Soybean Production
Jude Sullivan
4/20/2022
Soybean plant development
Introduction
Soybean is the key ingredient behind multiple household food items like tofu, edamame, tempeh, miso, soy sauce, and soy milk. However, most of the soybean that is produced is used to feed animals like cows (~70%). This is just one example of a resource that could be greatly conserved if humans ate at lower trophic levels (i.e., if we consumed soy products and other food items, like corn and grains, directly rather than feeding them to animals).
Objectives
- Identify which countries produced the most soybean in 2013 and what they used it for
- Ex: How much of the soybean that has been produced in recent years has been used for human compared to animal consumption?
- Explore how soybean production and usage in the United States have changed over time
- Ex: Has the popularity of soybean increased for human consumption in the United States over time?
Setting Up
#Import data
soybean_use <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-04-06/soybean_use.csv')## Rows: 9897 Columns: 6## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (2): entity, code
## dbl (4): year, human_food, animal_feed, processed##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.as_tibble(soybean_use)## # A tibble: 9,897 x 6
## entity code year human_food animal_feed processed
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Africa <NA> 1961 33000 6000 14000
## 2 Africa <NA> 1962 43000 7000 17000
## 3 Africa <NA> 1963 31000 7000 5000
## 4 Africa <NA> 1964 43000 6000 14000
## 5 Africa <NA> 1965 34000 6000 12000
## 6 Africa <NA> 1966 41000 6000 2000
## 7 Africa <NA> 1967 47000 6000 4000
## 8 Africa <NA> 1968 50000 7000 3000
## 9 Africa <NA> 1969 52000 6000 6000
## 10 Africa <NA> 1970 52000 6000 8000
## # ... with 9,887 more rowssoybean_use.csv Variable Table
| variable | class | description |
|---|---|---|
| entity | character | Country |
| code | character | Country Code |
| year | double | Year |
| human_food | double | Use for human food (tempeh, tofu, etc) |
| animal_feed | double | Used for animal food |
| processed | double | Processed into vegetable oil, biofuel, processed animal feed |
Objective 1 - Global Comparisons
#Sums soybean production that is used directly for human food, for animal food, and that is processed into other items
soybean_use <- soybean_use|>
group_by(entity, code, year) |>
mutate(use_total = c_across(human_food:processed) |>
sum(na.rm = TRUE)) |>
ungroup()
#Filters by the most recent year (2013) and shows which entities had the greatest combined soybean production
soybean_use_2013 <- soybean_use |>
rowwise() |>
group_by(entity) |>
filter(year == 2013) |>
arrange(-use_total)
#Finding percentage of human food, animal food, and processed item usage from the total
soybean_use_2013 |>
group_by(entity) |>
mutate(
human_percent = (human_food / use_total) * 100,
