Reading the raw data into a dataframe
Caps.df <- read.csv(paste("FAO.csv", sep=""))
#View(Caps.df)
dim(Caps.df)
## [1] 21477 63
library(psych)
## Warning: package 'psych' was built under R version 3.4.3
describe(Caps.df)
## vars n mean sd median trimmed mad
## Area.Abbreviation* 1 21477 83.40 48.94 82.00 83.04 63.75
## Area.Code 2 21477 125.45 72.87 120.00 124.66 91.92
## Area* 3 21477 87.38 49.96 87.00 87.32 63.75
## Item.Code 4 21477 2694.21 148.97 2640.00 2685.22 154.19
## Item* 5 21477 56.70 32.90 56.00 56.38 40.03
## Element.Code 6 21477 5211.69 146.82 5142.00 5181.75 0.00
## Element* 7 21477 1.82 0.39 2.00 1.90 0.00
## Unit* 8 21477 1.00 0.00 1.00 1.00 0.00
## latitude 9 21477 20.45 24.63 20.59 21.67 27.40
## longitude 10 21477 15.79 66.01 19.15 15.27 42.37
## Y1961 11 17938 195.26 1864.12 1.00 14.17 1.48
## Y1962 12 17938 200.78 1884.27 1.00 14.89 1.48
## Y1963 13 17938 205.46 1861.17 1.00 15.64 1.48
## Y1964 14 17938 209.93 1862.00 1.00 16.31 1.48
## Y1965 15 17938 217.56 2014.93 1.00 17.04 1.48
## Y1966 16 17938 225.99 2100.23 1.00 17.79 1.48
## Y1967 17 17938 230.42 2132.24 1.00 18.42 1.48
## Y1968 18 17938 238.42 2189.17 2.00 18.96 2.97
## Y1969 19 17938 244.34 2266.96 2.00 19.65 2.97
## Y1970 20 17938 250.26 2322.97 2.00 20.52 2.97
## Y1971 21 17938 254.24 2372.63 2.00 21.28 2.97
## Y1972 22 17938 257.45 2421.96 2.00 21.65 2.97
## Y1973 23 17938 267.32 2528.04 2.00 22.16 2.97
## Y1974 24 17938 267.13 2365.41 2.00 23.02 2.97
## Y1975 25 17938 274.44 2464.38 2.00 23.81 2.97
## Y1976 26 17938 276.57 2427.37 2.00 24.71 2.97
## Y1977 27 17938 285.96 2555.25 2.00 25.48 2.97
## Y1978 28 17938 299.79 2757.47 2.00 26.54 2.97
## Y1979 29 17938 305.84 2768.37 2.00 27.34 2.97
## Y1980 30 17938 305.67 2730.43 3.00 28.09 4.45
## Y1981 31 17938 311.66 2774.27 3.00 28.90 4.45
## Y1982 32 17938 320.98 2931.21 3.00 29.91 4.45
## Y1983 33 17938 326.91 3002.93 3.00 30.25 4.45
## Y1984 34 17938 339.56 3101.63 3.00 31.40 4.45
## Y1985 35 17938 344.35 3094.24 3.00 32.28 4.45
## Y1986 36 17938 351.74 3231.48 3.00 33.12 4.45
## Y1987 37 17938 361.94 3312.10 3.00 34.26 4.45
## Y1988 38 17938 363.98 3236.74 4.00 35.47 5.93
## Y1989 39 17938 372.35 3349.60 4.00 36.12 5.93
## Y1990 40 18062 375.42 3422.82 4.00 36.26 5.93
## Y1991 41 18062 379.45 3453.92 4.00 36.51 5.93
## Y1992 42 20490 386.01 3509.29 4.00 37.48 5.93
## Y1993 43 20865 389.31 3555.65 4.00 38.44 5.93
## Y1994 44 20865 397.08 3714.32 4.00 38.91 5.93
## Y1995 45 20865 404.49 3754.28 4.00 39.62 5.93
## Y1996 46 20865 415.26 3962.39 5.00 40.64 7.41
## Y1997 47 20865 421.62 4036.10 5.00 41.33 7.41
## Y1998 48 20865 428.88 4149.06 5.00 41.69 7.41
## Y1999 49 20865 441.68 4340.53 5.00 43.35 7.41
## Y2000 50 21128 451.77 4649.58 5.00 44.28 7.41
## Y2001 51 21128 458.72 4751.60 6.00 45.35 8.90
## Y2002 52 21128 465.46 4868.63 6.00 46.24 8.90
## Y2003 53 21128 472.69 4911.22 6.00 47.43 8.90
## Y2004 54 21128 486.69 5001.78 6.00 49.46 8.90
## Y2005 55 21128 493.15 5100.06 6.00 50.37 8.90
## Y2006 56 21373 496.32 5134.82 7.00 50.90 10.38
## Y2007 57 21373 508.48 5298.94 7.00 52.48 10.38
## Y2008 58 21373 522.84 5496.70 7.00 53.32 10.38
## Y2009 59 21373 524.58 5545.94 7.00 54.03 10.38
## Y2010 60 21373 535.49 5721.09 7.00 54.82 10.38
## Y2011 61 21373 553.40 5883.07 8.00 56.52 11.86
## Y2012 62 21477 560.57 6047.95 8.00 57.50 11.86
## Y2013 63 21477 575.56 6218.38 8.00 59.31 11.86
## min max range skew kurtosis se
## Area.Abbreviation* 1.0 169.00 168.00 0.05 -1.23 0.33
## Area.Code 1.0 276.00 275.00 0.10 -1.05 0.50
## Area* 1.0 174.00 173.00 0.01 -1.19 0.34
