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 is the subset of Caps.df which gives only those entries in which element is food .

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

#Sum of the columns i.e total production of food in a particular year in accordance with area(country)

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

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

#Arranging the food production in descending order to get highestfood producing country in the year 1961.

attach(food)
newdata <- food[order(-Y1961),] 
head (newdata)

##       Area.Abbreviation Area.Code                     Area Item.Code
## 4240                CHN        41          China, mainland      2907
## 4144                CHN        41          China, mainland      2533
## 9140                IND       100                    India      2905
## 4238                CHN        41          China, mainland      2905
## 4250                CHN        41          China, mainland      2918
## 20494               USA       231 United States of America      2848
##                           Item Element.Code Element        Unit latitude
## 4240             Starchy Roots         5142    Food 1000 tonnes    35.86
## 4144            Sweet potatoes         5142    Food 1000 tonnes    35.86
## 9140  Cereals - Excluding Beer         5142    Food 1000 tonnes    20.59
## 4238  Cereals - Excluding Beer         5142    Food 1000 tonnes    35.86
## 4250                Vegetables         5142    Food 1000 tonnes    35.86
## 20494  Milk - Excluding Butter         5142    Food 1000 tonnes    37.09
##       longitude Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968 Y1969
## 4240     104.20 74071 75097 66180 58955 75697 85502 84238 83039 90687
## 4144     104.20 64319 66000 57920 50175 65635 75009 73534 72464 79230
## 9140      78.96 63431 68508 66208 70097 66185 70573 77541 77619 80419
## 4238     104.20 59256 66145 74029 81048 88579 93392 93557 93501 93301
## 4250     104.20 52968 49318 43635 39097 41449 40808 41524 43742 45077
## 20494    -95.71 50289 49326 50260 50967 50757 51203 50105 50615 51097
##        Y1970  Y1971  Y1972  Y1973  Y1974  Y1975  Y1976  Y1977  Y1978
## 4240   97335  91746  90527 106285 103361 101046  92605 101221 106643
## 4144   84300  79462  77140  90255  89135  87570  81161  87564  91621
## 9140   85402  79236  83283  89011  83944  93623  84205  98428 102904
## 4238  104244 108121 110178 114836 118872 123842 126359 128840 142403
## 4250   36375  40620  37069  41444  41519  42878  42458  44566  49398
## 20494  50953  51728  52566  53138  51668  50813  53143  53532  54115
##        Y1979  Y1980  Y1981  Y1982  Y1983  Y1984  Y1985  Y1986  Y1987
## 4240   92699  90914  83511  86397  87305  82582  70293  68165  72810
## 4144   78843  78892  71909  74022  74399  68385  56914  56110  60192
## 9140   97639 100504 105221 104664 119483 117647 115612 120294 126697
## 4238  147401 151742 157179 172222 182221 187020 188438 189999 190010
## 4250   49720  47949  55676  59900  67862  77006  83353  98119 102903
## 20494  55150  54872  55620  57172  58802  59085  61872  62840  64812
##        Y1988  Y1989  Y1990  Y1991  Y1992  Y1993  Y1994  Y1995  Y1996
## 4240   67908  67708  78631  77257  76962  82080  77473  84856  78925
## 4144   53457  52927  60230  60377  57135  62384  59531  63273  55554
## 9140  137361 144533 131438 142702 150268 147774 148213 151752 155558
## 4238  189180 192403 201072 193224 197464 202770 204581 208137 210855
## 4250  110505 112335 115743 118523 133090 155981 171463 184812 206540
## 20494  63388  63253  65333  65997  67639  67017  68659  69678  69152
##        Y1997  Y1998  Y1999  Y2000  Y2001  Y2002  Y2003  Y2004  Y2005
## 4240   82436  91583  93943  98625  99140 101765 101416 106009 105309
## 4144   48812  50440  54148  53061  54422  52425  51857  50647  50376
## 9140  156366 160755 162620 167346 165306 162715 163169 165749 167463
## 4238  209235 210700 209491 207521 206399 205081 202404 201977 202337
## 4250  221456 229928 255625 311110 327370 352172 354850 360767 373694
## 20494  69395  70016  71557  73112  74753  76332  76422  76008  76887
##        Y2006  Y2007  Y2008  Y2009  Y2010  Y2011  Y2012  Y2013
## 4240   85157  86491  92753  90151  93641  94671  94969  95208
## 4144   41957  35788  37731  34582  32795  33380  33621  33623
## 9140  172010 177260 181605 179175 184552 184648 181267 185884
## 4238  202349 200735 203218 201290 204167 208701 207802 209038
## 4250  388100 402975 425537 434724 451838 462696 479028 489299
## 20494  76329  78349  77704  79447  78596  78752  81287  81513

