Before attempting to run these commands, you should have first downloaded and unzipped the 'energy-nat' folder onto your computer (download it here) and have processed the English and Dutch commuting data examples. The resulting objects need to be on your R workspace when you run through this code, as it refers to data loaded from these sessions.
load("en-nl.RData")
gors$label <- gortex$V3 # set the zone codes to identify the English zones
## Loading required package: sp
gors$ZONE_CODE <- gortex$V3 # again, for reference
gors$EAV <- gors$ET/gors$all.all # average energy costs: total divided by the population
gors@data[, c(1:5, 115, 114, 116)]
## ZONE_CODE ZONE_LABEL all.all metro.all train.all ET
## 0 NE North East 1032968 22266 9119 34635581
## 1 NW North West 2900020 15955 54356 95402871
## 2 YaTH Yorkshire and The Humber 2182839 7839 31830 74293853
## 3 EM East Midlands 1917728 1287 18849 73848971
## 4 WM West Midlands 2334567 4262 35408 81396375
## 5 EoE East of England 2579378 21688 156054 106379285
## 6 Ln London 3319134 625224 404414 69060893
## 7 SE South East 3888755 8949 218822 158129081
## 8 SW South West 2286107 1916 21171 81036299
## moden.other label
## 0 0 NE
## 1 0 NW
## 2 0 YaTH
## 3 0 EM
## 4 0 WM
## 5 0 EoE
## 6 0 Ln
## 7 0 SE
## 8 0 SW
summary(gors$EAV) # browsing out dataset
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.8 33.5 34.9 34.7 38.5 41.2
sum(gors$ET)/sum(gors$all.all) # EN population-weighted average, lower than average of GORs
## [1] 34.5
r3$pops <- c(2415, 1180, 3530, 2583, 482, 642, 1968, 576, 1143, 1105, 356, 378)
sum(r3$pops) # Adding populaitons of NL provinces to calculate per person averages
## [1] 16358
summary(r3$etot)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 32.2 38.6 41.1 40.6 42.2 52.7
sum(r3$Totaal.vervoerwijzen * r3$etot)/((mean(r3$Totaal.vervoerwijzen)) * 12) # Netherlands region average (unweighted)
## [1] 40.72
r3@data[, c("name", "code_hasc", "Vervoerwijzen", "Totaal.vervoerwijzen", "etot",
"pops")] # re-check data
## name code_hasc Vervoerwijzen Totaal.vervoerwijzen etot pops
## 1 Noord-Brabant NL.NB Noord-Brabant 0.49 40.48 2415
## 2 Utrecht NL.UT Utrecht 0.50 37.84 1180
## 3 Zuid-Holland NL.ZH Zuid-Holland 0.47 32.21 3530
## 4 Noord-Holland NL.NH Noord-Holland 0.50 34.79 2583
## 5 Drenthe NL.DR Drenthe 0.52 42.10 482
## 6 Friesland NL.FR Friesland 0.47 42.42 642
## 7 Gelderland NL.GE Gelderland 0.51 41.69 1968
## 8 Groningen NL.GR Groningen 0.46 41.79 576
## 9 Limburg NL.LI Limburg 0.48 38.89 1143
## 10 Overijssel NL.OV Overijssel 0.51 40.19 1105
## 11 Flevoland NL.FL Flevoland 0.52 52.67 356
## 12 Zeeland NL.ZE Zeeland 0.47 42.73 378
r3$etot * r3$pops
## [1] 97749 44647 113716 89868 20290 27231 82046 24072 44450 44415
## [11] 18752 16151
sum(gors$ET)/sum(gors$all.all) # En pop weighted
## [1] 34.5
sum(r3$etot * r3$pops)/sum(r3$pops) # NL pop-weighted av. also lower, due to cities
## [1] 38.11
38.1/34.5 # 10% difference...
