Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Shorten District by Plan vector

Description

If x is repeated for each district, it returns a plan level value. Otherwise it returns x.

Usage

by_plan(x, ndists)

Arguments

Value

x or plan level subset of x

Examples

by_plan(letters)
by_plan(rep(letters, each = 2))

Calculate Boyce Clark Ratio

Description

Calculate Boyce Clark Ratio

Usage

comp_bc(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Boyce, R., & Clark, W. 1964. The Concept of Shape in Geography. Geographical Review, 54(4), 561-572.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_bc(plans = nh$r_2020, shp = nh)

# Or many plans:

# slower, beware!
comp_bc(plans = nh_m[, 3:5], shp = nh)

Calculate Box Reock Compactness

Description

Box reock is the ratio of the area of the district by the area of the minimum bounding box (of any rotation). Scores are bounded between 0 and 1, where 1 is most compact.

Usage

comp_box_reock(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

#' data(nh)
data(nh_m)
# For a single plan:
comp_box_reock(plans = nh$r_2020, shp = nh)

# Or many plans:

# slower, beware!
comp_box_reock(plans = nh_m[, 3:5], shp = nh)

Calculate Convex Hull Compactness

Description

Calculate Convex Hull Compactness

Usage

comp_ch(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_ch(plans = nh$r_2020, shp = nh)

# Or many plans:
comp_ch(plans = nh_m[, 3:5], shp = nh)

Calculate Edges Removed Compactness

Description

Calculate Edges Removed Compactness

Usage

comp_edges_rem(plans, shp, adj)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Matthew P. Dube and Jesse Tyler Clark. 2016. Beyond the circle: Measuring district compactness using graph theory. In Annual Meeting of the Northeastern Political Science Association

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_edges_rem(plans = nh$r_2020, shp = nh, nh$adj)

# Or many plans:
comp_edges_rem(plans = nh_m[, 3:5], shp = nh, nh$adj)

Calculate Fryer Holden Compactness

Description

Calculate Fryer Holden Compactness

Usage

comp_fh(plans, shp, total_pop, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Fryer R, Holden R. 2011. Measuring the Compactness of Political Districting Plans. Journal of Law and Economics.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_fh(plans = nh$r_2020, shp = nh, total_pop = pop)

# Or many plans:
comp_fh(plans = nh_m[, 3:5], shp = nh, pop)

Calculate Fraction Kept Compactness

Description

Calculate Fraction Kept Compactness

Usage

comp_frac_kept(plans, shp, adj)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Matthew P. Dube and Jesse Tyler Clark. 2016. Beyond the circle: Measuring district compactness using graph theory. In Annual Meeting of the Northeastern Political Science Association

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_frac_kept(plans = nh$r_2020, shp = nh, nh$adj)

# Or many plans:
comp_frac_kept(plans = nh_m[, 3:5], shp = nh, nh$adj)

Calculate Log Spanning Tree Compactness

Description

Calculate Log Spanning Tree Compactness

Usage

comp_log_st(plans, shp, counties = NULL, adj)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Cory McCartan and Kosuke Imai. 2020. Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_log_st(plans = nh$r_2020, shp = nh, counties = county, adj = nh$adj)

# Or many plans:
comp_log_st(plans = nh_m[, 3:5], shp = nh, counties = county, adj = nh$adj)

Calculate Length Width Compactness

Description

Calculate Length Width Compactness

Usage

comp_lw(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Harris, Curtis C. 1964. “A scientific method of districting”. Behavioral Science 3(9), 219–225.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_lw(plans = nh$r_2020, shp = nh)

# Or many plans:

# slower, beware!
comp_lw(plans = nh_m[, 3:5], shp = nh)

Calculate Polsby Popper Compactness

Description

Calculate Polsby Popper Compactness

Usage

comp_polsby(
  plans,
  shp,
  use_Rcpp,
  perim_path,
  perim_df,
  epsg = 3857,
  ncores = 1
)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Cox, E. 1927. A Method of Assigning Numerical and Percentage Values to the Degree of Roundness of Sand Grains. Journal of Paleontology, 1(3), 179-183.

