knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(pacta.loanbook) plot_table <- function(dataset_name) { table <- data_dictionary table <- dplyr::filter(table, .data[["dataset"]] == dataset_name) table_plot <- gt::gt(dplyr::select(table, -"dataset")) table_plot <- gt::cols_width( .data = table_plot, column ~ gt::px(150), typeof ~ gt::px(90) ) table_plot <- gt::tab_style( data = table_plot, style = gt::cell_text(size = "smaller"), locations = gt::cells_body(columns = 1:2) ) table_plot <- gt::tab_options( data = table_plot, ihtml.active = TRUE, ihtml.use_pagination = FALSE, ihtml.use_sorting = TRUE, ihtml.use_highlight = TRUE ) gt::fmt_passthrough(table_plot) }
The output of match_name()
has the following columns:
plot_table("match_name_output")
The output of prioritize()
has the following columns:
plot_table("prioritize_output")
The output of target_market_share()
has the following columns:
plot_table("target_market_share_output")
The {pacta.loanbook}
package provides a demo target_market_share()
output dataset market_share
.
market_share
The output of target_sda()
has the following columns:
plot_table("target_sda_output")
The {pacta.loanbook}
package provides a demo target_sda()
output dataset sda
.
sda
The output of prep_techmix()
has the following columns:
plot_table("prep_techmix_output")
The output of prep_emission_intensity()
has the following columns:
plot_table("prep_emission_intensity_output")
The output of prep_trajectory()
has the following columns:
plot_table("prep_trajectory_output")
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