pkgname <- "easyalluvial"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('easyalluvial')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("add_imp_plot")
### * add_imp_plot
flush(stderr()); flush(stdout())
### Name: add_imp_plot
### Title: add bar plot of important features to model response alluvial
### plot
### Aliases: add_imp_plot
### ** Examples
## Not run:
##D df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
##D
##D train = caret::train( disp ~ .
##D , df
##D , method = 'rf'
##D , trControl = caret::trainControl( method = 'none' )
##D , importance = TRUE )
##D
##D pred_train = caret::predict.train(train, df)
##D
##D p = alluvial_model_response_caret(train, degree = 4, pred_train = pred_train)
##D
##D p_grid = add_marginal_histograms(p, data_input = df)
##D
##D p_grid = add_imp_plot(p_grid, p, data_input = df)
## End(Not run)
cleanEx()
nameEx("add_marginal_histograms")
### * add_marginal_histograms
flush(stderr()); flush(stdout())
### Name: add_marginal_histograms
### Title: add marginal histograms to alluvial plot
### Aliases: add_marginal_histograms
### ** Examples
## Not run:
##D p = alluvial_wide(mtcars2, max_variables = 3)
##D p_grid = add_marginal_histograms(p, mtcars2)
## End(Not run)
cleanEx()
nameEx("alluvial_long")
### * alluvial_long
flush(stderr()); flush(stdout())
### Name: alluvial_long
### Title: alluvial plot of data in long format
### Aliases: alluvial_long
### ** Examples
## Not run:
##D data = quarterly_flights
##D
##D alluvial_long( data, key = qu, value = mean_arr_delay, id = tailnum, fill_by = 'last_variable' )
##D
##D # more flow coloring variants ------------------------------------
##D
##D alluvial_long( data, key = qu, value = mean_arr_delay, id = tailnum, fill_by = 'first_variable' )
##D alluvial_long( data, key = qu, value = mean_arr_delay, id = tailnum, fill_by = 'all_flows' )
##D alluvial_long( data, key = qu, value = mean_arr_delay, id = tailnum, fill_by = 'value' )
##D
##D # color by additional variable carrier ---------------------------
##D
##D alluvial_long( data, key = qu, value = mean_arr_delay, fill = carrier, id = tailnum )
##D
##D # use same color coding for flows and y levels -------------------
##D
##D palette = c('green3', 'tomato')
##D
##D alluvial_long( data, qu, mean_arr_delay, tailnum, fill_by = 'value'
##D , col_vector_flow = palette
##D , col_vector_value = palette )
##D
##D
##D # reorder levels ------------------------------------------------
##D
##D alluvial_long( data, qu, mean_arr_delay, tailnum, fill_by = 'first_variable'
##D , order_levels_value = c('on_time', 'late') )
##D
##D alluvial_long( data, qu, mean_arr_delay, tailnum, fill_by = 'first_variable'
##D , order_levels_key = c('Q4', 'Q3', 'Q2', 'Q1') )
##D
##D require(dplyr)
##D require(magrittr)
##D
##D order_by_carrier_size = data %>%
##D group_by(carrier) %>%
##D count() %>%
##D arrange( desc(n) ) %>%
##D .[['carrier']]
##D
##D alluvial_long( data, qu, mean_arr_delay, tailnum, carrier
##D , order_levels_fill = order_by_carrier_size )
##D
## End(Not run)
cleanEx()
nameEx("alluvial_model_response")
### * alluvial_model_response
flush(stderr()); flush(stdout())
### Name: alluvial_model_response
### Title: create model response plot
### Aliases: alluvial_model_response
### ** Examples
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)
alluvial_model_response(pred, dspace, imp, degree = 3)
# partial dependency plotting method
## Not run:
##D pred = get_pdp_predictions(df, imp
##D , .f_predict = randomForest:::predict.randomForest
##D , m
##D , degree = 3
##D , bins = 5)
##D
##D
##D alluvial_model_response(pred, dspace, imp, degree = 3, method = 'pdp')
##D
## End(Not run)
cleanEx()
nameEx("alluvial_model_response_caret")
### * alluvial_model_response_caret
flush(stderr()); flush(stdout())
### Name: alluvial_model_response_caret
### Title: create model response plot for caret models
### Aliases: alluvial_model_response_caret
### ** Examples
if(check_pkg_installed("caret", raise_error = FALSE)) {
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
train = caret::train( disp ~ .,
df,
method = 'rf',
trControl = caret::trainControl( method = 'none' ),
importance = TRUE )