Polsby, Daniel D., and Robert D. Popper. 1991. “The Third Criterion: Compactness as a procedural safeguard against partisan gerrymandering.” Yale Law & Policy Review 9 (2): 301–353.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_polsby(plans = nh$r_2020, shp = nh)

# Or many plans:
comp_polsby(plans = nh_m[, 3:5], shp = nh)

Calculate Reock Compactness

Description

Calculate Reock Compactness

Usage

comp_reock(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Reock, E. 1961. A Note: Measuring Compactness as a Requirement of Legislative Apportionment. Midwest Journal of Political Science, 5(1), 70-74.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_reock(plans = nh$r_2020, shp = nh)

# Or many plans:
comp_reock(plans = nh_m[, 3:5], shp = nh)

Calculate Schwartzberg Compactness

Description

Calculate Schwartzberg Compactness

Usage

comp_schwartz(
  plans,
  shp,
  use_Rcpp,
  perim_path,
  perim_df,
  epsg = 3857,
  ncores = 1
)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Schwartzberg, Joseph E. 1966. Reapportionment, Gerrymanders, and the Notion of Compactness. Minnesota Law Review. 1701.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_schwartz(plans = nh$r_2020, shp = nh)

# Or many plans:
comp_schwartz(plans = nh_m[, 3:5], shp = nh)

Calculate Skew Compactness

Description

Skew is defined as the ratio of the radii of the largest inscribed circle with the smallest bounding circle. Scores are bounded between 0 and 1, where 1 is most compact.

Usage

comp_skew(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

S.N. Schumm. 1963. Sinuosity of alluvial rivers on the Great Plains. Bulletin of the Geological Society of America, 74. 1089-1100.

Examples

data(nh)
data(nh_m)
# For a single plan:
comp_skew(plans = nh$r_2020, shp = nh)

# Or many plans:

# slower, beware!
comp_skew(plans = nh_m[, 3:5], shp = nh)

Calculate X Symmetry Compactness

Description

X symmetry is the overlapping area of a shape and its projection over the x-axis.

Usage

comp_x_sym(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Aaron Kaufman, Gary King, and Mayya Komisarchik. 2021. How to Measure Legislative District Compactness If You Only Know it When You See It. American Journal of Political Science. 65, 3. Pp. 533-550.

Examples

#' data(nh)
data(nh_m)
# For a single plan:
comp_x_sym(plans = nh$r_2020, shp = nh)

# Or many plans:

# slower, beware!
comp_x_sym(plans = nh_m[, 3:5], shp = nh)

Calculate Y Symmetry Compactness

Description

Y symmetry is the overlapping area of a shape and its projection over the y-axis.

Usage

comp_y_sym(plans, shp, epsg = 3857, ncores = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Aaron Kaufman, Gary King, and Mayya Komisarchik. 2021. How to Measure Legislative District Compactness If You Only Know it When You See It. American Journal of Political Science. 65, 3. Pp. 533-550.

Examples

#' data(nh)
data(nh_m)
# For a single plan:
comp_y_sym(plans = nh$r_2020, shp = nh)

# Or many plans:

# slower, beware!
comp_y_sym(plans = nh_m[, 3:5], shp = nh)

Compute Talismanic Redistricting Competitiveness Metric

Description

Compute Talismanic Redistricting Competitiveness Metric

Usage

compet_talisman(plans, shp, rvote, dvote, alpha = 1, beta = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Wendy K. Tam Cho and Yan Y. Liu Toward a Talismanic Redistricting Tool. Election Law Journal. 15, 4. Pp. 351-366.

