Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 1
Challenge Overview
Today’s challenge is to
read in a dataset, and
describe the dataset using both words and any supporting information (e.g., tables, etc)
Read in the Data
Read in one (or more) of the following data sets, using the correct R package and command.
- railroad_2012_clean_county.csv ⭐
- birds.csv ⭐⭐
- FAOstat*.csv ⭐⭐
- wild_bird_data.xlsx ⭐⭐⭐
- StateCounty2012.xls ⭐⭐⭐⭐
Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.
Code
birds <- read_csv("_data/birds.csv")
spec(birds)cols(
`Domain Code` = col_character(),
Domain = col_character(),
`Area Code` = col_double(),
Area = col_character(),
`Element Code` = col_double(),
Element = col_character(),
`Item Code` = col_double(),
Item = col_character(),
`Year Code` = col_double(),
Year = col_double(),
Unit = col_character(),
Value = col_double(),
Flag = col_character(),
`Flag Description` = col_character()
)Code
# birds.csv has 14 columns, and the results of the column specifications indicates that the first line of the .csv contains column names.
dim(birds)[1] 30977 14Code
# birds.csv has 30977 rows and 14 columns
head(birds)# A tibble: 6 × 14
Domai…¹ Domain Area …² Area Eleme…³ Element Item …⁴ Item Year …⁵ Year Unit
<chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <chr>
1 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1961 1961 1000…
2 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1962 1962 1000…
3 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1963 1963 1000…
4 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1964 1964 1000…
5 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1965 1965 1000…
6 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1966 1966 1000…
# … with 3 more variables: Value <dbl>, Flag <chr>, `Flag Description` <chr>,
# and abbreviated variable names ¹`Domain Code`, ²`Area Code`,
# ³`Element Code`, ⁴`Item Code`, ⁵`Year Code`Code
colnames(birds) [1] "Domain Code" "Domain" "Area Code" "Area"
[5] "Element Code" "Element" "Item Code" "Item"
[9] "Year Code" "Year" "Unit" "Value"
[13] "Flag" "Flag Description"Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.
Describe the data
Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).
Code
# Some of the columns are intuitive (i.e. Area = country, Unit = quantity) but others can be explored further.
table(select(birds, "Domain Code")) Domain Code
QA
30977 Code
table(select(birds, "Domain"))Domain
Live Animals
30977 Code
# There's only 1 "Domain" type: Live Animals, and the code is: QA
table(select(birds, "Area"))Area
Afghanistan
58
Africa
290
Albania
232
Algeria
232
American Samoa
58
Americas
232
Angola
58
Antigua and Barbuda
58
Argentina
232
Armenia
54
Aruba
29
Asia
290
Australia
174
Australia and New Zealand
232
Austria
232
Azerbaijan
54
Bahamas
58
Bahrain
58
Bangladesh
116
Barbados
116
Belarus
81
Belgium
76
Belgium-Luxembourg
156
Belize
174
Benin
58
Bermuda
110
Bhutan
58
Bolivia (Plurinational State of)
174
Bosnia and Herzegovina
108
Botswana
58
Brazil
174
Brunei Darussalam
116
Bulgaria
232
Burkina Faso
58
Burundi
86
Cabo Verde
58
Cambodia
116
Cameroon
58
Canada
232
Caribbean
232
Cayman Islands
53
Central African Republic
116
Central America
174
Central Asia
108
Chad
58
Chile
116
China, Hong Kong SAR
279
China, Macao SAR
58
China, mainland
174
China, Taiwan Province of
232
Colombia
58
Comoros
58
Congo
58
Cook Islands
109
Costa Rica
58
Côte d'Ivoire
86
Croatia
108
Cuba
58
Cyprus
277
Czechia
104
Czechoslovakia
128
Democratic People's Republic of Korea
116
Democratic Republic of the Congo
58
Denmark
232
Dominica
58
Dominican Republic
58
Eastern Africa
232
Eastern Asia
290
Eastern Europe
232
Ecuador
232
Egypt
290
El Salvador
58
Equatorial Guinea
116
Eritrea
26
Estonia
108
Eswatini
58
Ethiopia
26
Ethiopia PDR
