R/extract_layer_output.R
extract_layer_output.RdConvenience function to reorganize soil layer time series data from
layer_output list entry produced with run_LWFB90. The data is transformed to a
wide format, by casting the variables with the layer number using data.table's
dcast-function.
extract_layer_output(
x,
layers = NULL,
value_vars = NULL,
layer_index_name = "nl",
sep = ""
)Data.frame or data.table with layer data organized in rows and
identified by a layer index column named layer_index_nm.
Integer vector to select a subset of layers. If not supplied, values from all layers will be returned.
Character vector containing names of value-variables to be
extracted from x. If not supplied, value_vars will be
guessed.
Column containing layer index. Defaults to 'nl' as in
layer_output.
Separation character for constructing names from variable name and layer index.
A data.table with the layers' values of the variables organized in columns with the names being made up of the variable name and the layer index.
# create a data.frame with monthly values
# identifiers: layer number, yr and mo
df <- expand.grid(nl = 1:5,
yr = 2002,
mo = 1:12)
df
#> nl yr mo
#> 1 1 2002 1
#> 2 2 2002 1
#> 3 3 2002 1
#> 4 4 2002 1
#> 5 5 2002 1
#> 6 1 2002 2
#> 7 2 2002 2
#> 8 3 2002 2
#> 9 4 2002 2
#> 10 5 2002 2
#> 11 1 2002 3
#> 12 2 2002 3
#> 13 3 2002 3
#> 14 4 2002 3
#> 15 5 2002 3
#> 16 1 2002 4
#> 17 2 2002 4
#> 18 3 2002 4
#> 19 4 2002 4
#> 20 5 2002 4
#> 21 1 2002 5
#> 22 2 2002 5
#> 23 3 2002 5
#> 24 4 2002 5
#> 25 5 2002 5
#> 26 1 2002 6
#> 27 2 2002 6
#> 28 3 2002 6
#> 29 4 2002 6
#> 30 5 2002 6
#> 31 1 2002 7
#> 32 2 2002 7
#> 33 3 2002 7
#> 34 4 2002 7
#> 35 5 2002 7
#> 36 1 2002 8
#> 37 2 2002 8
#> 38 3 2002 8
#> 39 4 2002 8
#> 40 5 2002 8
#> 41 1 2002 9
#> 42 2 2002 9
#> 43 3 2002 9
#> 44 4 2002 9
#> 45 5 2002 9
#> 46 1 2002 10
#> 47 2 2002 10
#> 48 3 2002 10
#> 49 4 2002 10
#> 50 5 2002 10
#> 51 1 2002 11
#> 52 2 2002 11
#> 53 3 2002 11
#> 54 4 2002 11
#> 55 5 2002 11
#> 56 1 2002 12
#> 57 2 2002 12
#> 58 3 2002 12
#> 59 4 2002 12
#> 60 5 2002 12
#add a value variable
df$var <- runif(nrow(df), -1,0)
extract_layer_output(df)
#> Key: <yr, mo>
#> yr mo var1 var2 var3 var4 var5
#> <num> <int> <num> <num> <num> <num> <num>
#> 1: 2002 1 -0.3427016 -0.8781354 -0.68115196 -0.37693897 -0.5530026
#> 2: 2002 2 -0.2917066 -0.1099710 -0.89569350 -0.36958828 -0.1584411
#> 3: 2002 3 -0.6357376 -0.3770772 -0.53925249 -0.83658772 -0.4861331
#> 4: 2002 4 -0.8567336 -0.3497013 -0.84470547 -0.49169014 -0.9141296
#> 5: 2002 5 -0.4969733 -0.9614281 -0.17596712 -0.71401443 -0.7532491
#> 6: 2002 6 -0.6020064 -0.3679071 -0.05142115 -0.65988076 -0.3726340
#> 7: 2002 7 -0.1750846 -0.8548846 -0.03286087 -0.91396101 -0.7595388
#> 8: 2002 8 -0.3611163 -0.4966049 -0.74947674 -0.02096623 -0.3434204
#> 9: 2002 9 -0.9101511 -0.3694571 -0.12323234 -0.06971846 -0.5805141
#> 10: 2002 10 -0.5044401 -0.6472953 -0.75126513 -0.04127210 -0.6400399