animal_percent = (animal_feed / use_total) * 100,
processed_percent = (processed / use_total) * 100
)## # A tibble: 200 x 10
## # Groups: entity [200]
## entity code year human_food animal_feed processed use_total human_percent
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 World OWID~ 2013 10649000 17478000 227311000 255438000 4.17
## 2 Americas <NA> 2013 970000 5599000 127032000 133601000 0.726
## 3 Asia <NA> 2013 8667000 10017000 80582000 99266000 8.73
## 4 Eastern~ <NA> 2013 6906000 9664000 64196000 80766000 8.55
## 5 South A~ <NA> 2013 819000 1926000 75405000 78150000 1.05
## 6 China CHN 2013 5072000 9530000 59162000 73764000 6.88
## 7 Norther~ <NA> 2013 45000 2707000 48776000 51528000 0.0873
## 8 United ~ USA 2013 12000 2240000 47189000 49441000 0.0243
## 9 Brazil BRA 2013 725000 637000 35513000 36875000 1.97
## 10 Argenti~ ARG 2013 0 986000 34041000 35027000 0
## # ... with 190 more rows, and 2 more variables: animal_percent <dbl>,
## # processed_percent <dbl>kable(soybean_use_2013)| entity | code | year | human_food | animal_feed | processed | use_total |
|---|---|---|---|---|---|---|
| World | OWID_WRL | 2013 | 10649000 | 17478000 | 227311000 | 255438000 |
| Americas | NA | 2013 | 970000 | 5599000 | 127032000 | 133601000 |
| Asia | NA | 2013 | 8667000 | 10017000 | 80582000 | 99266000 |
| Eastern Asia | NA | 2013 | 6906000 | 9664000 | 64196000 | 80766000 |
| South America | NA | 2013 | 819000 | 1926000 | 75405000 | 78150000 |
| China | CHN | 2013 | 5072000 | 9530000 | 59162000 | 73764000 |
| Northern America | NA | 2013 | 45000 | 2707000 | 48776000 | 51528000 |
| United States | USA | 2013 | 12000 | 2240000 | 47189000 | 49441000 |
| Brazil | BRA | 2013 | 725000 | 637000 | 35513000 | 36875000 |
| Argentina | ARG | 2013 | 0 | 986000 | 34041000 | 35027000 |
| Europe | NA | 2013 | 139000 | 1732000 | 16672000 | 18543000 |
| European Union | NA | 2013 | 118000 | 646000 | 13376000 | 14140000 |
| Low Income Food Deficit Countries | NA | 2013 | 1259000 | 125000 | 9871000 | 11255000 |
| Southern Asia | NA | 2013 | 580000 | 1000 | 9764000 | 10345000 |
| India | IND | 2013 | 484000 | NA | 9400000 | 9884000 |
| Europe, Western | NA | 2013 | 100000 | 320000 | 6388000 | 6808000 |
| Southern Europe | NA | 2013 | 10000 | 223000 | 6209000 | 6442000 |
| Land Locked Developing Countries | NA | 2013 | 242000 | 507000 | 5345000 | 6094000 |
| South Eastern Asia | NA | 2013 | 1047000 | 95000 | 4411000 | 5553000 |
| Eastern Europe | NA | 2013 | 23000 | 1097000 | 2930000 | 4050000 |
| Africa | NA | 2013 | 866000 | 127000 | 2948000 | 3941000 |
| Central America | NA | 2013 | 34000 | 960000 | 2740000 | 3734000 |
| Net Food Importing Developing Countries | NA | 2013 | 641000 | 63000 | 2921000 | 3625000 |
| Germany | DEU | 2013 | 72000 | 80000 | 3282000 | 3434000 |
| Mexico | MEX | 2013 | 2000 | 959000 | 2440000 | 3401000 |
