Use acast or dcast depending on whether you want
vector/matrix/array output or data frame output. Data frames can have at
most two dimensions.
dcast(
data,
formula,
fun.aggregate = NULL,
...,
margins = NULL,
subset = NULL,
fill = NULL,
drop = TRUE,
value.var = guess_value(data)
)
acast(
data,
formula,
fun.aggregate = NULL,
...,
margins = NULL,
subset = NULL,
fill = NULL,
drop = TRUE,
value.var = guess_value(data)
)Arguments
- data
molten data frame, see
melt.- formula
casting formula, see details for specifics.
- fun.aggregate
aggregation function needed if variables do not identify a single observation for each output cell. Defaults to length (with a message) if needed but not specified.
- ...
further arguments are passed to aggregating function
- margins
vector of variable names (can include "grand\_col" and "grand\_row") to compute margins for, or TRUE to compute all margins . Any variables that can not be margined over will be silently dropped.
- subset
quoted expression used to subset data prior to reshaping, e.g.
subset = .(variable=="length").- fill
value with which to fill in structural missings, defaults to value from applying
fun.aggregateto 0 length vector- drop
should missing combinations dropped or kept?
- value.var
name of column which stores values, see
guess_valuefor default strategies to figure this out.
Details
The cast formula has the following format:
x_variable + x_2 ~ y_variable + y_2 ~ z_variable ~ ...
The order of the variables makes a difference. The first varies slowest,
and the last fastest. There are a couple of special variables: "..."
represents all other variables not used in the formula and "." represents
no variable, so you can do formula = var1 ~ ..
Alternatively, you can supply a list of quoted expressions, in the form
list(.(x_variable, x_2), .(y_variable, y_2), .(z)). The advantage
of this form is that you can cast based on transformations of the
variables: list(.(a + b), (c = round(c))). See the documentation
for . for more details and alternative formats.
If the combination of variables you supply does not uniquely identify one
row in the original data set, you will need to supply an aggregating
function, fun.aggregate. This function should take a vector of
numbers and return a single summary statistic.
See also
Examples
#Air quality example
names(airquality) <- tolower(names(airquality))
aqm <- melt(airquality, id=c("month", "day"), na.rm=TRUE)
acast(aqm, day ~ month ~ variable)
#> , , ozone
#>
#> 5 6 7 8 9
#> 1 41 NA 135 39 96
#> 2 36 NA 49 9 78
#> 3 12 NA 32 16 73
#> 4 18 NA NA 78 91
#> 5 NA NA 64 35 47
#> 6 28 NA 40 66 32
#> 7 23 29 77 122 20
#> 8 19 NA 97 89 23
#> 9 8 71 97 110 21
#> 10 NA 39 85 NA 24
#> 11 7 NA NA NA 44
#> 12 16 NA 10 44 21
#> 13 11 23 27 28 28
#> 14 14 NA NA 65 9
#> 15 18 NA 7 NA 13
#> 16 14 21 48 22 46
#> 17 34 37 35 59 18
#> 18 6 20 61 23 13
#> 19 30 12 79 31 24
#> 20 11 13 63 44 16
#> 21 1 NA 16 21 13
#> 22 11 NA NA 9 23
#> 23 4 NA NA NA 36
#> 24 32 NA 80 45 7
#> 25 NA NA 108 168 14
#> 26 NA NA 20 73 30
#> 27 NA NA 52 NA NA
#> 28 23 NA 82 76 14
#> 29 45 NA 50 118 18
#> 30 115 NA 64 84 20
#> 31 37 NA 59 85 NA
#>
#> , , solar.r
#>
#> 5 6 7 8 9
#> 1 190 286 269 83 167
#> 2 118 287 248 24 197
#> 3 149 242 236 77 183
#> 4 313 186 101 NA 189
#> 5 NA 220 175 NA 95
#> 6 NA 264 314 NA 92
#> 7 299 127 276 255 252
#> 8 99 273 267 229 220
#> 9 19 291 272 207 230
#> 10 194 323 175 222 259
#> 11 NA 259 139 137 236
#> 12 256 250 264 192 259
#> 13 290 148 175 273 238
#> 14 274 332 291 157 24
#> 15 65 322 48 64 112
#> 16 334 191 260 71 237
#> 17 307 284 274 51 224
#> 18 78 37 285 115 27
#> 19 322 120 187 244 238
#> 20 44 137 220 190 201
#> 21 8 150 7 259 238
#> 22 320 59 258 36 14
#> 23 25 91 295 255 139
#> 24 92 250 294 212 49
#> 25 66 135 223 238 20
#> 26 266 127 81 215 193
#> 27 NA 47 82 153 145
#> 28 13 98 213 203 191
#> 29 252 31 275 225 131
#> 30 223 138 253 237 223
#> 31 279 NA 254 188 NA
#>
#> , , wind
#>
#> 5 6 7 8 9
#> 1 7.4 8.6 4.1 6.9 6.9
#> 2 8.0 9.7 9.2 13.8 5.1
#> 3 12.6 16.1 9.2 7.4 2.8
#> 4 11.5 9.2 10.9 6.9 4.6
#> 5 14.3 8.6 4.6 7.4 7.4
#> 6 14.9 14.3 10.9 4.6 15.5
#> 7 8.6 9.7 5.1 4.0 10.9
#> 8 13.8 6.9 6.3 10.3 10.3
#> 9 20.1 13.8 5.7 8.0 10.9
#> 10 8.6 11.5 7.4 8.6 9.7
#> 11 6.9 10.9 8.6 11.5 14.9
#> 12 9.7 9.2 14.3 11.5 15.5
#> 13 9.2 8.0 14.9 11.5 6.3
#> 14 10.9 13.8 14.9 9.7 10.9
#> 15 13.2 11.5 14.3 11.5 11.5
#> 16 11.5 14.9 6.9 10.3 6.9
#> 17 12.0 20.7 10.3 6.3 13.8
#> 18 18.4 9.2 6.3 7.4 10.3
#> 19 11.5 11.5 5.1 10.9 10.3
#> 20 9.7 10.3 11.5 10.3 8.0
#> 21 9.7 6.3 6.9 15.5 12.6
#> 22 16.6 1.7 9.7 14.3 9.2
#> 23 9.7 4.6 11.5 12.6 10.3
#> 24 12.0 6.3 8.6 9.7 10.3
#> 25 16.6 8.0 8.0 3.4 16.6
#> 26 14.9 8.0 8.6 8.0 6.9
#> 27 8.0 10.3 12.0 5.7 13.2
#> 28 12.0 11.5 7.4 9.7 14.3
#> 29 14.9 14.9 7.4 2.3 8.0
#> 30 5.7 8.0 7.4 6.3 11.5
#> 31 7.4 NA 9.2 6.3 NA
#>
#> , , temp
#>
#> 5 6 7 8 9
#> 1 67 78 84 81 91
#> 2 72 74 85 81 92
#> 3 74 67 81 82 93
#> 4 62 84 84 86 93
#> 5 56 85 83 85 87
#> 6 66 79 83 87 84
#> 7 65 82 88 89 80
#> 8 59 87 92 90 78
#> 9 61 90 92 90 75
#> 10 69 87 89 92 73
#> 11 74 93 82 86 81
#> 12 69 92 73 86 76
#> 13 66 82 81 82 77
#> 14 68 80 91 80 71
#> 15 58 79 80 79 71
#> 16 64 77 81 77 78
#> 17 66 72 82 79 67
#> 18 57 65 84 76 76
#> 19 68 73 87 78 68
#> 20 62 76 85 78 82
#> 21 59 77 74 77 64
#> 22 73 76 81 72 71
#> 23 61 76 82 75 81
#> 24 61 76 86 79 69
#> 25 57 75 85 81 63
#> 26 58 78 82 86 70
#> 27 57 73 86 88 77
#> 28 67 80 88 97 75
#> 29 81 77 86 94 76
#> 30 79 83 83 96 68
#> 31 76 NA 81 94 NA
