This tutorial provides a basic introduction to magpie objects in R. If you would like to get more details on the concept of and the idea behind magpie objects you can have a look at magclass-concept, where this is explained in more detail by comparing the magpie class to other data classes in R.
Generation of magpie objects can either be done from scratch, by reading in files or by converting objects from other classes to magpie objects. For creation of a new magpie object you can use new.magpie, conversion of an existing object happens with as.magpie:
library(magclass)
# creating a magpie object with 2 regions, 2 years and 2 different values
m <- new.magpie(cells_and_regions = c("AFR", "CPA"),
years = c(1995, 2000),
names = c("bla", "blub"),
sets = c("region", "year", "value"),
fill = 0)
print(m)
#> , , value = bla
#>
#> year
#> region y1995 y2000
#> AFR 0 0
#> CPA 0 0
#>
#> , , value = blub
#>
#> year
#> region y1995 y2000
#> AFR 0 0
#> CPA 0 0
# converting a simple vector with one value per region to a magpie object
v <- c(ENG = 10, USA = 20, BRA = 30, CHN = 40, IND = 50)
m2 <- as.magpie(v)
str(m2)
#> Formal class 'magpie' [package "magclass"] with 1 slot
#> ..@ .Data: num [1:5, 1, 1] 10 20 30 40 50
#> .. ..- attr(*, "dimnames")=List of 3
#> .. .. ..$ fake: chr [1:5] "ENG" "USA" "BRA" "CHN" ...
#> .. .. ..$ NA : NULL
#> .. .. ..$ NA : NULLIn the example above the names were automatically detected as regions, if for some reason the automatic detection fails you can also indicate to what type of dimension the data belongs. Lets assume in the following example, that the names are not representing regions but something else. The argument spatial=0 indicates the non-existence of a spatial dimension, but it can also be used to point to the spatial dimension in the data.
m3 <- as.magpie(v, spatial = 0)
str(m3)
#> Formal class 'magpie' [package "magclass"] with 1 slot
#> ..@ .Data: num [1, 1, 1:5] 10 20 30 40 50
#> .. ..- attr(*, "dimnames")=List of 3
#> .. .. ..$ fake: chr "GLO"
#> .. .. ..$ NA : NULL
#> .. .. ..$ NA : chr [1:5] "ENG" "USA" "BRA" "CHN" ...For the following a example data set containing population data is used. For convenience we will just shorten its name:
pm <- maxample("pop")Let’s first have a look at the content of the magpie object:
Show the structure of the object:
str(pm)
#> Formal class 'magpie' [package "magclass"] with 1 slot
#> ..@ .Data: num [1:10, 1:16, 1:2] 553 1281 554 276 452 ...
#> .. ..- attr(*, "dimnames")=List of 3
#> .. .. ..$ i : chr [1:10] "AFR" "CPA" "EUR" "FSU" ...
#> .. .. ..$ t : chr [1:16] "y1995" "y2005" "y2015" "y2025" ...
#> .. .. ..$ scenario: chr [1:2] "A2" "B1"Show the first elements:
head(pm)
#> An object of class "magpie"
#> , , scenario = A2
#>
#> t
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> AFR 552.6664 696.44 889.18 1124.11 1389.33 1659.73
#> CPA 1280.6350 1429.53 1518.46 1592.09 1640.95 1671.94
#> EUR 554.4384 582.36 593.76 605.27 614.58 618.97
#>
#> , , scenario = B1
#>
#> t
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> AFR 552.6664 721.85 932.04 1118.33 1267.33 1383.24
#> CPA 1280.6350 1429.26 1499.74 1531.12 1518.73 1463.68
#> EUR 554.4384 587.21 603.63 613.98 619.48 617.12Show the last elements:
tail(pm)
#> An object of class "magpie"
#> , , scenario = A2
#>
#> t
#> i y2095 y2105 y2115 y2125 y2135 y2145
#> PAO 166.31 167.49 167.49 167.49 167.49 167.49
#> PAS 843.52 839.53 839.53 839.53 839.53 839.53
#> SAS 3007.86 2972.39 2972.39 2972.39 2972.39 2972.39
#>
#> , , scenario = B1
#>
#> t
#> i y2095 y2105 y2115 y2125 y2135 y2145
#> PAO 140.82 138.80 138.80 138.80 138.80 138.80
#> PAS 536.24 507.06 507.06 507.06 507.06 507.06
#> SAS 1629.07 1528.15 1528.15 1528.15 1528.15 1528.15Which regions are in the data and how many?