## [1] 1.104
(sum(gors$ET)/sum(gors$all.all))/(sum(r3$etot * r3$pops)/sum(r3$pops))
## [1] 0.9052
msplit.nl <- r3@data[, 7:14]
for (i in 1:8) {
msplit.nl[, i] <- r3@data[, i + 6] * r3$pops
}
m.nl <- colSums(msplit.nl)
names(m.nl)[4] <- "Metro"
m.nl <- m.nl * 500
m.nl <- data.frame(tot = m.nl, prop = m.nl/sum(m.nl) * 100)
m.nl$mode <- row.names(m.nl)
m.nl <- m.nl[-c(3, 5, 8), ]
m.nl$Country <- "Netherlands"
head(gors@data)
## ZONE_CODE ZONE_LABEL all.all metro.all train.all bus.all
## 0 NE North East 1032968 22266 9119 113224
## 1 NW North West 2900020 15955 54356 248267
## 2 YaTH Yorkshire and The Humber 2182839 7839 31830 228839
## 3 EM East Midlands 1917728 1287 18849 133858
## 4 WM West Midlands 2334567 4262 35408 204347
## 5 EoE East of England 2579378 21688 156054 102838
## moto.all car.all carp.all taxi.all bike.all walk.all other.all all.0
## 0 6967 570214 94389 7113 16786 105271 8311 203866
## 1 26731 1694380 217559 23400 65961 297720 13217 613763
## 2 20865 1226371 159789 11624 63384 238491 9626 451932
## 3 20018 1157931 133260 7926 62644 201247 7400 409400
## 4 20844 1400069 167936 10240 52545 222347 7746 469182
## 5 28637 1518613 150642 11693 100193 233737 11798 517466
## metro.0 train.0 bus.0 moto.0 car.0 carp.0 taxi.0 bike.0 walk.0 other.0
## 0 1262 290 18335 777 74720 17162 2263 5997 82115 945
## 1 645 1472 35746 3550 251492 47740 8386 25902 236241 2589
## 2 558 893 27700 2735 169493 30703 3695 27401 186529 2225
## 3 27 463 16994 2646 166752 27968 2574 28918 161307 1751
## 4 194 969 27080 2890 198110 35468 2858 22379 177361 1873
## 5 295 2423 14177 3825 217932 31620 3127 51009 190497 2561
## all.2 metro.2 train.2 bus.2 moto.2 car.2 carp.2 taxi.2 bike.2 walk.2
## 0 226278 5448 651 45659 1881 123674 28721 2577 6446 10740
## 1 654798 3258 4643 105994 7971 395005 72322 7780 24555 31942
## 2 496207 2652 1569 96720 6491 282753 51362 4221 23413 26056
## 3 390775 56 568 55778 5604 242977 41119 2875 21505 19445
## 4 524963 648 2039 84970 6040 327234 55768 3792 19573 23913
## 5 437395 375 2845 41326 6946 284912 42480 3150 32933 21399
## other.2 all.5 metro.5 train.5 bus.5 moto.5 car.5 carp.5 taxi.5 bike.5
## 0 481 213822 8169 973 30061 2082 140657 23623 860 2617
## 1 1328 565533 5870 12094 70809 7430 397622 47143 2629 9521
## 2 970 425312 2667 3741 66368 5973 291890 36384 1563 7305
## 3 848 337671 62 1290 38214 5455 249655 28336 955 6983
## 4 986 449380 1035 6626 62047 5587 321791 36226 1487 6195
## 5 1029 354182 558 5243 21966 5688 278093 27082 997 8043
## walk.5 other.5 all.10 metro.10 train.10 bus.10 moto.10 car.10 carp.10
## 0 4336 444 171898 5934 2430 13203 1529 130423 14224
## 1 11395 1020 416059 4215 18164 22992 5020 329283 26077
## 2 8709 712 308919 875 10122 25377 3628 239106 20426
## 3 6122 599 279593 253 3443 13888 3863 233038 17423
## 4 7642 744 330188 1138 9932 