| Spain | ESP | 2013 | 1000 | 16000 | 3300000 | 3317000 |
| Paraguay | PRY | 2013 | 0 | 301000 | 2940000 | 3241000 |
| Japan | JPN | 2013 | 933000 | 104000 | 1911000 | 2948000 |
| Russia | RUS | 2013 | 7000 | 600000 | 1930000 | 2537000 |
| Taiwan | TWN | 2013 | 395000 | NA | 2060000 | 2455000 |
| Netherlands | NLD | 2013 | 2000 | 0 | 2440000 | 2442000 |
| Western Asia | NA | 2013 | 133000 | 136000 | 2136000 | 2405000 |
| Indonesia | IDN | 2013 | 279000 | NA | 2109000 | 2388000 |
| Northern Africa | NA | 2013 | 73000 | 2000 | 2168000 | 2243000 |
| Bolivia | BOL | 2013 | 0 | 2000 | 2117000 | 2119000 |
| Canada | CAN | 2013 | 33000 | 467000 | 1588000 | 2088000 |
| Italy | ITA | 2013 | 1000 | 151000 | 1582000 | 1734000 |
| Egypt | EGY | 2013 | 52000 | NA | 1647000 | 1699000 |
| Thailand | THA | 2013 | 143000 | NA | 1428000 | 1571000 |
| Turkey | TUR | 2013 | 128000 | NA | 1160000 | 1288000 |
| South Korea | KOR | 2013 | 411000 | 30000 | 814000 | 1255000 |
| Northern Europe | NA | 2013 | 7000 | 91000 | 1144000 | 1242000 |
| Ukraine | UKR | 2013 | 1000 | 444000 | 686000 | 1131000 |
| Least Developed Countries | NA | 2013 | 430000 | 57000 | 352000 | 839000 |
| Portugal | PRT | 2013 | 0 | 40000 | 708000 | 748000 |
| United Kingdom | GBR | 2013 | 3000 | 37000 | 670000 | 710000 |
| Vietnam | VNM | 2013 | 527000 | 95000 | 74000 | 696000 |
| Southern Africa | NA | 2013 | 70000 | 8000 | 611000 | 689000 |
| South Africa | ZAF | 2013 | 67000 | 8000 | 611000 | 686000 |
| France | FRA | 2013 | 4000 | 99000 | 516000 | 619000 |
| Western Africa | NA | 2013 | 478000 | 61000 | 35000 | 574000 |
| Saudi Arabia | SAU | 2013 | 1000 | 84000 | 474000 | 559000 |
| Malaysia | MYS | 2013 | 1000 | NA | 539000 | 540000 |
| Nigeria | NGA | 2013 | 451000 | 61000 | 20000 | 532000 |
| Tunisia | TUN | 2013 | 21000 | NA | 459000 | 480000 |
| Colombia | COL | 2013 | 43000 | NA | 386000 | 429000 |
| Norway | NOR | 2013 | 1000 | 15000 | 404000 | 420000 |
| Eastern Africa | NA | 2013 | 220000 | 55000 | 129000 | 404000 |
| Israel | ISR | 2013 | 1000 | 34000 | 337000 | 372000 |
| North Korea | PRK | 2013 | 73000 | NA | 249000 | 322000 |
| Greece | GRC | 2013 | 0 | 2000 | 287000 | 289000 |
| Iran | IRN | 2013 | 0 | NA | 288000 | 288000 |
| Venezuela | VEN | 2013 | 0 | NA | 265000 | 265000 |
| Costa Rica | CRI | 2013 | 8000 | NA | 244000 | 252000 |
| Serbia | SRB | 2013 | 0 | 11000 | 230000 | 241000 |
| Asia, Central | NA | 2013 | 1000 | 122000 | 75000 | 198000 |
| Small island developing States | NA | 2013 | 77000 | 5000 | 114000 | 196000 |
| Caribbean | NA | 2013 | 72000 | 5000 | 110000 | 187000 |
| Myanmar | MMR | 2013 | 21000 | NA | 165000 | 186000 |
| Kazakhstan | KAZ | 2013 | 1000 | 115000 | 68000 | 184000 |
| United Arab Emirates | ARE | 2013 | 1000 | 4000 | 165000 | 170000 |
| Cuba | CUB | 2013 | 71000 | 5000 | 80000 | 156000 |
| Belgium | BEL | 2013 | 1000 | 61000 | 91000 | 153000 |