32
Europe
290
Falkland Islands (Malvinas)
58
Fiji
174
Finland
166
France
290
French Guyana
116
French Polynesia
116
Gabon
58
Gambia
58
Georgia
54
Germany
232
Ghana
58
Greece
290
Grenada
58
Guadeloupe
154
Guam
58
Guatemala
58
Guinea
58
Guinea-Bissau
58
Guyana
58
Haiti
232
Honduras
58
Hungary
232
Iceland
58
India
116
Indonesia
116
Iran (Islamic Republic of)
232
Iraq
58
Ireland
232
Israel
212
Italy
116
Jamaica
58
Japan
116
Jordan
248
Kazakhstan
54
Kenya
58
Kiribati
58
Kuwait
58
Kyrgyzstan
108
Lao People's Democratic Republic
174
Latvia
54
Lebanon
92
Lesotho
58
Liberia
116
Libya
58
Liechtenstein
58
Lithuania
108
Luxembourg
19
Madagascar
232
Malawi
58
Malaysia
116
Mali
58
Malta
164
Martinique
160
Mauritania
58
Mauritius
232
Melanesia
174
Mexico
174
Micronesia
116
Micronesia (Federated States of)
56
Middle Africa
174
Mongolia
58
Montenegro
13
Montserrat
58
Morocco
116
Mozambique
232
Myanmar
290
Namibia
116
Nauru
58
Nepal
116
Netherlands
166
Netherlands Antilles (former)
58
New Caledonia
58
New Zealand
232
Nicaragua
58
Niger
58
Nigeria
58
Niue
58
North Macedonia
27
Northern Africa
290
Northern America
232
Northern Europe
232
Norway
174
Oceania
232
Oman
86
Pacific Islands Trust Territory
60
Pakistan
116
Palestine
58
Panama
174
Papua New Guinea
174
Paraguay
232
Peru
58
Philippines
232
Poland
232
Polynesia
116
Portugal
131
Puerto Rico
58
Qatar
58
Republic of Korea
228
Republic of Moldova
54
Réunion
157
Romania
232
Russian Federation
108
Rwanda
128
Saint Helena, Ascension and Tristan da Cunha
58
Saint Kitts and Nevis
58
Saint Lucia
58
Saint Pierre and Miquelon
92
Saint Vincent and the Grenadines
58
Samoa
58
Sao Tome and Principe
174
Saudi Arabia
89
Senegal
58
Serbia
52
Serbia and Montenegro
56
Seychelles
116
Sierra Leone
116
Singapore
116
Slovakia
104
Slovenia
108
Solomon Islands
58
Somalia
58
South-eastern Asia
290
South Africa
232
South America
232
South Sudan
7
Southern Africa
290
Southern Asia
232
Southern Europe
290
Spain
176
Sri Lanka
116
Sudan
7
Sudan (former)
51
Suriname
116
Sweden
116
Switzerland
232
Syrian Arab Republic
290
Tajikistan
27
Thailand
174
Timor-Leste
58
Togo
58
Tokelau
58
Tonga
58
Trinidad and Tobago
58
Tunisia
93
Turkey
232
Turkmenistan
54
Tuvalu
58
Uganda
58
Ukraine
108
United Arab Emirates
58
United Kingdom of Great Britain and Northern Ireland
232
United Republic of Tanzania
116
United States of America
174
United States Virgin Islands
58
Uruguay
232
USSR
62
Uzbekistan
54
Vanuatu
58
Venezuela (Bolivarian Republic of)
58
Viet Nam
116
Wallis and Futuna Islands
58
Western Africa
116
Western Asia
290
Western Europe
290
World
290
Yemen
58
Yugoslav SFR
124
Zambia
58
Zimbabwe
174 Code
n_distinct(select(birds, "Area"))[1] 248Code
# Based on the "Area" and "Flag Description," which there are 248 UN Food and Agriculture recognized countries
table(select(birds, "Item"))Item
Chickens Ducks Geese and guinea fowls
13074 6909 4136
Pigeons, other birds Turkeys
1165 5693 Code
# The types of birds under items are: chickens, Ducks Geese and guinea fowls, pigeons/other birds, turkeys
table(select(birds, "Year"))Year
1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976
493 493 493 493 494 495 495 495 498 498 498 498 498 499 499 499
1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
498 498 497 496 498 498 495 498 499 499 500 502 503 512 514 569
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
574 574 574 574 574 574 574 575 575 575 575 575 575 576 576 576
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
576 576 576 577 577 577 577 577 577 577 Code
# The records span from 1961 to 2018.
# To summarize, there are 248 countries (under UN FAO), from 1961-2018, where "Value" indicates each year's total value for each type of livestock bird (chicken, ducks geese and guinea fowls, pigeons and other birds, turkeys)