getRegions(pm)
#> [1] "AFR" "CPA" "EUR" "FSU" "LAM" "MEA" "NAM" "PAO" "PAS" "SAS"
nregions(pm)
#> [1] 10Which years and how many?
getYears(pm)
#> [1] "y1995" "y2005" "y2015" "y2025" "y2035" "y2045" "y2055" "y2065" "y2075"
#> [10] "y2085" "y2095" "y2105" "y2115" "y2125" "y2135" "y2145"
nyears(pm)
#> [1] 16Which data elements?
getNames(pm)
#> [1] "A2" "B1"…and splitted in subdimensions
getNames(pm, fulldim = TRUE)
#> $scenario
#> [1] "A2" "B1"…and how many?
ndata(pm)
#> [1] 2What are the sets (dimensions names) of the data?
getSets(pm)
#> d1.1 d2.1 d3.1
#> "i" "t" "scenario"are there any comments which come with the data?
getComment(pm)
#> NULLlet’s have a look at the full dimensionality of the object
getItems(pm, split = TRUE)
#> [[1]]
#> [[1]]$i
#> [1] "AFR" "CPA" "EUR" "FSU" "LAM" "MEA" "NAM" "PAO" "PAS" "SAS"
#>
#>
#> [[2]]
#> [[2]]$t
#> [1] "y1995" "y2005" "y2015" "y2025" "y2035" "y2045" "y2055" "y2065" "y2075"
#> [10] "y2085" "y2095" "y2105" "y2115" "y2125" "y2135" "y2145"
#>
#>
#> [[3]]
#> [[3]]$scenario
#> [1] "A2" "B1"if this is a bit confusing, this is the condensed version analogous to the standard 3 dim subsetting
dimnames(pm)
#> $i
#> [1] "AFR" "CPA" "EUR" "FSU" "LAM" "MEA" "NAM" "PAO" "PAS" "SAS"
#>
#> $t
#> [1] "y1995" "y2005" "y2015" "y2025" "y2035" "y2045" "y2055" "y2065" "y2075"
#> [10] "y2085" "y2095" "y2105" "y2115" "y2125" "y2135" "y2145"
#>
#> $scenario
#> [1] "A2" "B1"These functions can also be used to manipulate the object:
set a comment
getComment(pm) <- "This is a comment!"
getComment(pm)
#> [1] "This is a comment!"…or alternatively
pm2 <- setComment(pm, "This is comment for pm2!")
getComment(pm2)
#> [1] "This is comment for pm2!"rename 1st region in “RRR”
getRegions(pm)[1] <- "RRR"
#> Warning: 'getRegions<-' is deprecated.
#> Use 'getItems' instead.
#> See help("Deprecated")rename region set in “zones”
getSets(pm)[2] <- "year"The other functions such as getYears,… can be used in a similar manner.