| Austria | AUT | 2013 | 17000 | 80000 | 43000 | 140000 |
| Romania | ROU | 2013 | 0 | 20000 | 115000 | 135000 |
| Cambodia | KHM | 2013 | 66000 | NA | 65000 | 131000 |
| Zambia | ZMB | 2013 | 106000 | NA | 20000 | 126000 |
| Zimbabwe | ZWE | 2013 | 8000 | NA | 91000 | 99000 |
| Bangladesh | BGD | 2013 | 95000 | NA | NA | 95000 |
| Croatia | HRV | 2013 | 0 | 3000 | 87000 | 90000 |
| Hungary | HUN | 2013 | 0 | 17000 | 72000 | 89000 |
| Oceania | NA | 2013 | 7000 | 3000 | 77000 | 87000 |
| Australia & New Zealand | NA | 2013 | 6000 | 3000 | 77000 | 86000 |
| Australia | AUS | 2013 | 4000 | 3000 | 77000 | 84000 |
| Malawi | MWI | 2013 | 29000 | 55000 | NA | 84000 |
| Ecuador | ECU | 2013 | 0 | NA | 81000 | 81000 |
| Nepal | NPL | 2013 | 0 | NA | 64000 | 64000 |
| Morocco | MAR | 2013 | 0 | NA | 62000 | 62000 |
| Poland | POL | 2013 | 2000 | NA | 51000 | 53000 |
| Peru | PER | 2013 | 48000 | NA | 2000 | 50000 |
| Czechia | CZE | 2013 | 9000 | NA | 38000 | 47000 |
| Guatemala | GTM | 2013 | 5000 | 1000 | 41000 | 47000 |
| Ethiopia | ETH | 2013 | 42000 | NA | NA | 42000 |
| Finland | FIN | 2013 | 0 | NA | 41000 | 41000 |
| Uruguay | URY | 2013 | 0 | NA | 35000 | 35000 |
| Middle Africa | NA | 2013 | 25000 | NA | 6000 | 31000 |
| Moldova | MDA | 2013 | 0 | 16000 | 14000 | 30000 |
| Philippines | PHL | 2013 | 3000 | NA | 26000 | 29000 |
| Chile | CHL | 2013 | 1000 | NA | 25000 | 26000 |
| Rwanda | RWA | 2013 | 23000 | NA | NA | 23000 |
| Sweden | SWE | 2013 | 1000 | NA | 21000 | 22000 |
| Barbados | BRB | 2013 | 0 | NA | 21000 | 21000 |
| Burkina Faso | BFA | 2013 | 21000 | NA | NA | 21000 |
| Switzerland | CHE | 2013 | 4000 | NA | 17000 | 21000 |
| Bosnia and Herzegovina | BIH | 2013 | 6000 | NA | 14000 | 20000 |
| Hong Kong | HKG | 2013 | 20000 | NA | NA | 20000 |
| Slovakia | SVK | 2013 | 0 | NA | 19000 | 19000 |
| Uganda | UGA | 2013 | 1000 | NA | 18000 | 19000 |
| Ireland | IRL | 2013 | 1000 | 12000 | 4000 | 17000 |
| Benin | BEN | 2013 | 0 | NA | 14000 | 14000 |
| Cameroon | CMR | 2013 | 14000 | NA | NA | 14000 |
| Panama | PAN | 2013 | 14000 | NA | NA | 14000 |
| Denmark | DNK | 2013 | 0 | 13000 | 0 | 13000 |
| Sri Lanka | LKA | 2013 | 0 | 1000 | 12000 | 13000 |
| Laos | LAO | 2013 | 6000 | NA | 6000 | 12000 |
| Angola | AGO | 2013 | 11000 | NA | NA | 11000 |
| Azerbaijan | AZE | 2013 | 0 | 11000 | NA | 11000 |
| Kenya | KEN | 2013 | 9000 | NA | NA | 9000 |
| Lithuania | LTU | 2013 | 0 | 9000 | NA | 9000 |
| Trinidad and Tobago | TTO | 2013 | 0 | NA | 9000 | 9000 |
| Uzbekistan | UZB | 2013 | NA | 2000 | 7000 | 9000 |
| Nicaragua | NIC | 2013 | 0 | NA | 7000 | 7000 |
| Bulgaria | BGR | 2013 | 0 | 0 | 6000 | 6000 |
| Estonia | EST | 2013 | 0 | 6000 | NA | 6000 |
| Gabon | GAB | 2013 | 0 | NA | 6000 | 6000 |
| Honduras | HND | 2013 | 0 | NA | 6000 | 6000 |
| Belize | BLZ | 2013 | 1000 | 0 | 4000 | 5000 |
| El Salvador | SLV | 2013 | 5000 | NA | NA | 5000 |
