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cavedave/crops2.csv

Last active June 10, 2018 18:29
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crops2.csv
Year Corn Wheat Oats Barley Root Potatoes Turnips SugarBeet Flax fruit Hay&Pasture Crops&Pasture
1847 1026 272 625 120 231 89 118 na 10 1266 na na
1848 953 245 566 119 365 258 91 na 9 1327 na na
1849 976 243 584 127 362 219 115 na 10 1347 na na
1850 974 213 619 118 414 275 109 na 14 1403 na na
1851 962 173 641 126 434 270 124 na 24 1420 3438 4858
1852 921 125 672 109 429 275 113 na 22 1372 3568 4940
1853 879 113 639 113 447 279 128 na 28 1354 3671 5025
1854 844 143 596 95 449 308 108 na 26 1318 3702 5021
1855 864 155 612 89 459 309 119 na 17 1340 3742 5082
1856 848 181 588 71 496 345 117 na 18 1362 3751 5113
1857 847 192 565 81 511 359 115 na 16 1373 3694 5067
1858 827 185 561 74 512 360 112 na 14 1354 3723 5077
1859 800 157 568 69 520 374 107 na 20 1340 3778 5118
1860 794 157 560 71 507 361 106 na 18 1319 3828 5147
1861 792 133 574 78 496 349 111 na 19 1306 3845 5151
1862 766 118 565 76 469 311 124 na 18 1253 3905 5158
1863 728 87 566 69 466 315 115 na 25 1218 3915 5134
1864 698 96 526 70 466 322 110 na 38 1203 3923 5126
1865 668 93 496 72 474 330 109 na 31 1174 3994 5168
1866 658 102 488 61 466 323 104 na 33 1157 4021 5177
1867 638 88 475 69 449 308 109 na 34 1121 4066 5187
1868 660 93 486 75 459 320 103 na 28 1147 4044 5191
1869 663 87 481 90 463 320 106 na 33 1159 4050 5209
1870 645 80 461 97 470 319 111 na 28 1143 4063 5206
1871 628 74 458 89 473 322 107 na 22 1122 4117 5239
1872 611 66 451 88 458 301 110 na 17 1085 4165 5250
1873 563 50 415 92 424 272 110 na 16 1003 4233 5236
1874 550 57 403 85 418 270 105 na 12 980 4264 5244
1875 554 47 408 93 423 272 105 na 11 988 4256 5244
1876 536 35 407 88 420 266 108 na 16 972 4273 5245
1877 541 42 403 90 415 262 104 na 14 970 4178 5148
1878 531 48 380 97 404 254 103 na 13 948 4168 5116
1879 511 48 356 101 396 252 98 na 14 921 4198 5119
1880 516 45 378 87 385 247 95 na 18 919 4202 5121
1881 519 47 383 83 391 257 93 na 16 926 4167 5093
1882 511 46 385 74 385 252 93 na 12 907 4151 5058
1883 486 27 381 73 379 242 96 na 9 874 4166 5041
1884 460 19 371 67 375 240 95 na 8 844 4229 5073
1885 459 20 363 71 374 239 92 na 11 844 4227 5071
1886 461 19 365 72 375 239 93 na 14 850 4212 5062
1887 450 18 362 65 377 238 93 na 14 841 4195 5036
1888 451 29 348 68 378 239 91 na 13 841 4165 5006
1889 443 28 335 74 373 235 91 na 12 828 4193 5021
1890 436 28 329 73 372 232 92 na 9 818 4228 5046
1891 429 24 328 71 364 222 93 na 6 799 4240 5039
1892 428 22 330 70 357 218 93 na 6 791 4262 5053
1893 425 16 336 67 350 212 93 na 6 782 4288 5070
1894 424 14 340 66 355 211 96 na 10 790 4266 5055
1895 413 10 329 69 351 208 97 na 9 773 4283 5056
1896 403 11 318 69 349 206 95 na 7 759 4304 5063
1897 400 13 313 68 340 199 95 na 5 745 4331 5076
1898 395 15 313 63 337 195 94 na 3 735 4328 5063
1899 388 15 301 67 335 194 91 na 3 726 4340 5066
1900 382 15 293 69 334 191 91 na 4 721 4355 5076
1901 374 13 293 64 330 186 88 na 5 709 4358 5067
1902 371 13 287 67 326 183 88 na 4 701 4374 5076