# Highest production Of Food in Year 1961 --> China

attach(food)

## The following objects are masked from food (pos = 3):
## 
##     Area, Area.Abbreviation, Area.Code, Element, Element.Code,
##     Item, Item.Code, latitude, longitude, Unit, Y1961, Y1962,
##     Y1963, Y1964, Y1965, Y1966, Y1967, Y1968, Y1969, Y1970, Y1971,
##     Y1972, Y1973, Y1974, Y1975, Y1976, Y1977, Y1978, Y1979, Y1980,
##     Y1981, Y1982, Y1983, Y1984, Y1985, Y1986, Y1987, Y1988, Y1989,
##     Y1990, Y1991, Y1992, Y1993, Y1994, Y1995, Y1996, Y1997, Y1998,
##     Y1999, Y2000, Y2001, Y2002, Y2003, Y2004, Y2005, Y2006, Y2007,
##     Y2008, Y2009, Y2010, Y2011, Y2012, Y2013

newdata <- food[order(-Y2013),] 
head(newdata)

##      Area.Abbreviation Area.Code            Area Item.Code
## 4250               CHN        41 China, mainland      2918
## 4187               CHN        41 China, mainland      2605
## 4238               CHN        41 China, mainland      2905
## 9140               IND       100           India      2905
## 4251               CHN        41 China, mainland      2919
## 9152               IND       100           India      2918
##                          Item Element.Code Element        Unit latitude
## 4250               Vegetables         5142    Food 1000 tonnes    35.86
## 4187        Vegetables, Other         5142    Food 1000 tonnes    35.86
## 4238 Cereals - Excluding Beer         5142    Food 1000 tonnes    35.86
## 9140 Cereals - Excluding Beer         5142    Food 1000 tonnes    20.59
## 4251  Fruits - Excluding Wine         5142    Food 1000 tonnes    35.86
## 9152               Vegetables         5142    Food 1000 tonnes    20.59
##      longitude Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 Y1967 Y1968 Y1969
## 4250    104.20 52968 49318 43635 39097 41449 40808 41524 43742 45077
## 4187    104.20 44728 41160 36386 32669 34685 34236 34854 36717 37728
## 4238    104.20 59256 66145 74029 81048 88579 93392 93557 93501 93301
## 9140     78.96 63431 68508 66208 70097 66185 70573 77541 77619 80419
## 4251    104.20  2551  2450  2604  2663  2837  2965  3068  3183  3285
## 9152     78.96 16925 17664 18395 19054 19749 20590 21273 22022 22938
##       Y1970  Y1971  Y1972  Y1973  Y1974  Y1975  Y1976  Y1977  Y1978  Y1979
## 4250  36375  40620  37069  41444  41519  42878  42458  44566  49398  49720
## 4187  30575  34157  31171  35066  35077  36252  35826  37557  41436  42003
## 4238 104244 108121 110178 114836 118872 123842 126359 128840 142403 147401
## 9140  85402  79236  83283  89011  83944  93623  84205  98428 102904  97639
## 4251   3217   3297   3836   4509   4491   4651   4729   4911   5604   6029
## 9152  23887  24873  25929  26792  27723  28571  29470  30430  31430  32303
##       Y1980  Y1981  Y1982  Y1983  Y1984  Y1985  Y1986  Y1987  Y1988  Y1989
## 4250  47949  55676  59900  67862  77006  83353  98119 102903 110505 112335
## 4187  40278  47454  51314  58986  68033  73729  88358  92925 100127 101761
## 4238 151742 157179 172222 182221 187020 188438 189999 190010 189180 192403
## 9140 100504 105221 104664 119483 117647 115612 120294 126697 137361 144533
## 4251   5776   6665   6511   8110   8428   9962  11657  14468  14279  16006
## 9152  33240  34554  35939  37464  38727  39633  40922  42515  43470  44280
##       Y1990  Y1991  Y1992  Y1993  Y1994  Y1995  Y1996  Y1997  Y1998  Y1999
## 4250 115743 118523 133090 155981 171463 184812 206540 221456 229928 255625
## 4187 104353 106348 120635 140753 153779 165620 183886 197828 204909 228507
## 4238 201072 193224 197464 202770 204581 208137 210855 209235 210700 209491
## 9140 131438 142702 150268 147774 148213 151752 155558 156366 160755 162620
## 4251  16443  19126  21085  26682  30870  36887  41112  44888  46667  53727
## 9152  45238  46057  46713  49039  50161  52564  53177  50390  59530  65964
##       Y2000  Y2001  Y2002  Y2003  Y2004  Y2005  Y2006  Y2007  Y2008  Y2009
## 4250 311110 327370 352172 354850 360767 373694 388100 402975 425537 434724
## 4187 278584 293005 313932 314464 318852 330278 343450 355019 373622 377460
## 4238 207521 206399 205081 202404 201977 202337 202349 200735 203218 201290
## 9140 167346 165306 162715 163169 165749 167463 172010 177260 181605 179175
## 4251  54191  58492  60725  65659  72824  76285  81520  86208  91401  98779
## 9152  67010  72832  63824  72528  60491  65549  74760  80659  83421  82568
##       Y2010  Y2011  Y2012  Y2013
## 4250 451838 462696 479028 489299
## 4187 393516 402338 419262 426850
## 4238 204167 208701 207802 209038
## 9140 184552 184648 181267 185884
## 4251 104835 114968 126174 130129
## 9152  91969  98339 104773 111082

