Analysis.md

Gross Domestic Product Analysis

Ivelin Angelov, Laura Bishop, Ethan Graham, Scott Gozdzialski
10-03-2017

Introduction

The global economy is accelerating at a rapid pace. The World Bank, which is the world's largest development institution, looks at influencing factors like climate change, conflict, food security, education, agriculture, finance, and trade. Questions are being asked about Gross Domestic Product (GDP) and Income categories for 189 countries in which there is sufficient data to analyze.

The data source for this analysis is from the World Bank's Education Statistics (http://datatopics.worldbank.org/education/) web site for basic information on a country, including income grouping, date of last census data, systems of trade, government accounting approach, and many others basic data points except GDP. The GDP information, also from the World Bank, is not a part of the Education Statistics information collection and dissemination.

It is important to note that the World Bank's Education Statistics (EDSstat) does differentiate between countries that participate in the Organization of Economic Cooperation and Development (OECD). The OECD was founded in 1960 and contains 35 member countries today, including the United States, United Kingdom, and Germany. The goal of this organization is to help developing countries create and sustain economic prosperity. In this analysis, the "Income Group" used in the ranking of GDP for a country does contain an indicator of whether a high income country is part of the OECD.

This analysis takes a closer look at the relationship between GDP and Income Groups.

1. With the merged data frame how many IDs matched?

After merging the Data from the EDStats with the GDP data, it is expected that some of the rows will not perfectly match up. Looking at the data from both sets and merging them together ends up with some data points that do not have information in the GDP, those values are excluded from analysis. So how many use full observations are present?

# Load the merged tidy data file
Data = read.table(file.path("data", "merged_data.csv"), header = TRUE, encoding='UTF-8')
cat('There are', nrow(Data), 'rows of clean merged data for further analysis.')

## There are 189 rows of clean merged data for further analysis.

2. With the the data frame in decending order by GDP(United States last) what is the 13th value?

Here is an example of which county lie at the thirteenth position if the data is in descending order. This lists row in unordered data frame, three letter country code, country name, GDP in millions of US dollars, and finally which Organization of Economic Cooperation and Development group it belongs.

# reorder by GDP
Data = Data[order(Data$GDP),]
# print 13-th
cat('The 13th country in the sorted dataset is:', as.character(Data$CountryName[13]))

## The 13th country in the sorted dataset is: St. Kitts and Nevis

3. What are the average GDP rankings for the "High Income: OECD" and "High Income:nonOECD" groups?

There are five different economic groups as classified by the Organization of Economic Cooperation and Development (OECD). They are the high income and member of the OECD, the high income and non-member OECD, the upper middle income, the middle income, and the low income groups. Comparing average incomes of the high income OECD to average incomes in high income nonOECD indicates if there is a difference between being a member of OECD or not.

Data$GDP.rankings = rank(Data$GDP)
OECD_rankings = Data$GDP.rankings[Data$Income.Group == "High income: OECD"]
cat('The "High income: OECD" group has an average GDP of', mean(OECD_rankings),'millions in US dollars.')

## The "High income: OECD" group has an average GDP of 157.0333 millions in US dollars.

nonOECD_rankings = Data$GDP.rankings[Data$Income.Group == "High income: nonOECD"]
cat('The high income nonOECD group had an average GDP of', mean(nonOECD_rankings), 'millions in US dollars.')

## The high income nonOECD group had an average GDP of 98.34783 millions in US dollars.

Based on the analysis the OECD group has a higher average GDP.

4. Plot the GDP for all of the countries. Use ggplot2 to color your plot by Income Group.

Plotting the data frame's data helps visualize all the data concisely. The chart below is all the data in the data frame.

# Load needed packages
require(ggplot2)
# Allows for setting up the ggplot
require(scales) 
# For conclusion
require (Hmisc) 
# For histograms in conclusion
require (lattice) 

# Gets rid of the exponential numbers on the Y axis
options(scipen=10000)  

# Sets up GGPLOT2 scatter plot
ggplot (Data, aes(x=Data$Income.Group, y=Data$GDP)) + 
  # Sets up Y axis with labels and range
  scale_y_continuous(
    name="GDP -- In USD$ Millions",
    labels= scales::comma,
    expand = c(0,0),
    limits=c(0, 20000000),
    breaks=seq(0,18000000, 2000000)
    ) + 
  # Sets up x axis for abbreviates of categorical variable to prevent overlap and promote readability
  scale_x_discrete(
    "Income Group",
    labels = c("Low income" = "LI", "High income: nonOECD" = "nHI", "High income: OECD" = "oHI", "Lower middle income" = "LMI", "Upper middle income" = "UMI")
  ) + 
  # Sets up aspect ratio so consistent with each output
  theme(aspect.ratio = 2/1) + 
  # Sets up colored points
  geom_point(aes(color = Data$Income.Group)) +  
  # Changes legend title
  scale_color_discrete (name="Income Group")

