Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Poultry Across the World from 1960 to 2018
challenge_2
birds
In-depth Analysis of Poultry Productrion from 1960 to 2018
Introduction:
I continued my research using the birds.csv data file. In this analysis, I studied the median, means and calculated the coefficient of variation (mean / standard deviation). The data includes the estimates stocks of poultry from approximately 250 areas across the globe. This data spans from 1961 to 2018.
In this analysis, I discovered that production grew exponentially likely due to demand stemming from population growth. This is reflected when comparing the median, mean and coefficient of variation.
Code
### pull the data from the _data folder
library(readr)
birds <- read_csv ("_data/birds.csv")
view (birds)
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`As you can see from the above header, the data is organized by country, type of animal, the value, the item, year and several other data points. For the purpose of this analysis, we will focus on the area, item, year and the value. Refer to the below chart:
Code
arranged_values<- select(birds, "Area", "Item", "Year", "Value")
arranged_values %>%
arrange(desc('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 rowsSummary Statistics
The below table represents the percentages per type of poultry from the listed countries. As you can see, chicken production makes up for almost 43% if all poultry produced globally. The second most produced poultry product was Duck (22%), than Turkey (18%), followed by Geese and Guinea Fowls (13%) and all other birds(~4%).
Code
Bird <- select(birds, "Item")
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 Code
###median and mean of items
arranged_values%>%
group_by(Item)%>%
select(Value)%>%
summarise (
avg_value = mean (Value, na.rm = TRUE),
med_value = median(Value, na.rm = TRUE)
)# A tibble: 5 × 3
Item avg_value med_value
<chr> <dbl> <dbl>
1 Chickens 207931. 10784.
2 Ducks 23072. 510
3 Geese and guinea fowls 10292. 258
4 Pigeons, other birds 6163. 2800
5 Turkeys 15228. 528 Next, I analyzed the median and average values of the type of poultry exported. You will notice that the average values are exponentially larger than the median values. This indicates that there is an extremely large value difference in the data.
Code
arranged_values%>%
group_by(Year)%>%
select(Value)%>%
summarise (
avg_year = mean (Value, na.rm = TRUE),
med_year = median(Value, na.rm = TRUE)
)# A tibble: 58 × 3
Year avg_year med_year
<dbl> <dbl> <dbl>
1 1961 36752. 1033
2 1962 37787. 1014
3 1963 38736. 1106
4 1964 39325. 1103
5 1965 40334. 1104
6 1966 41229. 1088.
7 1967 43240. 1193
8 1968 44420. 1252.
9 1969 45607. 1267
10 1970 47706. 1259
# … with 48 more rowsAfter breaking up the averages and median values per year, I noticed that around the 1980s poultry production began to grow at a significantly higher rate. The largest growths in production were seen from the 1980s to about 2010. After 2010, the production seems to increase at a more steady rate.
Graphs
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")
The above graph presents a graphical representation of poultry production by type per year. As you can see, chicken represents the largest amount of exported poultry. Additionally, you will notice only chicken and ducks show a significant increase over the years.
Code
arranged_values# 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 rowsCode
ggplot(data = arranged_values)+
geom_line(mapping = aes(x = Year, y = Value))
The above graph shows the amount of poultry produced from 1960 - 2018. As you can see there a steady increase in production especially from 1980 to 2000. The increase in production could be a result from an increase in demand from population growth.
Describe the data
I have included the coefficient of variation below. This measures the ratio of standard deviation to the mean.