19604 3849 268705 19556
## 5 5851 661 379857 5595 10362 12773 5257 314866 21326
## taxi.10 bike.10 walk.10 other.10 all.20 metro.20 train.20 bus.20 moto.20
## 0 349 912 2528 366 43847 209 874 1886 289
## 1 993 3099 5486 730 134197 375 6722 3353 1279
## 2 587 2254 5994 550 97625 81 5844 3822 834
## 3 402 2308 4488 487 108726 53 2744 3180 1044
## 4 553 1994 4303 554 123409 271 6253 3214 1132
## 5 638 3423 4840 777 201209 8198 24218 4809 2827
## car.20 carp.20 taxi.20 bike.20 walk.20 other.20 all.30 metro.30 train.30
## 0 36558 3011 87 133 690 110 15191 60 275
## 1 113043 6804 233 571 1494 323 55566 87 2787
## 2 78927 5703 124 576 1517 197 43290 40 2760
## 3 93250 5506 128 679 1927 215 43203 15 1269
## 4 103689 6186 146 569 1704 245 45058 27 2517
## 5 148596 8543 284 1153 2182 399 107616 2903 30318
## bus.30 moto.30 car.30 carp.30 taxi.30 bike.30 walk.30 other.30 all.40
## 0 501 83 12908 964 28 63 265 44 12328
## 1 1175 375 47145 2633 94 213 928 129 44734
## 2 1227 290 35170 2418 54 384 838 109 35478
## 3 855 364 37408 1967 68 281 886 90 35511
## 4 652 317 38530 2024 42 179 670 100 33450
## 5 1992 1439 65387 3679 117 504 1029 248 108875
## metro.40 train.40 bus.40 moto.40 car.40 carp.40 taxi.40 bike.40 walk.40
## 0 75 371 469 54 10015 942 20 44 241
## 1 90 2585 1325 241 37013 2079 66 187 965
## 2 49 1997 1258 185 28600 1978 43 278 951
## 3 14 1328 803 248 30351 1547 45 274 767
## 4 28 1932 562 223 28392 1495 47 117 540
## 5 1308 46724 2095 1104 52868 2826 93 482 1091
## other.40 all.60 metro.60 train.60 bus.60 moto.60 car.60 carp.60 taxi.60
## 0 97 29571 582 2190 1595 137 18073 2033 112
## 1 183 61724 786 3841 2838 343 42584 3064 264
## 2 139 56350 625 3358 3027 335 39047 3163 212
## 3 134 61249 620 6705 2207 409 43348 2717 102
## 4 114 56449 685 3860 2769 320 41135 2635 142
## 5 284 90977 1133 27617 1870 670 50983 2739 175
## bike.60 walk.60 other.60 abbed mfh moden.all moden.metro moden.train
## 0 260 2420 2169 NE 79308 0 175640 342044
## 1 763 4738 2503 NW 242474 0 146915 1286024
## 2 778 4284 1521 YaTH 184181 0 70707 956464
## 3 746 3501 894 EM 173308 0 36823 990636
## 4 611 3415 877 WM 208823 0 63421 1032208
## 5 951 3440 1399 EoE 243485 0 298160 7771360
## moden.bus moden.moto moden.car moden.carp moden.taxi moden.bike
## 0 2635479 134196 31200791 0 98861 8081
## 1 5469084 498587 87555150 0 307831 30385
## 2 5415379 374630 67189678 0 168455 26777
## 3 3355704 383665 68868720 0 111118 25895
## 4 4631191 391319 75019779 0 152437 22530
## 5 3134069 646855 94272899 0 132046 38230
## moden.walk moden.other ET label EAV
## 0 40489 0 34635581 NE 33.53
## 1 108896 0 95402871 NW 32.90
## 2 91762 0 74293853 YaTH 34.04
## 3 76410 0 73848971 EM 38.51
## 4 83491 0 81396375 WM 34.87
## 5 85665 0 106379285 EoE 41.24
m.en <- colSums(gors@data[, c(5, 6, 8, 9, 11, 12, 4)])/sum(gors$all.all)