alluvial_model_response_caret(train, df, degree = 3)
}
# partial dependency plotting method
## Not run:
##D future::plan("multisession")
##D alluvial_model_response_caret(train, df, degree = 3, method = 'pdp', parallel = TRUE)
##D
## End(Not run)
cleanEx()
nameEx("alluvial_model_response_parsnip")
### * alluvial_model_response_parsnip
flush(stderr()); flush(stdout())
### Name: alluvial_model_response_parsnip
### Title: create model response plot for parsnip models
### Aliases: alluvial_model_response_parsnip
### ** Examples
if(check_pkg_installed("parsnip", raise_error = FALSE)) {
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = parsnip::rand_forest(mode = "regression") %>%
parsnip::set_engine("randomForest") %>%
parsnip::fit(disp ~ ., data = df)
alluvial_model_response_parsnip(m, df, degree = 3)
}
## Not run:
##D # workflow ---------------------------------
##D m <- parsnip::rand_forest(mode = "regression") %>%
##D parsnip::set_engine("randomForest")
##D
##D rec_prep = recipes::recipe(disp ~ ., df) %>%
##D recipes::prep()
##D
##D wf <- workflows::workflow() %>%
##D workflows::add_model(m) %>%
##D workflows::add_recipe(rec_prep) %>%
##D parsnip::fit(df)
##D
##D alluvial_model_response_parsnip(wf, df, degree = 3)
##D
##D # partial dependence plotting method -----
##D future::plan("multisession")
##D alluvial_model_response_parsnip(m, df, degree = 3, method = 'pdp', parallel = TRUE)
## End(Not run)
cleanEx()
nameEx("alluvial_wide")
### * alluvial_wide
flush(stderr()); flush(stdout())
### Name: alluvial_wide
### Title: alluvial plot of data in wide format
### Aliases: alluvial_wide
### ** Examples
## Not run:
##D alluvial_wide( data = mtcars2, id = ids
##D , max_variables = 3
##D , fill_by = 'first_variable' )#'
##D # more coloring variants----------------------
##D alluvial_wide( data = mtcars2, id = ids
##D , max_variables = 5
##D , fill_by = 'last_variable' )
##D
##D alluvial_wide( data = mtcars2, id = ids
##D , max_variables = 5
##D , fill_by = 'all_flows' )
##D
##D alluvial_wide( data = mtcars2, id = ids
##D , max_variables = 5
##D , fill_by = 'first_variable' )
##D
##D # manually order variable values and colour by stratum value
##D
##D alluvial_wide( data = mtcars2, id = ids
##D , max_variables = 5
##D , fill_by = 'values'
##D , order_levels = c('4', '8', '6') )
## End(Not run)
cleanEx()
nameEx("check_pkg_installed")
### * check_pkg_installed
flush(stderr()); flush(stdout())
### Name: check_pkg_installed
### Title: check if package is installed
### Aliases: check_pkg_installed
### ** Examples
check_pkg_installed("easyalluvial")
cleanEx()
nameEx("get_data_space")
### * get_data_space
flush(stderr()); flush(stdout())
### Name: get_data_space
### Title: calculate data space
### Aliases: get_data_space
### ** Examples
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp)
cleanEx()
nameEx("get_pdp_predictions")
### * get_pdp_predictions
flush(stderr()); flush(stdout())
### Name: get_pdp_predictions
### Title: get predictions compatible with the partial dependence plotting
### method
### Aliases: get_pdp_predictions
### ** Examples
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
pred = get_pdp_predictions(df, imp
, m
, degree = 3
, bins = 5)
# parallel processing --------------------------
## Not run:
##D future::plan("multisession")
##D
##D # note that we have to pass the predict method via .f_predict otherwise
##D # it will not be available in the worker's environment.
##D
##D pred = get_pdp_predictions(df, imp
##D , m
##D , degree = 3
##D , bins = 5,
##D , parallel = TRUE
##D , .f_predict = randomForest:::predict.randomForest)
## End(Not run)
cleanEx()
nameEx("manip_bin_numerics")
### * manip_bin_numerics
flush(stderr()); flush(stdout())
### Name: manip_bin_numerics
### Title: bin numerical columns
### Aliases: manip_bin_numerics
### ** Examples
summary( mtcars2 )
summary( manip_bin_numerics(mtcars2) )
summary( manip_bin_numerics(mtcars2, bin_labels = 'mean'))
summary( manip_bin_numerics(mtcars2, bin_labels = 'cuts'
, scale = FALSE, center = FALSE, transform = FALSE))
cleanEx()
nameEx("manip_factor_2_numeric")
### * manip_factor_2_numeric
flush(stderr()); flush(stdout())