## Item.Code 2511.0 2961.00 450.00 0.49 -1.20 1.02
## Item* 1.0 115.00 114.00 0.06 -1.15 0.22
## Element.Code 5142.0 5521.00 379.00 1.63 0.66 1.00
## Element* 1.0 2.00 1.00 -1.63 0.66 0.00
## Unit* 1.0 1.00 0.00 NaN NaN 0.00
## latitude -40.9 64.96 105.86 -0.36 -0.59 0.17
## longitude -172.1 179.41 351.51 -0.05 0.10 0.45
## Y1961 0.0 112227.00 112227.00 32.37 1401.06 13.92
## Y1962 0.0 109130.00 109130.00 31.41 1311.22 14.07
## Y1963 0.0 106356.00 106356.00 29.98 1219.00 13.90
## Y1964 0.0 104234.00 104234.00 29.52 1190.87 13.90
## Y1965 0.0 119378.00 119378.00 32.04 1385.12 15.04
## Y1966 0.0 118495.00 118495.00 31.46 1311.73 15.68
## Y1967 0.0 118725.00 118725.00 31.25 1290.40 15.92
## Y1968 0.0 127512.00 127512.00 31.40 1329.87 16.35
## Y1969 0.0 134937.00 134937.00 32.46 1414.27 16.93
## Y1970 0.0 131871.00 131871.00 32.11 1360.62 17.34
## Y1971 0.0 143407.00 143407.00 33.30 1488.43 17.72
## Y1972 0.0 147793.00 147793.00 33.83 1536.66 18.08
## Y1973 0.0 142439.00 142439.00 32.43 1375.90 18.88
## Y1974 0.0 118872.00 118872.00 29.36 1133.29 17.66
## Y1975 0.0 123842.00 123842.00 29.97 1174.50 18.40
## Y1976 0.0 126359.00 126359.00 29.45 1152.38 18.12
## Y1977 0.0 128840.00 128840.00 29.97 1175.07 19.08
## Y1978 0.0 142403.00 142403.00 31.14 1263.39 20.59
## Y1979 0.0 147401.00 147401.00 31.08 1285.98 20.67
## Y1980 0.0 151742.00 151742.00 30.40 1233.71 20.39
## Y1981 0.0 157179.00 157179.00 30.76 1278.62 20.71
## Y1982 0.0 172222.00 172222.00 32.22 1400.17 21.89
## Y1983 0.0 182221.00 182221.00 31.52 1353.80 22.42
## Y1984 0.0 187020.00 187020.00 31.42 1349.02 23.16
## Y1985 0.0 188438.00 188438.00 31.48 1370.92 23.10
## Y1986 0.0 189999.00 189999.00 31.71 1353.56 24.13
## Y1987 0.0 190010.00 190010.00 31.02 1282.64 24.73
## Y1988 0.0 189180.00 189180.00 30.25 1237.33 24.17
## Y1989 0.0 192403.00 192403.00 30.52 1244.33 25.01
## Y1990 0.0 201072.00 201072.00 31.10 1291.35 25.47
## Y1991 0.0 193224.00 193224.00 30.30 1205.59 25.70
## Y1992 0.0 197464.00 197464.00 30.73 1242.22 24.52
## Y1993 0.0 202770.00 202770.00 30.90 1257.42 24.62
## Y1994 0.0 204581.00 204581.00 31.89 1312.62 25.71
## Y1995 0.0 208137.00 208137.00 31.47 1296.31 25.99
## Y1996 0.0 210855.00 210855.00 32.59 1371.73 27.43
## Y1997 0.0 221456.00 221456.00 33.29 1438.83 27.94
## Y1998 0.0 229928.00 229928.00 33.12 1425.17 28.72
## Y1999 0.0 255625.00 255625.00 34.26 1537.56 30.05
## Y2000 0.0 311110.00 311110.00 38.81 2013.96 31.99
## Y2001 0.0 327370.00 327370.00 39.95 2155.74 32.69
## Y2002 0.0 352172.00 352172.00 42.34 2448.74 33.49
## Y2003 0.0 354850.00 354850.00 42.04 2417.89 33.79
## Y2004 0.0 360767.00 360767.00 41.81 2395.99 34.41
## Y2005 0.0 373694.00 373694.00 42.68 2504.09 35.09
## Y2006 0.0 388100.00 388100.00 44.38 2731.14 35.12
## Y2007 0.0 402975.00 402975.00 44.63 2762.91 36.25
## Y2008 0.0 425537.00 425537.00 45.58 2900.74 37.60
## Y2009 0.0 434724.00 434724.00 46.17 2978.46 37.94
## Y2010 0.0 451838.00 451838.00 46.88 3064.53 39.13
## Y2011 0.0 462696.00 462696.00 46.43 3012.93 40.24
## Y2012 -169.0 479028.00 479197.00 47.18 3099.38 41.27
## Y2013 -246.0 489299.00 489545.00 46.56 3018.57 42.43
head(Caps.df)
