Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 1
challenge_1
railroads
faostat
wildbirds
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
bird <- read_csv('_data/birds.csv')
bird# A tibble: 30,977 × 14
Domain Cod…¹ Domain Area …² Area Eleme…³ Element Item …⁴ Item Year …⁵ Year
<chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
1 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1961 1961
2 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1962 1962
3 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1963 1963
4 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1964 1964
5 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1965 1965
6 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1966 1966
7 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1967 1967
8 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1968 1968
9 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1969 1969
10 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1970 1970
# … with 30,967 more rows, 4 more variables: Unit <chr>, 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
as_tibble(bird)# A tibble: 30,977 × 14
Domain Cod…¹ Domain Area …² Area Eleme…³ Element Item …⁴ Item Year …⁵ Year
<chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
1 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1961 1961
2 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1962 1962
3 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1963 1963
4 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1964 1964
5 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1965 1965
6 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1966 1966
7 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1967 1967
8 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1968 1968
9 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1969 1969
10 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1970 1970
# … with 30,967 more rows, 4 more variables: Unit <chr>, 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`Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.
Describe the data
Description: The birds dataset contains approximately 30k rows and 14 columns on different stock birds from 248 regions like Afghanistan, Albania, Jamaica and Micronesia. All these birds belong to the domain of “Live Animals”. The birds are categorized in one of the following categories: “Chickens”, “Ducks”, “Geese and guinea fowls”, “Turkeys”, “Pigeons, other birds”. The dataset spans form the year 1961 to 2018. It was gathered form various official and unofficial sources like FAO estimates, unofficial figures likely gathered from livestock farms, as well as aggregates calculated from a combination of these sources. This dataset is likely useful to analyse when, where and how many different livestock birds were grown.
Code
nrow(bird)[1] 30977Code
colnames(bird)[4] <- c("area")
unique_areas <- unique(bird$area)
unique_areas [1] "Afghanistan"
[2] "Albania"
[3] "Algeria"
[4] "American Samoa"
[5] "Angola"
[6] "Antigua and Barbuda"
[7] "Argentina"
[8] "Armenia"
[9] "Aruba"
[10] "Australia"
[11] "Austria"
[12] "Azerbaijan"
[13] "Bahamas"
[14] "Bahrain"
[15] "Bangladesh"
[16] "Barbados"
[17] "Belarus"
[18] "Belgium"
[19] "Belgium-Luxembourg"
[20] "Belize"
[21] "Benin"
[22] "Bermuda"
[23] "Bhutan"
[24] "Bolivia (Plurinational State of)"
[25] "Bosnia and Herzegovina"
[26] "Botswana"
[27] "Brazil"
[28] "Brunei Darussalam"
[29] "Bulgaria"
[30] "Burkina Faso"
[31] "Burundi"
[32] "Cabo Verde"
[33] "Cambodia"
[34] "Cameroon"
[35] "Canada"
[36] "Cayman Islands"
[37] "Central African Republic"
[38] "Chad"
[39] "Chile"
[40] "China, Hong Kong SAR"
[41] "China, Macao SAR"
[42] "China, mainland"
[43] "China, Taiwan Province of"
[44] "Colombia"
[45] "Comoros"
[46] "Congo"
[47] "Cook Islands"
[48] "Costa Rica"
[49] "Côte d'Ivoire"
[50] "Croatia"
[51] "Cuba"
[52] "Cyprus"
[53] "Czechia"
[54] "Czechoslovakia"
[55] "Democratic People's Republic of Korea"
[56] "Democratic Republic of the Congo"
[57] "Denmark"
[58] "Dominica"
[59] "Dominican Republic"
[60] "Ecuador"
[61] "Egypt"
[62] "El Salvador"
[63] "Equatorial Guinea"
[64] "Eritrea"
[65] "Estonia"
[66] "Eswatini"
[67] "Ethiopia"
[68] "Ethiopia PDR"
[69] "Falkland Islands (Malvinas)"
[70] "Fiji"
[71] "Finland"
[72] "France"
[73] "French Guyana"
[74] "French Polynesia"
[75] "Gabon"
[76] "Gambia"
[77] "Georgia"
[78] "Germany"
[79] "Ghana"
[80] "Greece"
[81] "Grenada"
[82] "Guadeloupe"
[83] "Guam"
[84] "Guatemala"
[85] "Guinea"
[86] "Guinea-Bissau"
[87] "Guyana"
[88] "Haiti"
[89] "Honduras"
[90] "Hungary"
[91] "Iceland"
[92] "India"
[93] "Indonesia"
[94] "Iran (Islamic Republic of)"
[95] "Iraq"
[96] "Ireland"
[97] "Israel"
[98] "Italy"