#>
acast(aqm, month ~ variable, mean)
#> ozone solar.r wind temp
#> 5 23.61538 181.2963 11.622581 65.54839
#> 6 29.44444 190.1667 10.266667 79.10000
#> 7 59.11538 216.4839 8.941935 83.90323
#> 8 59.96154 171.8571 8.793548 83.96774
#> 9 31.44828 167.4333 10.180000 76.90000
acast(aqm, month ~ variable, mean, margins = TRUE)
#> ozone solar.r wind temp (all)
#> 5 23.61538 181.2963 11.622581 65.54839 68.70696
#> 6 29.44444 190.1667 10.266667 79.10000 87.38384
#> 7 59.11538 216.4839 8.941935 83.90323 93.49748
#> 8 59.96154 171.8571 8.793548 83.96774 79.71207
#> 9 31.44828 167.4333 10.180000 76.90000 71.82689
#> (all) 42.12931 185.9315 9.957516 77.88235 80.05722
dcast(aqm, month ~ variable, mean, margins = c("month", "variable"))
#> month ozone solar.r wind temp (all)
#> 1 5 23.61538 181.2963 11.622581 65.54839 68.70696
#> 2 6 29.44444 190.1667 10.266667 79.10000 87.38384
#> 3 7 59.11538 216.4839 8.941935 83.90323 93.49748
#> 4 8 59.96154 171.8571 8.793548 83.96774 79.71207
#> 5 9 31.44828 167.4333 10.180000 76.90000 71.82689
#> 6 (all) 42.12931 185.9315 9.957516 77.88235 80.05722
library(plyr) # needed to access . function
acast(aqm, variable ~ month, mean, subset = .(variable == "ozone"))
#> 5 6 7 8 9
#> ozone 23.61538 29.44444 59.11538 59.96154 31.44828
acast(aqm, variable ~ month, mean, subset = .(month == 5))
#> 5
#> ozone 23.61538
#> solar.r 181.29630
#> wind 11.62258
#> temp 65.54839
#Chick weight example
names(ChickWeight) <- tolower(names(ChickWeight))
chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE)
dcast(chick_m, time ~ variable, mean) # average effect of time
#> time weight
#> 1 0 41.06000
#> 2 2 49.22000
#> 3 4 59.95918
#> 4 6 74.30612
#> 5 8 91.24490
#> 6 10 107.83673
#> 7 12 129.24490
#> 8 14 143.81250
#> 9 16 168.08511
#> 10 18 190.19149
#> 11 20 209.71739
#> 12 21 218.68889
dcast(chick_m, diet ~ variable, mean) # average effect of diet
#> diet weight
#> 1 1 102.6455
#> 2 2 122.6167
#> 3 3 142.9500
#> 4 4 135.2627
acast(chick_m, diet ~ time, mean) # average effect of diet & time
#> 0 2 4 6 8 10 12 14 16
#> 1 41.4 47.25 56.47368 66.78947 79.68421 93.05263 108.5263 123.3889 144.6471
#> 2 40.7 49.40 59.80000 75.40000 91.70000 108.50000 131.3000 141.9000 164.7000
#> 3 40.8 50.40 62.20000 77.90000 98.40000 117.10000 144.4000 164.5000 197.4000
#> 4 41.0 51.80 64.50000 83.90000 105.60000 126.00000 151.4000 161.8000 182.0000
#> 18 20 21
#> 1 158.9412 170.4118 177.7500
#> 2 187.7000 205.6000 214.7000
#> 3 233.1000 258.9000 270.3000
#> 4 202.9000 233.8889 238.5556
# How many chicks at each time? - checking for balance
acast(chick_m, time ~ diet, length)
#> 1 2 3 4
#> 0 20 10 10 10
#> 2 20 10 10 10
#> 4 19 10 10 10
#> 6 19 10 10 10
#> 8 19 10 10 10
#> 10 19 10 10 10
#> 12 19 10 10 10
#> 14 18 10 10 10
#> 16 17 10 10 10
#> 18 17 10 10 10
#> 20 17 10 10 9
#> 21 16 10 10 9
acast(chick_m, chick ~ time, mean)
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 18 39 35 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
#> 16 41 45 49 51 57 51 54 NaN NaN NaN NaN NaN
#> 15 41 49 56 64 68 68 67 68 NaN NaN NaN NaN
#> 13 41 48 53 60 65 67 71 70 71 81 91 96
#> 9 42 51 59 68 85 96 90 92 93 100 100 98
#> 20 41 47 54 58 65 73 77 89 98 107 115 117
#> 10 41 44 52 63 74 81 89 96 101 112 120 124
#> 8 42 50 61 71 84 93 110 116 126 134 125 NaN
#> 17 42 51 61 72 83 89 98 103 113 123 133 142
#> 19 43 48 55 62 65 71 82 88 106 120 144 157
#> 4 42 49 56 67 74 87 102 108 136 154 160 157
#> 6 41 49 59 74 97 124 141 148 155 160 160 157
#> 11 43 51 63 84 112 139 168 177 182 184 181 175
#> 3 43 39 55 67 84 99 115 138 163 187 198 202
#> 1 42 51 59 64 76 93 106 125 149 171 199 205
#> 12 41 49 56 62 72 88 119 135 162 185 195 205
#> 2 40 49 58 72 84 103 122 138 162 187 209 215
#> 5 41 42 48 60 79 106 141 164 197 199 220 223
#> 14 41 49 62 79 101 128 164 192 227 248 259 266
#> 7 41 49 57 71 89 112 146 174 218 250 288 305
#> 24 42 52 58 74 66 68 70 71 72 72 76 74
#> 30 42 48 59 72 85 98 115 122 143 151 157 150
#> 22 41 55 64 77 90 95 108 111 131 148 164 167
#> 23 43 52 61 73 90 103 127 135 145 163 170 175
#> 27 39 46 58 73 87 100 115 123 144 163 185 192
#> 28 39 46 58 73 92 114 145 156 184 207 212 233
#> 26 42 48 57 74 93 114 136 147 169 205 236 251
#> 25 40 49 62 78 102 124 146 164 197 231 259 265
#> 29 39 48 59 74 87 106 134 150 187 230 279 309
#> 21 40 50 62 86 125 163 217 240 275 307 318 331
#> 33 39 50 63 77 96 111 137 144 151 146 156 147
#> 37 41 48 56 68 80 83 103 112 135 157 169 178
#> 36 39 48 61 76 98 116 145 166 198 227 225 220
#> 31 42 53 62 73 85 102 123 138 170 204 235 256
#> 39 42 50 61 78 89 109 130 146 170 214 250 272
#> 38 41 49 61 74 98 109 128 154 192 232 280 290
#> 32 41 49 65 82 107 129 159 179 221 263 291 305
#> 40 41 55 66 79 101 120 154 182 215 262 295 321
#> 34 41 49 63 85 107 134 164 186 235 294 327 341
#> 35 41 53 64 87 123 158 201 238 287 332 361 373
#> 44 42 51 65 86 103 118 127 138 145 146 NaN NaN
#> 45 41 50 61 78 98 117 135 141 147 174 197 196
#> 43 42 55 69 96 131 157 184 188 197 198 199 200
#> 41 42 51 66 85 103 124 155 153 175 184 199 204
#> 47 41 53 66 79 100 123 148 157 168 185 210 205
#> 49 40 53 64 85 108 128 152 166 184 203 233 237
#> 46 40 52 62 82 101 120 144 156 173 210 231 238
#> 50 41 54 67 84 105 122 155 175 205 234 264 264
#> 42 42 49 63 84 103 126 160 174 204 234 269 281
#> 48 39 50 62 80 104 125 154 170 222 261 303 322
acast(chick_m, chick ~ time, mean, subset = .(time < 10 & chick < 20))
#> 0 2 4 6 8
#> 18 39 35 NaN NaN NaN
#> 16 41 45 49 51 57