mean_1961<-aggregate(Y1961~Area,data=Caps.df,FUN = mean)
mean_2013<-aggregate(Y2013~Area,data=Caps.df,FUN = mean)

attach(mean_1961)

## The following objects are masked from food (pos = 3):
## 
##     Area, Y1961

## The following objects are masked from food (pos = 4):
## 
##     Area, Y1961

newdata <- mean_1961[order(-Y1961),] 
head(newdata)

##                         Area     Y1961
## 139 United States of America 3967.0000
## 30           China, mainland 3269.0342
## 64                     India 2310.1866
## 52                   Germany 1447.8844
## 48                    France  988.4643
## 108                   Poland  959.1324

attach(mean_2013)

## The following object is masked from mean_1961:
## 
##     Area

## The following objects are masked from food (pos = 4):
## 
##     Area, Y2013

## The following objects are masked from food (pos = 5):
## 
##     Area, Y2013

newdata1 <- mean_2013[order(-Y2013),] 
head(newdata1)

##                         Area     Y2013
## 35           China, mainland 21857.226
## 74                     India  9974.575
## 166 United States of America  6657.014
## 22                    Brazil  3211.438
## 130       Russian Federation  2707.394
## 117                  Nigeria  2297.122

data<-(mean_2013[1:146,] - mean_1961[1:146,])

## Warning in Ops.factor(left, right): '-' not meaningful for factors

head(data)

##   Area       Y2013
## 1   NA 162.9638554
## 2   NA  53.3739837
## 3   NA 521.5564516
## 4   NA 401.8807339
## 5   NA   0.2307692
## 6   NA 304.3983740

attach(data)

## The following objects are masked from mean_2013:
## 
##     Area, Y2013

## The following object is masked from mean_1961:
## 
##     Area

## The following objects are masked from food (pos = 5):
## 
##     Area, Y2013

## The following objects are masked from food (pos = 6):
## 
##     Area, Y2013

newdata2 <- data[order(Y2013),] 
head(newdata2)