5. Cut the GDP rankings into 5 seperate quantile groups. Making a table versus income group. How many countries are "lower middle income" but within the 38 nations with the highest GDP?

The chart above indicates that the groups are not built on GDP alone. The selection into the high income and member of the OECD, the high income and non-member OECD, the upper middle income, the middle income, and the low income groups come from many variables. These data shows some members of the lower income groups may have the higher GDP then members of the high income group. How many of the countries in the top 38 GDPs are actual in the lower middle income?

# Split the rankings to 5 quantile groups
OrderData <- Data[order(Data$GDP.rankings), c(1:4)]
colNames <- c("Country Name", "Country Code", "GDP in Millions USD$", "Income Group")
# Load the thml table package
require('htmlTable')
# Print a table
htmlTable(
  OrderData[c('CountryName', 'CountryCode', 'GDP', 'Income.Group')],
  caption = "Ranking in Income Groups"
)

Ranking in Income Groups
	CountryName	CountryCode	GDP	Income.Group
173	Tuvalu	TUV	40	Lower middle income
92	Kiribati	KIR	175	Lower middle income
113	Marshall Islands	MHL	182	Lower middle income
137	Palau	PLW	228	Upper middle income
155	S�o Tom� and Principe	STP	263	Lower middle income
59	Micronesia, Fed. Sts.	FSM	326	Lower middle income
169	Tonga	TON	472	Lower middle income
46	Dominica	DMA	480	Upper middle income
39	Comoros	COM	596	Low income
184	Samoa	WSM	684	Lower middle income
180	St. Vincent and the Grenadines	VCT	713	Upper middle income
69	Grenada	GRD	767	Upper middle income
93	St. Kitts and Nevis	KNA	767	Upper middle income
183	Vanuatu	VUT	787	Lower middle income
66	Guinea-Bissau	GNB	822	Low income
65	Gambia, The	GMB	917	Low income
151	Solomon Islands	SLB	1008	Low income
161	Seychelles	SYC	1129	Upper middle income
8	Antigua and Barbuda	ATG	1134	Upper middle income
100	St. Lucia	LCA	1239	Upper middle income
168	Timor-Leste	TMP	1293	Lower middle income
22	Belize	BLZ	1493	Lower middle income
99	Liberia	LBR	1734	Low income
28	Bhutan	BTN	1780	Lower middle income
40	Cape Verde	CPV	1827	Lower middle income
30	Central African Republic	CAF	2184	Low income
111	Maldives	MDV	2222	Lower middle income
102	Lesotho	LSO	2448	Lower middle income
12	Burundi	BDI	2472	Low income
1	Aruba	ABW	2584	High income: nonOECD
71	Guyana	GUY	2851	Lower middle income
52	Eritrea	ERI	3092	Low income
160	Swaziland	SWZ	3744	Lower middle income
152	Sierra Leone	SLE	3796	Low income
164	Togo	TGO	3814	Low income
57	Fiji	FJI	3908	Upper middle income
120	Mauritania	MRT	4199	Low income
26	Barbados	BRB	4225	High income: nonOECD
122	Malawi	MWI	4264	Low income
117	Montenegro	MNE	4373	Upper middle income
156	Suriname	SUR	5012	Upper middle income
23	Bermuda	BMU	5474	High income: nonOECD
64	Guinea	GIN	5632	Low income
108	Monaco	MCO	6075	High income: nonOECD
95	Kosovo	KSV	6445	Lower middle income
90	Kyrgyz Republic	KGZ	6475	Low income
125	Niger	NER	6773	Low income
166	Tajikistan	TJK	6972	Low income
146	Rwanda	RWA	7103	Low income
109	Moldova	MDA	7253	Lower middle income
14	Benin	BEN	7557	Low income
75	Haiti	HTI	7843	Low income
19	Bahamas, The	BHS	8149	High income: nonOECD
116	Malta	MLT	8722	High income: nonOECD
97	Lao PDR	LAO	9418	Low income
114	Macedonia, FYR	MKD	9613	Upper middle income
189	Zimbabwe	ZWE	9802	Low income
7	Armenia	ARM	9951	Lower middle income
110	Madagascar	MDG	9975	Low income
118	Mongolia	MNG	10271	Lower middle income
115	Mali	MLI	10308	Low income