m.en <- colSums(gors@data[, c(4:13)])
m.en <- data.frame(tot = m.en, prop = m.en/sum(m.en) * 100)
wones <- c(4, 6, 8, 9, 11, 12) - 3
m.en <- m.en[wones, ]
m.en$mode <- c("Metro", "Bus", "Car.d", "Car.p", "Bicycle", "Walk")
m.en$Country <- "England"
m.all <- rbind(m.en, m.nl)
head(m.all)
## tot prop mode Country
## metro.all 709386 3.480 Metro England
## bus.all 1685361 8.267 Bus England
## car.all 12324166 60.453 Car.d England
## carp.all 1370685 6.724 Car.p England
## bike.all 634588 3.113 Bicycle England
## walk.all 2241901 10.997 Walk England
p <- ggplot(m.all, aes(y = prop, fill = mode))
p + geom_bar(aes(x = mode), stat = "identity") + facet_grid(facets = . ~ Country) +
scale_fill_brewer(type = "qual", palette = 7) + ylab("% of commuter trips")
names(gors)[4:15]
## [1] "metro.all" "train.all" "bus.all" "moto.all" "car.all"
## [6] "carp.all" "taxi.all" "bike.all" "walk.all" "other.all"
## [11] "all.0" "metro.0"
md
## X X0 V1 V2 V3 V4 V5 V6 V7 V8
## 1 1 0 0.000 0.000 0.00 0.00 0.00 0.00 0.00 0.0
## 2 metro 0 2.333 5.555 11.35 20.51 36.12 0.00 0.00 91.0
## 3 train 0 2.100 5.833 11.28 21.15 36.98 52.68 74.45 138.4
## 4 bus 0 2.450 5.310 10.84 19.48 38.73 56.00 78.40 154.7
## 5 moto 0 2.000 5.717 9.80 21.28 32.90 50.40 0.00 0.0
## 6 card 0 2.176 5.451 11.00 21.00 36.40 50.07 70.38 143.6
## 7 carp 0 2.155 5.492 11.09 21.30 37.14 51.00 67.20 133.0
## 8 taxi 0 2.400 4.200 12.60 16.80 0.00 0.00 0.00 0.0
## 9 cycle 0 2.063 5.458 10.53 16.10 0.00 0.00 0.00 0.0
## 10 walk 0 1.679 4.885 11.20 19.13 35.00 0.00 0.00 0.0
## 11 other 0 1.400 5.950 10.60 18.90 38.85 52.50 58.80 182.0
md <- md[, -1]
t.en <- colSums(gors@data[, 3:101])
md
## X0 V1 V2 V3 V4 V5 V6 V7 V8
## 1 0 0.000 0.000 0.00 0.00 0.00 0.00 0.00 0.0
## 2 0 2.333 5.555 11.35 20.51 36.12 0.00 0.00 91.0
## 3 0 2.100 5.833 11.28 21.15 36.98 52.68 74.45 138.4
## 4 0 2.450 5.310 10.84 19.48 38.73 56.00 78.40 154.7
## 5 0 2.000 5.717 9.80 21.28 32.90 50.40 0.00 0.0
## 6 0 2.176 5.451 11.00 21.00 36.40 50.07 70.38 143.6
## 7 0 2.155 5.492 11.09 21.30 37.14 51.00 67.20 133.0
## 8 0 2.400 4.200 12.60 16.80 0.00 0.00 0.00 0.0
## 9 0 2.063 5.458 10.53 16.10 0.00 0.00 0.00 0.0
## 10 0 1.679 4.885 11.20 19.13 35.00 0.00 0.00 0.0
## 11 0 1.400 5.950 10.60 18.90 38.85 52.50 58.80 182.0
md$mdtotald <- 0
for (i in 1:11) {
md[i, "mdtotald"] <- sum(md[i, 1:9] * colSums(gors@data[, seq(from = 4,
by = 11, length.out = 9) + i - 2]))
}
md
## X0 V1 V2 V3 V4 V5 V6 V7 V8 mdtotald
## 1 0 0.000 0.000 0.00 0.00 0.00 0.00 0.00 0.0 0
## 2 0 2.333 5.555 11.35 20.51 36.12 0.00 0.00 91.0 10571205
## 3 0 2.100 5.833 11.28 21.15 36.98 52.68 74.45 138.4 39191501
## 4 0 2.450 5.310 10.84 19.48 38.73 56.00 78.40 154.7 18905134
## 5 0 2.000 5.717 9.80 21.28 32.90 50.40 0.00 0.0 2947787
## 6 0 2.176 5.451 11.00 21.00 36.40 50.07 70.38 143.6 230221407
## 7 0 2.155 5.492 11.09 21.30 37.14 51.00 67.20 133.0 17313725
## 8 0 2.400 4.200 12.60 16.80 0.00 0.00 0.00 0.0 501664
## 9 0 2.063 5.458 10.53 16.10 0.00 0.00 0.00 0.0 3082122
## 10 0 1.679 4.885 11.20 19.13 35.00 0.00 0.00 0.0 6319569
## 11 0 1.400 5.950 10.60 18.90 38.85 52.50 58.80 182.0 2985675
md$dist <- md$mdtotald/t.en[1:11]