#> 11: 2002 11 -0.8010138 -0.2737147 -0.83186755 -0.77351867 -0.1544599
#> 12: 2002 12 -0.6541464 -0.9326802 -0.97298055 -0.88195543 -0.9863046
# add more variables
df$var1 <- runif(nrow(df), 1,2)
df$var2 <- runif(nrow(df), 2,3)
# extract specific layers
extract_layer_output(df,layers = 2:4, sep = "_layer")
#> Key: <yr, mo>
#> yr mo var_layer2 var_layer3 var_layer4 var1_layer2 var1_layer3
#> <num> <int> <num> <num> <num> <num> <num>
#> 1: 2002 1 -0.8781354 -0.68115196 -0.37693897 1.251578 1.814231
#> 2: 2002 2 -0.1099710 -0.89569350 -0.36958828 1.628450 1.566586
#> 3: 2002 3 -0.3770772 -0.53925249 -0.83658772 1.316116 1.588247
#> 4: 2002 4 -0.3497013 -0.84470547 -0.49169014 1.924520 1.880309
#> 5: 2002 5 -0.9614281 -0.17596712 -0.71401443 1.906492 1.719318
#> 6: 2002 6 -0.3679071 -0.05142115 -0.65988076 1.091970 1.936429
#> 7: 2002 7 -0.8548846 -0.03286087 -0.91396101 1.737795 1.429432
#> 8: 2002 8 -0.4966049 -0.74947674 -0.02096623 1.569741 1.286187
#> 9: 2002 9 -0.3694571 -0.12323234 -0.06971846 1.696489 1.770643
#> 10: 2002 10 -0.6472953 -0.75126513 -0.04127210 1.700768 1.616499
#> 11: 2002 11 -0.2737147 -0.83186755 -0.77351867 1.039941 1.159275
#> 12: 2002 12 -0.9326802 -0.97298055 -0.88195543 1.431522 1.401598
#> var1_layer4 var2_layer2 var2_layer3 var2_layer4
#> <num> <num> <num> <num>
#> 1: 1.001646 2.107332 2.934998 2.466505
#> 2: 1.417966 2.072994 2.739590 2.896378
#> 3: 1.763934 2.356315 2.635668 2.018531
#> 4: 1.794478 2.571561 2.338220 2.850974
#> 5: 1.242587 2.677988 2.962416 2.501423
#> 6: 1.759171 2.690719 2.111790 2.166162
#> 7: 1.029104 2.373411 2.650485 2.361006
#> 8: 1.437341 2.097978 2.983512 2.652000
#> 9: 1.530409 2.099010 2.179128 2.874177
#> 10: 1.429831 2.774802 2.137970 2.728306
#> 11: 1.809297 2.151496 2.056559 2.412473
#> 12: 1.922565 2.394607 2.546368 2.159229
#extract specific variables
extract_layer_output(df, layers = 2:4, value_vars = c("var1", "var2"), sep = "_layer")
#> Key: <yr, mo>
#> yr mo var1_layer2 var1_layer3 var1_layer4 var2_layer2 var2_layer3
#> <num> <int> <num> <num> <num> <num> <num>
#> 1: 2002 1 1.251578 1.814231 1.001646 2.107332 2.934998
#> 2: 2002 2 1.628450 1.566586 1.417966 2.072994 2.739590
#> 3: 2002 3 1.316116 1.588247 1.763934 2.356315 2.635668
#> 4: 2002 4 1.924520 1.880309 1.794478 2.571561 2.338220
#> 5: 2002 5 1.906492 1.719318 1.242587 2.677988 2.962416
#> 6: 2002 6 1.091970 1.936429 1.759171 2.690719 2.111790
#> 7: 2002 7 1.737795 1.429432 1.029104 2.373411 2.650485
#> 8: 2002 8 1.569741 1.286187 1.437341 2.097978 2.983512
#> 9: 2002 9 1.696489 1.770643 1.530409 2.099010 2.179128
#> 10: 2002 10 1.700768 1.616499 1.429831 2.774802 2.137970
#> 11: 2002 11 1.039941 1.159275 1.809297 2.151496 2.056559
#> 12: 2002 12 1.431522 1.401598 1.922565 2.394607 2.546368
#> var2_layer4
#> <num>
#> 1: 2.466505
#> 2: 2.896378
#> 3: 2.018531
#> 4: 2.850974
#> 5: 2.501423
#> 6: 2.166162
#> 7: 2.361006
#> 8: 2.652000
#> 9: 2.874177
#> 10: 2.728306
#> 11: 2.412473
#> 12: 2.159229