Now that we have had a look into the structure of the object let’s extract some subsets out of it. There are different methods that can be used to extract data from a magpie object. Here are some examples:
Return all A2 related data for LAM and the years 2005 and 2015
pm["LAM", c(2005, 2015), "A2"]
#> An object of class "magpie"
#> , , scenario = A2
#>
#> year
#> i y2005 y2015
#> LAM 558.29 646.02Return data for regions which have “AS” in its name (pmatch allows for partial matching of the given search string)
pm["AS", , , pmatch = TRUE]
#> An object of class "magpie"
#> , , scenario = A2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045 y2055 y2065 y2075
#> PAS 383.2277 534.73 604.94 668.49 723.13 767.30 798.68 819.21 834.31
#> SAS 1269.9243 1505.02 1796.76 2095.48 2369.60 2600.68 2783.75 2920.70 3006.60
#> year
#> i y2085 y2095 y2105 y2115 y2125 y2135 y2145
#> PAS 844.38 843.52 839.53 839.53 839.53 839.53 839.53
#> SAS 3040.10 3007.86 2972.39 2972.39 2972.39 2972.39 2972.39
#>
#> , , scenario = B1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045 y2055 y2065 y2075
#> PAS 383.2277 530.67 590.42 639.68 674.98 692.45 689.79 668.98 634.64
#> SAS 1269.9243 1475.64 1687.80 1870.96 1999.15 2072.68 2090.96 2049.18 1953.77
#> year
#> i y2085 y2095 y2105 y2115 y2125 y2135 y2145
#> PAS 590.05 536.24 507.06 507.06 507.06 507.06 507.06
#> SAS 1811.83 1629.07 1528.15 1528.15 1528.15 1528.15 1528.15If you want to specifically select from one dimension from which you have the dimension name:
mselect(pm, scenario = "B1", i = c("FSU", "LAM"))
#> An object of class "magpie"
#> , , scenario = B1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045 y2055 y2065 y2075 y2085
#> FSU 276.3431 296.84 305.26 309.78 311.47 309.03 301.99 292.46 281.39 269.77
#> LAM 451.9981 552.79 623.20 681.60 723.44 747.70 753.98 743.05 718.79 683.68
#> year
#> i y2095 y2105 y2115 y2125 y2135 y2145
#> FSU 257.52 251.04 251.04 251.04 251.04 251.04
#> LAM 637.69 611.88 611.88 611.88 611.88 611.88Or you can use alternatively:
pm[list(i = c("FSU", "LAM")), , list(scenario = "B1")]
#> An object of class "magpie"
#> , , scenario = B1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045 y2055 y2065 y2075 y2085
#> FSU 276.3431 296.84 305.26 309.78 311.47 309.03 301.99 292.46 281.39 269.77
#> LAM 451.9981 552.79 623.20 681.60 723.44 747.70 753.98 743.05 718.79 683.68
#> year
#> i y2095 y2105 y2115 y2125 y2135 y2145
#> FSU 257.52 251.04 251.04 251.04 251.04 251.04
#> LAM 637.69 611.88 611.88 611.88 611.88 611.88Now we can perform some calculations with it.
take a subset of the data as an example
d <- head(pm)create a new object with some fancy calculations
d2 <- d^2 + 12 * d + 99 / exp(d)
getNames(d2) <- c("NEWSCEN1", "NEWSCEN2")
getSets(d2)[3] <- "newscen"
d2
#> An object of class "magpie"
#> , , newscen = NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 312072.1 493386.0 801311.2 1277113 1946909.7 2774620.4
#> CPA 1655393.6 2060710.5 2323942.2 2553856 2712408.1 2815446.4
#> EUR 314055.2 346131.5 359676.1 373615 385083.6 390551.5
#>
#> , , newscen = NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 312072.1 529729.6 879883.0 1264081.9 1621333.2 1929951.8
#> CPA 1655393.6 2059935.3 2267216.9 2362701.9 2324765.5 2159923.5
#> EUR 314055.2 351862.1 371612.7 384339.2 391189.2 388242.5multiply both data sets with each other
d <- d * d2
d
