Man pages for erblast/oetteR
Collection of personal R functions
| f_anova_stats | generate a datatframe with anova results from a data_ls list |
| f_boxcox | f_boxcox |
| f_clean_data | f_clean_data |
| f_clean_data_no_changes | wrapper for f_clean_data without modifications to data |
| f_content | opens an html index file for additional content |
| f_content_get_file_type | returns files of specified type from vignette path |
| f_content_get_path | returns content path |
| f_content_render | renders content |
| f_datatable_minimal | convert dataframe to DT:datatable with minimal features |
| f_datatable_universal | convert dataframe to DT:datatable inlcuding most usefull... |
| f_html_breaks | create a taglist with n lines of html line breaks |
| f_html_filename_2_link | convert a filename + path or a file_path to a html link |
| f_html_get_title_from_Rmd | get title from Rmd file |
| f_html_padding | add some padding around html objects |
| f_html_table_html_and_rmd_link | create a DT::datatable that pairs Rmd and rendered html... |
| f_manip_append_2_list | append object to list |
| f_manip_bin_numerics | bin numerical columns |
| f_manip_bring_to_pos_range | bring vector to positice range |
| f_manip_data_2_model_matrix_format | brings data to model.matrix format |
| f_manip_double_2_int | converts columns of type double to integer if maximum number... |
| f_manip_factor_2_numeric | converts factor to numeric preserving numeric levels and... |
| f_manip_get_most_common_level | get most common level from vector |
| f_manip_get_response_variable_from_formula | get response variable from formula |
| f_manip_get_variables_from_formula | get variables from formula |
| f_manip_matrix_2_tibble | converts matrices to tibble, preserving row.names |
| f_manip_summarize_2_median_and_most_common_factor | takes a data_ls list created by f_clean_data() and returns a... |
| f_manip_transpose_tibble | transpose a tibble |
| f_model_add_predictions_2_grid_regression | add predictions to grid (regression models) |
| f_model_data_grid | generates a data grid based on a formula |
| f_model_importance | model importance |
| f_model_importance_pl_add_plots_regression | add plots based on variable importance to pipelearner... |
| f_model_importance_plot | plot model importance |
| f_model_importance_plot_tableplot | tableplot of important variables |
| f_model_importance_pl_plots_as_html | print plots of variable importance in modelling dataframe to... |
| f_model_importance_randomForest | extract variable importance for randomForest model |
| f_model_importance_rpart | extract variable importance for rpart |
| f_model_importance_svm | extract variable importance for svm |
| f_model_plot_var_dep_over_spec_var_range | plot vmodel varaible dependency over the range of a specified... |
| f_model_plot_variable_dependency_regression | plot model dependency on most important variables |
| f_model_seq_range | generates sequence of variable spanning from min to max |
| f_pca | calculate principle components for a dataset |
| f_pca_plot_components | plot principle components as a dot plot |
| f_pca_plot_variance_explained | plot varaince explained of principle components |
| f_plot_adjust_col_vector_length | adjust length of color vector, by repeating colors |
| f_plot_alluvial | plot alluvial on tidy data |
| f_plot_alluvial_1v1 | plot alluvial of gathered data |
| f_plot_color_code_variables | color code all variables in a data_ls list. |
| f_plot_col_vector74 | generate a most distinctive color scale |
| f_plot_generate_comparison_pairs | generates comparison pairs for 'ggpubr::stat_compare_means()' |
| f_plot_hist | Plot Histograms |
| f_plot_obj_2_html | generate a separate html file from a list of various objects |
| f_plot_pretty_points | plot prettier dot plot |
| f_plot_profit_bars_plus_area | plot revenues cost and profit development over time with bars... |
| f_plot_profit_lines | plot revenues cost and profit development over time as an... |
| f_plot_time | plot variable distribution over time as reduced overlapping... |
| f_prediction_intervall | calculate raw prediction intervalls |
| f_predict_plot_model_performance_regression | plot model performance |
| f_predict_plot_regression_alluvials | plot residuals of different models as aaluvials |
| f_predict_plot_regression_distribution | Plot distribution of model predictions vs observed |
| f_predict_pl_regression | adds predictions to learned pipelearner dataframe |
| f_predict_pl_regression_summarize | summarize prediction by f_predict_pl_regression() |
| f_predict_regression_add_predictions | adds predictions, residuals, abolute residuals, squared... |
| f_shiny_multiview | run multiview shiny app |
| f_shiny_som | self organizing map shiny app |
| f_sim_profit | simulate profit |
| f_stat_chi_square | generate a datatframe with chi square results from a data_ls... |
| f_stat_combine_anova_with_chi_square | combines anova with chi square results into single dataframe |
| f_stat_diff_of_means_medians | calculates maximum difference in group means and medians |
| f_stat_group_ana | analyse group difference of dataset |
| f_stat_group_ana_taglist | analyse group difference of dataset |
| f_stat_group_counts_percentages | create a aggregated data frame with percentagers and counts... |
| f_stat_group_mean_medians | create a aggregated data frame with means and medians for... |
| f_stat_max_diff_of_freq | calculate the maximal difference in frequencies between to... |
| f_stat_shapiro | wrapper for shapiro.test() |
| f_stat_stars | calculate significant level from p value |
| f_train_lasso | wrapper for cv.glmnet and cv.HDtweedie |
| f_train_lasso_manual_cv | wrapper for glmnet and HDtweedie |
| make_container_for_function_calls | container for function calls, can be used as a progress bar |
