Package 'drhutools'

Title: Political Science Academic Research Gears
Description: Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, set up color-blind palette, and test for Type I error (false positives) in Qualitative Comparative Analysis (QCA) for crisp-set, multi-value, and fuzzy-set variants.
Authors: Yue Hu [aut, cre], Qian Qiu [ctb], Wen Deng [ctb], Bear Braumoeller [ctb] (QCAfalsePositive functions)
Maintainer: Yue Hu <[email protected]>
License: GPL (>= 3)
Version: 1.1.2
Built: 2026-06-25 00:10:48 UTC
Source: https://github.com/sammo3182/drhutools

Help Index

Determinants of the Arab Spring Uprising

Description

A dataset containing fuzzy-set membership scores for ten sets of state characteristics across 20 states in northern Africa and the Arab Peninsula at the time of the Arab Spring uprisings of 2010–12.

Usage

Arab.Spring

Format

A data frame with 20 rows and 10 variables, measured as fuzzy-set membership scores:

Gdppc

Per-capita GDP.

Gini

Economic inequality.

Unemp

Unemployment.

Urban

Degree of urbanization.

Youth

Size of youth bulge.

Mobile

Mobile phone usage.

Internet

Internet penetration.

Fuel

Fuel-dependence of economy.

Pol

Regime fragility.

Success

Social movement success.

Value

A data.frame object

Source

Hussain, Muzammil M., and Philip N. Howard. 2013. "What Best Explains Successful Protest Cascades? ICTs and the Fuzzy Causes of the Arab Spring." International Studies Review 15(1): 48–66.

Visualizing the experimental outcome with cumulative distribution functions.

Description

Packed ggplot2 function to compare the empirical cumulative distribution functions (ECDF) between the treatment and control groups in an experiment or quasi-experiment.

Arguments

data

A data.frame including two columns, one recording the outcome and the other recording the assignment. The assignment column must be named as group.
ks_test

A logical option to indicate whether to show the Kolmogorov-Smirnov test result in the bottom-right corner. The default value is FALSE.
point_size

An integer to indicate the size of the points at the largest difference. The default value is 3.
point_color

An character or function to indicate the color of the points at the largest difference. The default value is drhutools::gb_cols("red").
link_color

An character or function to indicate the color of the link at the largest difference. The default value is drhutools::gb_cols("red").

Value

A list of ggplot2 objects comparing the ECDFs between the control and treatment groups and identifying at most three largest differences.

Examples

data("PlantGrowth")

plot_plant <- cdplot(PlantGrowth, ks_test = TRUE)
plot_plant

A Simple Binomial Test for Type I Error in csQCA

Description

A Binomial test for crisp-set qualitative comparative analysis (csQCA), designed to calculate the probability of a false positive given the number of hypotheses implicitly tested and the number of confirming cases.

Usage

csQCAbinTest(freq.y, configs, total.configs, adj.method = "holm")

Arguments

freq.y

The frequency with which the dependent variable occurs in the sample (the number of 1s divided by the total number of cases).
configs

A list of configurations and the number of cases in which each configuration occurs.
total.configs

The total number of configurations used in the original csQCA analysis. This will generally equal the number of lines in the truth table used for Boolean minimization.
adj.method

The method used to calculate adjusted p-values (see stats::p.adjust() for details).

Value

An object containing the results of the Binomial test.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

test <- csQCAbinTest(freq.y=0.7, configs=list(aB=5, bCD=3, Ce=2),
   total.configs=20)
summary(test)

Folder hierarchy creator for academic research

Description

Folder hierarchy creator for academic research

Usage

folderSystem(
  overwrite = FALSE,
  language = NULL,
  format = NULL,
  bibliography = NULL,
  csl = NULL
)

Arguments

overwrite

Logical. Should existing template files in ⁠paper/⁠ be overwritten? Defaults to FALSE, leaving any files the user has already edited untouched.
language

Character or NULL. Manuscript language: "english" or "chinese". NULL (default) prompts interactively.
format

Character or NULL. Output format: "typst" or "word". NULL (default) prompts interactively.
bibliography

Character or NULL. Bibliography layout: "single" or "separate". NULL (default) prompts interactively (skipped, and forced to "single", when format = "word").
csl

Character or NULL. Citation style: "apsa" or "apa". NULL (default) prompts interactively.

