Package 'rdss'

Title: Companion Datasets and Functions for Research Design in the Social Sciences
Description: Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. 'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book.
Authors: Graeme Blair [aut, cre] , Alexander Coppock [aut] , Macartan Humphreys [aut]
Maintainer: Graeme Blair <[email protected]>
License: MIT + file LICENSE
Version: 1.0.12
Built: 2024-10-25 08:20:49 UTC
Source: https://github.com/cranhaven/cranhaven.r-universe.dev

Help Index

Add parentheses around standard error estimates

Description

Add parentheses around standard error estimates

Usage

add_parens(x, digits = 3)

Arguments

x

Numeric vector
digits

Number of digits to retain

Value

A character vector with enclosing parentheses

Examples

std.error <- c(0.12, 0.001, 1.2)
add_parens(std.error)

Best predictor function from causal_forest

Description

Best predictor function from causal_forest

Usage

best_predictor(data, covariate_names, cuts = 20)

Arguments

data

A data.frame of covariates
covariate_names

A character vector of covariates to assess
cuts

Either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which each covariate is to be cut.

Value

a data.frame of the best predictors

Replication data for Bonilla and Tillery (2020), American Political Science Review (obtained from Dataverse 10.7910/DVN/IUZDQI)

Description

Replication data for Bonilla and Tillery (2020), American Political Science Review (obtained from Dataverse 10.7910/DVN/IUZDQI)

Usage

bonilla_tillery

Format

A data.frame

Tidy helper function for causal_forest function

Description

Runs estimates estimation function from interference package and returns tidy data frame output

Usage

causal_forest_handler(data, covariate_names, share_train = 0.5, ...)

Arguments

data

A data.frame
covariate_names

Names of covariates
share_train

Share of units to be used for training
...

Options to causal_forest

Details

https://draft.declaredesign.org/complex-designs.html#discovery-using-causal-forests

See ?causal_forest for further details

Value

a data.frame of estimates

Examples

library(DeclareDesign)
library(ggplot2)

dat <- fabricate(
   N = 1000,
   A = rnorm(N),
   B = rnorm(N),
   Z = complete_rs(N),
   Y = A*Z + rnorm(N))

# note: remove num.threads = 1 to use more processors
estimates <- causal_forest_handler(data = dat, covariate_names = c("A", "B"), num.threads = 1)

ggplot(data = estimates, aes(A, pred)) + geom_point()

Replication data for David Clingingsmith, Asim Ijaz Khwaja, Michael Kremer (2020): Estimating the Impact of The Hajj: Religion and Tolerance in Islam's Global Gathering. The Quarterly Journal of Economics, Volume 124, Issue 3, August 2009, Pages 1133-1170

Description

Replication data for David Clingingsmith, Asim Ijaz Khwaja, Michael Kremer (2020): Estimating the Impact of The Hajj: Religion and Tolerance in Islam's Global Gathering. The Quarterly Journal of Economics, Volume 124, Issue 3, August 2009, Pages 1133-1170

Usage

clingingsmith_etal

Format

A data.frame

Conjoint experiment assignment handler: conducts complete random assignment of all attribute levels

Description

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Usage

conjoint_assignment(data, levels_list)

Arguments

data

A data.frame
levels_list

List of conjoint levels to assign

Value

a data.frame with random assignment added

Conjoint experiment inquiries handler

Description

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Usage

conjoint_inquiries(data, levels_list, utility_fn)

Arguments

data

A data.frame
levels_list

List of conjoint levels
utility_fn

a function that takes data and returns an additional column called U, which represents the utility of the choice

Value

a data.frame of estimand values

Conjoint experiment assignment handler: conducts complete random assignment of all attribute levels

Description

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Usage

conjoint_measurement(data, utility_fn)

Arguments

data

A data.frame
utility_fn

a function that takes data and returns an additional column called U, which represents the utility of the choice

Value

a data.frame

Access color palette used in the book "Research Design: Declare, Diagnose, Redesign" (Blair, Coppock, Humphreys)

Description

Based on Karthik Ram's wesanderson package (https://github.com/karthik/wesanderson)

Usage

dd_palette(name, n)

Arguments

name

Color palette name (character)
n

Number of colors

Details

Available color palettes:

color_palette = c("#72B4F3", "#F38672", "#C6227F")

grey_palette = c("#72B4F3", "#F38672", "#C6227F", gray(0.8))

dd_dark_blue = "#3564ED"

dd_light_blue = "#72B4F3"

dd_orange = "#F38672"

dd_purple = "#7E43B6"

dd_gray = gray(0.2)

dd_pink = "#C6227F"

dd_light_gray = gray(0.8)

dd_dark_blue_alpha = "#3564EDA0"

dd_light_blue_alpha = "#72B4F3A0"

Value

character vector of colors

Tidy helper function for did_multiplegt

Description

Runs did_multiplegt estimation function and returns tidy data frame output

Usage

did_multiplegt_tidy(data, ...)