Code
###Standard deviation of each country from the mean
arranged_values# 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 rowsCode
SD_function <- function(x) {
(x - mean(x, na.rm=T))/ sd(x, na.rm=T)
}
SD_function(arranged_values$Value) [1] -1.314309e-01 -1.311534e-01 -1.310146e-01 -1.305983e-01 -1.303208e-01
[6] -1.299045e-01 -1.287943e-01 -1.292245e-01 -1.292106e-01 -1.296269e-01
[11] -1.296269e-01 -1.296269e-01 -1.294881e-01 -1.294881e-01 -1.293494e-01
[16] -1.293494e-01 -1.292106e-01 -1.293494e-01 -1.290718e-01 -1.290718e-01
[21] -1.287943e-01 -1.287943e-01 -1.286555e-01 -1.285167e-01 -1.283780e-01
[26] -1.282392e-01 -1.283780e-01 -1.285167e-01 -1.286555e-01 -1.287943e-01
[31] -1.289331e-01 -1.290718e-01 -1.292106e-01 -1.293494e-01 -1.287915e-01
[36] -1.268515e-01 -1.268515e-01 -1.270902e-01 -1.276841e-01 -1.284390e-01
[41] -1.284557e-01 -1.210842e-01 -1.207428e-01 -1.198824e-01 -1.179507e-01
[46] -1.228549e-01 -1.254152e-01 -1.231199e-01 -1.238082e-01 -1.200684e-01
[51] -1.193884e-01 -1.196187e-01 -1.212271e-01 -1.225524e-01 -1.214908e-01
[56] -1.214408e-01 -1.191178e-01 -1.183143e-01 -1.357606e-01 -1.356260e-01
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[66] -1.357314e-01 -1.355538e-01 -1.349432e-01 -1.349405e-01 -1.349335e-01
[71] -1.348974e-01 -1.346227e-01 -1.340676e-01 -1.337900e-01 -1.335125e-01
[76] -1.335125e-01 -1.333737e-01 -1.331073e-01 -1.329574e-01 -1.326799e-01
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[96] -1.326258e-01 -1.323885e-01 -1.322816e-01 -1.320068e-01 -1.317834e-01
[101] -1.313532e-01 -1.316849e-01 -1.314712e-01 -1.316086e-01 -1.314143e-01
[106] -1.310146e-01 -1.308231e-01 -1.306746e-01 -1.288526e-01 -1.247782e-01
[111] -1.255637e-01 -1.247796e-01 -1.260813e-01 -1.273594e-01 -1.270805e-01
[116] -1.278617e-01 NA NA NA NA
[121] NA NA NA NA NA
[126] NA NA NA NA NA
[131] NA NA NA NA NA
[136] NA NA NA NA NA
[141] NA NA NA NA NA
[146] NA NA NA NA -1.374536e-01
[151] -1.374536e-01 -1.374467e-01 -1.374883e-01 -1.375299e-01 -1.375188e-01
[156] -1.374675e-01 -1.375507e-01 -1.373773e-01 -1.373925e-01 -1.372218e-01
[161] -1.372205e-01 -1.372593e-01 -1.368430e-01 -1.365655e-01 -1.364267e-01
[166] -1.364267e-01 -1.365655e-01 NA NA NA
[171] NA NA NA NA NA
[176] NA NA NA NA NA
[181] NA NA NA NA NA
[186] NA NA NA NA NA
[191] NA NA NA NA NA
[196] NA NA NA NA NA
[201] NA NA NA NA NA
[206] NA NA -1.376201e-01 -1.376201e-01 -1.376146e-01
[211] -1.376409e-01 -1.376687e-01 -1.376756e-01 -1.376062e-01 -1.375646e-01
[216] -1.375716e-01 -1.375785e-01 -1.374647e-01 -1.374633e-01 -1.375369e-01
[221] -1.372593e-01 -1.368430e-01 -1.368430e-01 -1.368430e-01 -1.368430e-01
[226] NA NA NA NA NA
[231] NA NA NA NA NA
[236] NA NA NA NA NA
[241] NA NA NA NA NA
[246] NA NA NA NA NA
[251] NA NA NA NA NA
[256] NA NA NA NA NA
[261] NA NA NA NA NA
[266] -1.371316e-01 -1.371122e-01 -1.371053e-01 -1.371733e-01 -1.372413e-01
[271] -1.372593e-01 -1.371205e-01 -1.371622e-01 -1.369957e-01 -1.370179e-01
[276] -1.367334e-01 -1.367320e-01 -1.368430e-01 -1.364267e-01 -1.361491e-01
[281] -1.361491e-01 -1.361491e-01 -1.361491e-01 -1.369457e-01 -1.369832e-01
[286] -1.369054e-01 -1.371566e-01 -1.373745e-01 -1.373856e-01 -1.373939e-01