Details

The function constructs a standardized folder hierarchy encompassing ⁠codes/⁠, ⁠data/⁠, ⁠output/⁠, ⁠paper/⁠, and ⁠document/⁠. These directories are designated for storing programming scripts, data sets, processed outputs, manuscript drafts (including related images and submission documents), and assorted materials, respectively. This organization facilitates easy retrieval of project components, ensuring that members of Dr. Hu's Amazing Team can efficiently navigate and manage shared resources. This structure is advocated for collaborative projects to maintain uniformity across team operations.

In addition, the function assembles a ready-to-write manuscript template under ⁠paper/⁠ from the choices below. When an argument is left NULL and the session is interactive, the function asks for the choice; otherwise it falls back to the first option.

  • language — "english" writes paper_en.qmd; "chinese" writes paper_cn.qmd (Chinese heading numbering, dual abstract, online appendix; modelled on the bundled organizedParticipation.qmd).

  • format — "typst" (PDF via Typst) or "word" (.docx).

  • bibliography — "single" produces one reference list via Quarto's citeproc; "separate" produces separate main-text and appendix lists via the Alexandria Typst package (Typst only; Word output is forced to "single").

  • csl — "apsa" (American Political Science Association) or "apa" (APA 7th edition).

Only the support files required by the chosen configuration are copied into ⁠paper/⁠ (the selected .csl, the matching .bib file(s), and the relevant Lua filters).

Value

Invisibly returns NULL. Called for its side effect of creating local folders, placeholder files, a default .gitignore, and the configured manuscript template under the project's working directory.

A Simple Permutation Test for Type I Error in fsQCA

Description

A permutation test for fuzzy-set qualitative comparative analysis (fsQCA), designed to calculate the probability of a false positive given the number of hypotheses implicitly tested and the distribution of the data.

Usage

fsQCApermTest(
  y,
  configs,
  total.configs,
  num.iter = 10000,
  my.seed = 123,
  adj.method = "holm"
)

Arguments

y

The outcome variable of interest.
configs

A list of configurations to be tested against y.
total.configs

The total number of configurations used in the original fsQCA analysis. This will generally equal the number of lines in the truth table used for Boolean minimization.
num.iter

The number of iterations to use for the permutation test. Larger numbers of iterations result in more precise p-values.
my.seed

The seed used to generate random numbers.
adj.method

The method used to calculate adjusted p-values (see stats::p.adjust() for details).

Value

An object containing the aggregate results of the permutation test as well as the individual permutations.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

intersect  <- pmin(social.revolutions$breakdown, social.revolutions$pop.ins)
intersect2 <- pmin(social.revolutions$breakdown, (1 - social.revolutions$pop.ins))
intersect3 <- pmin((1 - social.revolutions$breakdown), social.revolutions$pop.ins)
intersect4 <- pmin((1 - social.revolutions$breakdown), (1 - social.revolutions$pop.ins))

test <- fsQCApermTest(y = social.revolutions$soc.rev,
  configs = list(BI = intersect, Bi = intersect2,
                 bI = intersect3, bi = intersect4),
  total.configs = 4)
summary(test)
plot(test)

Function to extract colors as hex codes.

Description

Function to extract colors as hex codes.

Usage

gb_cols(...)

Arguments

...

A character vector indicating the names of colors. Options includes "gold", "black", "orange", "red", "light green", "green", "light blue", "blue", "light grey", "grey", and "dark grey".

Value

A character of hex codes.

Function to interpolate a gold-black color palette.

Description

Function to interpolate a gold-black color palette.

Usage

gb_pal(palette = "main", reverse = FALSE, ...)

Arguments

palette

A character vector indicating the name of palette in gb_palettes. Available palettes:

  • main: Gold and black colors.

  • tricol: Gold, black, and dark grey to create a gradual effect.

  • digitMixed: Five-pack colors specified for digital publications.

  • printMixed: Five-pack colors specified for printed publications.

  • full: A palette including all the colors gb_cols can call.

reverse

A logic vector indicating whether the palette should be reversed; the default is FALSE.
...

Additional arguments to pass to colorRampPalette()

Value

A function that takes an integer argument (the required number of colors) and returns a character vector of colors interpolating the given sequence.