Arguments

data

a data.frame
...

options passed to did_multiplegt

Details

See https://book.declaredesign.org/observational-causal.html#difference-in-differences

Value

a data.frame of estimates

Tidy helper function for estimator_AS function

Description

Runs estimates estimation function from interference package and returns tidy data frame output

Usage

estimator_AS_tidy(data, permutatation_matrix, adj_matrix)

Arguments

data

a data.frame
permutatation_matrix

a permutation matrix of random assignments
adj_matrix

an adjacency matrix

Details

The estimator_AS_tidy function requires the 'interference' package, which is not yet available on CRAN.

To use this function:

  1. install the developer version of interference via remotes::install_github('szonszein/interference') and

  2. install the developer version of rdss via remotes::install_github('DeclareDesign/rdss@remotes')

See https://book.declaredesign.org/experimental-causal.html#experiments-over-networks

Value

a data.frame of estimates

Shapefile of Fairfax County, Virginia, voting precincts

Description

An sf object containing the boundaries of voting precincts for Fairfax County, Virginia as well as precinct ID, name, district, polling place name, address, city, zip code, area, length, and geometry (polygons)

Usage

fairfax

Format

An sf object with 236 rows and 10 variables:

Replication data for Foos, John, Muller, and Cunningham (2021), Journal of Politics (derived from from Dataverse 10.7910/DVN/NDPXND)

Description

Replication data for Foos, John, Muller, and Cunningham (2021), Journal of Politics (derived from from Dataverse 10.7910/DVN/NDPXND)

Usage

foos_etal

Format

A data.frame

Round and pad a number to a specific decimal place

Description

Round and pad a number to a specific decimal place

Usage

format_num(x, digits = 3)

Arguments

x

Numeric vector
digits

Number of digits to retain

Value

a character vector of formatted numbers

Examples

std.error <- c(0.12, 0.001, 1.2)
format_num(std.error)

Helper function to obtain the observed exposure for the Aronow and Samii estimator

Description

See https://book.declaredesign.org/experimental-causal.html#experiments-over-networks

Usage

get_exposure_AS(obs_exposure)

Arguments

obs_exposure

A numeric vector

Value

a data.frame of observed exposure to a treatment created using the interference package

Download a replication file from the dataverse archive for Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign

Description

See https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HYVPO5 for further details and the code used to create these files.

Usage

get_rdss_file(name, verbose = TRUE)

Arguments

name

quoted name of the file on the dataverse archive
verbose

print declaration code if requesting a declaration

Details

The available names include:

Design declaration objects:

declaration_9.5
declaration_2.1
declaration_2.2
declaration_4.1
declaration_5.1
declaration_7.1
declaration_9.1
declaration_9.2
declaration_9.3
declaration_9.4
declaration_9.6
declaration_9.7
declaration_10.1
declaration_10.2
declaration_10.3
declaration_10.4
declaration_10a
declaration_11.1
declaration_11.2
declaration_11.3
declaration_11.4
declaration_11.5
declaration_12.1a
declaration_12.1b
declaration_12.1c
declaration_12.1d
declaration_13.1
declaration_13.2
declaration_15.1
declaration_15.2
declaration_15.3a
declaration_15.3b
declaration_15.3c
declaration_15.4
declaration_15.5
declaration_15.6
declaration_16.1a
declaration_16.1b
declaration_16.2
declaration_16.3
declaration_16.4
declaration_16.5
declaration_16.6
declaration_17.1
declaration_17.2
declaration_17.3
declaration_17.4
declaration_17.5
declaration_17.6_a
declaration_17.6_b
declaration_18.1
declaration_18.2
declaration_18.3
declaration_18.4
declaration_18.5
declaration_18.6
declaration_18.7
declaration_18.8
declaration_18.9a
declaration_18.9b
declaration_18.9c
declaration_18.10
declaration_18.11
declaration_18.12
declaration_18.13
declaration_19.1
declaration_19.2
declaration_19.3
declaration_19.4
declaration_23.1a
declaration_23.1b
declaration_23.1c
declaration_23.1d