[291] -1.268515e-01 -1.260189e-01 -1.253250e-01 -1.243536e-01 -1.232434e-01
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[306] -1.149713e-01 -1.144162e-01 -1.138070e-01 -1.133754e-01 -1.032604e-01
[311] -9.632186e-02 -8.244475e-02 -6.856765e-02 -5.469054e-02 -5.052741e-02
[316] -4.636428e-02 -4.358886e-02 -4.081344e-02 -4.081344e-02 -3.526260e-02
[321] -3.387489e-02 -3.248718e-02 -2.971176e-02 -1.444694e-02 3.593296e-03
[326] -2.994212e-02 -1.652851e-02 3.551064e-02 7.756427e-03 1.469498e-02
[331] 1.469498e-02 2.857208e-02 3.551064e-02 3.551064e-02 3.551064e-02
[336] 3.551064e-02 3.551064e-02 3.551064e-02 3.551064e-02 3.689835e-02
[341] 3.828606e-02 4.036762e-02 4.244919e-02 4.244919e-02 4.595454e-02
[346] 5.088369e-02 4.941272e-02 5.086288e-02 -1.379254e-01 -1.379185e-01
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[391] -1.378907e-01 -1.378907e-01 -1.378907e-01 -1.378907e-01 -1.378907e-01
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[556] -1.379004e-01 -1.379004e-01 -1.379004e-01 -1.379004e-01 -1.379004e-01
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[716] -5.191512e-02 -4.636428e-02 -4.913970e-02 -4.358886e-02 -4.358886e-02
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[1586] -1.372024e-01 -1.372010e-01 -1.157498e-01 -1.101990e-01 -1.032604e-01
[1591] -9.632186e-02 -8.660788e-02 -7.550620e-02 -6.856765e-02 -6.162910e-02
[1596] -5.469054e-02 -4.610617e-02 -5.469054e-02 -6.162910e-02 -6.719104e-02
[1601] -6.719104e-02 -6.719104e-02 -6.719104e-02 -8.041176e-02 -4.523052e-02
[1606] -4.059418e-02 -6.566039e-02 -6.132241e-02 -5.672493e-02 -5.185129e-02
[1611] -5.317378e-02 -4.454360e-02 -3.871522e-02 -3.252326e-02 -2.594412e-02
[1616] -1.999778e-02 -1.152859e-02 -6.598050e-03 1.599156e-03 1.364587e-02
[1621] 2.274370e-02 3.273522e-02 5.258548e-03 1.455621e-02 2.440895e-02
[1626] 3.495555e-02 4.619601e-02 6.004536e-02 7.331187e-02 8.746652e-02
[1631] 1.016073e-01 1.166223e-01 1.324006e-01 1.491641e-01 1.568937e-01
[1636] 1.692776e-01 1.784934e-01 1.877230e-01 1.990745e-01 2.076006e-01
[1641] 2.163446e-01 2.253078e-01 2.344986e-01 2.439212e-01 2.535824e-01
[1646] -1.282392e-01 -1.254638e-01 -1.247699e-01 -1.240761e-01 -1.233822e-01
[1651] -1.219945e-01 -1.213006e-01 -1.206068e-01 -1.199129e-01 -1.194647e-01
[1656] -1.196354e-01 -1.199129e-01 -1.201905e-01 -1.204680e-01 -1.206068e-01
[1661] -1.207456e-01 -1.207456e-01 -1.129896e-01 -1.104932e-01 -1.163590e-01
[1666] -1.150643e-01 -1.136904e-01 -1.122347e-01 -1.129744e-01 -1.129744e-01
[1671] -1.115867e-01 -1.101990e-01 -1.088113e-01 -1.074235e-01 -1.060358e-01
[1676] -1.046481e-01 -1.032604e-01 -1.018727e-01 -1.004850e-01 -9.909728e-02
[1681] -9.770957e-02 -9.632186e-02 -9.493415e-02 -9.354644e-02 -9.215873e-02
[1686] -9.100693e-02 -8.984125e-02 -8.863394e-02 -8.744051e-02 -8.621932e-02
[1691] -8.512303e-02 -8.372145e-02 -8.266679e-02 -8.073232e-02 -7.872985e-02
[1696] -7.672739e-02 -7.453480e-02 -7.237969e-02 -7.014825e-02 -6.784326e-02
[1701] -6.545918e-02 -6.299460e-02 -6.044538e-02 -1.375188e-01 -1.375105e-01
[1706] -1.375022e-01 -1.374925e-01 -1.374952e-01 -1.374952e-01 -1.374925e-01
[1711] -1.374883e-01 -1.374841e-01 -1.374813e-01 -1.373981e-01 -1.371205e-01