1903 371 11 293 63 324 181 88 na 4 699 4381 5080
1904 363 9 287 63 320 179 88 na 4 687 4395 5082
1905 359 11 283 62 317 177 86 na 4 680 4416 5096
1906 374 13 287 70 316 178 86 na 4 696 4249 4945
1907 373 12 290 67 308 172 86 na 4 687 4216 4904
1908 361 12 286 61 309 169 87 na 3 676 4245 4921
1909 358 14 276 64 307 167 86 na 3 670 4224 4894
1910 375 15 289 67 313 173 86 na 3 693 4222 4915
1911 365 15 284 63 314 172 86 na 5 687 4246 4933
1912 365 15 281 66 315 170 86 na 5 688 4233 4921
1913 367 11 284 68 312 168 88 na 5 687 4239 4926
1914 364 12 281 68 314 167 88 na 4 684 4251 4935
1915 387 29 299 57 314 172 85 na 4 708 4224 4932
1916 383 26 294 60 310 172 84 na 7 702 4227 4929
1917 537 43 420 71 357 210 95 na 9 907 4023 4929
1918 589 55 457 74 358 205 96 na 13 964 3856 4820
1919 510 24 410 74 310 172 90 na 9 833 3984 4816
1920 468 18 366 82 311 170 91 na 14 797 4015 4812
1921 419 16 332 68 306 166 87 na 3 732 4077 4808
1922 396 15 311 66 301 165 81 na 2 702 4143 4845
1923 372 15 295 59 296 161 80 na 3 676 4207 4882
1924 359 13 279 63 294 159 82 na 4 661 4259 4919
1925 343 9 272 59 285 154 81 na 4 636 4321 4956
1926 334 12 262 57 288 152 79 4 3 628 4332 4960
1927 326 14 261 49 280 148 74 7 2 612 4311 4922
1928 330 13 262 52 283 147 77 7 3 619 4284 4903
1929 331 12 270 48 279 147 76 5 3 616 4265 4880
1930 320 11 261 47 265 140 72 6 2 590 4199 4790
1931 309 8 252 47 265 140 74 2 0 577 4169 4746
1932 308 9 256 42 266 141 72 6 0 576 4143 4720
1933 326 20 257 48 260 138 69 6 0 589 4147 4736
1934 333 38 236 58 269 139 65 18 1 606 4127 4733
1935 372 66 249 56 267 136 61 23 2 644 4050 4694
1936 383 103 226 53 268 135 61 25 2 656 4041 4697
1937 375 89 232 53 265 132 60 25 2 644 4062 4706
1938 373 93 231 48 257 132 58 21 2 635 4078 4712
1939 351 103 217 30 248 128 57 17 2 604 4093 4697
1940 454 124 276 54 285 148 61 25 4 747 3942 4689
1941 572 187 317 66 323 173 64 32 6 905 3780 4685
1942 666 233 355 75 303 172 59 22 8 981 3698 4679
1943 673 206 379 85 307 165 58 34 11 995 3683 4678
1944 715 260 383 68 307 167 58 33 12 1039 3638 4677
1945 680 268 338 69 303 157 62 34 13 1001 3675 4676
1946 659 260 336 58 302 158 61 32 11 977 3704 4680
1947 632 235 334 59 293 155 64 25 7 937 3744 4681
1948 618 210 356 48 293 156 62 27 8 925 3759 4684
1949 491 147 278 64 268 142 56 24 6 770 3917 4688
1950 449 148 249 50 258 136 52 24 4 716 3972 4688
1951 434 114 251 68 251 130 52 24 5 695 3995 4690
1952 444 103 247 91 243 125 51 22 4 696 3994 4690
1953 452 143 231 76 251 125 51 26 2 709 3985 4694
1954 480 197 216 66 246 118 49 30 1 732 3969 4701
1955 454 145 221 86 240 116 52 22 1 699 4005 4705
1956 448 137 212 96 238 115 48 24 0 691 4036 4727
1957 476 164 186 124 230 108 44 29 0 711 4030 4741
1958 482 170 185 125 232 106 42 34 0 720 3997 4717
1959 439 114 187 135 226 105 43 28 0 669 4046 4716
1960 456 148 172 133 216 95 42 28 na 678 3868 4546
1961 437 140 149 146 205 86 39 32 na 647 3913 4560
1962 435 127 140 164 203 85 39 32 na 642 3973 4615
1963 405 94 134 174 202 83 39 36 na 612 4006 4618
1964 391 87 117 184 187 74 40 32 na 582 4079 4661
1965 380 74 115 188 179 71 43 27 na 564 4145 4709