| Kyrgyzstan | KGZ | 2013 | 0 | 4000 | NA | 4000 |
| Belarus | BLR | 2013 | 3000 | NA | NA | 3000 |
| Georgia | GEO | 2013 | 0 | 3000 | NA | 3000 |
| Latvia | LVA | 2013 | 0 | 0 | 3000 | 3000 |
| Liberia | LBR | 2013 | 3000 | NA | NA | 3000 |
| Botswana | BWA | 2013 | 2000 | NA | NA | 2000 |
| Mali | MLI | 2013 | 2000 | NA | NA | 2000 |
| New Zealand | NZL | 2013 | 2000 | NA | NA | 2000 |
| Slovenia | SVN | 2013 | 2000 | NA | NA | 2000 |
| Sudan | SDN | 2013 | NA | 2000 | NA | 2000 |
| Tanzania | TZA | 2013 | 2000 | NA | NA | 2000 |
| Timor | TLS | 2013 | 2000 | NA | NA | 2000 |
| Brunei | BRN | 2013 | 1000 | NA | NA | 1000 |
| Cote d’Ivoire | CIV | 2013 | 1000 | NA | NA | 1000 |
| Eswatini | SWZ | 2013 | 1000 | NA | NA | 1000 |
| Guyana | GUY | 2013 | 1000 | NA | NA | 1000 |
| Macao | MAC | 2013 | 1000 | NA | NA | 1000 |
| Mozambique | MOZ | 2013 | 1000 | NA | NA | 1000 |
| North Macedonia | MKD | 2013 | 1000 | NA | NA | 1000 |
| Oman | OMN | 2013 | 1000 | NA | NA | 1000 |
| Togo | TGO | 2013 | 0 | NA | 1000 | 1000 |
| Yemen | YEM | 2013 | 1000 | NA | NA | 1000 |
| Albania | ALB | 2013 | 0 | NA | 0 | 0 |
| Algeria | DZA | 2013 | 0 | 0 | NA | 0 |
| Antigua and Barbuda | ATG | 2013 | 0 | NA | NA | 0 |
| Armenia | ARM | 2013 | 0 | 0 | NA | 0 |
| Bahamas | BHS | 2013 | 0 | NA | NA | 0 |
| Bermuda | BMU | 2013 | 0 | NA | NA | 0 |
| Cape Verde | CPV | 2013 | 0 | NA | NA | 0 |
| Congo | COG | 2013 | 0 | NA | NA | 0 |
| Cyprus | CYP | 2013 | 0 | 0 | NA | 0 |
| Dominica | DMA | 2013 | 0 | NA | NA | 0 |
| Dominican Republic | DOM | 2013 | NA | NA | 0 | 0 |
| Fiji | FJI | 2013 | 0 | NA | NA | 0 |
| French Polynesia | PYF | 2013 | 0 | NA | NA | 0 |
| Gambia | GMB | 2013 | 0 | NA | NA | 0 |
| Ghana | GHA | 2013 | 0 | NA | NA | 0 |
| Grenada | GRD | 2013 | 0 | NA | NA | 0 |
| Guinea | GIN | 2013 | 0 | 0 | NA | 0 |
| Haiti | HTI | 2013 | 0 | NA | NA | 0 |
| Iceland | ISL | 2013 | 0 | 0 | NA | 0 |
| Iraq | IRQ | 2013 | NA | NA | 0 | 0 |
| Jamaica | JAM | 2013 | 0 | NA | 0 | 0 |
| Jordan | JOR | 2013 | 0 | NA | NA | 0 |
| Kiribati | KIR | 2013 | 0 | NA | NA | 0 |
| Kuwait | KWT | 2013 | 0 | NA | 0 | 0 |
| Lebanon | LBN | 2013 | 0 | 0 | 0 | 0 |
| Luxembourg | LUX | 2013 | 0 | 0 | 0 | 0 |
| Madagascar | MDG | 2013 | 0 | NA | NA | 0 |
| Maldives | MDV | 2013 | 0 | NA | NA | 0 |
| Malta | MLT | 2013 | 0 | 0 | NA | 0 |
| Mauritania | MRT | 2013 | 0 | NA | NA | 0 |
| Mauritius | MUS | 2013 | 0 | NA | NA | 0 |
| Melanesia | OWID_MNS | 2013 | 0 | NA | NA | 0 |
| Micronesia (region) | NA | 2013 | 0 | NA | NA | 0 |
| Mongolia | MNG | 2013 | 0 | NA | NA | 0 |
| Montenegro | MNE | 2013 | 0 | 0 | NA | 0 |
| Namibia | NAM | 2013 | 0 | NA | NA | 0 |
| New Caledonia | NCL | 2013 | 0 | NA | NA | 0 |
| Niger | NER | 2013 | 0 | NA | NA | 0 |
| Pakistan | PAK | 2013 | 0 | NA | 0 | 0 |
| Polynesia | OWID_PYA | 2013 | 0 | NA | NA | 0 |
| Saint Kitts and Nevis | KNA | 2013 | 0 | NA | NA | 0 |