Diagnosis objects:

diagnosis_2.1
diagnosis_4.1
diagnosis_9.1
diagnosis_9.2
diagnosis_9.3
diagnosis_9.4
diagnosis_9.5
diagnosis_9.6
diagnosis_9.7
simulation_10.1
diagnosis_10.1
diagnosis_10.2
diagnosis_10.3
diagnosis_10.4
diagnosis_10.5
diagnosis_10a
diagnosis_11.1
diagnosis_11.2
diagnosis_11.3
diagnosis_11.4
diagnosis_11.5
diagnosis_12.1
diagnosis_12.2
diagnosis_13.1
diagnosis_15.1
diagnosis_15.2
diagnosis_15.3
diagnosis_15.4
diagnosis_15.5
diagnosis_16.1
diagnosis_16.2
diagnosis_16.3
diagnosis_16.4
diagnosis_16.5
diagnosis_17.1
diagnosis_17.2
diagnosis_17.3
diagnosis_17.4
diagnosis_17.5
diagnosis_18.1
diagnosis_18.10_encouragment
diagnosis_18.10_placebo
diagnosis_18.11
diagnosis_18.12
diagnosis_18.13
diagnosis_18.2
diagnosis_18.3
diagnosis_18.4
diagnosis_18.5
diagnosis_18.6
diagnosis_18.7
diagnosis_18.8
diagnosis_18.9
diagnosis_19.1
diagnosis_19.2
diagnosis_19.3
diagnosis_19.4
diagnosis_19a
diagnosis_21a
diagnosis_21b
diagnosis_23.1
diagnosis_23a

Value

an r object

Examples

# Requires internet access
if(curl::has_internet()) {
  diagnosis_2.1 <- get_rdss_file("diagnosis_2.1")
  diagnosis_2.1
}

Add alpha transparency to a color defined in hexadecimal

Description

Add alpha transparency to a color defined in hexadecimal

Usage

hex_add_alpha(col, alpha)

Arguments

col

Original color code in hex
alpha

Level of alpha transparency to add

Value

color codes with alpha added

Voter file sample for Los Angeles County

Description

A dataset containing the party registration, age, census tract number, and voter turnout in 2012 for 1,000 randomly-sampled registered voters in Los Angeles County, California.

Usage

la_voter_file

Format

A data frame with 1000 rows and 4 variables:

party

political party registration

age

age of voter in years

census_tract

US Census tract number

voted_2012

voter turnout in 2012 election

Source

California Secretary of State.

Generate lags in grouped data

Description

See https://book.declaredesign.org/observational-causal.html#difference-in-differences

Usage

lag_by_group(x, groups, n = 1, order_by, default = NA)

Arguments

x

Vector of values
groups

Grouping variable
n

Positive integer of length 1, giving the number of positions to lead or lag by
order_by

Ordering variable withing group (e.g., time)
default

Value used for non-existent rows. Defaults to NA.

Value

vector of lagged values

Data used in student exercises for RDSS based on LAPOP survey of Brazil in 2018

Description

These data were resampled with replacement from LAPOP data (to 10,000 rows) for a subset of variables. These data cannot be used for scientific inferences, and are only useful for teaching purposes. ID numbers were scrambled so that individuals and municipalities cannot easily be identified.

Usage

lapop_brazil

Format

A data.frame

Details

Download the original data from https://www.vanderbilt.edu/lapop/raw-data.php

See https://www.vanderbilt.edu/lapop/core-surveys.php for survey questionnaire

Format confidence intervals for nice printing

Description

Format confidence intervals for nice printing

Usage

make_interval_entry(conf.low, conf.high, digits = 2)

Arguments

conf.low

a numeric vector of lower bounds
conf.high

a numeric vector of upper bounds
digits

number of digits to retain

Value

a character vector of intervals

Examples

conf.low <- c(-0.1652, 0.00304, -6.352)
conf.high <- c(0.3052, 0.00696, -1.648)

make_interval_entry(conf.low, conf.high)

Format estimates and standard errors for nice printing

Description

Format estimates and standard errors for nice printing

Usage

make_se_entry(estimate, std.error, digits = 2)

Arguments

estimate

a numeric vector of parameter estimates
std.error

a numeric vector of standard error estimates
digits

number of digits to retain

Value

a character vector of formatted estimates and standard errors

Examples

estimate <- c(0.07, 0.005, -4)
std.error <- c(0.12, 0.001, 1.2)

make_se_entry(estimate, std.error)

Post stratification estimator helper

Description

Calculates predicted values from a multilevel regression and the post-stratified state-level estimates

Usage

post_stratification_helper(model_fit, data, group, weights)

Arguments

model_fit

a model fit object from, e.g., glmer or lm_robust
data

a data.frame
group

unquoted name of the group variable to construct estimates for
weights

unquoted name of post-stratification weights variable

Details

Please see https://book.declaredesign.org/observational-descriptive.html#multi-level-regression-and-poststratification

Value

data.frame of post-stratified group-level estimates

Process tracing estimator

Description

Draw conclusions from a model given a query, data, and process tracing strategies

Usage

process_tracing_estimator(causal_model, query, data, strategies)

Arguments

causal_model

a model generated by CausalQueries
query

a causal query of interest
data

a single row dataset with data on nodes in the model
strategies

a vector describing sets of nodes to be examined e.g. c("X", "X-Y")