#> 15 41 49 56 64 68
#> 13 41 48 53 60 65
#> 9 42 51 59 68 85
acast(chick_m, time ~ diet, length)
#> 1 2 3 4
#> 0 20 10 10 10
#> 2 20 10 10 10
#> 4 19 10 10 10
#> 6 19 10 10 10
#> 8 19 10 10 10
#> 10 19 10 10 10
#> 12 19 10 10 10
#> 14 18 10 10 10
#> 16 17 10 10 10
#> 18 17 10 10 10
#> 20 17 10 10 9
#> 21 16 10 10 9
dcast(chick_m, diet + chick ~ time)
#> diet chick 0 2 4 6 8 10 12 14 16 18 20 21
#> 1 1 18 39 35 NA NA NA NA NA NA NA NA NA NA
#> 2 1 16 41 45 49 51 57 51 54 NA NA NA NA NA
#> 3 1 15 41 49 56 64 68 68 67 68 NA NA NA NA
#> 4 1 13 41 48 53 60 65 67 71 70 71 81 91 96
#> 5 1 9 42 51 59 68 85 96 90 92 93 100 100 98
#> 6 1 20 41 47 54 58 65 73 77 89 98 107 115 117
#> 7 1 10 41 44 52 63 74 81 89 96 101 112 120 124
#> 8 1 8 42 50 61 71 84 93 110 116 126 134 125 NA
#> 9 1 17 42 51 61 72 83 89 98 103 113 123 133 142
#> 10 1 19 43 48 55 62 65 71 82 88 106 120 144 157
#> 11 1 4 42 49 56 67 74 87 102 108 136 154 160 157
#> 12 1 6 41 49 59 74 97 124 141 148 155 160 160 157
#> 13 1 11 43 51 63 84 112 139 168 177 182 184 181 175
#> 14 1 3 43 39 55 67 84 99 115 138 163 187 198 202
#> 15 1 1 42 51 59 64 76 93 106 125 149 171 199 205
#> 16 1 12 41 49 56 62 72 88 119 135 162 185 195 205
#> 17 1 2 40 49 58 72 84 103 122 138 162 187 209 215
#> 18 1 5 41 42 48 60 79 106 141 164 197 199 220 223
#> 19 1 14 41 49 62 79 101 128 164 192 227 248 259 266
#> 20 1 7 41 49 57 71 89 112 146 174 218 250 288 305
#> 21 2 24 42 52 58 74 66 68 70 71 72 72 76 74
#> 22 2 30 42 48 59 72 85 98 115 122 143 151 157 150
#> 23 2 22 41 55 64 77 90 95 108 111 131 148 164 167
#> 24 2 23 43 52 61 73 90 103 127 135 145 163 170 175
#> 25 2 27 39 46 58 73 87 100 115 123 144 163 185 192
#> 26 2 28 39 46 58 73 92 114 145 156 184 207 212 233
#> 27 2 26 42 48 57 74 93 114 136 147 169 205 236 251
#> 28 2 25 40 49 62 78 102 124 146 164 197 231 259 265
#> 29 2 29 39 48 59 74 87 106 134 150 187 230 279 309
#> 30 2 21 40 50 62 86 125 163 217 240 275 307 318 331
#> 31 3 33 39 50 63 77 96 111 137 144 151 146 156 147
#> 32 3 37 41 48 56 68 80 83 103 112 135 157 169 178
#> 33 3 36 39 48 61 76 98 116 145 166 198 227 225 220
#> 34 3 31 42 53 62 73 85 102 123 138 170 204 235 256
#> 35 3 39 42 50 61 78 89 109 130 146 170 214 250 272
#> 36 3 38 41 49 61 74 98 109 128 154 192 232 280 290
#> 37 3 32 41 49 65 82 107 129 159 179 221 263 291 305
#> 38 3 40 41 55 66 79 101 120 154 182 215 262 295 321
#> 39 3 34 41 49 63 85 107 134 164 186 235 294 327 341
#> 40 3 35 41 53 64 87 123 158 201 238 287 332 361 373
#> 41 4 44 42 51 65 86 103 118 127 138 145 146 NA NA
#> 42 4 45 41 50 61 78 98 117 135 141 147 174 197 196
#> 43 4 43 42 55 69 96 131 157 184 188 197 198 199 200
#> 44 4 41 42 51 66 85 103 124 155 153 175 184 199 204
#> 45 4 47 41 53 66 79 100 123 148 157 168 185 210 205
#> 46 4 49 40 53 64 85 108 128 152 166 184 203 233 237
#> 47 4 46 40 52 62 82 101 120 144 156 173 210 231 238
#> 48 4 50 41 54 67 84 105 122 155 175 205 234 264 264
#> 49 4 42 42 49 63 84 103 126 160 174 204 234 269 281
#> 50 4 48 39 50 62 80 104 125 154 170 222 261 303 322
acast(chick_m, diet + chick ~ time)
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 1_18 39 35 NA NA NA NA NA NA NA NA NA NA
#> 1_16 41 45 49 51 57 51 54 NA NA NA NA NA
#> 1_15 41 49 56 64 68 68 67 68 NA NA NA NA
#> 1_13 41 48 53 60 65 67 71 70 71 81 91 96
#> 1_9 42 51 59 68 85 96 90 92 93 100 100 98
#> 1_20 41 47 54 58 65 73 77 89 98 107 115 117
#> 1_10 41 44 52 63 74 81 89 96 101 112 120 124
#> 1_8 42 50 61 71 84 93 110 116 126 134 125 NA
#> 1_17 42 51 61 72 83 89 98 103 113 123 133 142
#> 1_19 43 48 55 62 65 71 82 88 106 120 144 157
#> 1_4 42 49 56 67 74 87 102 108 136 154 160 157
#> 1_6 41 49 59 74 97 124 141 148 155 160 160 157
#> 1_11 43 51 63 84 112 139 168 177 182 184 181 175
#> 1_3 43 39 55 67 84 99 115 138 163 187 198 202
#> 1_1 42 51 59 64 76 93 106 125 149 171 199 205
#> 1_12 41 49 56 62 72 88 119 135 162 185 195 205
#> 1_2 40 49 58 72 84 103 122 138 162 187 209 215
#> 1_5 41 42 48 60 79 106 141 164 197 199 220 223
#> 1_14 41 49 62 79 101 128 164 192 227 248 259 266
#> 1_7 41 49 57 71 89 112 146 174 218 250 288 305
#> 2_24 42 52 58 74 66 68 70 71 72 72 76 74
#> 2_30 42 48 59 72 85 98 115 122 143 151 157 150
#> 2_22 41 55 64 77 90 95 108 111 131 148 164 167
#> 2_23 43 52 61 73 90 103 127 135 145 163 170 175
#> 2_27 39 46 58 73 87 100 115 123 144 163 185 192
#> 2_28 39 46 58 73 92 114 145 156 184 207 212 233
#> 2_26 42 48 57 74 93 114 136 147 169 205 236 251
#> 2_25 40 49 62 78 102 124 146 164 197 231 259 265
#> 2_29 39 48 59 74 87 106 134 150 187 230 279 309
#> 2_21 40 50 62 86 125 163 217 240 275 307 318 331
#> 3_33 39 50 63 77 96 111 137 144 151 146 156 147
#> 3_37 41 48 56 68 80 83 103 112 135 157 169 178
#> 3_36 39 48 61 76 98 116 145 166 198 227 225 220
#> 3_31 42 53 62 73 85 102 123 138 170 204 235 256
#> 3_39 42 50 61 78 89 109 130 146 170 214 250 272
#> 3_38 41 49 61 74 98 109 128 154 192 232 280 290
#> 3_32 41 49 65 82 107 129 159 179 221 263 291 305
#> 3_40 41 55 66 79 101 120 154 182 215 262 295 321
#> 3_34 41 49 63 85 107 134 164 186 235 294 327 341
#> 3_35 41 53 64 87 123 158 201 238 287 332 361 373
#> 4_44 42 51 65 86 103 118 127 138 145 146 NA NA
#> 4_45 41 50 61 78 98 117 135 141 147 174 197 196
#> 4_43 42 55 69 96 131 157 184 188 197 198 199 200
#> 4_41 42 51 66 85 103 124 155 153 175 184 199 204
#> 4_47 41 53 66 79 100 123 148 157 168 185 210 205
#> 4_49 40 53 64 85 108 128 152 166 184 203 233 237
#> 4_46 40 52 62 82 101 120 144 156 173 210 231 238
#> 4_50 41 54 67 84 105 122 155 175 205 234 264 264
