EMD can get very heavy with large datasets. For an example lemnatech dataset filtering for images from every 5th day there are 6332^2 = 40,094,224 pairwise EMD values. In long format that's a 40 million row dataframe, which is unwieldy. This function is to help reduce the size of datasets before comparing histograms and moving on with matrix methods or network analysis.
Usage
mv_ag(
df,
group,
mvCols = "frequencies",
n_per_group = 1,
outRows = NULL,
keep = NULL,
parallel = getOption("mc.cores", 1),
traitCol = "trait",
labelCol = "label",
valueCol = "value",
id = "image"
)Arguments
- df
A dataframe with multi value traits. This can be in wide or long format, data is assumed to be long if traitCol, valueCol, and labelCol are present.
- group
Vector of column names for variables which uniquely identify groups in the data to summarize data over. Typically this would be the design variables and a time variable.
- mvCols
Either a vector of column names/positions representing multi value traits or a character string that identifies the multi value trait columns as a regex pattern. Defaults to "frequencies".
- n_per_group
Number of rows to return for each group.
- outRows
Optionally this is a different way to specify how many rows to return. This will often not be exact so that groups have the same number of observations each.
- keep
A vector of single value traits to also average over groups, if there are a mix of single and multi value traits in your data.
- parallel
Optionally the groups can be run in parallel with this number of cores, defaults to 1 if the "mc.cores" option is not set globally.
- traitCol
Column with phenotype names, defaults to "trait".
- labelCol
Column with phenotype labels (units), defaults to "label".
- valueCol
Column with phenotype values, defaults to "value".
- id
Column that uniquely identifies images if the data is in long format. This is ignored when data is in wide format.
Examples
s1 <- mvSim(
dists = list(runif = list(min = 15, max = 150)),
n_samples = 10,
counts = 1000,
min_bin = 1,
max_bin = 180,
wide = TRUE
)
mv_ag(s1, group = "group", mvCols = "sim_", n_per_group = 2)
#> group sim_1 sim_2 sim_3 sim_4 sim_5 sim_6 sim_7 sim_8 sim_9 sim_10
#> runif.1 runif 0 0 0 0 0 0 0 0 0 0
#> runif.2 runif 0 0 0 0 0 0 0 0 0 0
#> sim_11 sim_12 sim_13 sim_14 sim_15 sim_16 sim_17 sim_18 sim_19 sim_20
#> runif.1 0 0 0 0 0.0068 0.0066 0.0068 0.007 0.0062 0.0056
#> runif.2 0 0 0 0 0.0076 0.0080 0.0094 0.008 0.0074 0.0064
#> sim_21 sim_22 sim_23 sim_24 sim_25 sim_26 sim_27 sim_28 sim_29 sim_30
#> runif.1 0.0062 0.0082 0.0072 0.0088 0.0056 0.0058 0.0090 0.0068 0.0064 0.0062
#> runif.2 0.0070 0.0064 0.0086 0.0086 0.0078 0.0064 0.0068 0.0072 0.0086 0.0056
#> sim_31 sim_32 sim_33 sim_34 sim_35 sim_36 sim_37 sim_38 sim_39 sim_40
#> runif.1 0.0058 0.0090 0.0058 0.0078 0.0092 0.0076 0.0066 0.0060 0.0064 0.0082
#> runif.2 0.0080 0.0076 0.0070 0.0056 0.0040 0.0088 0.0060 0.0062 0.0080 0.0086
#> sim_41 sim_42 sim_43 sim_44 sim_45 sim_46 sim_47 sim_48 sim_49 sim_50