#> An object of class "magpie"
#> , , scenario.newscen = A2.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 172471773 343613717 712509904 1435615002 2704899949 4605120609
#> CPA 2119955061 2945847491 3528813140 4065967778 4450926008 4707257368
#> EUR 174124277 201573119 213561266 226137981 236664659 241739628
#>
#> , , scenario.newscen = A2.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 172471773 368924875 782374360 1420967030 2252566752 3203198781
#> CPA 2119955061 2944739364 3442678113 3761633954 3814823856 3611262304
#> EUR 174124277 204910425 220648786 232628981 240417069 240310466
#>
#> , , scenario.newscen = B1.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 172471773 356150641 746854091 1428233254 2467376967 3837965851
#> CPA 2119955061 2945291059 3485309011 3910259280 4119415564 4120912807
#> EUR 174124277 203251869 217111267 229392153 238551555 241017118
#>
#> , , scenario.newscen = B1.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 172471773 382385289 820086132 1413660600 2054764104 2669586440
#> CPA 2119955061 2944183141 3400235879 3617580090 3530691083 3161436886
#> EUR 174124277 206616969 224316602 235976560 242333882 239592227Because we changed the names of the elements in the data dimension it is assumed that they reflect different dimensions. Therefor the object is blown up in size instead of just having the elements multiplied pairwise with each other as it happens when two objects with identical dimensionality are multiplied with each other:
d2 * d2
#> An object of class "magpie"
#> , , newscen = NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035
#> RRR 9.738901e+10 2.434297e+11 6.420997e+11 1.631017e+12 3.790457e+12
#> CPA 2.740328e+12 4.246528e+12 5.400707e+12 6.522178e+12 7.357158e+12
#> EUR 9.863068e+10 1.198070e+11 1.293669e+11 1.395882e+11 1.482893e+11
#> year
#> i y2045
#> RRR 7.698518e+12
#> CPA 7.926739e+12
#> EUR 1.525304e+11
#>
#> , , newscen = NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035
#> RRR 9.738901e+10 2.806134e+11 7.741941e+11 1.597903e+12 2.628721e+12
#> CPA 2.740328e+12 4.243333e+12 5.140273e+12 5.582360e+12 5.404535e+12
#> EUR 9.863068e+10 1.238070e+11 1.380960e+11 1.477166e+11 1.530290e+11
#> year
#> i y2045
#> RRR 3.724714e+12
#> CPA 4.665269e+12
#> EUR 1.507323e+11sum over the data dimension
dimSums(d, dim = 3)
#> An object of class "magpie"
#> , , 1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 689887091 1451074522 3061824487 5698475887 9479607771 14315871681
#> CPA 8479820244 11780061055 13857036143 15355441102 15915856510 15600869364
#> EUR 696497109 816352383 875637922 924135674 957967165 962659440..over the second data dimension only
dimSums(d, dim = 3.2)
#> An object of class "magpie"
#> , , scenario = A2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 344943546 712538592 1494884264 2856582032 4957466700 7808319390
#> CPA 4239910122 5890586855 6971491254 7827601732 8265749863 8318519671
#> EUR 348248554 406483544 434210052 458766961 477081728 482050095
#>
#> , , scenario = B1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 344943546 738535930 1566940224 2841893854 4522141071 6507552291
#> CPA 4239910122 5889474200 6885544890 7527839370 7650106647 7282349693
#> EUR 348248554 409868839 441427869 465368713 480885436 480609345..or alternatively addressed by name
dimSums(d, dim = "newscen")
#> An object of class "magpie"
