context('parcats')
test_that('parcats_alluvial_wide'
,{
p = alluvial_wide(mtcars2, max_variables = 5)
parcats(p, marginal_histograms = FALSE)
expect_error( parcats(p, marginal_histograms = TRUE) )
parcats(p, marginal_histograms = TRUE, data_input = mtcars2)
df = select(mtcars2, mpg, disp, wt, am, qsec)
p = alluvial_wide(df)
parcats(p, marginal_histograms = TRUE, data_input = df)
})
test_that('parcats_alluvial_wide_num_only'
,{
df = mtcars2 %>%
select_if(is.numeric)
p = alluvial_wide(df, max_variables = 5)
parcats(p, data_input = df)
})
test_that('parcats_params',{
p = alluvial_wide(mtcars2, max_variables = 5)
# hoveron
parcats(p, hoveron = 'category', marginal_histograms = TRUE, data_input = mtcars2)
parcats(p, hoveron = 'color', marginal_histograms = TRUE, data_input = mtcars2)
parcats(p, hoveron = 'dimension', marginal_histograms = TRUE, data_input = mtcars2)
#hoverinfo
parcats(p, hoverinfo = 'count', marginal_histograms = FALSE)
parcats(p, hoverinfo = 'probability', marginal_histograms = FALSE)
parcats(p, hoveron = 'count+probability', marginal_histograms = FALSE)
# hovertemplate probably needs some kind of format string, too complicated will drop this
# parcats(p, hovertemplate = 'count', marginal_histograms = FALSE)
# parcats(p, hovertemplate = 'probability', marginal_histograms = FALSE)
# parcats(p, hovertemplate = 'category', marginal_histograms = FALSE)
# parcats(p, hovertemplate = 'categorycount', marginal_histograms = FALSE)
# parcats(p, hovertemplate = 'colorcount', marginal_histograms = FALSE)
# parcats(p, hovertemplate = 'bandcolorcount', marginal_histograms = FALSE)
# arrangement
parcats(p, arrangement = 'fixed', marginal_histograms = FALSE)
parcats(p, arrangement = 'perpendicular', marginal_histograms = FALSE)
parcats(p, arrangement = 'freeform', marginal_histograms = FALSE)
#bundlecolors
parcats(p, bundlecolors = TRUE, marginal_histograms = FALSE)
parcats(p, bundlecolors = FALSE, marginal_histograms = FALSE)
#sortpaths
parcats(p, sortpaths = 'forward', marginal_histograms = FALSE)
parcats(p, sortpaths = 'backward', marginal_histograms = FALSE)
#labelfont
parcats(p, labelfont = list(size = 18, color = 'blue'), marginal_histograms = FALSE)
#tickfont
parcats(p, tickfont = list(size = 18, color = 'blue'), marginal_histograms = FALSE)
})
test_that('parcats_alluvial_long'
,{
p = alluvial_long(quarterly_flights, key = qu, value = mean_arr_delay, id = tailnum)
parcats(p, marginal_histograms = FALSE)
p = alluvial_long(quarterly_flights, key = qu, value = mean_arr_delay, id = tailnum, fill = carrier)
parcats(p, marginal_histograms = FALSE)
p = alluvial_long(quarterly_sunspots, key = qu, value = spots, id = year)
parcats(p, marginal_histograms = FALSE)
})
test_that('parcats_alluvial_model_response'
,{
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp, degree = 3)
pred = predict(m, newdata = dspace)
p = alluvial_model_response(pred, dspace, imp, degree = 3)
parcats(p, marginal_histograms = FALSE, imp = FALSE)
parcats(p, marginal_histograms = TRUE, imp = FALSE, data_input = df)
parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)
# grid = add_marginal_histograms(p, df)
# pdb
pred = get_pdp_predictions(df, imp, m, degree = 3)
p = alluvial_model_response(pred, dspace, imp, degree = 3, method = 'pdp')
parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)
# categorical response ---------------------------
df = titanic %>%
select_if(is.factor)
set.seed(0)
m = randomForest::randomForest( Survived ~ ., df)
imp = m$importance
expect_warning( {dspace = get_data_space(df, imp, degree = 3, max_levels = 5)} )
expect_true( nrow(dspace) == 30 )
pred = predict(m, newdata = dspace,type = 'response')
p = alluvial_model_response(pred, dspace, imp, degree = 3)
parcats(p, marginal_histograms = FALSE, imp = FALSE)
suppressWarnings({
# warning is raised because number of factors is cut to 5
parcats(p, marginal_histograms = TRUE, imp = FALSE, data_input = df)
parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)
})
# grid = add_marginal_histograms(p, df)
})
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