#> runif.1 0.0070 0.0092 0.0080 0.0074 0.0080 0.0084 0.0078 0.0066 0.0048 0.0082
#> runif.2 0.0064 0.0076 0.0076 0.0082 0.0062 0.0084 0.0068 0.0086 0.0056 0.0112
#> sim_51 sim_52 sim_53 sim_54 sim_55 sim_56 sim_57 sim_58 sim_59 sim_60
#> runif.1 0.0068 0.0060 0.0072 0.0088 0.0068 0.0086 0.008 0.0084 0.0066 0.0080
#> runif.2 0.0082 0.0064 0.0074 0.0076 0.0072 0.0068 0.007 0.0062 0.0112 0.0076
#> sim_61 sim_62 sim_63 sim_64 sim_65 sim_66 sim_67 sim_68 sim_69 sim_70
#> runif.1 0.0072 0.0056 0.0088 0.0078 0.0068 0.0066 0.0056 0.0070 0.007 0.0078
#> runif.2 0.0062 0.0070 0.0050 0.0066 0.0072 0.0080 0.0088 0.0082 0.007 0.0080
#> sim_71 sim_72 sim_73 sim_74 sim_75 sim_76 sim_77 sim_78 sim_79 sim_80
#> runif.1 0.007 0.0066 0.0062 0.0070 0.0100 0.009 0.0082 0.0086 0.0074 0.0070
#> runif.2 0.008 0.0074 0.0078 0.0066 0.0092 0.006 0.0062 0.0062 0.0086 0.0086
#> sim_81 sim_82 sim_83 sim_84 sim_85 sim_86 sim_87 sim_88 sim_89 sim_90
#> runif.1 0.0076 0.0080 0.0082 0.0074 0.0100 0.0096 0.0074 0.0082 0.0096 0.0092
#> runif.2 0.0066 0.0072 0.0072 0.0056 0.0068 0.0066 0.0086 0.0104 0.0068 0.0078
#> sim_91 sim_92 sim_93 sim_94 sim_95 sim_96 sim_97 sim_98 sim_99 sim_100
#> runif.1 0.0076 0.0062 0.0078 0.0066 0.0078 0.0074 0.0096 0.0072 0.0082 0.0062
#> runif.2 0.0088 0.0082 0.0070 0.0084 0.0080 0.0064 0.0064 0.0098 0.0064 0.0074
#> sim_101 sim_102 sim_103 sim_104 sim_105 sim_106 sim_107 sim_108 sim_109
#> runif.1 0.0058 0.0088 0.0062 0.0074 0.0064 0.0078 0.004 0.0066 0.0070
#> runif.2 0.0082 0.0082 0.0086 0.0072 0.0062 0.0068 0.006 0.0074 0.0078
#> sim_110 sim_111 sim_112 sim_113 sim_114 sim_115 sim_116 sim_117 sim_118
#> runif.1 0.0062 0.0094 0.0078 0.0076 0.0086 0.0076 0.0068 0.0086 0.0066
#> runif.2 0.0092 0.0064 0.0066 0.0076 0.0074 0.0058 0.0082 0.0082 0.0080
#> sim_119 sim_120 sim_121 sim_122 sim_123 sim_124 sim_125 sim_126 sim_127
#> runif.1 0.0058 0.0068 0.0088 0.0072 0.0054 0.0074 0.0076 0.0100 0.0076
#> runif.2 0.0088 0.0068 0.0060 0.0078 0.0080 0.0064 0.0066 0.0062 0.0074
#> sim_128 sim_129 sim_130 sim_131 sim_132 sim_133 sim_134 sim_135 sim_136
#> runif.1 0.0088 0.0072 0.0090 0.0088 0.0078 0.0064 0.0084 0.0082 0.0066
#> runif.2 0.0074 0.0074 0.0066 0.0092 0.0084 0.0070 0.0076 0.0068 0.0088
#> sim_137 sim_138 sim_139 sim_140 sim_141 sim_142 sim_143 sim_144 sim_145
#> runif.1 0.0074 0.0076 0.0080 0.0064 0.0080 0.0086 0.0064 0.0080 0.0080
#> runif.2 0.0082 0.0058 0.0094 0.0064 0.0076 0.0096 0.0050 0.0074 0.0056
#> sim_146 sim_147 sim_148 sim_149 sim_150 sim_151 sim_152 sim_153 sim_154
#> runif.1 0.0072 0.0082 0.0056 0.0044 0 0 0 0 0
#> runif.2 0.0072 0.0068 0.0084 0.0068 0 0 0 0 0
#> sim_155 sim_156 sim_157 sim_158 sim_159 sim_160 sim_161 sim_162 sim_163
#> runif.1 0 0 0 0 0 0 0 0 0
#> runif.2 0 0 0 0 0 0 0 0 0
#> sim_164 sim_165 sim_166 sim_167 sim_168 sim_169 sim_170 sim_171 sim_172
#> runif.1 0 0 0 0 0 0 0 0 0
#> runif.2 0 0 0 0 0 0 0 0 0
#> sim_173 sim_174 sim_175 sim_176 sim_177 sim_178 sim_179 sim_180
#> runif.1 0 0 0 0 0 0 0 0
#> runif.2 0 0 0 0 0 0 0 0