#> , , scenario = A2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 344943546 712538592 1494884264 2856582032 4957466700 7808319390
#> CPA 4239910122 5890586855 6971491254 7827601732 8265749863 8318519671
#> EUR 348248554 406483544 434210052 458766961 477081728 482050095
#>
#> , , scenario = B1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 344943546 738535930 1566940224 2841893854 4522141071 6507552291
#> CPA 4239910122 5889474200 6885544890 7527839370 7650106647 7282349693
#> EUR 348248554 409868839 441427869 465368713 480885436 480609345sum over regions and first data dimension
dimSums(d, dim = c(1, 3.1))
#> An object of class "magpie"
#> , , newscen = NEWSCEN1
#>
#> year
#> d1 y1995 y2005 y2015 y2025 y2035 y2045
#> [1,] 4933102222 6995727898 8904158681 11295605448 14217834702 17754013380
#>
#> , , newscen = NEWSCEN2
#>
#> year
#> d1 y1995 y2005 y2015 y2025 y2035 y2045
#> [1,] 4933102222 7051760063 8890339872 10682447215 12135596744 13125387105apply a lowpass filter on the data
lowpass(d)
#> An object of class "magpie"
#> , , scenario.newscen = A2.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 215257259 393052278 801062132 1572159964 2862633877 4130065444
#> CPA 2326428169 2885115796 3517360387 4027918676 4418769290 4643174528
#> EUR 180986488 197707946 213708408 225625472 235301732 240470886
#>
#> , , scenario.newscen = A2.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 221585048 423173971 838660156 1469218793 2282324829 2965540774
#> CPA 2326151137 2863027975 3397932386 3695192469 3750635992 3662152692
#> EUR 181820814 201148478 219709244 231580954 238443396 240337117
#>
#> , , scenario.newscen = B1.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 218391490 407906787 819523019 1517674392 2550238260 3495318630
#> CPA 2326289061 2873961548 3456542090 3856310784 4067500804 4120538496
#> EUR 181406175 199434821 216716639 228611782 236878095 240400727
#>
#> , , scenario.newscen = B1.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 224950152 439332121 859054538 1425542859 2048193812 2515880856
#> CPA 2326012081 2852139305 3340558747 3541521785 3460099785 3253750435
#> EUR 182247450 202918704 222806683 234650901 240059138 240277641do a linear interpolation of the data over time (yearly)
time_interpolate(d, 2005:2030)
#> An object of class "magpie"
#> , , scenario.newscen = A2.NEWSCEN1
#>
#> year
#> i y2005 y2006 y2007 y2008 y2009 y2010
#> RRR 343613717 380503336 417392955 454282573 491172192 528061811
#> CPA 2945847491 3004144056 3062440621 3120737186 3179033751 3237330316
#> EUR 201573119 202771934 203970749 205169563 206368378 207567193
#> year
#> i y2011 y2012 y2013 y2014 y2015 y2016
#> RRR 564951429 601841048 638730667 675620285 712509904 784820414
#> CPA 3295626881 3353923446 3412220011 3470516576 3528813140 3582528604
#> EUR 208766008 209964822 211163637 212362452 213561266 214818938
#> year
#> i y2017 y2018 y2019 y2020 y2021 y2022
#> RRR 857130924 929441434 1001751943 1074062453 1146372963 1218683473
#> CPA 3636244068 3689959532 3743674995 3797390459 3851105923 3904821386
#> EUR 216076609 217334281 218591952 219849624 221107295 222364966
#> year
#> i y2023 y2024 y2025 y2026 y2027 y2028
#> RRR 1290993983 1363304493 1435615002 1562543497 1689471992 1816400486
#> CPA 3958536850 4012252314 4065967778 4104463601 4142959424 4181455247
#> EUR 223622638 224880309 226137981 227190648 228243316 229295984