Draw a standard map of China from a data frame

Description

goodmap() creates an interactive PNG (or animated GIF) map from a data frame. It supports two map types: point and polygon. Point maps plot markers from geographic coordinates; polygon maps shade administrative regions (province or city level) by the average of a value column. Setting animate = TRUE strings per-frame maps into a GIF.

Usage

goodmap(
  data_file,
  type = "point",
  level = NULL,
  animate = FALSE,
  animate_var = NULL,
  map_center = c(35.8617, 104.1954),
  zoom_level = 4,
  tile_source = "amap",
  coord = "WGS-84",
  color_type = "numeric",
  custom_colors = NULL,
  point_radius = 5,
  legend_opacity = 0.7,
  legend_name = NULL,
  width = 800,
  height = 900
)

Arguments

data_file

Data frame. For point maps it must include g_lat and g_lon columns. For polygon maps it must include prov (province level) or city (city level) columns holding the complete, official names rather than abbreviations. Use a tool such as regioncode to expand incomplete names beforehand.
type

A string, point (default) or polygon.
level

A string, province or city. Required when type = "polygon".
animate

Logical. Generate an animation across animate_var? Defaults to FALSE, which draws a single PNG from the whole data.
animate_var

A string naming the column to animate over. One frame is drawn per unique value. Required when animate = TRUE.
map_center

A length-2 numeric c(lat, lon) for the map centre. Default c(35.8617, 104.1954), roughly the centre of China.
zoom_level

Numeric initial zoom level. Default 4.
tile_source

A string, amap (Gaode/Amap Chinese basemap, default) or osm (OpenStreetMap). Amap tiles use the GCJ-02 coordinate system (see coord). Baidu is unsupported because its BD-09 projection is incompatible with the standard leaflet tile grid.
coord

A string giving the coordinate system of the point input (g_lat/g_lon): WGS-84 (default, GPS/international geocoders), GCJ-02 (Amap/Gaode/Tencent), or BD-09 (Baidu). Points are reprojected to match tile_source. Ignored for polygon maps, whose bundled boundaries are GCJ-02 encoded.
color_type

numeric (default) for continuous values or factor for discrete categories.
custom_colors

Optional vector of colours for the gradient. Default NULL uses the package's gold-black palette.
point_radius

Numeric marker radius for point maps. Default 5.
legend_opacity

Numeric legend opacity. Default 0.7.
legend_name

Legend title. Default Value.
width

Numeric image width. Default 800.
height

Numeric image height. Default 900.

Details

For point maps the marker colour and size follow the value_set column. For polygon maps each region is shaded by the mean of value_set for that city or province.

Value

The path to the rendered image, invisibly for animations. Called mainly for its side effect of displaying the map in the viewer.

Examples

## Not run: 
goodmap(
   toy_poly,
   type = "polygon",
   level = "province"
)

## End(Not run)

Leaflet Map Names Dataset

Description

A internal dataset providing city names and corresponding file names for mapping with Leaflet.

Usage

leafletcn.map.names

Format

A data frame with city names and file names.

Value

A data.frame object

A Simple Binomial Test for Type I Error in mvQCA

Description

A Binomial test for multi-value qualitative comparative analysis (mvQCA), designed to calculate the probability of a false positive given the number of hypotheses implicitly tested and the number of confirming cases.

Usage

mvQCAbinTest(freq.y, configs, total.configs, adj.method = "holm")

Arguments

freq.y

The frequency with which the dependent variable occurs in the sample (the number of 1s divided by the total number of cases).
configs

A list of configurations and the number of cases in which each configuration occurs.
total.configs

The total number of configurations used in the original mvQCA analysis. This will generally equal the number of lines in the truth table used for Boolean minimization.
adj.method

The method used to calculate adjusted p-values (see stats::p.adjust() for details).

Value

An object containing the results of the Binomial test.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

test <- mvQCAbinTest(freq.y=0.7, configs=list(aB=5, bCD=3, Ce=2),
   total.configs=20)
summary(test)

Calculate Vector of p-value Thresholds for Multiple Inference

Description

Internal function. Calculates the adjusted thresholds for multiple significance tests, assuming that the original threshold for a single test is p<0.05. Used by fsQCApermTest() to calculate confidence intervals.