## Area.Abbreviation Area.Code Area Item.Code
## 1 AFG 2 Afghanistan 2511
## 2 AFG 2 Afghanistan 2805
## 3 AFG 2 Afghanistan 2513
## 4 AFG 2 Afghanistan 2513
## 5 AFG 2 Afghanistan 2514
## 6 AFG 2 Afghanistan 2514
## Item Element.Code Element Unit latitude
## 1 Wheat and products 5142 Food 1000 tonnes 33.94
## 2 Rice (Milled Equivalent) 5142 Food 1000 tonnes 33.94
## 3 Barley and products 5521 Feed 1000 tonnes 33.94
## 4 Barley and products 5142 Food 1000 tonnes 33.94
## 5 Maize and products 5521 Feed 1000 tonnes 33.94
## 6 Maize and products 5142 Food 1000 tonnes 33.94
## longitude Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968 Y1969 Y1970
## 1 67.71 1928 1904 1666 1950 2001 1808 2053 2045 2154 1819
## 2 67.71 183 183 182 220 220 195 231 235 238 213
## 3 67.71 76 76 76 76 76 75 71 72 73 74
## 4 67.71 237 237 237 238 238 237 225 227 230 234
## 5 67.71 210 210 214 216 216 216 235 232 236 200
## 6 67.71 403 403 410 415 415 413 454 448 455 383
## Y1971 Y1972 Y1973 Y1974 Y1975 Y1976 Y1977 Y1978 Y1979 Y1980 Y1981 Y1982
## 1 1963 2215 2310 2335 2434 2512 2282 2454 2443 2129 2133 2068
## 2 205 233 246 246 255 263 235 254 270 259 248 217
## 3 71 70 72 76 77 80 60 65 64 64 60 55
## 4 223 219 225 240 244 255 185 203 198 202 189 174
## 5 201 216 228 231 234 240 228 234 228 226 210 199
## 6 386 416 439 445 451 463 439 451 440 437 407 384
## Y1983 Y1984 Y1985 Y1986 Y1987 Y1988 Y1989 Y1990 Y1991 Y1992 Y1993 Y1994
## 1 1994 1851 1791 1683 2194 1801 1754 1640 1539 1582 1840 1855
## 2 217 197 186 200 193 202 191 199 197 249 218 260
## 3 53 51 48 46 46 47 46 43 43 40 50 46
## 4 167 160 151 145 145 148 145 135 132 120 155 143
## 5 192 182 173 170 154 148 137 144 126 90 141 150
## 6 371 353 334 330 298 287 265 279 245 170 272 289
## Y1995 Y1996 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006
## 1 1853 2177 2343 2407 2463 2600 2668 2776 3095 3249 3486 3704
## 2 319 254 326 347 270 372 411 448 460 419 445 546
## 3 41 44 50 48 43 26 29 70 48 58 236 262
## 4 125 138 159 154 141 84 83 122 144 185 43 44
## 5 159 108 90 99 72 35 48 89 63 120 208 233
## 6 310 209 173 192 141 66 93 170 117 231 67 82
## Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013
## 1 4164 4252 4538 4605 4711 4810 4895
## 2 455 490 415 442 476 425 422
## 3 263 230 379 315 203 367 360
## 4 48 62 55 60 72 78 89
## 5 249 247 195 178 191 200 200
## 6 67 69 71 82 73 77 76
mytable <- with(Caps.df, table(Element))
mytable
## Element
## Feed Food
## 3949 17528
mytable <- xtabs(~ Element+Area, data=Caps.df)
mytable
## Area
## Element Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina
## Feed 10 25 22 15 13 24
## Food 73 98 102 94 104 99
## Area
## Element Armenia Australia Austria Azerbaijan Bahamas Bangladesh Barbados
## Feed 35 27 34 28 11 15 17
## Food 98 101 105 96 105 108 104
## Area
## Element Belarus Belgium Belize Benin Bermuda
## Feed 33 35 13 12 10
## Food 98 101 99 105 93
## Area
## Element Bolivia (Plurinational State of) Bosnia and Herzegovina Botswana
## Feed 20 27 16
## Food 102 97 109
## Area
## Element Brazil Brunei Darussalam Bulgaria Burkina Faso Cabo Verde Cambodia
## Feed 29 13 34 11 8 10
## Food 108 107 100 104 98 106
## Area
## Element Cameroon Canada Central African Republic Chad Chile
## Feed 16 34 10 16 26
## Food 109 106 99 87 102
## Area
## Element China, Hong Kong SAR China, Macao SAR China, mainland
## Feed 27 15 39
## Food 106 106 107
## Area
## Element China, Taiwan Province of Colombia Congo Costa Rica Côte d'Ivoire
## Feed 32 27 14 25 9
## Food 109 105 106 104 110
## Area
## Element Croatia Cuba Cyprus Czechia Democratic People's Republic of Korea
## Feed 30 36 29 30 20
## Food 99 101 106 99 77
## Area
## Element Denmark Djibouti Dominica Dominican Republic Ecuador Egypt
## Feed 36 8 17 15 29 27
## Food 103 99 94 97 105 107
## Area
## Element El Salvador Estonia Ethiopia Fiji Finland France French Polynesia
## Feed 24 36 9 20 32 37 21
## Food 106 99 107 105 101 103 100
## Area
## Element Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea
