, `Government Integrity` <chr>, `Tax
## # Burden` <chr>, `Gov't Spending` <chr>, `Fiscal Health` <chr>,
## # `Business Freedom` <chr>, `Labor Freedom` <chr>, `Monetary
## # Freedom` <chr>, `Trade Freedom` <chr>, `Investment Freedom` <chr>,
## # `Financial Freedom` <chr>, `Tariff Rate (%)` <chr>, `Income Tax Rate
## # (%)` <chr>, `Corporate Tax Rate (%)` <chr>, `Tax Burden % of
## # GDP` <chr>, `Gov't Expenditure % of GDP` <chr>, Country <chr>,
## # `Population (Millions)` <chr>, `GDP (Billions, PPP)` <chr>, `GDP
## # Growth Rate (%)` <chr>, `5 Year GDP Growth Rate (%)` <chr>, `GDP per
## # Capita (PPP)` <chr>, `Unemployment (%)` <chr>, `Inflation (%)` <chr>,
## # `FDI Inflow (Millions)` <chr>, `Public Debt (% of GDP)` <chr>IEF_simplified <- IEF[,c(2,4,5,7,30)]
head(IEF_simplified)## # A tibble: 6 x 5
## `Country Name` Region `World Rank` `2019 Score` `GDP per Capita ~
## <chr> <chr> <chr> <chr> <chr>
## 1 Afghanistan Asia-Pacific 152 51.5 1957.58
## 2 Albania Europe 52 66.5 12506.65
## 3 Algeria Middle East a~ 171 46.2 15237.20
## 4 Angola Sub-Saharan A~ 156 50.6 6752.58
## 5 Argentina Americas 148 52.2 20875.76
## 6 Armenia Europe 47 67.7 9455.94IEF_simplified$`2019 Score` <- as.double(IEF_simplified$`2019 Score`)
IEF_simplified$`GDP per Capita (PPP)` <- as.double(IEF_simplified$`GDP per Capita (PPP)`)
IEF_simplified$`World Rank` <- as.double(IEF_simplified$`World Rank`)
head(IEF_simplified)## # A tibble: 6 x 5
## `Country Name` Region `World Rank` `2019 Score` `GDP per Capita ~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia-Pacific 152 51.5 1958.
## 2 Albania Europe 52 66.5 12507.
## 3 Algeria Middle East a~ 171 46.2 15237.
## 4 Angola Sub-Saharan A~ 156 50.6 6753.
## 5 Argentina Americas 148 52.2 20876.
## 6 Armenia Europe 47 67.7 9456.#Note: No country is both a tax haven and an Oil/Gas Major in the data set: Therefore this outcome can be ignored.
Conditions <- read_csv("OilandGas&Tax Haven.csv")
head(Conditions)## # A tibble: 6 x 2
## `Country Name` `Oil and Gas Major/Tax Haven`
## <chr> <chr>
## 1 Afghanistan Neither
## 2 Albania Neither
## 3 Algeria Neither
## 4 Angola Neither
## 5 Argentina Neither
## 6 Armenia NeitherData <- IEF_simplified %>% left_join(Conditions, by = "Country Name")
head(Data) ## # A tibble: 6 x 6
## `Country Name` Region `World Rank` `2019 Score` `GDP per Capita~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia-~ 152 51.5 1958.
## 2 Albania Europe 52 66.5 12507.
## 3 Algeria Middl~ 171 46.2 15237.
## 4 Angola Sub-S~ 156 50.6 6753.
## 5 Argentina Ameri~ 148 52.2 20876.
## 6 Armenia Europe 47 67.7 9456.
## # ... with 1 more variable: `Oil and Gas Major/Tax Haven` <chr>#Remove NA values
Data_NA <- subset(Data,is.na(Data$`Oil and Gas Major/Tax Haven`))
Data_NA## # A tibble: 1 x 6
## `Country Name` Region `World Rank` `2019 Score` `GDP per Capita~
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 <NA> <NA> NA NA NA
## # ... with 1 more variable: `Oil and Gas Major/Tax Haven` <chr>#Only 1 NA Row: Therefore we can remove this row.
Data_fixed <- na.omit(Data)
Type <- Data_fixed$`Oil and Gas Major/Tax Haven`
theme_update(plot.title = element_text(hjust = 0.5)) #Make sure all Graph headings are in the center.