[1716] -1.367042e-01 -1.366348e-01 -1.365655e-01 -1.364267e-01 -1.362879e-01
[1721] -1.360798e-01 -1.356634e-01 -1.355941e-01 -1.354553e-01 -1.353165e-01
[1726] -1.354553e-01 -1.351777e-01 -1.353165e-01 -1.344839e-01 -1.342064e-01
[1731] -1.336513e-01 -1.328048e-01 -1.339288e-01 -1.339427e-01 -1.344006e-01
[1736] -1.344006e-01 -1.340121e-01 -1.335541e-01 -1.330407e-01 -1.331933e-01
[1741] -1.331517e-01 -1.330546e-01 -1.331517e-01 -1.339149e-01 -1.340260e-01
[1746] -1.340884e-01 -1.332766e-01 -1.332350e-01 -1.331656e-01 -1.329574e-01
[1751] -1.329574e-01 -1.329574e-01 -1.329574e-01 -1.329574e-01 -1.329574e-01
[1756] -1.329574e-01 -1.329574e-01 -1.329421e-01 -1.317085e-01 -1.328658e-01
[1761] -1.328464e-01 NA NA NA NA
[1766] NA NA NA NA NA
[1771] NA NA NA NA NA
[1776] NA NA NA NA NA
[1781] NA NA NA NA NA
[1786] NA NA NA NA NA
[1791] NA NA NA NA NA
[1796] NA NA -1.379074e-01 -1.379018e-01 -1.379254e-01
[1801] -1.378935e-01 -1.378935e-01 -1.378824e-01 -1.378741e-01 -1.378879e-01
[1806] -1.378768e-01 -1.378768e-01 -1.378768e-01 -1.378810e-01 -1.378879e-01
[1811] -1.378768e-01 -1.378699e-01 -1.378630e-01 -1.378560e-01 -1.378560e-01
[1816] -1.378560e-01 -1.378533e-01 -1.378533e-01 -1.378505e-01 -6.894233e-02
[1821] -7.281404e-02 -9.368521e-02 -9.646063e-02 -1.025666e-01 -1.013176e-01
[1826] -1.011788e-01 -1.004850e-01 -1.013176e-01 -1.013176e-01 -1.028441e-01
[1831] -1.042318e-01 -1.060358e-01 -1.053420e-01 -1.007625e-01 -1.006238e-01
[1836] -9.979113e-02 -9.743203e-02 -9.354644e-02 -8.910576e-02 -8.619157e-02
[1841] -8.355492e-02 -7.568938e-02 -7.537437e-02 -7.406853e-02 -7.411849e-02
[1846] -7.400609e-02 NA NA NA NA
[1851] NA NA NA NA NA
[1856] NA NA NA -1.365655e-01 -1.365655e-01
[1861] -1.365655e-01 -1.365655e-01 -1.365655e-01 -1.365655e-01 -1.364267e-01
[1866] -1.362879e-01 -1.360104e-01 -1.358716e-01 -1.354636e-01 -1.354400e-01
[1871] -1.353928e-01 -1.354553e-01 -1.354733e-01 -1.352152e-01 -1.352333e-01
[1876] -1.361491e-01 -1.365655e-01 -1.367042e-01 -1.365655e-01 -1.365655e-01
[1881] -1.364267e-01 -1.365655e-01 -1.365655e-01 -1.367042e-01 -1.367042e-01
[1886] -1.371774e-01 -1.371205e-01 -1.369818e-01 -1.368430e-01 -1.367042e-01
[1891] -1.365655e-01 -1.365655e-01 -1.364267e-01 -1.362879e-01 -1.361491e-01
[1896] -1.357550e-01 -1.357190e-01 -1.356857e-01 -1.357023e-01 -1.362518e-01
[1901] -8.383246e-02 -8.448607e-02 -8.532009e-02 -9.350203e-02 -8.729341e-02
[1906] -8.859370e-02 -9.234329e-02 -9.250565e-02 -9.286229e-02 -9.182568e-02
[1911] -9.025062e-02 -8.886153e-02 -8.852570e-02 -8.769169e-02 -9.118733e-02
[1916] -8.558237e-02 -9.867264e-02 -1.006168e-01 -9.309265e-02 NA
[1921] NA NA NA -1.376021e-01 -1.376271e-01
[1926] -1.376271e-01 -1.376201e-01 -1.376201e-01 -1.376062e-01 -1.375993e-01
[1931] -1.375951e-01 -1.375924e-01 -1.375924e-01 -1.376340e-01 -1.377173e-01
[1936] -1.376895e-01 -1.377256e-01 -1.376729e-01 NA NA
[1941] NA NA NA -1.379462e-01 -1.379268e-01
[1946] -1.379462e-01 -1.379462e-01 -1.379462e-01 -1.379462e-01 -1.379462e-01
[1951] -1.379462e-01 -1.379462e-01 -1.379462e-01 -1.379476e-01 -1.379504e-01
[1956] -1.379476e-01 -1.379476e-01 -1.376062e-01 -1.375646e-01 -1.376062e-01
[1961] -1.376062e-01 -1.376021e-01 -1.376548e-01 -1.377062e-01 -1.376881e-01