## Feed 16 10 35 43 10 41 21 25 12
## Food 104 98 98 104 110 102 99 105 100
## Area
## Element Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India
## Feed 7 20 19 28 39 29 26
## Food 84 99 101 104 103 96 108
## Area
## Element Indonesia Iran (Islamic Republic of) Iraq Ireland Israel Italy
## Feed 21 19 25 35 30 43
## Food 105 101 95 101 105 105
## Area
## Element Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan
## Feed 17 35 21 40 17 8 22 31
## Food 102 108 106 101 110 86 100 93
## Area
## Element Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia
## Feed 16 36 27 6 8
## Food 86 100 102 69 91
## Area
## Element Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali
## Feed 42 32 19 16 26 3 12
## Food 98 95 106 104 108 101 104
## Area
## Element Malta Mauritania Mauritius Mexico Mongolia Montenegro Morocco
## Feed 29 11 15 25 16 21 25
## Food 101 105 104 108 98 97 104
## Area
## Element Mozambique Myanmar Namibia Nepal Netherlands New Caledonia
## Feed 10 14 13 11 41 24
## Food 104 98 108 110 100 105
## Area
## Element New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama
## Feed 28 22 15 24 37 22 26 24
## Food 106 107 103 107 103 102 107 106
## Area
## Element Paraguay Peru Philippines Poland Portugal Republic of Korea
## Feed 20 25 34 33 32 27
## Food 102 107 108 103 102 107
## Area
## Element Republic of Moldova Romania Russian Federation Rwanda
## Feed 33 40 35 8
## Food 97 99 102 100
## Area
## Element Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines
## Feed 8 15 14
## Food 91 100 95
## Area
## Element Samoa Sao Tome and Principe Saudi Arabia Senegal Serbia
## Feed 19 6 21 13 29
## Food 89 85 109 107 98
## Area
## Element Sierra Leone Slovakia Slovenia Solomon Islands South Africa Spain
## Feed 11 30 31 9 34 45
## Food 100 100 101 91 106 105
## Area
## Element Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Tajikistan
## Feed 20 10 19 14 36 35 29
## Food 107 94 97 103 106 106 73
## Area
## Element Thailand The former Yugoslav Republic of Macedonia Timor-Leste
## Feed 24 30 9
## Food 107 100 77
## Area
## Element Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda
## Feed 11 17 21 27 24 17
## Food 104 100 105 101 66 110
## Area
## Element Ukraine United Arab Emirates United Kingdom
## Feed 38 29 39
## Food 96 92 104
## Area
## Element United Republic of Tanzania United States of America Uruguay
## Feed 19 36 24
## Food 110 105 100
## Area
## Element Uzbekistan Vanuatu Venezuela (Bolivarian Republic of) Viet Nam
## Feed 32 6 22 16
## Food 91 90 108 93
## Area
## Element Yemen Zambia Zimbabwe
## Feed 13 13 13
## Food 106 107 108
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1961,
main="Production In 1961",
col=c("yellow"),
vertical=TRUE,
xlab="Y1961" )
boxplot(Caps.df$Y1962,
main="Production In 1962",
col=c("yellow"),
vertical=TRUE,
xlab="Y1962" )
boxplot(Caps.df$Y1963,
main="Production In 1963",
col=c("yellow"),
vertical=TRUE,
xlab="Y1963" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1964,
main="Production In 1964",
col=c("yellow"),
vertical=TRUE,
xlab="Y1964" )
boxplot(Caps.df$Y1965,
main="Production In 1965",
col=c("yellow"),
vertical=TRUE,
xlab="Y1965" )
boxplot(Caps.df$Y1966,
main="Production In 1966",
col=c("yellow"),
vertical=TRUE,
xlab="Y1966" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1967,
main="Production In 1967",
col=c("yellow"),
vertical=TRUE,
xlab="Y1967" )
boxplot(Caps.df$Y1968,
main="Production In 1968",
col=c("yellow"),
vertical=TRUE,
xlab="Y1968" )
boxplot(Caps.df$Y1969,
main="Production In 1969",