p <- ggplot(data = Data_fixed,
aes(x = (Data_fixed$`2019 Score`),
y = (Data_fixed$`GDP per Capita (PPP)`),
colour = Type) )
p + ggtitle("Economic Freedom vs. GDP Per Capita") + xlab("Index of Economic Freedom") + ylab("GDP per Capita (PPP)") + geom_point() + geom_smooth(method = "lm")
#Data is still somewhat visually messy
p + ggtitle("Economic Freedom vs. GDP Per Capita") + xlab("Index of Economic Freedom") + ylab("GDP per Capita (PPP)") + geom_point() + facet_grid(~ Data_fixed$`Oil and Gas Major/Tax Haven`) + geom_smooth(method = "lm")
#Trends are easier to identify but scale is broken now.
#We can use a log function to fix this issue
p2 <- ggplot(data = Data_fixed,
aes(x = (Data_fixed$`2019 Score`),
y = (log(Data_fixed$`GDP per Capita (PPP)`)),
colour = Type,) )
p2a <- p2 + ggtitle("Overall Summary") + xlab("") + ylab("Log of GDP per Capita (PPP)") + geom_point() + facet_grid(~ Data_fixed$`Oil and Gas Major/Tax Haven`) + geom_smooth(method = "lm")
p2a
#Switiching to logs we can see the scale is more manageable, and the relationship is more observable.
#However,unlike the original plot no specific data points are identified, in addition it is going against the normal economic data display of GDP per Capita by transforming it into a log. Therefore additional plots need to be constructed.
#We can specify the specific Oil and Gas Majors and Tax haven's seperatly in individual scatterplots,then superimpose the labels over smaller number of observations.
Oil_and_Gas_Majors <- as.data.frame(Data_fixed %>% filter(`Oil and Gas Major/Tax Haven` == 'Oil and Gas Major'))
Oil_and_Gas_Majors## Country Name Region World Rank 2019 Score
## 1 Canada Americas 8 77.7
## 2 China Asia-Pacific 100 58.4
## 3 India Asia-Pacific 129 55.2
## 4 Indonesia Asia-Pacific 56 65.8
## 5 Iran Middle East and North Africa 155 51.1
## 6 Kuwait Middle East and North Africa 90 60.8
## 7 Norway Europe 26 73.0
## 8 Qatar Middle East and North Africa 28 72.6
## 9 Russia Europe 98 58.9
## 10 Saudi Arabia Middle East and North Africa 91 60.7
## 11 Turkmenistan Asia-Pacific 164 48.4
## 12 United Arab Emirates Middle East and North Africa 9 77.6
## 13 United States Americas 12 76.8
## GDP per Capita (PPP) Oil and Gas Major/Tax Haven
## 1 48265.25 Oil and Gas Major
## 2 16660.27 Oil and Gas Major
## 3 7182.80 Oil and Gas Major
## 4 12377.49 Oil and Gas Major
## 5 20199.59 Oil and Gas Major
## 6 66162.89 Oil and Gas Major
## 7 71830.88 Oil and Gas Major
## 8 124529.05 Oil and Gas Major
## 9 27834.09 Oil and Gas Major
## 10 54777.38 Oil and Gas Major
## 11 18125.68 Oil and Gas Major
## 12 67740.86 Oil and Gas Major
## 13 59501.11 Oil and Gas Majorp3 <- ggplot(data = Oil_and_Gas_Majors,
aes(x = (Oil_and_Gas_Majors$`2019 Score`),
y = (Oil_and_Gas_Majors$`GDP per Capita (PPP)`)
) )
p3a <- p3 + ggtitle("Oil and Gas Majors Summary") + xlab("") + ylab("") + geom_point() + geom_text(aes(label=Oil_and_Gas_Majors$`Country Name`), size=3) + geom_smooth(method = "lm")