Usage

p.threshold.adjust(total.configs, my.method)

Arguments

total.configs

The total number of hypotheses tested, or the number of configurations utilized by the Quine-McCluskey algorithm in fsQCA (including logical remainders, if they are used in the analysis).
my.method

The adjustment method used to calculate p-values (see stats::p.adjust() for details).

Value

Numeric vector giving adjusted p-value thresholds, from smallest to largest.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

p.threshold.adjust(10, "holm")

Plot Results of fsQCA Permutation Test

Description

Plots distributions of consistencies and counterexamples from permutation tests of fsQCA data, including confidence intervals adjusted to account for multiple inference. Also prints observed consistency values and number of counterexamples as black dots along the x-axis, for comparison.

Usage

## S3 method for class 'fsQCApt'
plot(x, y = x$config.names, statistic = "both", ...)

Arguments

x

Object returned by fsQCApermTest().
y

A vector of configurations to examine. Default behavior is to examine all configurations.
statistic

The statistic to examine (consistency, counterexamples, or both).
...

Additional parameters to pass on.

Value

Plots of distributions of consistencies, counterexamples, or both.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

intersect  <- pmin(social.revolutions$breakdown, social.revolutions$pop.ins)
intersect2 <- pmin(social.revolutions$breakdown, (1 - social.revolutions$pop.ins))
intersect3 <- pmin((1 - social.revolutions$breakdown), social.revolutions$pop.ins)
intersect4 <- pmin((1 - social.revolutions$breakdown), (1 - social.revolutions$pop.ins))

test <- fsQCApermTest(y = social.revolutions$soc.rev,
  configs = list(BI = intersect, Bi = intersect2,
                 bI = intersect3, bi = intersect4),
  total.configs = 4)
plot(test)
plot(test, "bi", statistic = "consistency")
plot(test, c("BI", "Bi"), statistic = "both")
plot(test, statistic = "consistency")
plot(test, "BI")

Fill scale constructors for gold & black colors

Description

Fill scale constructors for gold & black colors

Usage

scale_color_gb(palette = "main", discrete = TRUE, reverse = FALSE, ...)

Arguments

palette

A character vector indicating the name of palette in gb_palettes. Available palettes:

  • main: Gold and black colors.

  • tricol: Gold, black, and dark grey to create a gradual effect.

  • digitMixed: Five-pack colors specified for digital publications.

  • printMixed: Five-pack colors specified for printed publications.

  • full: A palette including all the colors gb_cols can call.

discrete

A logic vector indicating whether color aesthetic is discrete or not; the default is "main".
reverse

A logic vector indicating whether the palette should be reversed.
...

Additional arguments passed to discrete_scale() or scale_color_gradientn(), used respectively when discrete is TRUE or FALSE

Value

No return value, called for modifying the appearance of the plot.

Color scale constructors for gold & black colors

Description

Color scale constructors for gold & black colors

Usage

scale_fill_gb(palette = "main", discrete = TRUE, reverse = FALSE, ...)

Arguments

palette

A character vector indicating the name of palette in gb_palettes. Available palettes:

  • main: Gold and black colors.

  • tricol: Gold, black, and dark grey to create a gradual effect.

  • digitMixed: Five-pack colors specified for digital publications.

  • printMixed: Five-pack colors specified for printed publications.

  • full: A palette including all the colors gb_cols can call.

discrete

A logic vector indicating whether color aesthetic is discrete or not; the default is "main".
reverse

A logic vector indicating whether the palette should be reversed.
...

Additional arguments passed to discrete_scale() or scale_color_gradientn(), used respectively when discrete is TRUE or FALSE

Value

No return value, called for modifying the appearance of the plot.

Determinants of Social Revolutions

Description

A data frame containing hypothetical fuzzy-set membership scores for three sets of state characteristics across twenty cases.

Usage

social.revolutions

Format

A data frame with 20 rows and 3 variables, measured as degree of fuzzy-set membership:

soc.rev

Presence of social revolution.

breakdown

Degree of state breakdown.

pop.ins

Presence of popular insurrection.

Value

A data.frame object

Source

Ragin, Charles C. 2000. Fuzzy-Set Social Science. Chicago: University of Chicago Press, p. 220.

Summarize Binomial Tests for csQCA Data

Description

Displays number of confirming cases and raw and adjusted p-scores following Binomial test of csQCA data.