##     Area      Y2013
## 139   NA -3767.0079
## 30    NA -3230.6214
## 64    NA -2081.2215
## 52    NA -1383.6151
## 48    NA  -986.8517
## 17    NA  -782.5017

df = data.frame(Area,newdata2) 
df

##     Area Area.1         Y2013
## 139   NA     NA -3767.0078740
## 30    NA     NA -3230.6214025
## 64    NA     NA -2081.2215322
## 52    NA     NA -1383.6151230
## 48    NA     NA  -986.8516731
## 17    NA     NA  -782.5017156
## 108   NA     NA  -708.9832301
## 70    NA     NA  -636.6842342
## 72    NA     NA  -617.7591352
## 65    NA     NA  -490.9880952
## 134   NA     NA  -441.9568521
## 101   NA     NA  -372.8342337
## 137   NA     NA  -360.3958042
## 24    NA     NA  -297.0191898
## 103   NA     NA  -246.1424149
## 143   NA     NA  -211.5923853
## 89    NA     NA  -163.1702786
## 7     NA     NA  -147.5760691
## 39    NA     NA  -115.7708438
## 110   NA     NA  -112.7923400
## 47    NA     NA   -99.8029654
## 135   NA     NA   -97.6591572
## 10    NA     NA   -76.8422502
## 68    NA     NA   -71.7996930
## 106   NA     NA   -70.3920134
## 96    NA     NA   -70.1540993
## 38    NA     NA   -63.1202749
## 43    NA     NA   -62.0568270
## 93    NA     NA   -56.9925144
## 128   NA     NA   -52.1535188
## 92    NA     NA   -44.0214035
## 23    NA     NA   -43.2416667
## 53    NA     NA   -41.6527778
## 142   NA     NA   -39.2008159
## 95    NA     NA   -34.9701308
## 102   NA     NA   -29.1623153
## 122   NA     NA   -28.4709602
## 133   NA     NA   -26.8579020
## 60    NA     NA   -26.1185185
## 66    NA     NA   -25.4480769
## 123   NA     NA   -24.9548485
## 69    NA     NA   -20.1682540
## 31    NA     NA   -19.3943400
## 13    NA     NA   -15.5256763
## 44    NA     NA   -15.3628370
## 26    NA     NA   -14.9942297
## 81    NA     NA   -12.7794286
## 73    NA     NA    -9.5111181
## 19    NA     NA    -8.4675801
## 138   NA     NA    -6.4598837
## 99    NA     NA    -6.2761628
## 132   NA     NA    -5.8189588
## 105   NA     NA    -3.5672994
## 34    NA     NA    -3.5592287
## 86    NA     NA    -2.2099836
## 91    NA     NA    -0.7364341
## 58    NA     NA    -0.4741622
## 5     NA     NA     0.2307692
## 18    NA     NA     0.5124595
## 16    NA     NA     1.1285714
## 136   NA     NA     1.9648533
## 113   NA     NA     2.9675828
## 11    NA     NA     3.4058849
## 55    NA     NA     9.3346667
## 21    NA     NA    16.8458113
## 59    NA     NA    20.7453081
## 90    NA     NA    26.0030960
## 119   NA     NA    30.5489247
## 27    NA     NA    32.0040409
## 140   NA     NA    34.2388114
## 61    NA     NA    35.5215311
## 83    NA     NA    35.8596780
## 20    NA     NA    38.9065919
## 121   NA     NA    40.9592308
## 111   NA     NA    43.1882989
## 115   NA     NA    45.9243297
## 87    NA     NA    51.4010741
## 2     NA     NA    53.3739837
## 71    NA     NA    56.2224981
## 67    NA     NA    58.4452381
## 94    NA     NA    66.5280992
## 141   NA     NA    76.8511218
## 33    NA     NA    76.9493108
## 114   NA     NA    81.6020117
## 41    NA     NA    88.1527341
## 118   NA     NA    88.8714286
## 88    NA     NA    96.9569667
## 56    NA     NA   100.8997687
## 78    NA     NA   115.0720474
## 32    NA     NA   122.3342803
## 100   NA     NA   127.2787843
## 79    NA     NA   135.0592593
## 146   NA     NA   139.0421032
## 50    NA     NA   140.1705224
## 25    NA     NA   145.3003590
## 116   NA     NA   149.7327370
## 49    NA     NA   149.9037043
## 131   NA     NA   157.1836554
## 1     NA     NA   162.9638554
## 42    NA     NA   165.7208681
## 9     NA     NA   178.5945175
## 46    NA     NA   191.8511079
## 126   NA     NA   206.1755964
## 97    NA     NA   209.1819767
## 15    NA     NA   216.4783028
## 36    NA     NA   217.6619040
## 98    NA     NA   234.0820896
## 40    NA     NA   236.2433048
## 45    NA     NA   236.8942109
## 107   NA     NA   243.1602795
## 28    NA     NA   257.0490827
## 129   NA     NA   280.3350541
## 14    NA     NA   288.2960794
## 77    NA     NA   298.0240196
## 6     NA     NA   304.3983740
## 8     NA     NA   325.6867131
## 84    NA     NA   333.4585379
## 112   NA     NA   345.5372340
## 85    NA     NA   354.5673364
## 4     NA     NA   401.8807339
## 63    NA     NA   452.8490000
## 144   NA     NA   476.6554622
## 54    NA     NA   491.8806366
## 3     NA     NA   521.5564516
## 109   NA     NA   572.3452825
## 127   NA     NA   574.9748791
## 37    NA     NA   576.1144781
## 124   NA     NA   737.9742708
## 125   NA     NA   807.2816937
## 29    NA     NA   857.2613341
## 145   NA     NA   892.4933333
## 12    NA     NA   981.7403455
## 80    NA     NA  1025.4303849
## 57    NA     NA  1155.2339286
## 51    NA     NA  1207.4061636
## 76    NA     NA  1214.8927596
## 82    NA     NA  1293.4005245
## 62    NA     NA  1307.4417457
## 120   NA     NA  1418.0531057
## 104   NA     NA  1569.2648352
## 75    NA     NA  2038.3574806
## 117   NA     NA  2296.2210385
## 130   NA     NA  2702.8127652
## 22    NA     NA  3179.2310597
## 74    NA     NA  9905.9053355
## 35    NA     NA 21816.1251871