| Saint Lucia | LCA | 2013 | 0 | NA | NA | 0 |
| Saint Vincent and the Grenadines | VCT | 2013 | 0 | NA | NA | 0 |
| Samoa | WSM | 2013 | 0 | NA | NA | 0 |
| Senegal | SEN | 2013 | 0 | NA | NA | 0 |
| Sierra Leone | SLE | 2013 | 0 | NA | NA | 0 |
| Solomon Islands | SLB | 2013 | 0 | NA | NA | 0 |
| Suriname | SUR | 2013 | 0 | NA | NA | 0 |
| Tajikistan | TJK | 2013 | NA | 0 | NA | 0 |
| Vanuatu | VUT | 2013 | 0 | NA | NA | 0 |
#2013's Top 10 countries in soybean production
top_countries_2013 <- soybean_use_2013 |>
filter(entity %in% c("China", "United States", "Brazil", "Argentina", "India", "Germany", "Mexico", "Spain", "Paraguay", "Japan"))
kable(top_countries_2013)| entity | code | year | human_food | animal_feed | processed | use_total |
|---|---|---|---|---|---|---|
| China | CHN | 2013 | 5072000 | 9530000 | 59162000 | 73764000 |
| United States | USA | 2013 | 12000 | 2240000 | 47189000 | 49441000 |
| Brazil | BRA | 2013 | 725000 | 637000 | 35513000 | 36875000 |
| Argentina | ARG | 2013 | 0 | 986000 | 34041000 | 35027000 |
| India | IND | 2013 | 484000 | NA | 9400000 | 9884000 |
| Germany | DEU | 2013 | 72000 | 80000 | 3282000 | 3434000 |
| Mexico | MEX | 2013 | 2000 | 959000 | 2440000 | 3401000 |
| Spain | ESP | 2013 | 1000 | 16000 | 3300000 | 3317000 |
| Paraguay | PRY | 2013 | 0 | 301000 | 2940000 | 3241000 |
| Japan | JPN | 2013 | 933000 | 104000 | 1911000 | 2948000 |
ggplot(data = top_countries_2013) +
aes(x = reorder(entity, -use_total),
y = use_total) +
geom_bar(stat = "identity",
color = "darkgreen",
fill = "darkgreen") +
scale_x_discrete(guide = guide_axis(angle = 45)) +
labs(title = "Top 10 Soybean-Producing Countries in 2013")
Objective 2 - Exploring the USA
#Developing respective soybean usage percent variables for human, animal, and processed products
usa_soybean_use <- soybean_use |>
group_by(entity) |>
mutate(
human_percent = (human_food / use_total) * 100,
animal_percent = (animal_feed / use_total) * 100,
processed_percent = (processed / use_total) * 100
) |>
filter(entity %in% "United States")
kable(usa_soybean_use)| entity | code | year | human_food | animal_feed | processed | use_total | human_percent | animal_percent | processed_percent |
|---|---|---|---|---|---|---|---|---|---|
| United States | USA | 1961 | 0 | 36000 | 11050000 | 11086000 | 0.0000000 | 0.3247339 | 99.67527 |
| United States | USA | 1962 | 0 | 31000 | 11730000 | 11761000 | 0.0000000 | 0.2635830 | 99.73642 |
| United States | USA | 1963 | 0 | 24000 | 12873000 | 12897000 | 0.0000000 | 0.1860898 | 99.81391 |
| United States | USA | 1964 | 0 | 23000 | 11893000 | 11916000 | 0.0000000 | 0.1930178 | 99.80698 |
| United States | USA | 1965 | 0 | 24000 | 13036000 | 13060000 | 0.0000000 | 0.1837672 | 99.81623 |
| United States | USA | 1966 | 0 | 24000 | 14616000 | 14640000 | 0.0000000 | 0.1639344 | 99.83607 |
