eHDPrep (version 1.3.3)

metavariable_agg: Aggregate Data by Metavariable

Description

Variables in a numeric data frame are aggregated into metavariables via their most informative common ancestors identified in an ontological graph object (see metavariable_info). Metavariables are appended to the data frame.

Usage

metavariable_agg(graph, data, label_attr = "name", normalize_vals = TRUE)

Value

data with semantic aggregations derived from common ontological ancestry (metavariables) appended as new columns, each prefixed with "MV_" and suffixed by their aggregation function (e.g. "_SUM").

Arguments

graph

Graph containing ontological and dataset nodes. Must be in tidygraph format or coercible to this format. Must have been processed using metavariable_info.

data

Numeric data frame or matrix containing variables which are also in graph.

label_attr

Node attribute containing labels used for column names when creating metavariable aggregations. Default: "name"

normalize_vals

Should values be normalized before aggregation? Default: TRUE

Details

Metavariables are created from the aggregation of data variables via their most informative common ancestor (expected to have been calculated in metavariable_info). Metavariables are labelled using the syntax: MV_[label_attr]_[Aggregation function]. The data variables are aggregated row-wise by their maximum, minimum, mean, sum, and product. Metavariables with zero entropy (no information) are not appended to the data. See examples for where this function should be applied in the semantic enrichment workflow.

See Also

Other semantic enrichment functions: join_vars_to_ontol(), metavariable_info(), metavariable_variable_descendants()

Examples

require(magrittr)
require(dplyr)
data(example_ontology)
data(example_mapping_file)
data(example_data)

#' # define datatypes
tibble::tribble(~"var", ~"datatype",
"patient_id", "id",
"tumoursize", "numeric",
"t_stage", "ordinal_tstage",
"n_stage", "ordinal_nstage",
"diabetes_merged", "character",
"hypertension", "factor",
"rural_urban", "factor",
"marital_status", "factor",
"SNP_a", "genotype",
"SNP_b", "genotype",
"free_text", "freetext") -> data_types

# create post-QC data
example_data %>%
  merge_cols(diabetes_type, diabetes, "diabetes_merged", rm_in_vars = TRUE) %>%
  apply_quality_ctrl(patient_id, data_types,
                     bin_cats =c("No" = "Yes", "rural" = "urban"),
                     to_numeric_matrix = TRUE) %>%
                     suppressMessages() ->
                     post_qc_data

# minimal example on first four coloums of example data:
dplyr::slice(example_ontology, 1:7,24) %>%
   join_vars_to_ontol(example_mapping_file[1:3,], root = "root") %>%
   metavariable_info() %>%
   metavariable_agg(post_qc_data[1:10,1:4]) -> res
# see Note section of documentation for information on possible warnings.

# summary of result:
tibble::glimpse(res)

# \donttest{
# full example:
example_ontology %>%
   join_vars_to_ontol(example_mapping_file, root = "root") %>%
   metavariable_info() %>%
   metavariable_agg(post_qc_data) -> res
 # see Note section of documentation for information on possible warnings.

# summary of result:
tibble::glimpse(res)
# }