### Name: manip_factor_2_numeric
### Title: converts factor to numeric preserving numeric levels and order
### in character levels.
### Aliases: manip_factor_2_numeric
### ** Examples
fac_num = factor( c(1,3,8) )
fac_chr = factor( c('foo','bar') )
fac_chr_ordered = factor( c('a','b','c'), ordered = TRUE )
manip_factor_2_numeric( fac_num )
manip_factor_2_numeric( fac_chr )
manip_factor_2_numeric( fac_chr_ordered )
# does not work for decimal numbers
manip_factor_2_numeric(factor(c("A12", "B55", "10e4")))
manip_factor_2_numeric(factor(c("1.56", "4.56", "8.4")))
cleanEx()
nameEx("palette_filter")
### * palette_filter
flush(stderr()); flush(stdout())
### Name: palette_filter
### Title: color filters for any vector of hex color values
### Aliases: palette_filter
### ** Examples
require(magrittr)
palette_qualitative() %>%
palette_filter(thresh_similar = 0) %>%
palette_plot_intensity()
## Not run:
##D # more examples---------------------------
##D
##D palette_qualitative() %>%
##D palette_filter(thresh_similar = 25) %>%
##D palette_plot_intensity()
##D
##D palette_qualitative() %>%
##D palette_filter(thresh_similar = 0, blues = FALSE) %>%
##D palette_plot_intensity()
## End(Not run)
cleanEx()
nameEx("palette_increase_length")
### * palette_increase_length
flush(stderr()); flush(stdout())
### Name: palette_increase_length
### Title: increases length of palette by repeating colours
### Aliases: palette_increase_length
### ** Examples
require(magrittr)
length(palette_qualitative())
palette_qualitative() %>%
palette_increase_length(100) %>%
length()
cleanEx()
nameEx("palette_plot_intensity")
### * palette_plot_intensity
flush(stderr()); flush(stdout())
### Name: palette_plot_intensity
### Title: plot colour intensity of palette
### Aliases: palette_plot_intensity
### ** Examples
## Not run:
##D if(interactive()){
##D palette_qualitative() %>%
##D palette_filter( thresh = 25) %>%
##D palette_plot_intensity()
##D }
## End(Not run)
cleanEx()
nameEx("palette_plot_rgp")
### * palette_plot_rgp
flush(stderr()); flush(stdout())
### Name: palette_plot_rgp
### Title: plot rgb values of palette
### Aliases: palette_plot_rgp
### ** Examples
## Not run:
##D if(interactive()){
##D palette_qualitative() %>%
##D palette_filter( thresh = 50) %>%
##D palette_plot_rgp()
##D }
## End(Not run)
cleanEx()
nameEx("palette_qualitative")
### * palette_qualitative
flush(stderr()); flush(stdout())
### Name: palette_qualitative
### Title: compose palette from qualitative RColorBrewer palettes
### Aliases: palette_qualitative
### ** Examples
palette_qualitative()
cleanEx()
nameEx("plot_all_hists")
### * plot_all_hists
flush(stderr()); flush(stdout())
### Name: plot_all_hists
### Title: plot marginal histograms of alluvial plot
### Aliases: plot_all_hists
### ** Examples
## Not run:
##D p = alluvial_wide(mtcars2, max_variables = 3)
##D plot_all_hists(p, mtcars2)
## End(Not run)
cleanEx()
nameEx("plot_condensation")
### * plot_condensation
flush(stderr()); flush(stdout())
### Name: plot_condensation
### Title: Plot dataframe condensation potential
### Aliases: plot_condensation
### ** Examples
plot_condensation(mtcars2)
plot_condensation(mtcars2, first = 'disp')
cleanEx()
nameEx("plot_imp")
### * plot_imp
flush(stderr()); flush(stdout())
### Name: plot_imp
### Title: plot feature importance
### Aliases: plot_imp
### ** Examples
## Not run:
##D df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
##D
##D train = caret::train( disp ~ .
##D , df
##D , method = 'rf'
##D , trControl = caret::trainControl( method = 'none' )
##D , importance = TRUE )
##D
##D pred_train = caret::predict.train(train, df)
##D
##D p = alluvial_model_response_caret(train, degree = 3, pred_train = pred_train)
##D
##D plot_imp(p, mtcars2)
##D
## End(Not run)
cleanEx()
nameEx("tidy_imp")
### * tidy_imp
flush(stderr()); flush(stdout())
### Name: tidy_imp
### Title: tidy up dataframe containing model feature importance
### Aliases: tidy_imp
### ** Examples
# randomforest
df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
tidy_imp(imp, df)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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