f_plot_obj_2_html: generate a separate html file from a list of various objects

In erblast/oetteR: Collection of personal R functions

Description

lists of graphical objects like html(taglists), plots, tabplots, grids can be converted to html files

Usage

f_plot_obj_2_html(obj_list, type, output_file, title = "Plots",
  quiet = FALSE, ...)

Arguments

obj_list	htmltools::tagList
type	one of c('taglist','plots','tabplots','grids' , 'model_performance') some templates take additional arguments via the ... argument taglist taglist ceated with htmltools::tagList, a good container for html widgets plots a list with ggplot objects, takes additional arguments: fig.height: Default 5, fig.width: Default 7 tabplots a list of objects created with tabplot::tableplot, takes additional arguments: fig.height: Default 5, fig.width: Default 7, titles: list of titles must be same length as obj_list grids a list of grids created with gridExtra::arrangeGrob, takes additional argument: height: Default 30 model_performance takes a taglist created with f_predict_plot_model_performance_regression, takes the additional arguments: alluvial, plot objec created with f_predict_plot_regression_alluvials, dist, list of two plots created with f_predict_plot_regression_distribution, render_points_as_png: Default: TRUE, takes screenshots of point plots, otherwise plotly will load all points into memory which is not compatible with large data sets
output_file	file_name of the html file, without .html suffix
title	character vector of html document title, Default: 'Plots'
quiet	bollean, suppress markdown console print output, Default: FALSE
...	additional arguments passed to rmarkdown::render argument params

Examples

# type = taglist---------------------------------------------------------------

taglist = f_clean_data(mtcars) %>%
  f_boxcox() %>%
  f_pca() %>%
  f_pca_plot_components()

f_plot_obj_2_html(taglist, type = "taglist", output_file =  'test_me', title = 'Plots')

file.remove('test_me.html')

#type = tabplot-----------------------------------------------------------------

form = as.formula('disp~cyl+mpg+hp')
pipelearner::pipelearner(mtcars) %>%
  pipelearner::learn_models( rpart::rpart, form ) %>%
  pipelearner::learn_models( randomForest::randomForest, form ) %>%
  pipelearner::learn_models( e1071::svm, form ) %>%
  pipelearner::learn() %>%
  dplyr::mutate( imp = map2(fit, train, f_model_importance)
                 , tabplot = pmap( list( data = train
                                         , ranked_variables = imp
                                         , response_var = target
                                         , title = model
                 )
                 , f_model_importance_plot_tableplot
                 , limit = 5
                 )
  )  %>%
  .$tabplot %>%
  f_plot_obj_2_html( type = "tabplots", output_file =  'test_me', title = 'Plots')

file.remove('test_me.html')

#type = plots --------------------------------------------------------------------

data_ls = f_clean_data(mtcars)
form = as.formula('disp~cyl+mpg+hp')
variable_color_code = f_plot_color_code_variables(data_ls)

pipelearner::pipelearner(data_ls$data) %>%
 pipelearner::learn_models( rpart::rpart, form ) %>%
 pipelearner::learn_models( randomForest::randomForest, form ) %>%
 pipelearner::learn_models( e1071::svm, form ) %>%
 pipelearner::learn() %>%
 dplyr::mutate( imp = map2(fit, train, f_model_importance)
                , plots = pmap( list( m = fit
                                        , ranked_variables = imp
                                        , title = model
                                        )
                                   , f_model_plot_variable_dependency_regression
                                   , formula = form
                                   , data_ls = data_ls
                                   , variable_color_code = variable_color_code
                                  )
 )  %>%
 .$plots %>%
 f_plot_obj_2_html( type = "plots"
                    , output_file =  'test_me'
                    , title = 'Plots'
                    , fig.width = 30
                    , fig.height = 21)

file.remove('test_me.html')

#type = grids -------------------------------------------------------------------

data_ls = f_clean_data(mtcars)

form = as.formula('disp~cyl+mpg+hp+am+gear+drat+wt+vs+carb')

variable_color_code = f_plot_color_code_variables(data_ls)

grids = pipelearner::pipelearner(data_ls$data) %>%
  pipelearner::learn_models( rpart::rpart, form ) %>%
  pipelearner::learn_models( randomForest::randomForest, form ) %>%
  pipelearner::learn_models( e1071::svm, form ) %>%
  pipelearner::learn() %>%
  dplyr::mutate( imp = map2(fit, train, f_model_importance)
                 , range_var = map_chr(imp, function(x) head(x,1)$row_names )
                 , grid = pmap( list( m = fit
                                      , title = model
                                      , variables = imp
                                      , range_variable = range_var
                                      , data = test
                 )
                 , f_model_plot_var_dep_over_spec_var_range
                 , formula = form
                 , data_ls = data_ls
                 , variable_color_code = variable_color_code
                 , log_y = F
                 , limit = 12
                 )
  )  %>%
  .$grid

f_plot_obj_2_html( grids
                   , type = "grids"
                   , output_file = 'test_me'
                   , title = 'Grids'
                   , height = 30 )

file.remove('test_me.html')

#' #type = model_performance -------------------------------------------------------

form = displacement ~ cylinders + mpg

df = ISLR::Auto %>%
 mutate( name = paste( name, row_number() ) ) %>%
 pipelearner::pipelearner() %>%
 pipelearner::learn_models( rpart::rpart, form ) %>%
 pipelearner::learn_models( randomForest::randomForest, form ) %>%
 pipelearner::learn_models( e1071::svm, form ) %>%
 pipelearner::learn() %>%
 f_predict_pl_regression( 'name' ) %>%
 unnest(preds) %>%
 mutate( bins = cut(target1, breaks = 3 , dig.lab = 4)
         , title = model )

dist = f_predict_plot_regression_distribution(df
                                             , col_title = 'title'
                                             , col_pred = 'pred'
                                             , col_obs = 'target1')


alluvial = f_predict_plot_regression_alluvials(df
                                              , col_id = 'name'
                                              , col_title = 'title'
                                              , col_pred = 'pred'
                                              , col_obs = 'target1')


taglist = f_predict_plot_model_performance_regression(df)

f_plot_obj_2_html( taglist
                  , type = 'model_performance'
                  , output_file = 'test_me'
                  , dist = dist
                  , alluvial = alluvial
                  , render_points_as_png = TRUE
                 )


file.remove('test_me.html')

erblast/oetteR documentation built on May 27, 2019, 12:11 p.m.