| United States | USA | 1967 | 0 | 26000 | 15214000 | 15240000 | 0.0000000 | 0.1706037 | 99.82940 |
| United States | USA | 1968 | 0 | 25000 | 15676000 | 15701000 | 0.0000000 | 0.1592255 | 99.84077 |
| United States | USA | 1969 | 0 | 24000 | 16493000 | 16517000 | 0.0000000 | 0.1453048 | 99.85470 |
| United States | USA | 1970 | 0 | 24000 | 20058000 | 20082000 | 0.0000000 | 0.1195100 | 99.88049 |
| United States | USA | 1971 | 0 | 29000 | 20684000 | 20713000 | 0.0000000 | 0.1400087 | 99.85999 |
| United States | USA | 1972 | 0 | 29000 | 19623000 | 19652000 | 0.0000000 | 0.1475677 | 99.85243 |
| United States | USA | 1973 | 0 | 30000 | 19650000 | 19680000 | 0.0000000 | 0.1524390 | 99.84756 |
| United States | USA | 1974 | 0 | 33000 | 22344000 | 22377000 | 0.0000000 | 0.1474729 | 99.85253 |
| United States | USA | 1975 | 0 | 27000 | 19078000 | 19105000 | 0.0000000 | 0.1413243 | 99.85868 |
| United States | USA | 1976 | 0 | 33000 | 23542000 | 23575000 | 0.0000000 | 0.1399788 | 99.86002 |
| United States | USA | 1977 | 0 | 27000 | 21506000 | 21533000 | 0.0000000 | 0.1253889 | 99.87461 |
| United States | USA | 1978 | 0 | 27000 | 25221000 | 25248000 | 0.0000000 | 0.1069392 | 99.89306 |
| United States | USA | 1979 | 0 | 27000 | 27701000 | 27728000 | 0.0000000 | 0.0973745 | 99.90263 |
| United States | USA | 1980 | 0 | 27000 | 30573000 | 30600000 | 0.0000000 | 0.0882353 | 99.91176 |
| United States | USA | 1981 | 0 | 43000 | 27774000 | 27817000 | 0.0000000 | 0.1545817 | 99.84542 |
| United States | USA | 1982 | 0 | 47000 | 28033000 | 28080000 | 0.0000000 | 0.1673789 | 99.83262 |
| United States | USA | 1983 | 3000 | 35000 | 30155000 | 30193000 | 0.0099361 | 0.1159209 | 99.87414 |
| United States | USA | 1984 | 3000 | 40000 | 26753000 | 26796000 | 0.0111957 | 0.1492760 | 99.83953 |
| United States | USA | 1985 | 4000 | 12000 | 28032000 | 28048000 | 0.0142613 | 0.0427838 | 99.94295 |
| United States | USA | 1986 | 5000 | 55000 | 28658000 | 28718000 | 0.0174107 | 0.1915175 | 99.79107 |
| United States | USA | 1987 | 5000 | 52000 | 32087000 | 32144000 | 0.0155550 | 0.1617720 | 99.82267 |
| United States | USA | 1988 | 6000 | 44000 | 31952000 | 32002000 | 0.0187488 | 0.1374914 | 99.84376 |
| United States | USA | 1989 | 7000 | 54000 | 28795000 | 28856000 | 0.0242584 | 0.1871361 | 99.78861 |
| United States | USA | 1990 | 7000 | 55000 | 31189000 | 31251000 | 0.0223993 | 0.1759944 | 99.80161 |
| United States | USA | 1991 | 8000 | 56000 | 32305000 | 32369000 | 0.0247150 | 0.1730050 | 99.80228 |
| United States | USA | 1992 | 9000 | 62000 | 34130000 | 34201000 | 0.0263150 | 0.1812812 | 99.79240 |
| United States | USA | 1993 | 10000 | 53000 | 34809000 | 34872000 | 0.0286763 | 0.1519844 | 99.81934 |
| United States | USA | 1994 | 10000 | 71000 | 34728000 | 34809000 | 0.0287282 | 0.2039702 | 99.76730 |
| United States | USA | 1995 | 10000 | 62000 | 38238000 | 38310000 | 0.0261028 | 0.1618376 | 99.81206 |