1966 340 53 98 187 167 68 44 22 na 511 4243 4754
1967 358 76 96 183 166 65 44 26 na 527 4256 4783
1968 366 90 88 184 159 59 44 26 na 528 4274 4803
1969 362 82 77 198 150 55 40 25 na 515 4302 4817
1970 381 95 68 214 147 57 38 26 na 532 4263 4795
1971 390 91 60 235 141 52 35 30 na 535 4290 4825
1972 375 68 52 252 131 44 31 34 na 510 4319 4829
1973 354 58 50 243 126 48 28 30 na 484 4360 4844
1974 354 57 47 246 114 42 25 26 na 471 4378 4850
1975 342 45 49 245 114 41 22 33 na 460 4230 4690
1976 353 50 40 259 120 47 21 35 na 476 4217 4693
1977 382 48 35 298 123 53 18 35 na 508 4198 4706
1978 407 50 31 325 111 41 17 37 na 521 4179 4700
1979 429 50 28 350 107 41 15 35 na 539 4161 4700
1980 446 53 25 366 104 42 14 33 na 554 4142 4696
1981 429 49 23 354 98 36 12 35 na 529 4170 4701
1982 410 57 23 326 100 37 11 36 na 512 4173 4689
1983 390 59 23 305 97 33 10 36 na 491 4205 4701
1984 399 77 26 292 98 35 10 35 na 500 4193 4701
1985 390 78 25 284 95 32 9 34 na 488 4205 4703
1986 367 75 23 265 97 30 9 37 na 466 4209 4688
1987 338 56 23 255 97 29 9 37 na 438 4219 4671
1988 328 59 23 242 85 27 8 33 na 421 4233 4670
1989 324 61 22 237 83 25 8 32 na 414 4240 4673
1990 304 70 22 207 83 24 7 32 na 395 4267 4683
1991 304 86 21 193 79 20 6 33 na 392 3408 3800
1992 303 91 20 184 81 22 6 31 na 393 3424 3817
1993 291 79 20 181 na 22 5 32 na 404 3501 3905
1994 276 74 21 170 na 21 6 35 na 400 3529 3929
1995 279 71 20 179 na 22 5 35 na 399 3530 3929
1996 298 86 21 181 na 24 5 32 na 406 3506 3912
Raw
graph.r
library(ggplot2)
mydata = read.csv("crops2.csv",sep=' ')
head(mydata)
#Year Corn Wheat....
#1 1847 1,026 272 625 120 231 89 118 NA
#2 1848 953 245 566 119 365 258 91 NA
drop <- c("Flax", "SugarBeet", "Root","Turnips", "fruit","Crops.Pasture","Hay.Pasture")
mydata<-mydata[ , !(names(mydata) %in% drop)]
#make the data the right shape not wide long.
library(reshape2)
mydata2 <- melt(mydata, id.vars = c("Year"))
#get rid of columns being chars not numbers
mydata2 <- transform(mydata2, value = as.numeric(value))
names(mydata2)[names(mydata2) == 'variable'] <- 'crop'
p<-ggplot(mydata2, aes(x=Year, y=value, group=crop)) +
geom_line(aes(color=crop))#+
#geom_point(aes(color=crop))
p
library("ggthemes")
library("scales")
p<-p + ylab("1000's of hectares")
#p + theme_few() + scale_colour_few()
p<-p + theme_hc() +
scale_colour_hc()
p<-p +ggtitle("Land Farmed in Ireland 1847-1990") +
theme(plot.title = element_text(hjust = 0.5))
p<-p+annotate("text", x = 1985, y = -12, label = "@iamreddave data:cso.ie", size = 3.0)
p<- p+annotate("segment", x = 1914.6, xend = 1914.6, y = 1000, yend = 900, colour = "black")
p<- p+annotate("text", x = 1922.5, y = 950, label = "WW1 start", size=3, colour="black")
p<- p+annotate("segment", x = 1939.75, xend = 1939.75, y = 1000, yend = 900, colour = "black")
p<- p+annotate("text", x = 1947.5, y = 950, label = "WW2 start", size=3, colour="black")
p<- p+annotate("segment", x = 1973, xend = 1973, y = 1000, yend = 900, colour = "black")
p<- p+annotate("text", x = 1981, y = 950, label = "Joins EEC", size=3, colour="black")
ggsave(filename = "crops.png")
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