md$pdist <- md$mdtotald/sum(md$mdtotald)
md
## X0 V1 V2 V3 V4 V5 V6 V7 V8 mdtotald dist
## 1 0 0.000 0.000 0.00 0.00 0.00 0.00 0.00 0.0 0 0.000
## 2 0 2.333 5.555 11.35 20.51 36.12 0.00 0.00 91.0 10571205 14.902
## 3 0 2.100 5.833 11.28 21.15 36.98 52.68 74.45 138.4 39191501 41.253
## 4 0 2.450 5.310 10.84 19.48 38.73 56.00 78.40 154.7 18905134 11.217
## 5 0 2.000 5.717 9.80 21.28 32.90 50.40 0.00 0.0 2947787 11.817
## 6 0 2.176 5.451 11.00 21.00 36.40 50.07 70.38 143.6 230221407 18.680
## 7 0 2.155 5.492 11.09 21.30 37.14 51.00 67.20 133.0 17313725 12.631
## 8 0 2.400 4.200 12.60 16.80 0.00 0.00 0.00 0.0 501664 4.306
## 9 0 2.063 5.458 10.53 16.10 0.00 0.00 0.00 0.0 3082122 4.857
## 10 0 1.679 4.885 11.20 19.13 35.00 0.00 0.00 0.0 6319569 2.819
## 11 0 1.400 5.950 10.60 18.90 38.85 52.50 58.80 182.0 2985675 28.652
## pdist
## 1 0.000000
## 2 0.031837
## 3 0.118033
## 4 0.056936
## 5 0.008878
## 6 0.693355
## 7 0.052144
## 8 0.001511
## 9 0.009282
## 10 0.019033
## 11 0.008992
barplot(md$mdtotald, col = 1:11)
md$tdist <- md$mdtotald
md <- md[, -10]
md2 <- md[c(3, 4, 6, 7, 9, 10, 2), ]
md2
## X0 V1 V2 V3 V4 V5 V6 V7 V8 dist pdist
## 3 0 2.100 5.833 11.28 21.15 36.98 52.68 74.45 138.4 41.253 0.118033
## 4 0 2.450 5.310 10.84 19.48 38.73 56.00 78.40 154.7 11.217 0.056936
## 6 0 2.176 5.451 11.00 21.00 36.40 50.07 70.38 143.6 18.680 0.693355
## 7 0 2.155 5.492 11.09 21.30 37.14 51.00 67.20 133.0 12.631 0.052144
## 9 0 2.063 5.458 10.53 16.10 0.00 0.00 0.00 0.0 4.857 0.009282
## 10 0 1.679 4.885 11.20 19.13 35.00 0.00 0.00 0.0 2.819 0.019033
## 2 0 2.333 5.555 11.35 20.51 36.12 0.00 0.00 91.0 14.902 0.031837
## tdist
## 3 39191501
## 4 18905134
## 6 230221407
## 7 17313725
## 9 3082122
## 10 6319569
## 2 10571205
md2$Country <- "England"
sum(md$tdist)/sum(gors$all.all) # Total distance by all modes: 10 km
## [1] 14.8
# Distance NL
names(r3)
## [1] "c2" "Shape_Leng"
## [3] "name" "code_hasc"
## [5] "Vervoerwijzen" "Totaal.vervoerwijzen"
## [7] "Car.d" "Car.p"
## [9] "Train" "Bus.tram.metro"
## [11] "Moto" "Bicycle"
## [13] "Walk" "Other"
## [15] "Totaal.vervoerwijzen.1" "Car.d.1"
## [17] "Car.p.1" "Train.1"
## [19] "Bus.tram.metro.1" "Moto.1"
## [21] "Bicycle.1" "Walk.1"
## [23] "Other.1" "Totaal.vervoerwijzen.2"
## [25] "Car.d.2" "Car.p.2"
## [27] "Train.2" "Bus.tram.metro.2"
## [29] "Moto.2" "Bicycle.2"
## [31] "Walk.2" "Other.2"
## [33] "etot" "id"
## [35] "pops"
md.nl <- data.frame(dist = colSums(r3@data[, 16:23])/nrow(r3@data))
md.nl # v. useful infor: compare w.