#> 4_42 42 49 63 84 103 126 160 174 204 234 269 281
#> 4_48 39 50 62 80 104 125 154 170 222 261 303 322
acast(chick_m, chick ~ time ~ diet)
#> , , 1
#>
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 18 39 35 NA NA NA NA NA NA NA NA NA NA
#> 16 41 45 49 51 57 51 54 NA NA NA NA NA
#> 15 41 49 56 64 68 68 67 68 NA NA NA NA
#> 13 41 48 53 60 65 67 71 70 71 81 91 96
#> 9 42 51 59 68 85 96 90 92 93 100 100 98
#> 20 41 47 54 58 65 73 77 89 98 107 115 117
#> 10 41 44 52 63 74 81 89 96 101 112 120 124
#> 8 42 50 61 71 84 93 110 116 126 134 125 NA
#> 17 42 51 61 72 83 89 98 103 113 123 133 142
#> 19 43 48 55 62 65 71 82 88 106 120 144 157
#> 4 42 49 56 67 74 87 102 108 136 154 160 157
#> 6 41 49 59 74 97 124 141 148 155 160 160 157
#> 11 43 51 63 84 112 139 168 177 182 184 181 175
#> 3 43 39 55 67 84 99 115 138 163 187 198 202
#> 1 42 51 59 64 76 93 106 125 149 171 199 205
#> 12 41 49 56 62 72 88 119 135 162 185 195 205
#> 2 40 49 58 72 84 103 122 138 162 187 209 215
#> 5 41 42 48 60 79 106 141 164 197 199 220 223
#> 14 41 49 62 79 101 128 164 192 227 248 259 266
#> 7 41 49 57 71 89 112 146 174 218 250 288 305
#> 24 NA NA NA NA NA NA NA NA NA NA NA NA
#> 30 NA NA NA NA NA NA NA NA NA NA NA NA
#> 22 NA NA NA NA NA NA NA NA NA NA NA NA
#> 23 NA NA NA NA NA NA NA NA NA NA NA NA
#> 27 NA NA NA NA NA NA NA NA NA NA NA NA
#> 28 NA NA NA NA NA NA NA NA NA NA NA NA
#> 26 NA NA NA NA NA NA NA NA NA NA NA NA
#> 25 NA NA NA NA NA NA NA NA NA NA NA NA
#> 29 NA NA NA NA NA NA NA NA NA NA NA NA
#> 21 NA NA NA NA NA NA NA NA NA NA NA NA
#> 33 NA NA NA NA NA NA NA NA NA NA NA NA
#> 37 NA NA NA NA NA NA NA NA NA NA NA NA
#> 36 NA NA NA NA NA NA NA NA NA NA NA NA
#> 31 NA NA NA NA NA NA NA NA NA NA NA NA
#> 39 NA NA NA NA NA NA NA NA NA NA NA NA
#> 38 NA NA NA NA NA NA NA NA NA NA NA NA
#> 32 NA NA NA NA NA NA NA NA NA NA NA NA
#> 40 NA NA NA NA NA NA NA NA NA NA NA NA
#> 34 NA NA NA NA NA NA NA NA NA NA NA NA
#> 35 NA NA NA NA NA NA NA NA NA NA NA NA
#> 44 NA NA NA NA NA NA NA NA NA NA NA NA
#> 45 NA NA NA NA NA NA NA NA NA NA NA NA
#> 43 NA NA NA NA NA NA NA NA NA NA NA NA
#> 41 NA NA NA NA NA NA NA NA NA NA NA NA
#> 47 NA NA NA NA NA NA NA NA NA NA NA NA
#> 49 NA NA NA NA NA NA NA NA NA NA NA NA
#> 46 NA NA NA NA NA NA NA NA NA NA NA NA
#> 50 NA NA NA NA NA NA NA NA NA NA NA NA
#> 42 NA NA NA NA NA NA NA NA NA NA NA NA
#> 48 NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> , , 2
#>
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 18 NA NA NA NA NA NA NA NA NA NA NA NA
#> 16 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15 NA NA NA NA NA NA NA NA NA NA NA NA
#> 13 NA NA NA NA NA NA NA NA NA NA NA NA
#> 9 NA NA NA NA NA NA NA NA NA NA NA NA
#> 20 NA NA NA NA NA NA NA NA NA NA NA NA
#> 10 NA NA NA NA NA NA NA NA NA NA NA NA
#> 8 NA NA NA NA NA NA NA NA NA NA NA NA
#> 17 NA NA NA NA NA NA NA NA NA NA NA NA
#> 19 NA NA NA NA NA NA NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA NA NA NA NA NA NA
#> 6 NA NA NA NA NA NA NA NA NA NA NA NA
#> 11 NA NA NA NA NA NA NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA NA NA NA NA NA NA
#> 1 NA NA NA NA NA NA NA NA NA NA NA NA
#> 12 NA NA NA NA NA NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA NA NA NA NA NA
#> 5 NA NA NA NA NA NA NA NA NA NA NA NA
#> 14 NA NA NA NA NA NA NA NA NA NA NA NA
#> 7 NA NA NA NA NA NA NA NA NA NA NA NA
#> 24 42 52 58 74 66 68 70 71 72 72 76 74
#> 30 42 48 59 72 85 98 115 122 143 151 157 150
#> 22 41 55 64 77 90 95 108 111 131 148 164 167
#> 23 43 52 61 73 90 103 127 135 145 163 170 175
#> 27 39 46 58 73 87 100 115 123 144 163 185 192
#> 28 39 46 58 73 92 114 145 156 184 207 212 233
#> 26 42 48 57 74 93 114 136 147 169 205 236 251
#> 25 40 49 62 78 102 124 146 164 197 231 259 265
#> 29 39 48 59 74 87 106 134 150 187 230 279 309
#> 21 40 50 62 86 125 163 217 240 275 307 318 331
#> 33 NA NA NA NA NA NA NA NA NA NA NA NA
#> 37 NA NA NA NA NA NA NA NA NA NA NA NA
#> 36 NA NA NA NA NA NA NA NA NA NA NA NA
#> 31 NA NA NA NA NA NA NA NA NA NA NA NA
#> 39 NA NA NA NA NA NA NA NA NA NA NA NA
#> 38 NA NA NA NA NA NA NA NA NA NA NA NA
#> 32 NA NA NA NA NA NA NA NA NA NA NA NA
#> 40 NA NA NA NA NA NA NA NA NA NA NA NA
#> 34 NA NA NA NA NA NA NA NA NA NA NA NA
#> 35 NA NA NA NA NA NA NA NA NA NA NA NA
#> 44 NA NA NA NA NA NA NA NA NA NA NA NA
#> 45 NA NA NA NA NA NA NA NA NA NA NA NA
#> 43 NA NA NA NA NA NA NA NA NA NA NA NA
#> 41 NA NA NA NA NA NA NA NA NA NA NA NA
#> 47 NA NA NA NA NA NA NA NA NA NA NA NA
#> 49 NA NA NA NA NA NA NA NA NA NA NA NA
#> 46 NA NA NA NA NA NA NA NA NA NA NA NA
#> 50 NA NA NA NA NA NA NA NA NA NA NA NA
#> 42 NA NA NA NA NA NA NA NA NA NA NA NA
#> 48 NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> , , 3
#>
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 18 NA NA NA NA NA NA NA NA NA NA NA NA
#> 16 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15 NA NA NA NA NA NA NA NA NA NA NA NA
#> 13 NA NA NA NA NA NA NA NA NA NA NA NA
#> 9 NA NA NA NA NA NA NA NA NA NA NA NA
#> 20 NA NA NA NA NA NA NA NA NA NA NA NA
#> 10 NA NA NA NA NA NA NA NA NA NA NA NA
#> 8 NA NA NA NA NA NA NA NA NA NA NA NA
#> 17 NA NA NA NA NA NA NA NA NA NA NA NA
#> 19 NA NA NA NA NA NA NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA NA NA NA NA NA NA
#> 6 NA NA NA NA NA NA NA NA NA NA NA NA
#> 11 NA NA NA NA NA NA NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA NA NA NA NA NA NA
#> 1 NA NA NA NA NA NA NA NA NA NA NA NA