Examples

data(nh)
data(nh_m)
# For a single plan:
compet_talisman(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
compet_talisman(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Euclidean Distances

Description

Calculate Euclidean Distances

Usage

dist_euc(plans, ncores = 1)

Arguments

Value

matrix of plan distances

Examples

data(nh)
data(nh_m)
# For a single plan (distance is trivial, 0):
dist_euc(plans = nh$r_2020)

# Or many plans:
dist_euc(plans = nh_m[, 3:5])

Calculate Hamming Distances

Description

Calculate Hamming Distances

Usage

dist_ham(plans, ncores = 1)

Arguments

Value

matrix of plan distances

Examples

data(nh)
data(nh_m)
# For a single plan (distance is trivial, 0):
dist_ham(plans = nh$r_2020)

# Or many plans:
dist_ham(plans = nh_m[, 3:5])

Calculate Variation of Information Distances

Description

Calculate Variation of Information Distances

Usage

dist_info(plans, shp, total_pop, ncores = 1)

Arguments

Value

matrix of plan distances

Examples

data(nh)
data(nh_m)
# For a single plan (distance is trivial, 0):
dist_info(plans = nh$r_2020, shp = nh, total_pop = pop)

# Or many plans:
dist_info(plans = nh_m[, 3:5], shp = nh, total_pop = pop)

Calculate Manhattan Distances

Description

Calculate Manhattan Distances

Usage

dist_man(plans, ncores = 1)

Arguments

Value

matrix of plan distances

Examples

data(nh)
data(nh_m)
# For a single plan (distance is trivial, 0):
dist_man(plans = nh$r_2020)

# Or many plans:
dist_man(plans = nh_m[, 3:5])

Count Incumbent Pairings

Description

Count the number of incumbents paired with at least one other incumbent.

Usage

inc_pairs(plans, shp, inc)

Arguments

Value

vector of number of incumbents paired

Examples

data(nh)
data(nh_m)
# Use incumbent data:
fake_inc <- rep(FALSE, nrow(nh))
fake_inc[3:4] <- TRUE

# For a single plan:
inc_pairs(plans = nh$r_2020, shp = nh, inc = fake_inc)

# Or many plans:
inc_pairs(plans = nh_m[, 3:5], shp = nh, inc = fake_inc)

Return Functions Matching a Prefix

Description

This package uses prefixes for each function that correspond to the type of measure. This function returns the functions

Usage

list_fn(prefix)

Arguments

Value

character vector of functions

Examples

list_fn('part_')

New Hampshire Election and Demographic Data

Description

This data set contains demographic, election, and geographic information for the 326 voting tabulation districts in New Hampshire in 2020.

Usage

data("nh")

Format

A tibble with 326 rows and 45 columns

• GEOID20: 2020 VTD GEOID

• state: state name

• county: county name

• vtd: VTD portion of GEOID

• pop: total population

• pop_hisp: Hispanic population

• pop_white: White, not Hispanic population

• pop_black: Black, not Hispanic population

• pop_aian: American Indian and Alaska Native, not Hispanic population

• pop_asian: Asian, not Hispanic population

• pop_nhpi: Native Hawaiian and Pacific Islander, not Hispanic population

• pop_other: other race, not Hispanic population

• pop_two: multi-race, not Hispanic population

• vap: total voting-age population

• vap_hisp: Hispanic voting-age population

• vap_white: White, not Hispanic voting-age population

• vap_black: Black, not Hispanic voting-age population

• vap_aian: American Indian and Alaska Native, not Hispanic voting-age population

• vap_asian: Asian, not Hispanic voting-age population

• vap_nhpi: Native Hawaiian and Pacific Islander, not Hispanic voting-age population

