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print method for MAgPIE objects for conventient display of magpie data.

Usage

# S3 method for class 'magpie'
print(x, drop = TRUE, reshape = FALSE, ...)

Arguments

x

MAgPIE object

drop

argument which controls whether empty dimensions should be skipped or not.

reshape

argument that controls tabular representation of nested data dimension cross tables, FALSE will reproduce standard print behavior any pair of two dimension numbers will create a table for these two dims, and loop over the other dimensions

...

arguments to be passed to or from other methods.

Value

Invisibly, the MAgPIE object x.

Author

Jan Philipp Dietrich, Kristine Karstens, Felicitas Beier

Examples


pop <- maxample("pop")
print(pop)
#> , , scenario = A2
#> 
#>      t
#> i         y1995   y2005   y2015   y2025   y2035   y2045   y2055
#>   AFR  552.6664  696.44  889.18 1124.11 1389.33 1659.73 1924.19
#>   CPA 1280.6350 1429.53 1518.46 1592.09 1640.95 1671.94 1691.24
#>   EUR  554.4384  582.36  593.76  605.27  614.58  618.97  619.37
#>   FSU  276.3431  295.38  302.62  308.59  313.30  315.72  317.36
#>   LAM  451.9981  558.29  646.02  733.13  812.69  880.98  939.44
#>   MEA  277.7437  390.18  489.22  596.13  698.33  790.61  871.83
#>   NAM  292.1132  326.09  353.25  382.53  409.44  431.12  448.70
#>   PAO  133.7772  152.00  155.27  157.35  158.81  159.70  160.45
#>   PAS  383.2277  534.73  604.94  668.49  723.13  767.30  798.68
#>   SAS 1269.9243 1505.02 1796.76 2095.48 2369.60 2600.68 2783.75
#>      t
#> i       y2065   y2075   y2085   y2095   y2105   y2115   y2125   y2135
#>   AFR 2172.30 2387.96 2560.32 2671.07 2708.86 2708.86 2708.86 2708.86
#>   CPA 1719.25 1765.77 1832.31 1918.47 1965.05 1965.05 1965.05 1965.05
#>   EUR  618.74  622.03  630.52  642.20  648.98  648.98  648.98  648.98
#>   FSU  319.61  322.30  327.08  332.39  334.66  334.66  334.66  334.66
#>   LAM  989.54 1035.25 1079.39 1117.61 1134.64 1134.64 1134.64 1134.64
#>   MEA  942.81 1002.56 1052.19 1088.94 1103.31 1103.31 1103.31 1103.31
#>   NAM  465.22  481.39  494.88  505.93  511.41  511.41  511.41  511.41
#>   PAO  160.95  161.47  163.53  166.31  167.49  167.49  167.49  167.49
#>   PAS  819.21  834.31  844.38  843.52  839.53  839.53  839.53  839.53
#>   SAS 2920.70 3006.60 3040.10 3007.86 2972.39 2972.39 2972.39 2972.39
#>      t
#> i       y2145
#>   AFR 2708.86
#>   CPA 1965.05
#>   EUR  648.98
#>   FSU  334.66
#>   LAM 1134.64
#>   MEA 1103.31
#>   NAM  511.41
#>   PAO  167.49
#>   PAS  839.53
#>   SAS 2972.39
#> 
#> , , scenario = B1
#> 
#>      t
#> i         y1995   y2005   y2015   y2025   y2035   y2045   y2055
#>   AFR  552.6664  721.85  932.04 1118.33 1267.33 1383.24 1469.16
#>   CPA 1280.6350 1429.26 1499.74 1531.12 1518.73 1463.68 1370.97
#>   EUR  554.4384  587.21  603.63  613.98  619.48  617.12  606.77
#>   FSU  276.3431  296.84  305.26  309.78  311.47  309.03  301.99
#>   LAM  451.9981  552.79  623.20  681.60  723.44  747.70  753.98
#>   MEA  277.7437  398.92  502.51  598.73  682.80  754.14  811.59
#>   NAM  292.1132  325.04  349.85  376.11  399.68  418.70  434.27
#>   PAO  133.7772  153.07  157.37  159.07  159.51  158.10  155.21
#>   PAS  383.2277  530.67  590.42  639.68  674.98  692.45  689.79
#>   SAS 1269.9243 1475.64 1687.80 1870.96 1999.15 2072.68 2090.96
#>      t
#> i       y2065   y2075   y2085   y2095   y2105   y2115   y2125   y2135
#>   AFR 1510.27 1505.16 1454.54 1361.24 1304.59 1304.59 1304.59 1304.59
#>   CPA 1257.23 1139.25 1021.52  904.61  846.50  846.50  846.50  846.50
#>   EUR  592.52  579.18  567.73  554.61  547.06  547.06  547.06  547.06
#>   FSU  292.46  281.39  269.77  257.52  251.04  251.04  251.04  251.04
#>   LAM  743.05  718.79  683.68  637.69  611.88  611.88  611.88  611.88
#>   MEA  849.11  865.89  861.01  831.23  809.60  809.60  809.60  809.60
#>   NAM  449.98  468.05  486.99  503.86  511.44  511.44  511.44  511.44
#>   PAO  151.86  148.08  144.47  140.82  138.80  138.80  138.80  138.80
#>   PAS  668.98  634.64  590.05  536.24  507.06  507.06  507.06  507.06
#>   SAS 2049.18 1953.77 1811.83 1629.07 1528.15 1528.15 1528.15 1528.15
#>      t
#> i       y2145
#>   AFR 1304.59
#>   CPA  846.50
#>   EUR  547.06
#>   FSU  251.04
#>   LAM  611.88
#>   MEA  809.60
#>   NAM  511.44
#>   PAO  138.80
#>   PAS  507.06
#>   SAS 1528.15
#> 
print(pop[, 1, ], drop = FALSE)
#> , , scenario = A2
#> 
#>      t
#> i         y1995
#>   AFR  552.6664
#>   CPA 1280.6350
#>   EUR  554.4384
#>   FSU  276.3431
#>   LAM  451.9981
#>   MEA  277.7437
#>   NAM  292.1132
#>   PAO  133.7772
#>   PAS  383.2277
#>   SAS 1269.9243
#> 
#> , , scenario = B1
#> 
#>      t
#> i         y1995
#>   AFR  552.6664
#>   CPA 1280.6350
#>   EUR  554.4384
#>   FSU  276.3431
#>   LAM  451.9981
#>   MEA  277.7437
#>   NAM  292.1132
#>   PAO  133.7772
#>   PAS  383.2277
#>   SAS 1269.9243
#> 
print(pop[, 1, ])
#>      scenario
#> i            A2        B1
#>   AFR  552.6664  552.6664
#>   CPA 1280.6350 1280.6350
#>   EUR  554.4384  554.4384
#>   FSU  276.3431  276.3431
#>   LAM  451.9981  451.9981
#>   MEA  277.7437  277.7437
#>   NAM  292.1132  292.1132
#>   PAO  133.7772  133.7772
#>   PAS  383.2277  383.2277
#>   SAS 1269.9243 1269.9243