#> 12 NA NA NA NA NA NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA NA NA NA NA NA
#> 5 NA NA NA NA NA NA NA NA NA NA NA NA
#> 14 NA NA NA NA NA NA NA NA NA NA NA NA
#> 7 NA NA NA NA NA NA NA NA NA NA NA NA
#> 24 NA NA NA NA NA NA NA NA NA NA NA NA
#> 30 NA NA NA NA NA NA NA NA NA NA NA NA
#> 22 NA NA NA NA NA NA NA NA NA NA NA NA
#> 23 NA NA NA NA NA NA NA NA NA NA NA NA
#> 27 NA NA NA NA NA NA NA NA NA NA NA NA
#> 28 NA NA NA NA NA NA NA NA NA NA NA NA
#> 26 NA NA NA NA NA NA NA NA NA NA NA NA
#> 25 NA NA NA NA NA NA NA NA NA NA NA NA
#> 29 NA NA NA NA NA NA NA NA NA NA NA NA
#> 21 NA NA NA NA NA NA NA NA NA NA NA NA
#> 33 39 50 63 77 96 111 137 144 151 146 156 147
#> 37 41 48 56 68 80 83 103 112 135 157 169 178
#> 36 39 48 61 76 98 116 145 166 198 227 225 220
#> 31 42 53 62 73 85 102 123 138 170 204 235 256
#> 39 42 50 61 78 89 109 130 146 170 214 250 272
#> 38 41 49 61 74 98 109 128 154 192 232 280 290
#> 32 41 49 65 82 107 129 159 179 221 263 291 305
#> 40 41 55 66 79 101 120 154 182 215 262 295 321
#> 34 41 49 63 85 107 134 164 186 235 294 327 341
#> 35 41 53 64 87 123 158 201 238 287 332 361 373
#> 44 NA NA NA NA NA NA NA NA NA NA NA NA
#> 45 NA NA NA NA NA NA NA NA NA NA NA NA
#> 43 NA NA NA NA NA NA NA NA NA NA NA NA
#> 41 NA NA NA NA NA NA NA NA NA NA NA NA
#> 47 NA NA NA NA NA NA NA NA NA NA NA NA
#> 49 NA NA NA NA NA NA NA NA NA NA NA NA
#> 46 NA NA NA NA NA NA NA NA NA NA NA NA
#> 50 NA NA NA NA NA NA NA NA NA NA NA NA
#> 42 NA NA NA NA NA NA NA NA NA NA NA NA
#> 48 NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> , , 4
#>
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 18 NA NA NA NA NA NA NA NA NA NA NA NA
#> 16 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15 NA NA NA NA NA NA NA NA NA NA NA NA
#> 13 NA NA NA NA NA NA NA NA NA NA NA NA
#> 9 NA NA NA NA NA NA NA NA NA NA NA NA
#> 20 NA NA NA NA NA NA NA NA NA NA NA NA
#> 10 NA NA NA NA NA NA NA NA NA NA NA NA
#> 8 NA NA NA NA NA NA NA NA NA NA NA NA
#> 17 NA NA NA NA NA NA NA NA NA NA NA NA
#> 19 NA NA NA NA NA NA NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA NA NA NA NA NA NA
#> 6 NA NA NA NA NA NA NA NA NA NA NA NA
#> 11 NA NA NA NA NA NA NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA NA NA NA NA NA NA
#> 1 NA NA NA NA NA NA NA NA NA NA NA NA
#> 12 NA NA NA NA NA NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA NA NA NA NA NA
#> 5 NA NA NA NA NA NA NA NA NA NA NA NA
#> 14 NA NA NA NA NA NA NA NA NA NA NA NA
#> 7 NA NA NA NA NA NA NA NA NA NA NA NA
#> 24 NA NA NA NA NA NA NA NA NA NA NA NA
#> 30 NA NA NA NA NA NA NA NA NA NA NA NA
#> 22 NA NA NA NA NA NA NA NA NA NA NA NA
#> 23 NA NA NA NA NA NA NA NA NA NA NA NA
#> 27 NA NA NA NA NA NA NA NA NA NA NA NA
#> 28 NA NA NA NA NA NA NA NA NA NA NA NA
#> 26 NA NA NA NA NA NA NA NA NA NA NA NA
#> 25 NA NA NA NA NA NA NA NA NA NA NA NA
#> 29 NA NA NA NA NA NA NA NA NA NA NA NA
#> 21 NA NA NA NA NA NA NA NA NA NA NA NA
#> 33 NA NA NA NA NA NA NA NA NA NA NA NA
#> 37 NA NA NA NA NA NA NA NA NA NA NA NA
#> 36 NA NA NA NA NA NA NA NA NA NA NA NA
#> 31 NA NA NA NA NA NA NA NA NA NA NA NA
#> 39 NA NA NA NA NA NA NA NA NA NA NA NA
#> 38 NA NA NA NA NA NA NA NA NA NA NA NA
#> 32 NA NA NA NA NA NA NA NA NA NA NA NA
#> 40 NA NA NA NA NA NA NA NA NA NA NA NA
#> 34 NA NA NA NA NA NA NA NA NA NA NA NA
#> 35 NA NA NA NA NA NA NA NA NA NA NA NA
#> 44 42 51 65 86 103 118 127 138 145 146 NA NA
#> 45 41 50 61 78 98 117 135 141 147 174 197 196
#> 43 42 55 69 96 131 157 184 188 197 198 199 200
#> 41 42 51 66 85 103 124 155 153 175 184 199 204
#> 47 41 53 66 79 100 123 148 157 168 185 210 205
#> 49 40 53 64 85 108 128 152 166 184 203 233 237
#> 46 40 52 62 82 101 120 144 156 173 210 231 238
#> 50 41 54 67 84 105 122 155 175 205 234 264 264
#> 42 42 49 63 84 103 126 160 174 204 234 269 281
#> 48 39 50 62 80 104 125 154 170 222 261 303 322
#>
acast(chick_m, diet + chick ~ time, length, margins="diet")
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 1_18 1 1 0 0 0 0 0 0 0 0 0 0
#> 1_16 1 1 1 1 1 1 1 0 0 0 0 0
#> 1_15 1 1 1 1 1 1 1 1 0 0 0 0
#> 1_13 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_9 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_20 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_10 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_8 1 1 1 1 1 1 1 1 1 1 1 0
#> 1_17 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_19 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_4 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_6 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_11 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_3 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_1 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_12 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_2 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_5 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_14 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_7 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_24 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_30 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_22 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_23 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_27 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_28 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_26 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_25 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_29 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_21 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_33 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_37 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_36 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_31 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_39 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_38 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_32 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_40 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_34 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_35 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_44 1 1 1 1 1 1 1 1 1 1 0 0