#> year
#> i y2029 y2030
#> RRR 1943328981 2070257476
#> CPA 4219951070 4258446893
#> EUR 230348652 231401320
#>
#> , , scenario.newscen = A2.NEWSCEN2
#>
#> year
#> i y2005 y2006 y2007 y2008 y2009 y2010
#> RRR 368924875 410269823 451614772 492959720 534304669 575649617
#> CPA 2944739364 2994533238 3044327113 3094120988 3143914863 3193708738
#> EUR 204910425 206484261 208058097 209631933 211205769 212779605
#> year
#> i y2011 y2012 y2013 y2014 y2015 y2016
#> RRR 616994566 658339514 699684463 741029411 782374360 846233627
#> CPA 3243502613 3293296488 3343090363 3392884238 3442678113 3474573697
#> EUR 214353441 215927278 217501114 219074950 220648786 221846805
#> year
#> i y2017 y2018 y2019 y2020 y2021 y2022
#> RRR 910092894 973952161 1037811428 1101670695 1165529962 1229389229
#> CPA 3506469281 3538364865 3570260450 3602156034 3634051618 3665947202
#> EUR 223044825 224242844 225440864 226638883 227836903 229034922
#> year
#> i y2023 y2024 y2025 y2026 y2027 y2028
#> RRR 1293248496 1357107763 1420967030 1504127002 1587286974 1670446946
#> CPA 3697842786 3729738370 3761633954 3766952945 3772271935 3777590925
#> EUR 230232942 231430961 232628981 233407789 234186598 234965407
#> year
#> i y2029 y2030
#> RRR 1753606919 1836766891
#> CPA 3782909915 3788228905
#> EUR 235744216 236523025
#>
#> , , scenario.newscen = B1.NEWSCEN1
#>
#> year
#> i y2005 y2006 y2007 y2008 y2009 y2010
#> RRR 356150641 395220986 434291331 473361676 512432021 551502366
#> CPA 2945291059 2999292855 3053294650 3107296445 3161298240 3215300035
#> EUR 203251869 204637809 206023749 207409689 208795629 210181568
#> year
#> i y2011 y2012 y2013 y2014 y2015 y2016
#> RRR 590572711 629643056 668713401 707783746 746854091 814992008
#> CPA 3269301830 3323303626 3377305421 3431307216 3485309011 3527804038
#> EUR 211567508 212953448 214339388 215725328 217111267 218339356
#> year
#> i y2017 y2018 y2019 y2020 y2021 y2022
#> RRR 883129924 951267840 1019405756 1087543673 1155681589 1223819505
#> CPA 3570299065 3612794092 3655289119 3697784146 3740279173 3782774199
#> EUR 219567445 220795533 222023622 223251710 224479799 225707887
#> year
#> i y2023 y2024 y2025 y2026 y2027 y2028
#> RRR 1291957422 1360095338 1428233254 1532147626 1636061997 1739976368
#> CPA 3825269226 3867764253 3910259280 3931174909 3952090537 3973006165
#> EUR 226935976 228164064 229392153 230308093 231224033 232139974
#> year
#> i y2029 y2030
#> RRR 1843890739 1947805111
#> CPA 3993921794 4014837422
#> EUR 233055914 233971854
#>
#> , , scenario.newscen = B1.NEWSCEN2
#>
#> year
#> i y2005 y2006 y2007 y2008 y2009 y2010
#> RRR 382385289 426155374 469925458 513695542 557465627 601235711
#> CPA 2944183141 2989788415 3035393688 3080998962 3126604236 3172209510
#> EUR 206616969 208386932 210156896 211926859 213696822 215466785
#> year
#> i y2011 y2012 y2013 y2014 y2015 y2016
#> RRR 645005795 688775879 732545964 776316048 820086132 879443579
#> CPA 3217814784 3263420057 3309025331 3354630605 3400235879 3421970300
#> EUR 217236749 219006712 220776675 222546639 224316602 225482598
#> year
#> i y2017 y2018 y2019 y2020 y2021 y2022
#> RRR 938801026 998158473 1057515919 1116873366 1176230813 1235588260
#> CPA 3443704721 3465439142 3487173563 3508907984 3530642406 3552376827
#> EUR 226648593 227814589 228980585 230146581 231312577 232478572
#> year
#> i y2023 y2024 y2025 y2026 y2027 y2028