• vap_other: other race, not Hispanic voting-age population

• vap_two: multi-race, not Hispanic voting-age population

• pre_16_rep_tru: Votes for Republican president 2016

• pre_16_dem_cli: Votes for Democratic president 2016

• uss_16_rep_ayo: Votes for Republican senate 2016

• uss_16_dem_has: Votes for Democratic senate 2016

• gov_16_rep_sun: Votes for Republican governor 2016

• gov_16_dem_van: Votes for Democratic governor 2016

• gov_18_rep_sun: Votes for Republican governor 2018

• gov_18_dem_kel: Votes for Democratic governor 2018

• pre_20_dem_bid: Votes for Democratic president 2020

• pre_20_rep_tru: Votes for Republican president 2020

• uss_20_dem_sha: Votes for Democratic senate 2020

• uss_20_rep_mes: Votes for Republican senate 2020

• gov_20_dem_fel: Votes for Democratic governor 2020

• gov_20_rep_sun: Votes for Republican governor 2020

• arv_16: Average Republican vote 2016

• adv_16: Average Democratic vote 2016

• arv_18: Average Republican vote 2018

• adv_18: Average Democratic vote 2018

• arv_20: Average Republican vote 2020

• adv_20: Average Democratic vote 2020

• nrv: Normal Republican vote

• ndv: Normal Democratic vote

• geometry: sf geometry, simplified for size using rmapshaper

• r_2020: Republican proposed plan for 2020 Congressional districts

• d_2020: Democratic proposed plan for 2020 Congressional districts

• adj: zero-indexed adjacency graph

References

Voting and Election Science Team, 2020, "2020 Precinct-Level Election Results", https://doi.org/10.7910/DVN/K7760H, Harvard Dataverse, V23

Voting and Election Science Team, 2018, "2016 Precinct-Level Election Results", https://doi.org/10.7910/DVN/NH5S2I, Harvard Dataverse, V71

Voting and Election Science Team, 2019, "2018 Precinct-Level Election Results", https://doi.org/10.7910/DVN/UBKYRU, Harvard Dataverse, V48

Kenny & McCartan (2021, Aug. 10). ALARM Project: 2020 Redistricting Data Files. Retrieved from https://github.com/alarm-redist/census-2020/

Examples

data(nh)

Redistricting Plans for New Hampshire as matrix

Description

This data set contains two reference plans (d_2020 and r_2020) and 50 simulated plans for New Hampshire, based on 2020 demographics, simulated at a population tolerance of 0.05%.

Usage

data("nh_m")

Format

A matrix with 52 columns and 326 rows where each column is a plan

Examples

data(nh_m)

New Hampshire Election and Demographic Data as a redist_map

Description

This data set contains demographic, election, and geographic information for the 326 voting tabulation districts in New Hampshire in 2020.

Usage

data("nh_map")

Format

A redist_map with 326 rows and 45 columns

• GEOID20: 2020 VTD GEOID

• state: state name

• county: county name

• vtd: VTD portion of GEOID

• pop: total population

• pop_hisp: Hispanic population

• pop_white: White, not Hispanic population

• pop_black: Black, not Hispanic population

• pop_aian: American Indian and Alaska Native, not Hispanic population

• pop_asian: Asian, not Hispanic population

• pop_nhpi: Native Hawaiian and Pacific Islander, not Hispanic population

• pop_other: other race, not Hispanic population

• pop_two: multi-race, not Hispanic population

• vap: total voting-age population

• vap_hisp: Hispanic voting-age population

• vap_white: White, not Hispanic voting-age population

• vap_black: Black, not Hispanic voting-age population

• vap_aian: American Indian and Alaska Native, not Hispanic voting-age population

• vap_asian: Asian, not Hispanic voting-age population

• vap_nhpi: Native Hawaiian and Pacific Islander, not Hispanic voting-age population

• vap_other: other race, not Hispanic voting-age population

• vap_two: multi-race, not Hispanic voting-age population

• pre_16_rep_tru: Votes for Republican president 2016

• pre_16_dem_cli: Votes for Democratic president 2016

• uss_16_rep_ayo: Votes for Republican senate 2016

• uss_16_dem_has: Votes for Democratic senate 2016

• gov_16_rep_sun: Votes for Republican governor 2016

• gov_16_dem_van: Votes for Democratic governor 2016

• gov_18_rep_sun: Votes for Republican governor 2018

• gov_18_dem_kel: Votes for Democratic governor 2018

• pre_20_dem_bid: Votes for Democratic president 2020

• pre_20_rep_tru: Votes for Republican president 2020

• uss_20_dem_sha: Votes for Democratic senate 2020

• uss_20_rep_mes: Votes for Republican senate 2020

• gov_20_dem_fel: Votes for Democratic governor 2020

• gov_20_rep_sun: Votes for Republican governor 2020

• arv_16: Average Republican vote 2016

• adv_16: Average Democratic vote 2016

• arv_18: Average Republican vote 2018

• adv_18: Average Democratic vote 2018

• arv_20: Average Republican vote 2020

• adv_20: Average Democratic vote 2020

• nrv: Normal Republican vote

• ndv: Normal Democratic vote

• r_2020: Republican proposed plan for 2020 Congressional districts

• d_2020: Democratic proposed plan for 2020 Congressional districts

• adj: zero-indexed adjacency graph

• geometry: sf geometry, simplified for size using rmapshaper

References

Voting and Election Science Team, 2020, "2020 Precinct-Level Election Results", https://doi.org/10.7910/DVN/K7760H, Harvard Dataverse, V23

