Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Poultry Production Around the World 1960-2018
challenge_1
wildbirds
Code
### pull the data from the _data folder
library(readr)
birds <- read_csv ("_data/birds.csv")
view (birds)Introduction:
I analyzed the birds.csv data set. This data set contains the information about the type, country of origin, and amount of poultry that was produced around the world. This data is relatively straight forward based on the data values. I have included the first few rows below for reference.
Code
select(birds, "Area", "Item", "Year", "Value")# A tibble: 30,977 × 4
Area Item Year Value
<chr> <chr> <dbl> <dbl>
1 Afghanistan Chickens 1961 4700
2 Afghanistan Chickens 1962 4900
3 Afghanistan Chickens 1963 5000
4 Afghanistan Chickens 1964 5300
5 Afghanistan Chickens 1965 5500
6 Afghanistan Chickens 1966 5800
7 Afghanistan Chickens 1967 6600
8 Afghanistan Chickens 1968 6290
9 Afghanistan Chickens 1969 6300
10 Afghanistan Chickens 1970 6000
# … with 30,967 more rowsVisualization Analysis:
Code
Bird <- select(birds, "Item")
table(Bird)Item
Chickens Ducks Geese and guinea fowls
13074 6909 4136
Pigeons, other birds Turkeys
1165 5693 Code
prop.table(table(Bird))Item
Chickens Ducks Geese and guinea fowls
0.42205507 0.22303645 0.13351842
Pigeons, other birds Turkeys
0.03760855 0.18378152 I have included tables describing the type of poultry and their respective county. Additionally, I have included the proportions of each type of bird throughout the entirety of the data. As you can see from above, Chicken makes up about 42% of the poultry production, Ducks make up about 22%, Turkeys make up about 18%, Geese and Guinea fowls make up about 13% and Pigeons and other beards make up about 3.7%.
Code
### This section of code specifies the values on the below charts.
ggplot(data = birds, mapping = aes(x = Year, y = Value, color = Item)) +
geom_line() +
facet_wrap(vars(Item)) +
### Titles of the below charts including a title for each axis
labs(title = "Poultry Production from 1960-2018",
x = "Year",
y = "Amount of Poultry") +
### This section of code represents the texts formation of the below charts (Thanks, Megan!!)
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 6, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 6),
strip.text = element_text(face = "italic"),
text = element_text(size = 10))
The above code was borrowed from a similar analysis (Megan Galarneau). I believe this is probably the best way to efficiently represent the data in an attractive way.
Code
Bird <- select(birds, "Area")
prop.table(table(Bird))Area
Afghanistan
0.0018723569
Africa
0.0093617845
Albania
0.0074894276
Algeria
0.0074894276
American Samoa
0.0018723569
Americas
0.0074894276
Angola
0.0018723569
Antigua and Barbuda
0.0018723569
Argentina
0.0074894276
Armenia
0.0017432288
Aruba
0.0009361785
Asia
0.0093617845
Australia
0.0056170707
Australia and New Zealand
0.0074894276
Austria
0.0074894276
Azerbaijan
0.0017432288
Bahamas
0.0018723569
Bahrain
0.0018723569
Bangladesh
0.0037447138
Barbados
0.0037447138
Belarus
0.0026148433
Belgium
0.0024534332
Belgium-Luxembourg
0.0050359944
Belize
0.0056170707
Benin
0.0018723569
Bermuda
0.0035510217
Bhutan
0.0018723569
Bolivia (Plurinational State of)
0.0056170707
Bosnia and Herzegovina
0.0034864577
Botswana
0.0018723569
Brazil
0.0056170707
Brunei Darussalam
0.0037447138
Bulgaria
0.0074894276
Burkina Faso
0.0018723569
Burundi
0.0027762533
Cabo Verde
0.0018723569
Cambodia
0.0037447138
Cameroon
0.0018723569
Canada
0.0074894276
Caribbean
0.0074894276
Cayman Islands
0.0017109468
Central African Republic
0.0037447138
Central America
0.0056170707
Central Asia
0.0034864577
Chad
0.0018723569
Chile
0.0037447138
China, Hong Kong SAR
0.0090066824
China, Macao SAR
0.0018723569
China, mainland
0.0056170707
China, Taiwan Province of
0.0074894276
Colombia
0.0018723569
Comoros
0.0018723569
Congo
0.0018723569
Cook Islands
0.0035187397
Costa Rica
0.0018723569
Côte d'Ivoire
0.0027762533
Croatia
0.0034864577
Cuba
0.0018723569
Cyprus
0.0089421183
Czechia
0.0033573296
Czechoslovakia
0.0041320980
Democratic People's Republic of Korea
0.0037447138
Democratic Republic of the Congo
0.0018723569
Denmark
0.0074894276
Dominica
0.0018723569
Dominican Republic
0.0018723569
Eastern Africa
0.0074894276
Eastern Asia
0.0093617845
Eastern Europe
0.0074894276
Ecuador
0.0074894276
Egypt
0.0093617845
El Salvador
0.0018723569
Equatorial Guinea
0.0037447138
Eritrea
0.0008393324
Estonia
0.0034864577
Eswatini
0.0018723569
Ethiopia
0.0008393324
Ethiopia PDR
0.0010330245
Europe
0.0093617845
Falkland Islands (Malvinas)
0.0018723569
Fiji
0.0056170707
Finland
0.0053588146
France
0.0093617845
French Guyana
0.0037447138
French Polynesia
