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: 1.1.9999
Built: 2026-06-08 05:39:48 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.

The goodmap function is designed to create interactive PNG Map or GIF Map from a provided data file. It supports two types of maps: point and polygon. The function can visualize data by either plotting points based on geographical coordinates or highlighting regions polygon based on their administrative boundaries (province or city level). Additionally, the function can generate animated that showcase the change of data.

Description

If the map type is point, the color and size of the points will be determined by the value_set column in the data file, which means the different value of each point. If the map type is polygon, the color of the polygons will be determined by the average value of the value_set column for each city or province in the data file.

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

Dataframe. When generate point map, data_file should include required columns such as g_lat and g_lon. When generate polygon map, data_file should include required columns such as prov or city. The prov columns must be complete, official names rather than any shortened form or abbreviation. If there is only incomplete names or geocodes in your data_file, we recommend you to use function regioncode as a one-step solution to these conversion from incomplete names. Ensure the file is formatted correctly with appropriate column headers.
type

A string specifying the type of map to generate. Options are pointfor point maps using g_lat and g_lon, or polygon for maps with administrative boundaries.
level

A string specifying the level of administrative boundaries for polygon maps. Acceptable values are province or city. This parameter is required if type is polygon.
animate

A logical value indicating whether to generate an animation from the maps. The default is FALSE. If animate is FALSE, the whole data will be generated as a PNG file. If animate is TRUE, an animation will be generated from all panel data, the animate_var must be assigned.
animate_var

A string specifying the variable to animate over. The default is NULL, the whole data will be generated as a PNG file. If an animation is needed, data_file should include required the animate_set column. animate_set includes a series of unique values that represents year or month, or other categorical variable. The frame number of the animation depends on the animate_set.
map_center

A numeric vector of length 2 specifying the latitude and longitude for the center of the map view. Default is c(35.8617, 104.1954), which is approximately the center of China.
zoom_level

A numeric value specifying the zoom level for the map. Default is 3.
tile_source

A string specifying the basemap tile provider. Options are amap for the Gaode/Amap Chinese basemap (default), or osm for OpenStreetMap. Amap offers fuller Chinese labelling and coverage; note its tiles use the GCJ-02 coordinate system (see coord). Baidu is not supported because its BD-09 projection is incompatible with the standard leaflet tile grid.
coord

A string giving the coordinate system of the point input data (g_lat/g_lon). Options are WGS-84 (default, GPS/international geocoders), GCJ-02 (Amap/Gaode/Tencent geocoders), or BD-09 (Baidu geocoder). Points are reprojected to match tile_source so markers align with the basemap. This argument is ignored for polygon maps, whose boundaries are bundled in GCJ-02 and align with the Amap basemap.
color_type

If the data is discrete, such as types or categories, choose factor. If the data is continuous, such as temperature or pressure, choose numeric. Default is numeric.
custom_colors

A vector of colors for customizing the color gradient. Default is NULL, which uses the predefined color palette.
point_radius

A numeric value specifying the radius for point markers on point maps. Default is 5.
legend_opacity

A numeric value specifying the opacity of the legend. Default is 0.7.
legend_name

The name of the legend. Default is Value.
width

A numeric value specifying the width of the map images. Default is 800.
height

A numeric value specifying the height of the map images. Default is 900.

Value

Image 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)