[1966] -1.376895e-01 -1.376687e-01 -1.376895e-01 -1.376798e-01 -1.376826e-01
[1971] -1.376798e-01 -1.376978e-01 -1.376284e-01 -1.374841e-01 -1.375785e-01
[1976] -1.376465e-01 -9.437906e-02 -9.468852e-02 -9.552531e-02 -9.444428e-02
[1981] -9.449563e-02 -9.733211e-02 -9.706428e-02 -9.506182e-02 -9.287340e-02
[1986] -8.978713e-02 -8.753071e-02 -9.069747e-02 -9.096668e-02 -9.273463e-02
[1991] -9.352840e-02 -9.416813e-02 -9.374765e-02 -9.476207e-02 -9.624137e-02
[1996] -9.863239e-02 -1.002116e-01 -9.197277e-02 -9.389336e-02 -9.470101e-02
[2001] -9.504933e-02 -9.502157e-02 -9.378235e-02 -9.646063e-02 -9.535046e-02
[2006] -9.243627e-02 -9.063224e-02 -8.771805e-02 -8.230598e-02 -7.620006e-02
[2011] -7.217570e-02 -6.995536e-02 -7.134307e-02 -6.398820e-02 -7.046187e-02
[2016] -1.378130e-01 -1.378019e-01 -1.378477e-01 -1.378130e-01 -1.378533e-01
[2021] -1.378269e-01 -1.378144e-01 -1.378102e-01 -1.378172e-01 -1.378047e-01
[2026] -1.378394e-01 -1.378269e-01 -1.378047e-01 -1.377936e-01 -1.378269e-01
[2031] -1.377936e-01 -1.378269e-01 -1.378449e-01 -1.378283e-01 -1.378380e-01
[2036] -1.378408e-01 -1.378491e-01 -1.378602e-01 -1.378727e-01 -1.378796e-01
[2041] -1.378671e-01 -1.378630e-01 -1.378574e-01 -1.378630e-01 -1.378491e-01
[2046] -1.378241e-01 -1.378297e-01 -1.378463e-01 -1.378449e-01 -1.378005e-01
[2051] -1.378560e-01 -1.379032e-01 -1.379129e-01 -1.379046e-01 -1.379296e-01
[2056] -1.379324e-01 -1.379337e-01 -1.379351e-01 -1.379337e-01 -1.379351e-01
[2061] -1.379337e-01 -1.379060e-01 -1.379337e-01 -1.379032e-01 -1.379074e-01
[2066] -1.379324e-01 -1.379129e-01 -1.379310e-01 -1.379310e-01 -1.379213e-01
[2071] -1.379310e-01 -1.379226e-01 -1.379324e-01 -1.379310e-01 -1.379199e-01
[2076] -1.379282e-01 -1.379310e-01 -1.379310e-01 -1.379254e-01 -1.379254e-01
[2081] -1.379310e-01 -1.379324e-01 -1.379254e-01 -1.379282e-01 -1.379365e-01
[2086] -1.379337e-01 -1.379379e-01 -1.379393e-01 -1.379365e-01 -1.379379e-01
[2091] -1.379393e-01 -1.379407e-01 -1.379393e-01 -1.379213e-01 -1.379254e-01
[2096] -1.379129e-01 -1.379129e-01 -1.378935e-01 -1.378755e-01 -1.378782e-01
[2101] -1.378810e-01 -1.378297e-01 -1.377950e-01 -1.376298e-01 -1.377284e-01
[2106] -1.376687e-01 -1.376298e-01 -1.375882e-01 -1.375910e-01 -1.375743e-01
[2111] -1.375965e-01 -1.376409e-01 -1.375493e-01 -1.376562e-01 -1.376770e-01
[2116] -1.376493e-01 -1.377297e-01 -1.376770e-01 -1.376659e-01 -1.376548e-01
[2121] -1.375382e-01 -1.374938e-01 -1.375535e-01 -1.374952e-01 -1.374564e-01
[2126] -1.375702e-01 -1.376146e-01 -1.375757e-01 -1.375396e-01 -1.375410e-01
[2131] -1.374827e-01 -1.374314e-01 -1.377339e-01 -1.377450e-01 -1.377339e-01
[2136] -1.377256e-01 -1.377173e-01 -1.376409e-01 -1.376340e-01 -1.376201e-01
[2141] -1.376132e-01 -1.376062e-01 -1.375924e-01 -1.375785e-01 -1.375646e-01
[2146] -1.375507e-01 -1.375369e-01 -1.375230e-01 -1.375091e-01 -1.374952e-01
[2151] -1.374675e-01 -1.374120e-01 -1.373703e-01 -1.373009e-01 -1.371899e-01
[2156] -1.371205e-01 -1.371414e-01 -1.371136e-01 -1.367736e-01 -1.368430e-01
[2161] -1.369679e-01 -1.364267e-01 -1.363573e-01 -1.359410e-01 -1.360104e-01
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[2171] -1.365655e-01 -1.366348e-01 -1.360104e-01 -1.356496e-01 -1.359410e-01