p3a
Tax_Haven <- as.data.frame(Data_fixed %>% filter(`Oil and Gas Major/Tax Haven` == 'Tax Haven'))
Tax_Haven## Country Name Region World Rank 2019 Score GDP per Capita (PPP)
## 1 Hong Kong Asia-Pacific 1 90.2 61393.32
## 2 Ireland Europe 6 80.5 75538.36
## 3 Luxembourg Europe 17 75.9 106373.78
## 4 Netherlands Europe 13 76.8 53634.56
## 5 Singapore Asia-Pacific 2 89.4 93905.50
## 6 Switzerland Europe 4 81.9 61421.82
## Oil and Gas Major/Tax Haven
## 1 Tax Haven
## 2 Tax Haven
## 3 Tax Haven
## 4 Tax Haven
## 5 Tax Haven
## 6 Tax Havenp4 <- ggplot(data = Tax_Haven,
aes(x = (Tax_Haven$`2019 Score`),
y = (Tax_Haven$`GDP per Capita (PPP)`)
) )
p4a <- p4 + ggtitle("Tax Haven Summary") + geom_point() + xlab("") + ylab("") + geom_text(aes(label=Tax_Haven$`Country Name`), size=3) + geom_smooth(method = "lm") + expand_limits(y=0)
p4a
#We can now identify which countries are Oil/Gas Majors and Tax Havens and their respective trends.
#We can also create an individual scatterplot for the remainder of the countries.
Neither <- as.data.frame(Data_fixed %>% filter(`Oil and Gas Major/Tax Haven` == 'Neither'))
head(Neither)## Country Name Region World Rank 2019 Score
## 1 Afghanistan Asia-Pacific 152 51.5
## 2 Albania Europe 52 66.5
## 3 Algeria Middle East and North Africa 171 46.2
## 4 Angola Sub-Saharan Africa 156 50.6
## 5 Argentina Americas 148 52.2
## 6 Armenia Europe 47 67.7
## GDP per Capita (PPP) Oil and Gas Major/Tax Haven
## 1 1957.58 Neither
## 2 12506.65 Neither
## 3 15237.20 Neither
## 4 6752.58 Neither
## 5 20875.76 Neither
## 6 9455.94 Neitherp5 <- ggplot(data = Neither,
aes(x = (Neither$`2019 Score`),
y = (Neither$`GDP per Capita (PPP)`)
) )
p5a <- p5 + ggtitle("Neither Oil/Gas Major or Tax Haven Summary") + xlab("") + ylab("") + geom_point() + geom_smooth(method = "lm")
p5a
Data Reference
The Heritage Foundation. (2019). Country Rankings. Retrieved September 21, 2019, from the Heritage Foundation website: https://www.heritage.org/index/ranking
US Energy Information Administration. (2018). Production of Crude Oil including Lease Condenstate . Retrieved September 21, 2019, from the US EIA website: https://www.eia.gov/beta/international/data/browser/#/?pa=00000000000000000000000000000000002&c=ruvvvvvfvtvnvv1vrvvvvfvvvvvvfvvvou20evvvvvvvvvvvvuvo&ct=0&tl_id=5-A&vs=INTL.57-1-AFG-TBPD.A&vo=0&v=H&end=2018
Central Intelligence Agency (The World Factbook). (2015). Natural Gas -Production. Retrieved September 21, 2019, from the CIA World Factbook website: https://www.cia.gov/library/publications/the-world-factbook/rankorder/2249rank.html
Zucman G. (2019). The Missing Profits of Nations. Retrieved September 21, 2019, from the Zucman’s website: http://gabriel-zucman.eu/missingprofits/
Note the above link was mentioned in a wikipedia article: https://en.wikipedia.org/wiki/Tax_haven#Top_10_tax_havens
Reconstruction
