test.ena.group.R
In rENA: Epistemic Network Analysis

# suppressMessages(library(rENA, quietly = T, verbose = F))
# context("Test creating groups");
#
# # codeNames = c("E.data","S.data","E.design","S.design","S.professional","E.client","V.client","E.consultant","V.consultant","S.collaboration","I.engineer","I.intern","K.actuator","K.rom","K.materials","K.power");
# codeNames = c('Data','Technical.Constraints','Performance.Parameters','Client.and.Consultant.Requests','Design.Reasoning','Collaboration');
#
#
# test_that("Mean set points and edges (default)", {
#   df <- system.file("extdata", "rs.data.csv", package="rENA")
#   accum = rENA:::ena.accumulate.data.file(df, units.by = c("UserName","Condition"), conversations.by = c("ActivityNumber","GroupName"), codes = codeNames);
#   set = ena.make.set(accum, position.method = lws.positions.es)
#   means = ena.group(set, by=accum$metadata$Condition)
#   testthat::expect_equal(
#     label="Group result row count same has unique values of by",
#     object=nrow(means$points),
#     expected=length(unique(accum$metadata$Condition))
#   );
# })
# test_that("Mean arbitrary point matrix", {
#   df <- system.file("extdata", "rs.data.csv", package="rENA")
#   accum = rENA:::ena.accumulate.data.file(df, units.by = c("UserName","Condition"), conversations.by = c("ActivityNumber","GroupName"), codes = codeNames);
#   set = ena.make.set(accum, position.method = lws.positions.es)
#
#   means = ena.group(set$points.rotated, by =accum$metadata$Condition);
#   testthat::expect_equal(
#     label="Group result row count same has unique values of by",
#     object=nrow(means),
#     expected=length(unique(accum$metadata$Condition))
#   );
# });
# test_that("Use custom method", {
#   df <- system.file("extdata", "rs.data.csv", package="rENA")
#   accum = rENA:::ena.accumulate.data.file(df, units.by = c("UserName","Condition"), conversations.by = c("ActivityNumber","GroupName"), codes = codeNames);
#   set = ena.make.set(accum, position.method = lws.positions.es)
#   custom <- function(x) {
#     return(99)
#   };
#   means=ena.group(set$points.rotated, by =accum$metadata$Condition, method = custom)
#   testthat::expect_true(all(means==99))
#   # testthat::expect_equal(
#   #   label = "48 units by 2 dimensions",
#   #   object = dim(df.set.lws.es$points.rotated),
#   #   expected = c(48,2)
#   # );
#   # testthat::expect_equal(
#   #   label = "48 units",
#   #   object = length(df.set.lws.es$enadata$unit.names),
#   #   expected = 48
#   # );
# })
#