Nothing
context('alluvial wide')
test_that('alluvial_wide'
,{
data = mtcars2
max_variables = 5
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'first_variable' )
expect_doppelganger('wide_first', p)
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'last_variable' )
expect_doppelganger('wide_last', p)
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'all_flows' )
expect_doppelganger('wide_all_flows', p)
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'values' )
expect_doppelganger('wide_values', p)
# manually order variable values
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'values'
, order_levels = c('8', '4', '6') )
expect_doppelganger('wide_reorder_y_levels', p)
#check integritiy of returned dataframe
expect_equal( nrow(data), nrow(p$data_key) )
# id
p = alluvial_wide(data, id = ids )
expect_true( ! 'ID' %in% names(p$data_key) )
expect_true( length(unique(p$data_key$ids) ) == nrow(p$data_key) )
p = alluvial_wide(data, id = 'ids' )
expect_true( ! 'ID' %in% names(p$data_key) )
expect_true( length(unique(p$data_key$ids) ) == nrow(p$data_key) )
p = alluvial_wide(data, id = NULL)
expect_true( 'ID' %in% names(p$data_key) )
expect_true( length(unique(p$data_key$ID) ) == nrow(p$data_key) )
#check automatic angling of x axis labels
data = ISLR::Auto %>%
as_tibble() %>%
mutate( name_x = row_number()
, name_x = paste( name, name_x ) ) %>%
select( - name ) %>%
mutate_at( vars( c('cylinders', 'year', 'origin' ) ), as.factor )
p = alluvial_wide( data, id = name_x, max_variables = 5 )
expect_doppelganger('wide_ISLR_cars', p)
p = alluvial_wide( data, id = name_x, max_variables = 5, auto_rotate_xlabs = F )
expect_doppelganger('wide_ISLR_cars_rotate_labels', p)
# check NA behavoir, rename label ando order to front
data$cylinders[1:4] = NA
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'first_variable'
, NA_label = 'none'
, order_levels = 'none' )
#vdiffr detects difference when rendered with different OS
#expect_doppelganger('wide_NA_label', p)
# test statum options
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'first_variable'
, stratum_labels = F
, stratum_label_type = "none"
, stratum_width = 1/20 )
#vdiffr detects difference when rendered with different OS
#expect_doppelganger('wide_Strat_width', p)
# test warning for high flow numbers
expect_warning( alluvial_wide( data = ggplot2::diamonds) )
#gouped df
p = alluvial_wide( group_by(mtcars2, cyl), max_variables = 3 )
# plot attachments
expect_true( all( c('data_key', 'alluvial_type', 'alluvial_params') %in% names(p) ) )
# color of stratum same as fill variable
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'first_variable'
, colorful_fill_variable_stratum = T)
# renders differently on mac
# expect_doppelganger('colorful_fill_variable_stratum', p)
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'last_variable'
, colorful_fill_variable_stratum = T)
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'all_flows'
, colorful_fill_variable_stratum = T)
p = alluvial_wide( data = data
, max_variables = max_variables
, fill_by = 'values'
, colorful_fill_variable_stratum = T
, col_vector_value = palette_qualitative() %>% palette_filter( greys = F)
)
})
test_that('alluvial_wide_all_char_cols',{
p = mtcars2 %>%
select_if(is.factor) %>%
mutate_all(as.character) %>%
alluvial_wide()
expect_true("ggplot" %in% class(p))
})
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