## dist
## Car.d.1 24.582
## Car.p.1 27.097
## Train.1 38.350
## Bus.tram.metro.1 13.207
## Moto.1 8.858
## Bicycle.1 4.265
## Walk.1 2.544
## Other.1 22.011
md.nl$tdist <- md.nl[, 1] * colSums(r3@data[, 7:14])
md.nl$pdist <- md.nl$tdist/sum(md.nl$tdist)
md.nl$Country <- "Netherlands"
qplot(data = md.nl, x = row.names(md.nl), y = pdist)
md.nl$ptrips <- colSums(r3@data[, 7:14])/nrow(r3@data)
sum(md.nl$dist * md.nl$ptrips)
## [1] 16.03
md.nl
## dist tdist pdist Country ptrips
## Car.d.1 24.582 161.9107 0.841695 Netherlands 0.548887
## Car.p.1 27.097 13.2633 0.068950 Netherlands 0.040790
## Train.1 38.350 0.0000 0.000000 Netherlands 0.000000
## Bus.tram.metro.1 13.207 1.6355 0.008502 Netherlands 0.010319
## Moto.1 8.858 0.3656 0.001901 Netherlands 0.003440
## Bicycle.1 4.265 12.8688 0.066899 Netherlands 0.251442
## Walk.1 2.544 1.4011 0.007283 Netherlands 0.045891
## Other.1 22.011 0.9175 0.004770 Netherlands 0.003474
md.all <- rbind(md2[, c("dist", "pdist", "tdist", "Country")], md.nl[-which(row.names(md.nl) ==
"Moto.1" | row.names(md.nl) == "Other.1"), c("dist", "pdist", "tdist", "Country")])
md.all
## dist pdist tdist Country
## 3 41.253 0.118033 3.919e+07 England
## 4 11.217 0.056936 1.891e+07 England
## 6 18.680 0.693355 2.302e+08 England
## 7 12.631 0.052144 1.731e+07 England
## 9 4.857 0.009282 3.082e+06 England
## 10 2.819 0.019033 6.320e+06 England
## 2 14.902 0.031837 1.057e+07 England
## Car.d.1 24.582 0.841695 1.619e+02 Netherlands
## Car.p.1 27.097 0.068950 1.326e+01 Netherlands
## Train.1 38.350 0.000000 0.000e+00 Netherlands
## Bus.tram.metro.1 13.207 0.008502 1.635e+00 Netherlands
## Bicycle.1 4.265 0.066899 1.287e+01 Netherlands
## Walk.1 2.544 0.007283 1.401e+00 Netherlands
md.all$mode = c("Train", "Bus", "Car.d", "Car.p", "Bicycle", "Walk", "Metro",
"Car.d", "Car.p", "Train", "Metro", "Bicycle", "Walk")
md.all
## dist pdist tdist Country mode
## 3 41.253 0.118033 3.919e+07 England Train
## 4 11.217 0.056936 1.891e+07 England Bus
## 6 18.680 0.693355 2.302e+08 England Car.d
## 7 12.631 0.052144 1.731e+07 England Car.p
## 9 4.857 0.009282 3.082e+06 England Bicycle
## 10 2.819 0.019033 6.320e+06 England Walk
## 2 14.902 0.031837 1.057e+07 England Metro
## Car.d.1 24.582 0.841695 1.619e+02 Netherlands Car.d
## Car.p.1 27.097 0.068950 1.326e+01 Netherlands Car.p
## Train.1 38.350 0.000000 0.000e+00 Netherlands Train
## Bus.tram.metro.1 13.207 0.008502 1.635e+00 Netherlands Metro
## Bicycle.1 4.265 0.066899 1.287e+01 Netherlands Bicycle
## Walk.1 2.544 0.007283 1.401e+00 Netherlands Walk
qplot(data = md.all[-c(1, 10), ], x = mode, y = dist, fill = mode, geom = "bar") +
facet_grid(facets = . ~ Country) + scale_fill_brewer(type = "qual", palette = 7) +
ylab("Distance of commute (km)")