Details

See https://book.declaredesign.org/observational-causal.html#process-tracing

Value

a data.frame of estimates

Examples

# Simple example showing ambiguity in attribution
process_tracing_estimator(
  causal_model = CausalQueries::make_model("X -> Y"),
  query = "Y[X=1] > Y[X=0]",
  data = data.frame(X=1, Y = 1),
  strategies = "X-Y")

# Example where M=1 acts as a hoop test
process_tracing_estimator(
  causal_model = CausalQueries::make_model("X -> M -> Y") |>
   CausalQueries::set_restrictions("Y[M=1] < Y[M=0]") |>
   CausalQueries::set_restrictions("M[X=1] < M[X=0]"),
  query = "Y[X=1] > Y[X=0]",
  data = data.frame(X=1, Y = 1, M = 0),
  strategies = c("Y", "X-Y", "X-M-Y"))

Helper function for using rdrobust as a model in declare_estimator

Description

Helper function for using rdrobust as a model in declare_estimator

Usage

rdrobust_helper(data, y, x, subset = NULL, ...)

Arguments

data

a data.frame
y

unquoted name of the outcome variable
x

unquoted name of the running variable
subset

an optional vector specifying a subset of observations to be used in the fitting process
...

Other arguments to rdrobust

Value

rdrobust model fit object

rdss package

Description

Companion datasets and functions for the book "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" (book.declaredesign.org)

Author(s)

Maintainer: Graeme Blair [email protected] (ORCID)

Authors:

Helper function for rma function in metafor package

Description

See https://book.declaredesign.org/complex-designs.html#meta-analysis

Usage

rma_helper(data, yi, sei, method = "REML", ...)

Arguments

data

a data.frame
yi

unquoted variable name of estimates used in meta-analysis
sei

unquoted variable name of standard errors used in meta-analysis
method

character string to specify whether a fixed- or a random/mixed-effects model should be fitted. A fixed-effects model (with or without moderators) is fitted when using method = "FE". Random/mixed-effects models are fitted by setting method equal to one of the following: "DL", "HE", "SJ", "ML", "REML", "EB", "HS", "HSk", or "GENQ". Default is "REML".
...

Further options to be passed to rma

Details

See ?rma for further details

Value

a data.frame of estimates

Extract mu and tau parameters from rma model fit

Description

See https://book.declaredesign.org/complex-designs.html#meta-analysis

Usage

rma_mu_tau(fit)

Arguments

fit

Fit object from the rma function in the metafor package

Value

a data.frame of estimates

ggplot Theme used in the book "Research Design: Declare, Diagnose, Redesign" (Blair, Coppock, Humphreys)

Description

ggplot Theme used in the book "Research Design: Declare, Diagnose, Redesign" (Blair, Coppock, Humphreys)

Usage

theme_dd()

Value

ggplot theme

Tidy results from a stanreg regresion and exponentiate the estimated coefficient

Description

Note no standard errors or other summary statistics are provided

Note no standard errors or other summary statistics are provided

Usage

tidy_stan(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

tidy_stan(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x

A stanreg fit from stan_glm
conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.
conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. Defaults to FALSE. Note that standard errors are not included when exponentiate = TRUE.
...

Other arguments to broom.mixed::tidy

Details

See https://book.declaredesign.org/choosing-an-answer-strategy.html#bayesian-formalizations

See https://book.declaredesign.org/choosing-an-answer-strategy.html#bayesian-formalizations

Value

data.frame of results

data.frame of results

Tidy estimates from the amce estimator

Description

Runs amce estimation function and returns tidy data frame output

Usage

## S3 method for class 'amce'
tidy(x, alpha = 0.05, ...)

Arguments

x

an amce fit object from cjoint::amce
alpha

Confidence level
...

Extra arguments to pass to tidy

Details

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Value

a data.frame of estimates

Examples

library(cjoint)

data(immigrationconjoint)
data(immigrationdesign)

# Run AMCE estimator using all attributes in the design
results <- amce(Chosen_Immigrant ~  Gender + Education + `Language Skills` +
                  `Country of Origin` + Job + `Job Experience` + `Job Plans` +
                  `Reason for Application` + `Prior Entry`, data = immigrationconjoint,
                cluster = TRUE, respondent.id = "CaseID", design = immigrationdesign)

# Print summary
# tidy(results)

Tidy helper function for rdrobust function

Description

Runs rdrobust estimation function and returns tidy data frame output

Usage

## S3 method for class 'rdrobust'
tidy(x, ...)

Arguments

x

Model fit object from rdrobust
...

Other arguments (not used)

Details

See https://book.declaredesign.org/observational-causal.html#regression-discontinuity-designs

Value

a data.frame of estimates