| United States | USA | 1996 | 10000 | 67000 | 37286000 | 37363000 | 0.0267644 | 0.1793218 | 99.79391 |
| United States | USA | 1997 | 10000 | 76000 | 39082000 | 39168000 | 0.0255310 | 0.1940359 | 99.78043 |
| United States | USA | 1998 | 10000 | 78000 | 43464000 | 43552000 | 0.0229611 | 0.1790963 | 99.79794 |
| United States | USA | 1999 | 11000 | 76000 | 43273000 | 43360000 | 0.0253690 | 0.1752768 | 99.79935 |
| United States | USA | 2000 | 10000 | 76000 | 44625000 | 44711000 | 0.0223659 | 0.1699805 | 99.80765 |
| United States | USA | 2001 | 11000 | 78000 | 46259000 | 46348000 | 0.0237335 | 0.1682921 | 99.80797 |
| United States | USA | 2002 | 11000 | 77000 | 43948000 | 44036000 | 0.0249796 | 0.1748569 | 99.80016 |
| United States | USA | 2003 | 11000 | 650000 | 41622000 | 42283000 | 0.0260152 | 1.5372608 | 98.43672 |
| United States | USA | 2004 | 12000 | 2748000 | 46160000 | 48920000 | 0.0245298 | 5.6173344 | 94.35814 |
| United States | USA | 2005 | 12000 | 1932000 | 47321000 | 49265000 | 0.0243581 | 3.9216482 | 96.05399 |
| United States | USA | 2006 | 13000 | 2096000 | 49152000 | 51261000 | 0.0253604 | 4.0888785 | 95.88576 |
| United States | USA | 2007 | 13000 | 900000 | 49070000 | 49983000 | 0.0260088 | 1.8006122 | 98.17338 |
| United States | USA | 2008 | 12000 | 1960000 | 45233000 | 47205000 | 0.0254210 | 4.1521025 | 95.82248 |
| United States | USA | 2009 | 10000 | 2205000 | 46131000 | 48346000 | 0.0206842 | 4.5608737 | 95.41844 |
| United States | USA | 2010 | 11000 | 2205000 | 44852000 | 47068000 | 0.0233704 | 4.6847115 | 95.29192 |
| United States | USA | 2011 | 12000 | 2205000 | 46349000 | 48566000 | 0.0247086 | 4.5402133 | 95.43508 |
| United States | USA | 2012 | 11000 | 2014000 | 45964000 | 47989000 | 0.0229219 | 4.1967951 | 95.78028 |
| United States | USA | 2013 | 12000 | 2240000 | 47189000 | 49441000 | 0.0242714 | 4.5306527 | 95.44508 |
#USA's Soybean Production Use Over Time
usa_production_history <- soybean_use |>
filter(entity %in% "United States")
#I truncated the following x-axis to begin at 1980 since soybeans were not used for human food until 1983
ggplot(data = usa_production_history) +
aes(x = year,
y = human_food) +
geom_line(stat = "identity") +
coord_cartesian(xlim = c(1980,2013)) +
labs(title = "USA Human Food Soybean Use from 1961-2013")
ggplot(data = usa_production_history) +
aes(x = year,
y = animal_feed) +
geom_line(stat = "identity") +
facet_zoom(xlim = c(2000, 2013)) +
labs(title = "USA Animal Food Soybean Use from 1961-2013")
ggplot(data = usa_production_history) +
aes(x = year,
y = processed) +
geom_line(stat = "identity") +
labs(title = "USA Processed Soybean Use from 1961-2013")
ggplot(data = usa_production_history) +
aes(x = year,
y = use_total) +
geom_line(stat = "identity") +
facet_zoom(xlim = c(2000, 2013)) +
labs(title = "USA Total Soybean Production from 1961-2013")