15	Burkina Faso	BFA	10441	Low income
121	Mauritius	MUS	10486	Upper middle income
127	Nicaragua	NIC	10507	Lower middle income
4	Albania	ALB	12648	Upper middle income
163	Chad	TCD	12887	Low income
124	Namibia	NAM	13072	Upper middle income
82	Iceland	ISL	13579	High income: OECD
37	Congo, Rep.	COG	13678	Lower middle income
91	Cambodia	KHM	14038	Low income
149	Senegal	SEN	14046	Lower middle income
119	Mozambique	MOZ	14244	Low income
29	Botswana	BWA	14504	Upper middle income
85	Jamaica	JAM	14755	Upper middle income
138	Papua New Guinea	PNG	15654	Lower middle income
62	Georgia	GEO	15747	Lower middle income
27	Brunei Darussalam	BRN	16954	High income: nonOECD
187	Congo, Dem. Rep.	ZAR	17204	Low income
20	Bosnia and Herzegovina	BIH	17466	Upper middle income
67	Equatorial Guinea	GNQ	17697	High income: nonOECD
60	Gabon	GAB	18377	Upper middle income
73	Honduras	HND	18434	Lower middle income
130	Nepal	NPL	18963	Low income
175	Uganda	UGA	19881	Low income
2	Afghanistan	AFG	20497	Low income
188	Zambia	ZMB	20678	Low income
54	Estonia	EST	22390	High income: nonOECD
43	Cyprus	CYP	22767	High income: nonOECD
170	Trinidad and Tobago	TTO	23320	High income: nonOECD
153	El Salvador	SLV	23864	Lower middle income
35	C�te d'Ivoire	CIV	24680	Lower middle income
36	Cameroon	CMR	25322	Lower middle income
142	Paraguay	PRY	25502	Lower middle income
24	Bolivia	BOL	27035	Lower middle income
174	Tanzania	TZA	28242	Low income
105	Latvia	LVA	28373	High income: nonOECD
18	Bahrain	BHR	29044	High income: nonOECD
86	Jordan	JOR	31015	Lower middle income
167	Turkmenistan	TKM	35164	Lower middle income
185	Yemen, Rep.	YEM	35646	Lower middle income
134	Panama	PAN	36253	Upper middle income
154	Serbia	SRB	37489	Upper middle income
89	Kenya	KEN	40697	Low income
63	Ghana	GHA	40711	Low income
55	Ethiopia	ETH	41605	Low income
103	Lithuania	LTU	42344	Upper middle income
98	Lebanon	LBN	42945	Upper middle income
106	Macao SAR, China	MAC	43582	High income: nonOECD
41	Costa Rica	CRI	45104	Upper middle income
158	Slovenia	SVN	45279	High income: OECD
171	Tunisia	TUN	45662	Lower middle income
177	Uruguay	URY	49920	Upper middle income
70	Guatemala	GTM	50234	Lower middle income
17	Bulgaria	BGR	50972	Upper middle income
179	Uzbekistan	UZB	51113	Lower middle income
104	Luxembourg	LUX	55178	High income: OECD
148	Sudan	SDN	58769	Lower middle income
48	Dominican Republic	DOM	59047	Upper middle income
74	Croatia	HRV	59228	High income: nonOECD
101	Sri Lanka	LKA	59423	Lower middle income
21	Belarus	BLR	63267	Upper middle income
11	Azerbaijan	AZE	66605	Upper middle income
42	Cuba	CUB	68234	Upper middle income
132	Oman	OMN	69972	High income: nonOECD
162	Syrian Arab Republic	SYR	73672	Lower middle income
50	Ecuador	ECU	84040	Lower middle income
157	Slovak Republic	SVK	91149	High income: OECD
107	Morocco	MAR	95982	Lower middle income
140	Puerto Rico	PRI	101496	High income: nonOECD
3	Angola	AGO	114147	Lower middle income
16	Bangladesh	BGD	116355	Low income
76	Hungary	HUN	124600	High income: OECD
182	Vietnam	VNM	155820	Lower middle income
96	Kuwait	KWT	160913	High income: nonOECD
131	New Zealand	NZL	167347	High income: OECD
143	Qatar	QAT	171476	High income: nonOECD
176	Ukraine	UKR	176309	Lower middle income
144	Romania	ROM	192711	Upper middle income
44	Czech Republic	CZE	196446	High income: OECD
88	Kazakhstan	KAZ	203521	Upper middle income
135	Peru	PER	203790	Upper middle income
49	Algeria	DZA	205789	Upper middle income
81	Iraq	IRQ	210280	Lower middle income
79	Ireland	IRL	210771	High income: OECD