col=c("yellow"),
vertical=TRUE,
xlab="Y1969" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1970,
main="Production In 1970",
col=c("yellow"),
vertical=TRUE,
xlab="Y1970" )
boxplot(Caps.df$Y1971,
main="Production In 1971",
col=c("yellow"),
vertical=TRUE,
xlab="Y1971" )
boxplot(Caps.df$Y1972,
main="Production In 1972",
col=c("yellow"),
vertical=TRUE,
xlab="Y1972" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1973,
main="Production In 1973",
col=c("yellow"),
vertical=TRUE,
xlab="Y1973" )
boxplot(Caps.df$Y1974,
main="Production In 1974",
col=c("yellow"),
vertical=TRUE,
xlab="Y1974" )
boxplot(Caps.df$Y1975,
main="Production In 1975",
col=c("yellow"),
vertical=TRUE,
xlab="Y1975" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1976,
main="Production In 1976",
col=c("yellow"),
vertical=TRUE,
xlab="Y1976" )
boxplot(Caps.df$Y1977,
main="Production In 1977",
col=c("yellow"),
vertical=TRUE,
xlab="Y1977" )
boxplot(Caps.df$Y1978,
main="Production In 1978",
col=c("yellow"),
vertical=TRUE,
xlab="Y1978" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1979,
main="Production In 1979",
col=c("yellow"),
vertical=TRUE,
xlab="Y1979" )
boxplot(Caps.df$Y1980,
main="Production In 1980",
col=c("yellow"),
vertical=TRUE,
xlab="Y1980" )
boxplot(Caps.df$Y1981,
main="Production In 1981",
col=c("yellow"),
vertical=TRUE,
xlab="Y1981" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1982,
main="Production In 1982",
col=c("yellow"),
vertical=TRUE,
xlab="Y1982" )
boxplot(Caps.df$Y1983,
main="Production In 1983",
col=c("yellow"),
vertical=TRUE,
xlab="Y1983" )
boxplot(Caps.df$Y1984,
main="Production In 1984",
col=c("yellow"),
vertical=TRUE,
xlab="Y1984" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1985,
main="Production In 1985",
col=c("yellow"),
vertical=TRUE,
xlab="Y1964" )
boxplot(Caps.df$Y1986,
main="Production In 1986",
col=c("yellow"),
vertical=TRUE,
xlab="Y1986" )
boxplot(Caps.df$Y1987,
main="Production In 1987",
col=c("yellow"),
vertical=TRUE,
xlab="Y1987" )
par(mfrow=c(1, 2))
boxplot(Caps.df$Y1988,
main="Production In 1988",
col=c("yellow"),
vertical=TRUE,
xlab="Y1988" )
boxplot(Caps.df$Y1990,
main="Production In 1990",
col=c("yellow"),
vertical=TRUE,
xlab="Y1990" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1991,
main="Production In 1991",
col=c("yellow"),
vertical=TRUE,
xlab="Y1991" )
boxplot(Caps.df$Y1992,
main="Production In 1992",
col=c("yellow"),
vertical=TRUE,
xlab="Y1992" )
boxplot(Caps.df$Y1993,
main="Production In 1993",
col=c("yellow"),
vertical=TRUE,
xlab="Y1993" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1994,
main="Production In 1994",
col=c("yellow"),
vertical=TRUE,
xlab="Y1994" )
boxplot(Caps.df$Y1995,
main="Production In 1995",
col=c("yellow"),
vertical=TRUE,
xlab="Y1995" )
boxplot(Caps.df$Y1996,
main="Production In 1996",
col=c("yellow"),
vertical=TRUE,
xlab="Y1996" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y1997,
main="Production In 1997",
col=c("yellow"),
vertical=TRUE,
xlab="Y1997" )
boxplot(Caps.df$Y1998,
main="Production In 1998",
col=c("yellow"),
vertical=TRUE,
xlab="Y1998" )
boxplot(Caps.df$Y1999,
main="Production In 1999",
col=c("yellow"),
vertical=TRUE,
xlab="Y1999" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y2000,
main="Production In 2000",
col=c("yellow"),
vertical=TRUE,
xlab="Y2000" )
boxplot(Caps.df$Y2001,
main="Production In 2001",
col=c("yellow"),
vertical=TRUE,
xlab="Y2001" )
boxplot(Caps.df$Y2002,
main="Production In 2002",
col=c("yellow"),
vertical=TRUE,
xlab="Y2002" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y2003,