#> 4_45 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_43 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_41 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_47 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_49 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_46 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_50 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_42 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_48 1 1 1 1 1 1 1 1 1 1 1 1
#> (all)_(all) 50 50 49 49 49 49 49 48 47 47 46 45
acast(chick_m, diet + chick ~ time, length, drop = FALSE)
#> 0 2 4 6 8 10 12 14 16 18 20 21
#> 1_18 1 1 0 0 0 0 0 0 0 0 0 0
#> 1_16 1 1 1 1 1 1 1 0 0 0 0 0
#> 1_15 1 1 1 1 1 1 1 1 0 0 0 0
#> 1_13 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_9 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_20 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_10 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_8 1 1 1 1 1 1 1 1 1 1 1 0
#> 1_17 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_19 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_4 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_6 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_11 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_3 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_1 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_12 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_2 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_5 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_14 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_7 1 1 1 1 1 1 1 1 1 1 1 1
#> 1_24 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_30 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_22 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_23 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_27 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_28 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_26 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_25 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_29 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_21 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_33 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_37 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_36 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_31 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_39 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_38 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_32 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_40 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_34 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_35 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_44 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_45 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_43 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_41 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_47 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_49 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_46 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_50 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_42 0 0 0 0 0 0 0 0 0 0 0 0
#> 1_48 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_18 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_16 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_15 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_13 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_9 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_20 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_10 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_8 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_17 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_19 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_4 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_6 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_11 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_3 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_1 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_12 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_2 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_5 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_14 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_7 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_24 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_30 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_22 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_23 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_27 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_28 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_26 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_25 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_29 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_21 1 1 1 1 1 1 1 1 1 1 1 1