[2176] -1.358161e-01 -1.359549e-01 -1.358994e-01 -1.358439e-01 -1.358716e-01
[2181] -1.358716e-01 -1.358300e-01 -1.358161e-01 -1.358022e-01 -1.357883e-01
[2186] -1.357328e-01 -1.357120e-01 -1.357134e-01 -1.357037e-01 -1.356884e-01
[2191] -1.379448e-01 -1.379448e-01 -1.379448e-01 -1.379448e-01 -1.379448e-01
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[2256] -1.379351e-01 -1.379351e-01 -1.379351e-01 -1.379351e-01 -1.379351e-01
[2261] -1.379351e-01 -1.379337e-01 -1.379337e-01 -1.379337e-01 -1.379337e-01
[2266] -1.379324e-01 -1.379324e-01 -1.379324e-01 -1.379324e-01 -1.379324e-01
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[2291] -1.379115e-01 -1.379143e-01 -1.379115e-01 -1.379074e-01 -1.379157e-01
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[2301] -1.378879e-01 -1.378893e-01 -1.378893e-01 -1.378824e-01 -1.378824e-01
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[2311] -1.307371e-01 -1.305983e-01 -1.303208e-01 -1.300432e-01 -1.296269e-01
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[2331] -1.088113e-01 -1.143621e-01 -1.171375e-01 -1.199129e-01 -1.226884e-01
[2336] -1.246603e-01 -1.268515e-01 -1.265406e-01 -1.262215e-01 -1.258801e-01
[2341] -1.255415e-01 -1.251946e-01 -1.248379e-01 -1.244702e-01 -1.240761e-01
[2346] -1.216365e-01 -1.061926e-01 -1.049798e-01 -1.005683e-01 -1.022391e-01
[2351] -9.988550e-02 -1.022890e-01 -1.011525e-01 -1.164090e-01 -1.157512e-01
[2356] -1.149866e-01 -1.142414e-01 -1.134823e-01 -1.126996e-01 -1.118934e-01
[2361] -1.110593e-01 -1.101962e-01 -1.093067e-01 -1.083894e-01 -1.378782e-01
[2366] -1.378755e-01 -1.378491e-01 -1.378422e-01 -1.378352e-01 -1.378283e-01
[2371] -1.378255e-01 -1.378241e-01 -1.378227e-01 -1.378172e-01 -1.378005e-01
[2376] -1.378144e-01 -1.378352e-01 -1.378630e-01 -1.378699e-01 -1.378810e-01
[2381] -1.378699e-01 -1.378727e-01 -1.378866e-01 -1.378907e-01 -1.378866e-01
[2386] -1.378838e-01 -1.378699e-01 -1.378699e-01 -1.378630e-01 -1.378560e-01
[2391] -1.378560e-01 -1.378560e-01 -1.378422e-01 -1.378422e-01 -1.378630e-01
[2396] -1.378630e-01 -1.378768e-01 -1.378838e-01 -1.378838e-01 -1.378907e-01
[2401] -1.378907e-01 -1.378907e-01 -1.378907e-01 -1.378907e-01 -1.378907e-01
[2406] -1.378907e-01 -1.378907e-01 -1.378907e-01 -1.378907e-01 -1.378907e-01
[2411] -1.378907e-01 -1.378838e-01 -1.378838e-01 -1.378838e-01 -1.378838e-01
[2416] -1.378838e-01 -1.378838e-01 -1.378838e-01 -1.378838e-01 -1.378838e-01
[2421] -1.378824e-01 -1.378810e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[2426] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[2431] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[2436] -1.379518e-01 -1.379518e-01 -1.379532e-01 -1.379532e-01 -1.379532e-01
[2441] -1.379532e-01 -1.379532e-01 -1.379532e-01 -1.379532e-01 NA
[2446] NA NA NA NA NA
[2451] NA NA NA NA NA
[2456] NA NA NA NA NA
[2461] NA NA NA NA NA
[2466] NA NA NA NA NA
[2471] NA NA NA NA -1.378519e-01
[2476] -1.378491e-01 -1.378463e-01 -1.378435e-01 -1.378422e-01 -1.378394e-01
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[12541] -1.379518e-01 -1.379532e-01 -1.379532e-01 -1.379532e-01 NA
[12546] NA NA NA NA NA
[12551] NA NA NA NA NA
[12556] NA NA NA NA NA
[12561] NA NA NA NA NA
[12566] NA NA NA NA NA