Voting and Election Science Team, 2018, "2016 Precinct-Level Election Results", https://doi.org/10.7910/DVN/NH5S2I, Harvard Dataverse, V71

Voting and Election Science Team, 2019, "2018 Precinct-Level Election Results", https://doi.org/10.7910/DVN/UBKYRU, Harvard Dataverse, V48

Kenny & McCartan (2021, Aug. 10). ALARM Project: 2020 Redistricting Data Files. Retrieved from https://github.com/alarm-redist/census-2020/

Examples

data(nh_map)

Redistricting Plans for New Hampshire as redist_plans

Description

This data set contains two reference plans (d_2020 and r_2020) and 50 simulated plans for New Hampshire, based on 2020 demographics, simulated at a population tolerance of 0.05%.

Usage

data("nh_plans")

Format

A redist_plans with 104 rows and 3 columns

• draw: factor identifying the reference plans (d_2020 and r_2020) and 50 simulted plans

• district: district number (1 or 2)

• total_pop: total population in the district

Examples

data(nh_plans)

Calculate Partisan Bias

Description

Calculate Partisan Bias

Usage

part_bias(plans, shp, dvote, rvote, v = 0.5)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Jonathan N. Katz, Gary King, and Elizabeth Rosenblatt. 2020. Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies. American Political Science Review, 114, 1, Pp. 164-178.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_bias(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_bias(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Declination

Description

Calculate Declination

Usage

part_decl(plans, shp, dvote, rvote, normalize = TRUE, adjust = TRUE)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Gregory S. Warrington. 2018. "Quantifying Gerrymandering Using the Vote Distribution." Election Law Journal: Rules, Politics, and Policy. Pp. 39-57.http://doi.org/10.1089/elj.2017.0447

Examples

data(nh)
data(nh_m)
# For a single plan:
part_decl(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_decl(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Simplified Declination

Description

Calculate Simplified Declination

Usage

part_decl_simple(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Jonathan N. Katz, Gary King, and Elizabeth Rosenblatt. 2020. Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies. American Political Science Review, 114, 1, Pp. 164-178.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_decl_simple(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_decl_simple(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Dilution Asymmetry

Description

Calculate Dilution Asymmetry

Usage

part_dil_asym(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Sanford C. Gordon and Sidak Yntiso. 2024. Base Rate Neglect and the Diagnosis of Partisan Gerrymanders. Election Law Journal: Rules, Politics, and Policy. doi:10.1089/elj.2023.0005.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_dil_asym(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_dil_asym(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Democratic Seats

Description

Calculate Democratic Seats

Usage

part_dseats(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_dseats(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_dseats(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Democratic Vote Share

Description

Calculate Democratic Vote Share

Usage

part_dvs(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_dvs(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_dvs(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Efficiency Gap

Description

Calculate Efficiency Gap

Usage

part_egap(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Nicholas O. Stephanopoulos. 2015. Partisan Gerrymandering and the Efficiency Gap. The University of Chicago Law Review, 82, Pp. 831-900.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_egap(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_egap(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Efficiency Gap (Equal Population Assumption)

Description

Calculate Efficiency Gap (Equal Population Assumption)

Usage

part_egap_ep(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Nicholas O. Stephanopoulos. 2015. Partisan Gerrymandering and the Efficiency Gap. The University of Chicago Law Review, 82, Pp. 831-900.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_egap_ep(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_egap_ep(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Lopsided Wins

Description

Calculate Lopsided Wins

Usage

part_lop_wins(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Samuel S.-H. Wang. 2016. "Three Tests for Practical Evaluation of Partisan Gerrymandering." Stanford Law Review, 68, Pp. 1263 - 1321.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_lop_wins(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_lop_wins(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Mean Median Score

Description

Calculate Mean Median Score

Usage

part_mean_median(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Michael D. McDonald and Robin E. Best. 2015. Unfair Partisan Gerrymanders in Politics and Law: A Diagnostic Applied to Six Cases. Election Law Journal: Rules, Politics, and Policy. 14. 4. Pp. 312-330.