main="Production In 2003",
col=c("yellow"),
vertical=TRUE,
xlab="Y2003" )
boxplot(Caps.df$Y2004,
main="Production In 2004",
col=c("yellow"),
vertical=TRUE,
xlab="Y2004" )
boxplot(Caps.df$Y2005,
main="Production In 2005",
col=c("yellow"),
vertical=TRUE,
xlab="Y2005" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y2006,
main="Production In 2006",
col=c("yellow"),
vertical=TRUE,
xlab="Y2006" )
boxplot(Caps.df$Y2007,
main="Production In 2008",
col=c("yellow"),
vertical=TRUE,
xlab="Y2008" )
boxplot(Caps.df$Y2009,
main="Production In 2009",
col=c("yellow"),
vertical=TRUE,
xlab="Y2009" )
par(mfrow=c(1, 3))
boxplot(Caps.df$Y2010,
main="Production In 2010",
col=c("yellow"),
vertical=TRUE,
xlab="Y2010" )
boxplot(Caps.df$Y2011,
main="Production In 2011",
col=c("yellow"),
vertical=TRUE,
xlab="Y2011" )
boxplot(Caps.df$Y2012,
main="Production In 2012",
col=c("yellow"),
vertical=TRUE,
xlab="Y2012" )
boxplot(Caps.df$Y2013,
main="Production In 2013",
col=c("yellow"),
vertical=TRUE,
xlab="Y2013" )
plot(Caps.df$Element, Caps.df$Y1961,
xlab="Element", ylab="Y1961")
plot(Caps.df$Element, Caps.df$Y2013,
xlab="Element", ylab="2013")
boxplot(Y1961~Element,data=Caps.df,horizontal=TRUE,ylab="Food OR Feed",xlab="Production")
axis(side=2,at=c(1,2),labels = c("Feed","Food"))
hist(Caps.df$Y1961 ,
main="Production in Year 1961",
xlab="Production" )
food <- Caps.df[ which(Caps.df$Element== 'Food'), ]
head(food)
## Area.Abbreviation Area.Code Area Item.Code
## 1 AFG 2 Afghanistan 2511
## 2 AFG 2 Afghanistan 2805
## 4 AFG 2 Afghanistan 2513
## 6 AFG 2 Afghanistan 2514
## 7 AFG 2 Afghanistan 2517
## 8 AFG 2 Afghanistan 2520
## Item Element.Code Element Unit latitude
## 1 Wheat and products 5142 Food 1000 tonnes 33.94
## 2 Rice (Milled Equivalent) 5142 Food 1000 tonnes 33.94
## 4 Barley and products 5142 Food 1000 tonnes 33.94
## 6 Maize and products 5142 Food 1000 tonnes 33.94
## 7 Millet and products 5142 Food 1000 tonnes 33.94
## 8 Cereals, Other 5142 Food 1000 tonnes 33.94
## longitude Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968 Y1969 Y1970
## 1 67.71 1928 1904 1666 1950 2001 1808 2053 2045 2154 1819
## 2 67.71 183 183 182 220 220 195 231 235 238 213
## 4 67.71 237 237 237 238 238 237 225 227 230 234
## 6 67.71 403 403 410 415 415 413 454 448 455 383
## 7 67.71 17 18 19 20 21 22 23 24 25 26
## 8 67.71 0 0 0 0 0 0 0 0 0 0
## Y1971 Y1972 Y1973 Y1974 Y1975 Y1976 Y1977 Y1978 Y1979 Y1980 Y1981 Y1982
## 1 1963 2215 2310 2335 2434 2512 2282 2454 2443 2129 2133 2068
## 2 205 233 246 246 255 263 235 254 270 259 248 217
## 4 223 219 225 240 244 255 185 203 198 202 189 174
## 6 386 416 439 445 451 463 439 451 440 437 407 384
## 7 26 27 27 28 29 37 32 33 31 31 29 27
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## Y1983 Y1984 Y1985 Y1986 Y1987 Y1988 Y1989 Y1990 Y1991 Y1992 Y1993 Y1994
## 1 1994 1851 1791 1683 2194 1801 1754 1640 1539 1582 1840 1855
## 2 217 197 186 200 193 202 191 199 197 249 218 260
## 4 167 160 151 145 145 148 145 135 132 120 155 143
## 6 371 353 334 330 298 287 265 279 245 170 272 289
## 7 28 26 25 23 23 23 23 24 24 18 22 20
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## Y1995 Y1996 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006
## 1 1853 2177 2343 2407 2463 2600 2668 2776 3095 3249 3486 3704
## 2 319 254 326 347 270 372 411 448 460 419 445 546
## 4 125 138 159 154 141 84 83 122 144 185 43 44
## 6 310 209 173 192 141 66 93 170 117 231 67 82
## 7 21 17 20 21 17 20 20 18 16 15 21 11
## 8 0 0 0 0 0 0 0 0 1 2 1 1
## Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013