141	Portugal	PRT	212274	High income: OECD
133	Pakistan	PAK	225143	Lower middle income
56	Finland	FIN	247546	High income: OECD
68	Greece	GRC	249099	High income: OECD
136	Philippines	PHL	250182	Lower middle income
83	Israel	ISR	258217	High income: OECD
126	Nigeria	NGA	262597	Lower middle income
51	Egypt, Arab Rep.	EGY	262832	Lower middle income
72	Hong Kong SAR, China	HKG	263259	High income: nonOECD
33	Chile	CHL	269869	Upper middle income
150	Singapore	SGP	274701	High income: nonOECD
123	Malaysia	MYS	305033	Upper middle income
47	Denmark	DNK	314887	High income: OECD
5	United Arab Emirates	ARE	348595	High income: nonOECD
165	Thailand	THA	365966	Lower middle income
38	Colombia	COL	369606	Upper middle income
181	Venezuela, RB	VEN	381286	Upper middle income
186	South Africa	ZAF	384313	Upper middle income
10	Austria	AUT	394708	High income: OECD
6	Argentina	ARG	475502	Upper middle income
13	Belgium	BEL	483262	High income: OECD
139	Poland	POL	489795	High income: OECD
129	Norway	NOR	499667	High income: OECD
80	Iran, Islamic Rep.	IRN	514060	Upper middle income
159	Sweden	SWE	523806	High income: OECD
32	Switzerland	CHE	631173	High income: OECD
147	Saudi Arabia	SAU	711050	High income: nonOECD
128	Netherlands	NLD	770555	High income: OECD
172	Turkey	TUR	789257	Upper middle income
77	Indonesia	IDN	878043	Lower middle income
94	Korea, Rep.	KOR	1129598	High income: OECD
112	Mexico	MEX	1178126	Upper middle income
53	Spain	ESP	1322965	High income: OECD
9	Australia	AUS	1532408	High income: OECD
31	Canada	CAN	1821424	High income: OECD
78	India	IND	1841710	Lower middle income
84	Italy	ITA	2014670	High income: OECD
145	Russian Federation	RUS	2014775	Upper middle income
25	Brazil	BRA	2252664	Upper middle income
61	United Kingdom	GBR	2471784	High income: OECD
58	France	FRA	2612878	High income: OECD
45	Germany	DEU	3428131	High income: OECD
87	Japan	JPN	5959718	High income: OECD
34	China	CHN	8227103	Lower middle income
178	United States	USA	16244600	High income: OECD

top38 <- tail(Data, n = 38)
# An answer to the question: How many countries are "lower middle income" but within the 38 nations with the highest GDP
cat('There are', NROW(top38[top38$Income.Group == 'Lower middle income', ]), 'countries "lower middle income" but within the 38 nations with the highest GDP')

## There are 5 countries "lower middle income" but within the 38 nations with the highest GDP

Conclusion

There are many factors that influence a country's GDP and overall prosperity. This data analysis effort looks at GDP and the categories of Income Group as provided by the World Bank data. Many data points were looked at in this analysis of Gross Domestic Products of different countries around the world.

Before analysis is possible, the data must be in a clean and usable format. Cleaning and merging of the different dataframes included with some incomplete records, which were removed from further analysis, leaving 189 usable records.

To check the cleaned data, the 13th nation from the top when sorted in descending order is manually and programmatically found to be St. Kitts and Nevis. Comparing the OECD and nonOECD groups shows a difference in their GDP, where the OECD high income group has a higher average GDP than the nonOECD high income group. Using data visualization tools helped determine how strong GDP is in the OECD scoring and checked the number of nations in the top 38 GDPs that were ranked in the OECD lower middle income group, which is 5.