## Mapping a variable to y and also using stat="bin". With stat="bin", it
## will attempt to set the y value to the count of cases in each group. This
## can result in unexpected behavior and will not be allowed in a future
## version of ggplot2. If you want y to represent counts of cases, use
## stat="bin" and don't map a variable to y. If you want y to represent
## values in the data, use stat="identity". See ?geom_bar for examples.
## (Deprecated; last used in version 0.9.2)
## Mapping a variable to y and also using stat="bin". With stat="bin", it
## will attempt to set the y value to the count of cases in each group. This
## can result in unexpected behavior and will not be allowed in a future
## version of ggplot2. If you want y to represent counts of cases, use
## stat="bin" and don't map a variable to y. If you want y to represent
## values in the data, use stat="identity". See ?geom_bar for examples.
## (Deprecated; last used in version 0.9.2)
md.all$ptrips <- 0
md.all$ptrips[8:13] <- colMeans(r3@data[, c(1, 2, 3, 4, 6, 7) + 6])
md.all$ptrips[1:7] <- colSums(gors@data[, c(5, 6, 8, 9, 11, 12, 4)])/sum(gors$all.all)
md.all
## dist pdist tdist Country mode ptrips
## 3 41.253 0.118033 3.919e+07 England Train 0.04233
## 4 11.217 0.056936 1.891e+07 England Bus 0.07510
## 6 18.680 0.693355 2.302e+08 England Car.d 0.54917
## 7 12.631 0.052144 1.731e+07 England Car.p 0.06108
## 9 4.857 0.009282 3.082e+06 England Bicycle 0.02828
## 10 2.819 0.019033 6.320e+06 England Walk 0.09990
## 2 14.902 0.031837 1.057e+07 England Metro 0.03161
## Car.d.1 24.582 0.841695 1.619e+02 Netherlands Car.d 0.54889
## Car.p.1 27.097 0.068950 1.326e+01 Netherlands Car.p 0.04079
## Train.1 38.350 0.000000 0.000e+00 Netherlands Train 0.00000
## Bus.tram.metro.1 13.207 0.008502 1.635e+00 Netherlands Metro 0.01032
## Bicycle.1 4.265 0.066899 1.287e+01 Netherlands Bicycle 0.25144
## Walk.1 2.544 0.007283 1.401e+00 Netherlands Walk 0.04589
sum(md.all$dist[1:7] * md.all$ptrips[1:7]) # 14.5 av dist uk
## [1] 14.51
sum(md.all$dist[8:13] * md.all$ptrips[8:13])
## [1] 15.92
md.all$dist[md.all$mode == "Car.d"][2]/md.all$dist[md.all$mode == "Car.d"][1]
## [1] 1.316
library(rgeos)
## rgeos version: 0.2-17, (SVN revision 392) GEOS runtime version:
## 3.3.8-CAPI-1.7.8 Polygon checking: TRUE
gors$area <- gArea(gors, byid = T)/1e+06
r3$area <- gArea(r3, byid = T)/1e+06
r3$popdens <- r3$pops * 1000/r3$area
gors$popdens <- gors$all.all * 2/gors$area
r3$EAV <- r3$etot
pden.all <- rbind(r3@data[, c("popdens", "EAV")], gors@data[, c("popdens", "EAV")])
pden.all$Country <- c(rep("Netherlands", 12), rep("England", 9))
qplot(data = pden.all, x = popdens, y = EAV, color = Country) + ylab("Energy costs of commuting (MJ/trip)") +
xlab("Population density (ppl/sqkm)") + geom_abline(intercept = sum(gors$ET)/sum(gors$all.all),
slope = 0, colour = "red") + geom_abline(intercept = sum(r3$etot * r3$pops)/sum(r3$pops),
slope = 0, colour = "blue")