## 1 4164 4252 4538 4605 4711 4810 4895
## 2 455 490 415 442 476 425 422
## 4 48 62 55 60 72 78 89
## 6 67 69 71 82 73 77 76
## 7 19 21 18 14 14 14 12
## 8 0 0 0 0 0 0 0
feed <- Caps.df[ which(Caps.df$Element == 'Feed'), ]
head(feed)
## Area.Abbreviation Area.Code Area Item.Code
## 3 AFG 2 Afghanistan 2513
## 5 AFG 2 Afghanistan 2514
## 10 AFG 2 Afghanistan 2536
## 11 AFG 2 Afghanistan 2537
## 15 AFG 2 Afghanistan 2549
## 57 AFG 2 Afghanistan 2848
## Item Element.Code Element Unit latitude
## 3 Barley and products 5521 Feed 1000 tonnes 33.94
## 5 Maize and products 5521 Feed 1000 tonnes 33.94
## 10 Sugar cane 5521 Feed 1000 tonnes 33.94
## 11 Sugar beet 5521 Feed 1000 tonnes 33.94
## 15 Pulses, Other and products 5521 Feed 1000 tonnes 33.94
## 57 Milk - Excluding Butter 5521 Feed 1000 tonnes 33.94
## longitude Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968 Y1969 Y1970
## 3 67.71 76 76 76 76 76 75 71 72 73 74
## 5 67.71 210 210 214 216 216 216 235 232 236 200
## 10 67.71 45 45 45 45 31 14 19 30 34 15
## 11 67.71 0 0 0 0 0 16 23 31 28 9
## 15 67.71 1 1 1 1 1 1 2 1 1 1
## 57 67.71 28 28 32 32 36 40 44 47 47 40
## Y1971 Y1972 Y1973 Y1974 Y1975 Y1976 Y1977 Y1978 Y1979 Y1980 Y1981 Y1982
## 3 71 70 72 76 77 80 60 65 64 64 60 55
## 5 201 216 228 231 234 240 228 234 228 226 210 199
## 10 0 0 28 32 20 28 24 24 34 61 50 43
## 11 13 13 6 0 0 10 16 13 6 15 0 0
## 15 1 1 2 2 2 2 2 2 2 2 2 2
## 57 38 41 45 46 47 48 42 43 44 44 44 44
## Y1983 Y1984 Y1985 Y1986 Y1987 Y1988 Y1989 Y1990 Y1991 Y1992 Y1993 Y1994
## 3 53 51 48 46 46 47 46 43 43 40 50 46
## 5 192 182 173 170 154 148 137 144 126 90 141 150
## 10 38 46 23 25 3 45 54 47 29 29 29 29
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 15 2 2 2 2 2 2 1 2 2 2 2 2
## 57 48 48 40 24 29 29 29 45 48 51 61 76
## Y1995 Y1996 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006
## 3 41 44 50 48 43 26 29 70 48 58 236 262
## 5 159 108 90 99 72 35 48 89 63 120 208 233
## 10 29 29 28 28 28 29 29 29 51 50 29 61
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 15 3 3 3 2 2 3 3 3 3 3 2 3
## 57 88 101 110 114 132 106 64 128 119 121 117 112
## Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013
## 3 263 230 379 315 203 367 360
## 5 249 247 195 178 191 200 200
## 10 65 54 114 83 83 69 81
## 11 0 0 0 0 0 0 0
## 15 3 3 5 4 5 4 4
## 57 116 113 115 114 114 121 123
agg<-aggregate(food[,11:63], by=list(Area=food$Area), sum)
head(agg)
## Area Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968
## 1 Afghanistan 8761 8694 8458 9430 9753 9445 10501 10682
## 2 Albania 1612 1641 1643 1767 1789 1798 1844 1940
## 3 Algeria 7405 7141 6798 7157 7425 7481 7912 8709
## 4 Angola 4716 4657 5124 5154 5399 5549 5685 5537
## 5 Antigua and Barbuda 90 92 103 93 82 73 64 57
## 6 Argentina 33850 33231 33692 34628 36863 36206 37590 39353
## Y1969 Y1970 Y1971 Y1972 Y1973 Y1974 Y1975 Y1976 Y1977 Y1978 Y1979 Y1980
## 1 10977 9776 9785 10439 10997 11243 11588 12274 10530 11456 11394 10986
## 2 2060 2210 2224 2290 2388 2493 2515 2758 2912 2976 2956 2975
## 3 8890 9231 9764 10519 10943 12223 13624 13678 14420 15229 16506 18040
## 4 6059 6300 6433 6327 6465 6340 6049 6251 6495 6747 6692 6752
## 5 68 75 85 57 58 56 59 55 53 57 61 72
## 6 41153 42002 41457 40072 40091 44485 44838 43626 44267 44382 45807 46865
## Y1981 Y1982 Y1983 Y1984 Y1985 Y1986 Y1987 Y1988 Y1989 Y1990 Y1991 Y1992
## 1 10763 10542 10402 9916 9255 8515 9484 8864 8708 9220 9142 9169
## 2 3164 3286 3490 3401 3250 3460 3375 3434 3638 3859 3909 4301
## 3 18702 19084 20285 20910 23754 24494 25398 25693 27548 26932 28794 30362
## 4 