sector_test: Performs sector test to analyze uneven distribution of...

In jlederluis/digitanalysis: Digit Analysis

Performs sector test to analyze uneven distribution of percent repeats across sectors (supposed to be uniform). A wrapper function for repeat_test.

Description

Performs sector test to analyze uneven distribution of percent repeats across sectors (supposed to be uniform). A wrapper function for repeat_test.

Usage

sector_test(
  digitdata,
  break_out,
  category,
  category_instance_analyzing,
  data_column = NA,
  duplicate_matching_cols = "all",
  break_out_grouping = NA,
  category_grouping = NA,
  rounding_patterns_to_omit = NA,
  plot = TRUE,
  remove_all_category_visualize = FALSE
)

Arguments

digitdata	A object of class DigitAnalysis.
break_out	The data column (non-numeric!) to split up the dataset based on different categories in the column if specified as an character. The first division (usually x-axis) shown in plots.
category	The column for splitting the data into sectors for separate analysis. The second division (usually variables) shown in plots.
category_instance_analyzing	The instance of the category to perform t test on.
data_column	The data column to omit rounding numbers upon. Must be a single column.
duplicate_matching_cols	An array of names of data columns two rows need to match exactly in order to be defined as a repeat. Must include data_column. Default to 'all', meaning matching all columns in 'number' slot of digitdata.
break_out_grouping	A list of arrays, or defaulted to NA. Only effective if break_out is not NA. Each the names of the elements in the list is the break_out name Each array contains the values belonging to that break_out If it is remain as NA as default, while break_out is not NA, then break_out_grouping will default to every individual item in break_out will be in a separate group.
category_grouping	A list of arrays, or defaulted to NA. Only effective if category is not NA. Each the names of the elements in the list is the category name Each array contains the values belonging to that category If it is remain as NA as default, while category is not NA, then category_grouping will default to every individual item in category will be in a separate group.
rounding_patterns_to_omit	The patterns to be counted as rounding digits to skip. Defaulted to NA. An array of characters such as c('0','00','000','5','50','500', '75', '25'). n_zeros_pattern might be helpful for generating strings of 0s.
plot	TRUE or FALSE or 'Save': If TRUE, display the plots and return them. If 'Save', return the plots but suppress display. If FALSE, no plot is produced. Default to TRUE.
remove_all_category_visualize	TRUE or FALSE: If TRUE, remove visualization of 'All Category' dataset for plots

Value

• A table of p-values for repeat test by sector on each category

• Plots for each break out element with variables as categories specified by category_grouping if plot = TRUE or 'Save'

• If NaN is in returned table, it means that there are no occurances of the data of the sector in that category –> 0/0 in percentage

Examples

sector_test(digitdata, break_out='column', category='sector', category_instance_analyzing='X')
sector_test(digitdata, break_out='A', category='B', category_instance_analyzing='X', data_column='Y', rounding_patterns_to_omit='00')

jlederluis/digitanalysis documentation built on Nov. 5, 2023, 11:46 a.m.