newdata3 <- df[order(-Y2013),] 
head(newdata3)

##     Area Area.1     Y2013
## 95    NA     NA -34.97013
## 121   NA     NA  40.95923
## 135   NA     NA -97.65916
## 29    NA     NA 857.26133
## 84    NA     NA 333.45854
## 111   NA     NA  43.18830

Hypothesis : Production Of Feed In year 1961 is more than production of Food .

# p-value is 0.003269 .Therefore the Hypothesis is checked and found correct

t.test(Y1961~Element,data = Caps.df, var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  Y1961 by Element
## t = 2.9416, df = 17936, p-value = 0.003269
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   36.09606 180.26645
## sample estimates:
## mean in group Feed mean in group Food 
##           284.7115           176.5303

t.test(Y2013~Element,data = Caps.df, var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  Y2013 by Element
## t = 0.95075, df = 21475, p-value = 0.3417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -110.5572  318.8387
## sample estimates:
## mean in group Feed mean in group Food 
##           660.5498           556.4090

#case 1:
fit <- lm(Y2013~Y2012,data = Caps.df)
summary(fit)

## 
## Call:
## lm(formula = Y2013 ~ Y2012, data = Caps.df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4448.6    -0.8     0.3     0.8 15418.8 
## 
## Coefficients:
##               Estimate Std. Error  t value Pr(>|t|)    
## (Intercept) -0.3473938  1.7039622   -0.204    0.838    
## Y2012        1.0273573  0.0002805 3661.991   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 248.7 on 21475 degrees of freedom
## Multiple R-squared:  0.9984, Adjusted R-squared:  0.9984 
## F-statistic: 1.341e+07 on 1 and 21475 DF,  p-value: < 2.2e-16

#case 2:
fit1 <- lm(Y1962~Y1961,data = Caps.df)
summary(fit1)

## 
## Call:
## lm(formula = Y1962 ~ Y1961, data = Caps.df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -5185     -4     -4     -4   6427 
## 
## Coefficients:
##              Estimate Std. Error  t value Pr(>|t|)    
## (Intercept) 4.0118070  1.1034405    3.636 0.000278 ***
## Y1961       1.0077249  0.0005887 1711.692  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 147 on 17936 degrees of freedom
##   (3539 observations deleted due to missingness)
## Multiple R-squared:  0.9939, Adjusted R-squared:  0.9939 
## F-statistic: 2.93e+06 on 1 and 17936 DF,  p-value: < 2.2e-16