7077 6861 6983 7249 7933 7458 7664 7712 7940 8035 7906 8405
## 5 75 71 72 80 77 77 77 81 81 78 77 85
## 6 46178 45043 45743 46653 48853 47676 50600 50074 48738 47363 50901 53920
## Y1993 Y1994 Y1995 Y1996 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003 Y2004
## 1 10207 10741 11365 12101 12963 13431 13761 13172 12763 14432 15505 15838
## 2 4699 5374 5419 5439 4902 4978 5120 5163 5390 5550 5628 5647
## 3 32016 30690 32070 31411 31546 33638 34649 34948 36547 38889 41297 44044
## 4 8262 9373 9826 10196 10278 11226 11422 11667 12787 13985 14953 15919
## 5 86 85 81 81 83 83 90 93 89 90 89 92
## 6 55594 57940 58477 59128 61363 62676 63404 62469 62955 55200 55516 54026
## Y2005 Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013
## 1 16474 16975 17856 18087 19045 19642 19908 21184 21471
## 2 5725 5864 5785 6093 6182 6573 6780 6909 6952
## 3 45161 46468 45681 47480 52666 54267 58375 60816 63455
## 4 16882 18243 19765 21779 24465 25992 27455 27968 30121
## 5 113 108 122 115 114 115 118 113 119
## 6 57581 58116 59078 61350 60976 61534 63810 64614 65063
agg1<-aggregate(feed[,11:63], by=list(Area=feed$Area), sum)
head(agg1)
## Area Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968
## 1 Afghanistan 720 720 736 740 720 724 788 826
## 2 Albania 94 108 124 122 95 197 202 229
## 3 Algeria 83 94 63 98 84 55 74 130
## 4 Angola 118 118 116 132 128 128 148 148
## 5 Antigua and Barbuda 2 2 2 2 2 0 0 2
## 6 Argentina 9552 7553 6527 7010 8073 10532 9847 11004
## Y1969 Y1970 Y1971 Y1972 Y1973 Y1974 Y1975 Y1976 Y1977 Y1978 Y1979 Y1980
## 1 838 678 648 682 762 774 760 816 744 762 756 824
## 2 170 185 152 188 187 235 307 339 346 401 396 349
## 3 113 124 127 192 142 195 418 570 742 985 1239 1165
## 4 160 160 170 172 174 186 162 162 150 176 152 154
## 5 0 2 0 0 0 0 0 0 0 0 0 4
## 6 10876 12201 13595 13068 14990 14653 14589 13807 13493 13897 12935 10526
## Y1981 Y1982 Y1983 Y1984 Y1985 Y1986 Y1987 Y1988 Y1989 Y1990 Y1991 Y1992
## 1 732 686 666 658 572 534 468 542 535 562 496 424
## 2 474 468 517 452 414 454 430 423 517 422 294 414
## 3 1524 2110 1985 2657 3182 3465 3292 3134 4683 3258 2812 2544
## 4 162 162 150 134 132 138 130 128 130 128 134 146
## 5 2 2 0 0 0 0 0 0 0 0 0 0
## 6 12460 13702 12914 13374 13374 12177 12982 11758 10757 9689 8629 11191
## Y1993 Y1994 Y1995 Y1996 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003 Y2004
## 1 566 606 640 570 562 582 554 397 346 638 568 704
## 2 514 626 682 734 651 723 834 925 940 945 895 990
## 3 2464 2847 2120 2211 2094 2576 3055 2813 3428 3808 4055 4575
## 4 146 174 186 198 198 578 562 2860 4464 6260 6698 9622
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 14510 14129 11987 9813 11440 12181 11746 12242 11144 11153 11047 10532
## Y2005 Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013
## 1 1184 1342 1392 1294 1616 1388 1192 1522 1536
## 2 994 1047 959 1075 1134 1334 1334 1312 1319
## 3 4401 4599 4252 3436 4839 5804 7477 8549 8706
## 4 9814 10004 10112 10274 12520 12408 13118 10096 18518
## 5 2 2 0 0 0 0 0 0 0
## 6 14735 14030 15070 14589 7446 11508 14954 11332 15780
plot(agg$Area,agg$Y1961)
hist(agg$Y1961,by = agg$Area)
## Warning in plot.window(xlim, ylim, "", ...): "by" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "by" is not a graphical parameter
## Warning in axis(1, ...): "by" is not a graphical parameter
## Warning in axis(2, ...): "by" is not a graphical parameter
#mytable <- xtabs(~Area+Y1961, data=Caps.df)
#addmargins(mytable)
#chisq.test(mytable)
#mytable <- xtabs(~Area+Y2013, data=Caps.df)
#addmargins(mytable)
#chisq.test(mytable)