Usage

## S3 method for class 'csQCAbt'
summary(object, ...)

Arguments

object

Object returned by csQCAbinTest().
...

Additional parameters to pass on.

Value

Matrix of values for counterexamples and consistency.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

test <- csQCAbinTest(freq.y=0.7, configs=list(aB=5, bCD=3, Ce=2),
   total.configs=20)
summary(test)

Summarize Permutation Tests for fsQCA Data

Description

Displays observed values, confidence intervals, and raw and adjusted p-scores for both consistency and counterexamples following permutation test of fsQCA data.

Usage

## S3 method for class 'fsQCApt'
summary(object, ...)

Arguments

object

Object returned by fsQCApermTest().
...

Additional parameters to pass on.

Value

Two matrices of values for counterexamples and consistency.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

intersect  <- pmin(social.revolutions$breakdown, social.revolutions$pop.ins)
intersect2 <- pmin(social.revolutions$breakdown, (1 - social.revolutions$pop.ins))
intersect3 <- pmin((1 - social.revolutions$breakdown), social.revolutions$pop.ins)
intersect4 <- pmin((1 - social.revolutions$breakdown), (1 - social.revolutions$pop.ins))

test <- fsQCApermTest(y = social.revolutions$soc.rev,
  configs = list(BI = intersect, Bi = intersect2,
                 bI = intersect3, bi = intersect4),
  total.configs = 4)
summary(test)

Summarize Binomial Tests for mvQCA Data

Description

Displays number of confirming cases and raw and adjusted p-scores following Binomial test of mvQCA data.

Usage

## S3 method for class 'mvQCAbt'
summary(object, ...)

Arguments

object

Object returned by mvQCAbinTest().
...

Additional parameters to pass on.

Value

Matrix of values for counterexamples and consistency.

Note

Adopted from the archived CRAN package QCAfalsePositive by Bear Braumoeller. The original package has been removed from CRAN and is no longer maintained. It is included in drhutools for continued accessibility.

Examples

test <- mvQCAbinTest(freq.y=0.7, configs=list(aB=5, bCD=3, Ce=2),
   total.configs=20)
summary(test)

Toy Dataset for goodmap

Description

A sample dataset designed to illustrate the functionality of the goodmap function.

Usage

toy_poly

Format

A geographic data frame with 6 rows and 5 variables:

id

Optional data identifier.

city

Prefectural names, required for city-level map plotting.

prov

Provincial names, required for provincial-level map plotting.

animate_set

A numeric vector used to group data for animated plotting.

value_set

Values used to fill each polygon in the map.

Value

A data.frame object

Calculate psychological traits based on

Description

Calculate psychological traits based on

Usage

traits(survey)

Arguments

survey

Psychological survey data as a data.frame.

Details

The current version can calculate the TOSCA-3SC scores and Grit-O score.

Value

A data frame with trait scores for each observations

References

Duckworth, Angela L., Christopher Peterson, Michael D. Matthews, and Dennis R. Kelly. 2007. "Grit: Perseverance and Passion for Long-Term Goals." Journal of Personality and Social Psychology 92(6): 1087-1101. doi:10.1037/0022-3514.92.6.1087.

Tangney, June P. 1990. "Assessing Individual Differences in Proneness to Shame and Guilt: Development of the Self-Conscious Affect and Attribution Inventory." Journal of Personality and Social Psychology 59(1): 102-11. doi:10.1037/0022-3514.59.1.102.

Examples

column_names <- c("Q3|R3", "Q3|R4", "Q4|R3", "Q4|R4", "Q5|R5", "Q5|R6", "Q6|R3", "Q6|R4", "Q7|R3", 
"Q7|R4", "Q8|R5", "Q8|R6", "Q9|R5", "Q9|R6", "Q10|R5", "Q10|R6", "Q11|R5", "Q11|R6", "Q12|R3", 
"Q12|R4", "Q13|R3", "Q13|R4", "Q14|1", "Q15|1", "Q16|1", "Q17|1", "Q18|1", "Q19|1", "Q20|1", 
"Q21|1", "Q22|1", "Q23|1", "Q24|1", "Q25|1")

toy_data <- data.frame(matrix(sample(1:5, 10 * length(column_names), replace = TRUE), 
ncol = length(column_names)))

names(toy_data) <- column_names

traits(toy_data)