#> RRR 1294945706 1354303153 1413660600 1477770950 1541881301 1605991651
#> CPA 3574111248 3595845669 3617580090 3608891189 3600202289 3591513388
#> EUR 233644568 234810564 235976560 236612292 237248024 237883756
#> year
#> i y2029 y2030
#> RRR 1670102001 1734212352
#> CPA 3582824487 3574135586
#> EUR 238519489 239155221split the data, do some calculations and bind it back together
d1 <- d[, 1:3, ] * 100
d2 <- d[, 4:6, ] * (-1)
dd <- mbind(d1, d2)
dd
#> An object of class "magpie"
#> , , scenario.newscen = A2.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035
#> RRR 17247177286 34361371712 71250990410 -1435615002 -2704899949
#> CPA 211995506103 294584749142 352881314048 -4065967778 -4450926008
#> EUR 17412427723 20157311917 21356126643 -226137981 -236664659
#> year
#> i y2045
#> RRR -4605120609
#> CPA -4707257368
#> EUR -241739628
#>
#> , , scenario.newscen = A2.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035
#> RRR 17247177286 36892487484 78237435983 -1420967030 -2252566752
#> CPA 211995506103 294473936352 344267811303 -3761633954 -3814823856
#> EUR 17412427723 20491042497 22064878583 -232628981 -240417069
#> year
#> i y2045
#> RRR -3203198781
#> CPA -3611262304
#> EUR -240310466
#>
#> , , scenario.newscen = B1.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035
#> RRR 17247177286 35615064104 74685409122 -1428233254 -2467376967
#> CPA 211995506103 294529105935 348530901109 -3910259280 -4119415564
#> EUR 17412427723 20325186949 21711126748 -229392153 -238551555
#> year
#> i y2045
#> RRR -3837965851
#> CPA -4120912807
#> EUR -241017118
#>
#> , , scenario.newscen = B1.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035
#> RRR 17247177286 38238528941 82008613234 -1413660600 -2054764104
#> CPA 211995506103 294418314075 340023587875 -3617580090 -3530691083
#> EUR 17412427723 20661696919 22431660178 -235976560 -242333882
#> year
#> i y2045
#> RRR -2669586440
#> CPA -3161436886
#> EUR -239592227set all values greater than 0.5 to 0.51
d[d > 0.5] <- 0.51
d
#> An object of class "magpie"
#> , , scenario.newscen = A2.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 0.51 0.51 0.51 0.51 0.51 0.51
#> CPA 0.51 0.51 0.51 0.51 0.51 0.51
#> EUR 0.51 0.51 0.51 0.51 0.51 0.51
#>
#> , , scenario.newscen = A2.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 0.51 0.51 0.51 0.51 0.51 0.51
#> CPA 0.51 0.51 0.51 0.51 0.51 0.51
#> EUR 0.51 0.51 0.51 0.51 0.51 0.51
#>
#> , , scenario.newscen = B1.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 0.51 0.51 0.51 0.51 0.51 0.51
#> CPA 0.51 0.51 0.51 0.51 0.51 0.51
#> EUR 0.51 0.51 0.51 0.51 0.51 0.51
#>
#> , , scenario.newscen = B1.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 0.51 0.51 0.51 0.51 0.51 0.51
#> CPA 0.51 0.51 0.51 0.51 0.51 0.51
#> EUR 0.51 0.51 0.51 0.51 0.51 0.51round the data
round(d, 0)
#> An object of class "magpie"
#> , , scenario.newscen = A2.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 1 1 1 1 1 1
#> CPA 1 1 1 1 1 1
#> EUR 1 1 1 1 1 1
#>
#> , , scenario.newscen = A2.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 1 1 1 1 1 1
#> CPA 1 1 1 1 1 1
#> EUR 1 1 1 1 1 1
#>
#> , , scenario.newscen = B1.NEWSCEN1
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 1 1 1 1 1 1
#> CPA 1 1 1 1 1 1
#> EUR 1 1 1 1 1 1
#>
#> , , scenario.newscen = B1.NEWSCEN2
#>
#> year
#> i y1995 y2005 y2015 y2025 y2035 y2045
#> RRR 1 1 1 1 1 1
#> CPA 1 1 1 1 1 1
#> EUR 1 1 1 1 1 1