0.0037447138
Gabon
0.0018723569
Gambia
0.0018723569
Georgia
0.0017432288
Germany
0.0074894276
Ghana
0.0018723569
Greece
0.0093617845
Grenada
0.0018723569
Guadeloupe
0.0049714304
Guam
0.0018723569
Guatemala
0.0018723569
Guinea
0.0018723569
Guinea-Bissau
0.0018723569
Guyana
0.0018723569
Haiti
0.0074894276
Honduras
0.0018723569
Hungary
0.0074894276
Iceland
0.0018723569
India
0.0037447138
Indonesia
0.0037447138
Iran (Islamic Republic of)
0.0074894276
Iraq
0.0018723569
Ireland
0.0074894276
Israel
0.0068437873
Italy
0.0037447138
Jamaica
0.0018723569
Japan
0.0037447138
Jordan
0.0080059399
Kazakhstan
0.0017432288
Kenya
0.0018723569
Kiribati
0.0018723569
Kuwait
0.0018723569
Kyrgyzstan
0.0034864577
Lao People's Democratic Republic
0.0056170707
Latvia
0.0017432288
Lebanon
0.0029699454
Lesotho
0.0018723569
Liberia
0.0037447138
Libya
0.0018723569
Liechtenstein
0.0018723569
Lithuania
0.0034864577
Luxembourg
0.0006133583
Madagascar
0.0074894276
Malawi
0.0018723569
Malaysia
0.0037447138
Mali
0.0018723569
Malta
0.0052942506
Martinique
0.0051651225
Mauritania
0.0018723569
Mauritius
0.0074894276
Melanesia
0.0056170707
Mexico
0.0056170707
Micronesia
0.0037447138
Micronesia (Federated States of)
0.0018077929
Middle Africa
0.0056170707
Mongolia
0.0018723569
Montenegro
0.0004196662
Montserrat
0.0018723569
Morocco
0.0037447138
Mozambique
0.0074894276
Myanmar
0.0093617845
Namibia
0.0037447138
Nauru
0.0018723569
Nepal
0.0037447138
Netherlands
0.0053588146
Netherlands Antilles (former)
0.0018723569
New Caledonia
0.0018723569
New Zealand
0.0074894276
Nicaragua
0.0018723569
Niger
0.0018723569
Nigeria
0.0018723569
Niue
0.0018723569
North Macedonia
0.0008716144
Northern Africa
0.0093617845
Northern America
0.0074894276
Northern Europe
0.0074894276
Norway
0.0056170707
Oceania
0.0074894276
Oman
0.0027762533
Pacific Islands Trust Territory
0.0019369209
Pakistan
0.0037447138
Palestine
0.0018723569
Panama
0.0056170707
Papua New Guinea
0.0056170707
Paraguay
0.0074894276
Peru
0.0018723569
Philippines
0.0074894276
Poland
0.0074894276
Polynesia
0.0037447138
Portugal
0.0042289441
Puerto Rico
0.0018723569
Qatar
0.0018723569
Republic of Korea
0.0073602996
Republic of Moldova
0.0017432288
Réunion
0.0050682765
Romania
0.0074894276
Russian Federation
0.0034864577
Rwanda
0.0041320980
Saint Helena, Ascension and Tristan da Cunha
0.0018723569
Saint Kitts and Nevis
0.0018723569
Saint Lucia
0.0018723569
Saint Pierre and Miquelon
0.0029699454
Saint Vincent and the Grenadines
0.0018723569
Samoa
0.0018723569
Sao Tome and Principe
0.0056170707
Saudi Arabia
0.0028730994
Senegal
0.0018723569
Serbia
0.0016786648
Serbia and Montenegro
0.0018077929
Seychelles
0.0037447138
Sierra Leone
0.0037447138
Singapore
0.0037447138
Slovakia
0.0033573296
Slovenia
0.0034864577
Solomon Islands
0.0018723569
Somalia
0.0018723569
South Africa
0.0074894276
South America
0.0074894276
South Sudan
0.0002259741
South-eastern Asia
0.0093617845
Southern Africa
0.0093617845
Southern Asia
0.0074894276
Southern Europe
0.0093617845
Spain
0.0056816348
Sri Lanka
0.0037447138
Sudan
0.0002259741
Sudan (former)
0.0016463828
Suriname
0.0037447138
Sweden
0.0037447138
Switzerland
0.0074894276
Syrian Arab Republic
0.0093617845
Tajikistan
0.0008716144
Thailand
0.0056170707
Timor-Leste
0.0018723569
Togo
0.0018723569
Tokelau
0.0018723569
Tonga
0.0018723569
Trinidad and Tobago
0.0018723569
Tunisia
0.0030022275
Turkey
0.0074894276
Turkmenistan
0.0017432288
Tuvalu
0.0018723569
Uganda
0.0018723569
Ukraine
0.0034864577
United Arab Emirates
0.0018723569
United Kingdom of Great Britain and Northern Ireland
0.0074894276
United Republic of Tanzania
0.0037447138
United States of America
0.0056170707
United States Virgin Islands
0.0018723569
Uruguay
0.0074894276
USSR
0.0020014850
Uzbekistan
0.0017432288
Vanuatu
0.0018723569
Venezuela (Bolivarian Republic of)
0.0018723569
Viet Nam
0.0037447138
Wallis and Futuna Islands
0.0018723569
Western Africa
0.0037447138
Western Asia
0.0093617845
Western Europe
0.0093617845
World
0.0093617845
Yemen
0.0018723569
Yugoslav SFR
0.0040029699
Zambia
0.0018723569
Zimbabwe
0.0056170707 In this section, I have included each country and their respective poultry production. I attempted to use graphs, but I was unable to find an attractive way to include all the information without going through and combining the countries via region. My geography is a somewhat rusty.
I have included above the proportions of the productions from each country listed in the data set. I have separated the two tables to avoid any confusion.
Conclusion:
I have come to the conclusion that this is a data set that is describing the poultry production of every country. It is separated via country, production, year and type of poultry produced.