[12571] -1.370720e-01 -1.370414e-01 -1.370137e-01 -1.369845e-01 -1.369540e-01
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[19811] -1.339982e-01 -1.339288e-01 -1.338594e-01 -1.337207e-01 -1.336513e-01
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[19821] -1.325689e-01 -1.323329e-01 -1.320554e-01 -1.317085e-01 -1.319166e-01
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[19831] -1.300432e-01 -1.296269e-01 -1.296269e-01 -1.296269e-01 -1.296269e-01
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[20001] -1.368444e-01 -1.366043e-01 -1.366793e-01 -1.370414e-01 -1.370414e-01
[20006] -1.370373e-01 NA NA NA NA
[20011] NA NA NA NA NA
[20016] NA NA NA NA NA
[20021] NA NA NA NA NA
[20026] NA NA NA NA NA
[20031] NA NA NA NA -1.201724e-01
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[25936] -1.377603e-01 -1.377561e-01 -1.377561e-01 -1.377561e-01 NA
[25941] NA NA NA NA NA
[25946] NA NA NA NA NA
[25951] NA NA NA NA NA
[25956] NA NA NA NA NA
[25961] NA NA NA NA NA
[25966] NA NA NA -1.379476e-01 -1.379435e-01
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[30646] -1.378879e-01 -1.378768e-01 -1.378824e-01 -1.378782e-01 -1.378810e-01
[30651] -1.378796e-01 -1.378796e-01 -1.378713e-01 -1.378699e-01 -1.378685e-01
[30656] -1.378644e-01 -1.378616e-01 -1.378616e-01 -1.378546e-01 -1.378533e-01
[30661] -1.378533e-01 -1.378533e-01 -1.378519e-01 -1.378449e-01 -1.378435e-01
[30666] -1.378422e-01 -1.378422e-01 -1.378394e-01 -1.378324e-01 -1.378297e-01
[30671] -1.378297e-01 -1.378283e-01 -1.378283e-01 -1.378283e-01 -1.378213e-01
[30676] -1.378144e-01 -1.378144e-01 -1.378130e-01 -1.378075e-01 -1.378047e-01
[30681] -1.377894e-01 -1.377908e-01 -1.377908e-01 -1.377880e-01 -1.377825e-01
[30686] -1.377783e-01 -1.377742e-01 -1.379476e-01 -1.379462e-01 -1.379448e-01
[30691] -1.379435e-01 -1.379421e-01 -1.379407e-01 -1.379365e-01 -1.379324e-01
[30696] -1.379296e-01 -1.379268e-01 -1.379254e-01 -1.379240e-01 -1.379213e-01
[30701] -1.379185e-01 -1.379171e-01 -1.379157e-01 -1.379129e-01 -1.379115e-01
[30706] -1.379102e-01 -1.379004e-01 -1.379032e-01 -1.379018e-01 -1.379018e-01
[30711] -1.378990e-01 -1.378935e-01 -1.378921e-01 -1.378907e-01 -1.378879e-01
[30716] -1.378852e-01 -1.378810e-01 -1.378782e-01 -1.378782e-01 -1.378741e-01
[30721] -1.378699e-01 -1.378657e-01 -1.378644e-01 -1.378644e-01 -1.378602e-01
[30726] -1.378588e-01 -1.378519e-01 -1.378505e-01 -1.378491e-01 -1.378491e-01
[30731] -1.378477e-01 -1.378477e-01 -1.378477e-01 -1.378408e-01 -1.378394e-01
[30736] -1.378394e-01 -1.378311e-01 -1.378297e-01 -1.378283e-01 -1.378283e-01
[30741] -1.378283e-01 -1.378269e-01 -1.378227e-01 -1.378213e-01 -1.378186e-01
[30746] -1.375147e-01 -1.375355e-01 -1.375549e-01 -1.375535e-01 -1.375313e-01
[30751] -1.375327e-01 -1.374467e-01 -1.374536e-01 -1.374591e-01 -1.373037e-01
[30756] -1.373259e-01 -1.373509e-01 -1.373218e-01 -1.372996e-01 -1.372871e-01
[30761] -1.373634e-01 -1.373495e-01 -1.372524e-01 -1.372385e-01 -1.372232e-01
[30766] -1.372052e-01 -1.371816e-01 -1.371858e-01 -1.371552e-01 -1.371372e-01
[30771] -1.370928e-01 -1.371053e-01 -1.370900e-01 -1.371011e-01 -1.370845e-01
[30776] -1.373662e-01 -1.373245e-01 -1.372690e-01 -1.372663e-01 -1.370095e-01