#> 2_33 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_37 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_36 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_31 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_39 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_38 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_32 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_40 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_34 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_35 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_44 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_45 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_43 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_41 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_47 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_49 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_46 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_50 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_42 0 0 0 0 0 0 0 0 0 0 0 0
#> 2_48 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_18 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_16 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_15 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_13 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_9 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_20 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_10 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_8 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_17 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_19 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_4 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_6 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_11 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_3 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_1 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_12 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_2 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_5 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_14 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_7 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_24 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_30 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_22 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_23 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_27 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_28 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_26 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_25 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_29 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_21 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_33 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_37 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_36 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_31 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_39 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_38 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_32 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_40 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_34 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_35 1 1 1 1 1 1 1 1 1 1 1 1
#> 3_44 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_45 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_43 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_41 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_47 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_49 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_46 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_50 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_42 0 0 0 0 0 0 0 0 0 0 0 0
#> 3_48 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_18 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_16 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_15 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_13 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_9 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_20 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_10 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_8 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_17 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_19 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_4 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_6 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_11 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_3 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_1 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_12 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_2 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_5 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_14 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_7 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_24 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_30 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_22 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_23 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_27 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_28 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_26 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_25 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_29 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_21 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_33 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_37 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_36 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_31 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_39 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_38 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_32 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_40 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_34 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_35 0 0 0 0 0 0 0 0 0 0 0 0
#> 4_44 1 1 1 1 1 1 1 1 1 1 0 0
#> 4_45 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_43 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_41 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_47 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_49 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_46 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_50 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_42 1 1 1 1 1 1 1 1 1 1 1 1
#> 4_48 1 1 1 1 1 1 1 1 1 1 1 1
#Tips example
dcast(melt(tips), sex ~ smoker, mean, subset = .(variable == "total_bill"))
#> Using sex, smoker, day, time as id variables
#> sex No Yes
#> 1 Female 18.10519 17.97788
#> 2 Male 19.79124 22.28450
ff_d <- melt(french_fries, id=1:4, na.rm=TRUE)
acast(ff_d, subject ~ time, length)
#> 1 2 3 4 5 6 7 8 9 10
#> 3 30 30 30 30 30 30 30 30 30 0
#> 10 30 30 30 30 30 30 30 30 30 30
#> 15 30 30 30 30 25 30 30 30 30 30
#> 16 30 30 30 30 30 30 30 29 30 30
#> 19 30 30 30 30 30 30 30 30 30 30
#> 31 30 30 30 30 30 30 30 30 0 30
#> 51 30 30 30 30 30 30 30 30 30 30
#> 52 30 30 30 30 30 30 30 30 30 30
#> 63 30 30 30 30 30 30 30 30 30 30
#> 78 30 30 30 30 30 30 30 30 30 30
#> 79 30 30 30 30 30 30 29 28 30 0
#> 86 30 30 30 30 30 30 30 30 0 30
acast(ff_d, subject ~ time, length, fill=0)
#> 1 2 3 4 5 6 7 8 9 10
#> 3 30 30 30 30 30 30 30 30 30 0
#> 10 30 30 30 30 30 30 30 30 30 30
#> 15 30 30 30 30 25 30 30 30 30 30
#> 16 30 30 30 30 30 30 30 29 30 30
#> 19 30 30 30 30 30 30 30 30 30 30
#> 31 30 30 30 30 30 30 30 30 0 30
#> 51 30 30 30 30 30 30 30 30 30 30
#> 52 30 30 30 30 30 30 30 30 30 30
#> 63 30 30 30 30 30 30 30 30 30 30
#> 78 30 30 30 30 30 30 30 30 30 30
#> 79 30 30 30 30 30 30 29 28 30 0
#> 86 30 30 30 30 30 30 30 30 0 30
dcast(ff_d, treatment ~ variable, mean, margins = TRUE)
#> treatment potato buttery grassy rancid painty (all)
#> 1 1 6.887931 1.780087 0.6491379 4.065517 2.583621 3.194478
#> 2 2 7.001724 1.973913 0.6629310 3.624569 2.455844 3.146413
#> 3 3 6.967965 1.717749 0.6805195 3.866667 2.525541 3.151688
#> 4 (all) 6.952518 1.823699 0.6641727 3.852230 2.521758 3.164218
dcast(ff_d, treatment + subject ~ variable, mean, margins="treatment")
#> treatment subject potato buttery grassy rancid painty
#> 1 1 3 6.216667 0.3722222 0.18888889 2.1055556 3.11111111
#> 2 1 10 9.955000 6.7500000 0.58500000 4.0200000 1.37500000
#> 3 1 15 3.360000 0.7200000 0.42000000 3.9650000 3.26000000
#> 4 1 16 6.495000 3.2600000 0.75500000 4.1200000 1.23000000
#> 5 1 19 9.385000 3.0550000 2.02000000 5.3600000 2.77500000
#> 6 1 31 8.844444 0.4444444 0.08888889 5.9444444 3.21111111
#> 7 1 51 10.675000 2.6400000 1.05000000 5.1500000 1.95500000
#> 8 1 52 5.060000 0.8050000 0.87500000 4.2850000 2.64500000
#> 9 1 63 6.775000 0.0250000 0.00000000 6.0550000 3.85500000
#> 10 1 78 3.620000 0.7350000 0.54000000 1.5050000 3.49000000
#> 11 1 79 8.061111 0.2823529 0.34444444 0.5666667 0.00000000
#> 12 1 86 4.183333 1.7722222 0.80555556 5.4944444 4.10555556
#> 13 2 3 6.738889 0.5888889 0.10555556 3.1388889 2.47777778
#> 14 2 10 9.995000 6.9800000 0.47500000 2.1500000 0.82000000
#> 15 2 15 4.405000 1.3150000 0.34000000 2.2850000 2.06000000
#> 16 2 16 6.450000 3.3736842 1.05500000 3.4000000 0.45500000
#> 17 2 19 8.640000 2.4500000 1.13500000 5.4050000 4.15500000
#> 18 2 31 8.033333 0.6166667 0.15555556 6.0500000 5.06111111
#> 19 2 51 9.985000 3.7950000 1.57000000 4.6700000 2.25500000
#> 20 2 52 5.515000 1.0250000 1.18000000 4.2250000 2.19500000
#> 21 2 63 8.415000 0.1050000 0.01000000 5.0900000 4.35500000
#> 22 2 78 3.780000 0.2950000 0.75500000 1.5500000 2.72500000
#> 23 2 79 7.938889 0.6941176 0.25555556 1.0333333 0.00000000
#> 24 2 86 3.994444 2.0611111 0.78333333 4.5222222 2.84444444
#> 25 3 3 5.294444 0.7666667 0.09444444 2.8555556 2.86666667
#> 26 3 10 10.030000 6.4500000 0.14500000 3.1100000 0.69000000
#> 27 3 15 3.963158 0.9894737 0.44210526 2.5473684 2.36842105
#> 28 3 16 6.860000 2.7000000 1.12500000 3.2000000 0.55500000
#> 29 3 19 8.740000 1.7250000 2.07000000 7.2400000 3.90500000
#> 30 3 31 9.027778 0.6500000 0.17222222 6.5777778 5.12777778
#> 31 3 51 10.220000 3.1300000 1.35000000 4.9150000 2.54500000
#> 32 3 52 5.475000 0.8650000 0.76500000 3.1600000 2.66000000
#> 33 3 63 8.060000 0.0650000 0.12500000 6.1850000 3.10000000
#> 34 3 78 4.000000 0.7050000 0.66500000 1.1850000 3.52000000
#> 35 3 79 7.733333 0.5722222 0.11666667 1.1777778 0.02777778
#> 36 3 86 3.866667 1.6333333 0.94444444 4.1055556 3.02777778
#> 37 (all) (all) 6.952518 1.8236994 0.66417266 3.8522302 2.52175793
if (require("lattice")) {
lattice::xyplot(`1` ~ `2` | variable, dcast(ff_d, ... ~ rep), aspect="iso")
}
#> Loading required package: lattice