[99] "Jamaica"
[100] "Japan"
[101] "Jordan"
[102] "Kazakhstan"
[103] "Kenya"
[104] "Kiribati"
[105] "Kuwait"
[106] "Kyrgyzstan"
[107] "Lao People's Democratic Republic"
[108] "Latvia"
[109] "Lebanon"
[110] "Lesotho"
[111] "Liberia"
[112] "Libya"
[113] "Liechtenstein"
[114] "Lithuania"
[115] "Luxembourg"
[116] "Madagascar"
[117] "Malawi"
[118] "Malaysia"
[119] "Mali"
[120] "Malta"
[121] "Martinique"
[122] "Mauritania"
[123] "Mauritius"
[124] "Mexico"
[125] "Micronesia (Federated States of)"
[126] "Mongolia"
[127] "Montenegro"
[128] "Montserrat"
[129] "Morocco"
[130] "Mozambique"
[131] "Myanmar"
[132] "Namibia"
[133] "Nauru"
[134] "Nepal"
[135] "Netherlands"
[136] "Netherlands Antilles (former)"
[137] "New Caledonia"
[138] "New Zealand"
[139] "Nicaragua"
[140] "Niger"
[141] "Nigeria"
[142] "Niue"
[143] "North Macedonia"
[144] "Norway"
[145] "Oman"
[146] "Pacific Islands Trust Territory"
[147] "Pakistan"
[148] "Palestine"
[149] "Panama"
[150] "Papua New Guinea"
[151] "Paraguay"
[152] "Peru"
[153] "Philippines"
[154] "Poland"
[155] "Portugal"
[156] "Puerto Rico"
[157] "Qatar"
[158] "Republic of Korea"
[159] "Republic of Moldova"
[160] "Réunion"
[161] "Romania"
[162] "Russian Federation"
[163] "Rwanda"
[164] "Saint Helena, Ascension and Tristan da Cunha"
[165] "Saint Kitts and Nevis"
[166] "Saint Lucia"
[167] "Saint Pierre and Miquelon"
[168] "Saint Vincent and the Grenadines"
[169] "Samoa"
[170] "Sao Tome and Principe"
[171] "Saudi Arabia"
[172] "Senegal"
[173] "Serbia"
[174] "Serbia and Montenegro"
[175] "Seychelles"
[176] "Sierra Leone"
[177] "Singapore"
[178] "Slovakia"
[179] "Slovenia"
[180] "Solomon Islands"
[181] "Somalia"
[182] "South Africa"
[183] "South Sudan"
[184] "Spain"
[185] "Sri Lanka"
[186] "Sudan"
[187] "Sudan (former)"
[188] "Suriname"
[189] "Sweden"
[190] "Switzerland"
[191] "Syrian Arab Republic"
[192] "Tajikistan"
[193] "Thailand"
[194] "Timor-Leste"
[195] "Togo"
[196] "Tokelau"
[197] "Tonga"
[198] "Trinidad and Tobago"
[199] "Tunisia"
[200] "Turkey"
[201] "Turkmenistan"
[202] "Tuvalu"
[203] "Uganda"
[204] "Ukraine"
[205] "United Arab Emirates"
[206] "United Kingdom of Great Britain and Northern Ireland"
[207] "United Republic of Tanzania"
[208] "United States of America"
[209] "United States Virgin Islands"
[210] "Uruguay"
[211] "USSR"
[212] "Uzbekistan"
[213] "Vanuatu"
[214] "Venezuela (Bolivarian Republic of)"
[215] "Viet Nam"
[216] "Wallis and Futuna Islands"
[217] "Yemen"
[218] "Yugoslav SFR"
[219] "Zambia"
[220] "Zimbabwe"
[221] "World"
[222] "Africa"
[223] "Eastern Africa"
[224] "Middle Africa"
[225] "Northern Africa"
[226] "Southern Africa"
[227] "Western Africa"
[228] "Americas"
[229] "Northern America"
[230] "Central America"
[231] "Caribbean"
[232] "South America"
[233] "Asia"
[234] "Central Asia"
[235] "Eastern Asia"
[236] "Southern Asia"
[237] "South-eastern Asia"
[238] "Western Asia"
[239] "Europe"
[240] "Eastern Europe"
[241] "Northern Europe"
[242] "Southern Europe"
[243] "Western Europe"
[244] "Oceania"
[245] "Australia and New Zealand"
[246] "Melanesia"
[247] "Micronesia"
[248] "Polynesia" Code
colnames(bird)[2] <- c("domain")
unique_domains <- unique(bird$domain)
unique_domains[1] "Live Animals"Code
colnames(bird)[6] <- c("element")
unique_element <- unique(bird$element)
unique_element[1] "Stocks"Code
colnames(bird)[8] <- c("item")
unique_items <- unique(bird$item)
unique_items[1] "Chickens" "Ducks" "Geese and guinea fowls"
[4] "Turkeys" "Pigeons, other birds" Code
summary(bird) Domain Code domain Area Code area
Length:30977 Length:30977 Min. : 1 Length:30977
Class :character Class :character 1st Qu.: 79 Class :character
Mode :character Mode :character Median : 156 Mode :character
Mean :1202
3rd Qu.: 231
Max. :5504
Element Code element Item Code item
Min. :5112 Length:30977 Min. :1057 Length:30977
1st Qu.:5112 Class :character 1st Qu.:1057 Class :character
Median :5112 Mode :character Median :1068 Mode :character
Mean :5112 Mean :1066
3rd Qu.:5112 3rd Qu.:1072
Max. :5112 Max. :1083
Year Code Year Unit Value
Min. :1961 Min. :1961 Length:30977 Min. : 0
1st Qu.:1976 1st Qu.:1976 Class :character 1st Qu.: 171
Median :1992 Median :1992 Mode :character Median : 1800
Mean :1991 Mean :1991 Mean : 99411
3rd Qu.:2005 3rd Qu.:2005 3rd Qu.: 15404
Max. :2018 Max. :2018 Max. :23707134
NA's :1036
Flag Flag Description
Length:30977 Length:30977
Class :character Class :character
Mode :character Mode :character
Code
colnames(bird)[14] <- c("flag")
unique_flag <- unique(bird$flag)
unique_flag[1] "FAO estimate"
[2] "Official data"
[3] "FAO data based on imputation methodology"
[4] "Data not available"
[5] "Unofficial figure"
[6] "Aggregate, may include official, semi-official, estimated or calculated data"