[30781] -1.369957e-01 -1.369957e-01 -1.369957e-01 -1.369957e-01 -1.369054e-01
[30786] -1.368569e-01 -1.367875e-01 -1.367875e-01 -1.367736e-01 -1.367597e-01
[30791] -1.367597e-01 -1.366834e-01 -1.366418e-01 -1.366140e-01 -1.366071e-01
[30796] -1.365752e-01 -1.364267e-01 -1.364170e-01 -1.364211e-01 -1.364295e-01
[30801] -1.363767e-01 -1.363531e-01 -1.363282e-01 -1.379504e-01 -1.379504e-01
[30806] -1.379504e-01 -1.379504e-01 -1.379504e-01 -1.379504e-01 -1.379504e-01
[30811] -1.379504e-01 -1.379504e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30816] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30821] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30826] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30831] -1.379518e-01 -1.379518e-01 NA NA NA
[30836] NA NA -1.379518e-01 -1.379518e-01 -1.379518e-01
[30841] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30846] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30851] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30856] -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01 -1.379518e-01
[30861] -1.379518e-01 -1.370081e-01 -1.369984e-01 -1.369693e-01 -1.369512e-01
[30866] -1.369457e-01 -1.369110e-01 -1.368513e-01 -1.368416e-01 -1.368791e-01
[30871] -1.368472e-01 -1.369166e-01 -1.369554e-01 -1.369443e-01 -1.369249e-01
[30876] -1.368319e-01 -1.367917e-01 -1.368402e-01 -1.367695e-01 -1.367528e-01
[30881] -1.367500e-01 -1.366987e-01 -1.367112e-01 -1.368555e-01 -1.369082e-01
[30886] -1.366793e-01 -1.366862e-01 -1.366515e-01 -1.365974e-01 -1.367722e-01
[30891] -1.367944e-01 -1.367597e-01 -1.366682e-01 -1.365904e-01 -1.365641e-01
[30896] -1.364378e-01 -1.363462e-01 -1.363393e-01 -1.363254e-01 -1.362199e-01
[30901] -1.362213e-01 -1.361922e-01 -1.362158e-01 -1.361894e-01 -1.361755e-01
[30906] -1.361408e-01 -1.362366e-01 -1.362102e-01 -1.362158e-01 -1.364239e-01
[30911] -1.361714e-01 -1.361658e-01 -1.361422e-01 -1.361256e-01 -1.361131e-01
[30916] -1.360784e-01 -1.360520e-01 -1.360576e-01 -1.360492e-01 -1.379379e-01
[30921] -1.379365e-01 -1.379351e-01 -1.379337e-01 -1.379324e-01 -1.379310e-01
[30926] -1.379296e-01 -1.379268e-01 -1.379240e-01 -1.379226e-01 -1.379213e-01
[30931] -1.379185e-01 -1.379185e-01 -1.379185e-01 -1.379185e-01 -1.379171e-01
[30936] -1.379171e-01 -1.379171e-01 -1.379171e-01 -1.379171e-01 -1.379171e-01
[30941] -1.379171e-01 -1.379185e-01 -1.379254e-01 -1.379226e-01 -1.379226e-01
[30946] -1.379226e-01 -1.379226e-01 -1.379046e-01 -1.379185e-01 -1.379185e-01
[30951] -1.379074e-01 -1.379074e-01 -1.379102e-01 -1.379088e-01 -1.379088e-01
[30956] -1.379088e-01 -1.379088e-01 -1.379088e-01 -1.379074e-01 -1.379074e-01
[30961] -1.379074e-01 -1.379088e-01 -1.379088e-01 -1.379088e-01 -1.379088e-01
[30966] -1.379088e-01 -1.379088e-01 -1.379088e-01 -1.379115e-01 -1.379115e-01
[30971] -1.379115e-01 -1.379115e-01 -1.379115e-01 -1.379115e-01 -1.379102e-01
[30976] -1.379115e-01 -1.379115e-01Conclusion:
Chicken makes up the majority of the poultry that is produced around the world. Additionally, poultry production has increased exponentially since the 1980s. The poultry production was likley brough on as demand increased with the growth of the population. We would have to study this further.