Examples

data(nh)
data(nh_m)
# zero for the two district case:
# For a single plan:
part_mean_median(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_mean_median(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Responsiveness

Description

Calculate Responsiveness

Usage

part_resp(plans, shp, dvote, rvote, v = 0.5, bandwidth = 0.01)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Jonathan N. Katz, Gary King, and Elizabeth Rosenblatt. 2020. Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies. American Political Science Review, 114, 1, Pp. 164-178.

Examples

data(nh)
data(nh_m)
# For a single plan:
part_resp(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_resp(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Ranked Marginal Deviation

Description

Calculate Ranked Marginal Deviation

Usage

part_rmd(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Gregory Herschlag, Han Sung Kang, Justin Luo, Christy Vaughn Graves, Sachet Bangia, Robert Ravier & Jonathan C. Mattingly (2020) Quantifying Gerrymandering in North Carolina, Statistics and Public Policy, 7:1, 30-38, DOI: 10.1080/2330443X.2020.1796400

Examples

data(nh)
data(nh_m)
# For a single plan:
part_rmd(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_rmd(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Smoothed Seat Count Deviation

Description

Calculate Smoothed Seat Count Deviation

Usage

part_sscd(plans, shp, dvote, rvote)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Gregory Herschlag, Han Sung Kang, Justin Luo, Christy Vaughn Graves, Sachet Bangia, Robert Ravier & Jonathan C. Mattingly (2020) Quantifying Gerrymandering in North Carolina, Statistics and Public Policy, 7:1, 30-38, DOI: 10.1080/2330443X.2020.1796400

Examples

data(nh)
data(nh_m)
# For a single plan:
part_sscd(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_sscd(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Calculate Tau Gap

Description

Calculate Tau Gap

Usage

part_tau_gap(plans, shp, dvote, rvote, tau = 1)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Gregory S. Warrington. 2018. "Quantifying Gerrymandering Using the Vote Distribution." Election Law Journal: Rules, Politics, and Policy. Pp. 39-57.http://doi.org/10.1089/elj.2017.0447

Examples

data(nh)
data(nh_m)
# For a single plan:
part_tau_gap(plans = nh$r_2020, shp = nh, rvote = nrv, dvote = ndv)

# Or many plans:
part_tau_gap(plans = nh_m[, 3:5], shp = nh, rvote = nrv, dvote = ndv)

Prep Polsby Popper Perimeter Tibble

Description

Replaces redist.prep.polsbypopper

Usage

prep_perims(shp, epsg = 3857, perim_path, ncores = 1)

Arguments

Value

tibble of perimeters and lengths

Examples

data(nh)
prep_perims(nh)

Compute Dissimilarity Index

Description

Compute Dissimilarity Index

Usage

seg_dissim(plans, shp, group_pop, total_pop)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

References

Douglas Massey and Nancy Denton. 1987. The Dimensions of Social Segregation. Social Forces.

Examples

data(nh)
data(nh_m)
# For a single plan:
seg_dissim(plans = nh$r_2020, shp = nh, group_pop = vap_hisp, total_pop = vap)

# Or many plans:
seg_dissim(plans = nh_m[, 3:5], shp = nh, group_pop = vap_hisp, total_pop = vap)

Compute Number of Administrative Units Split

Description

Compute Number of Administrative Units Split

Usage

splits_admin(plans, shp, admin)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)
# For a single plan:
splits_admin(plans = nh$r_2020, shp = nh, admin = county)

# Or many plans:
splits_admin(plans = nh_m[, 3:5], shp = nh, admin = county)

Count the Number of Splits in Each Administrative Unit

Description

Tallies the number of unique administrative unit-districts. An unsplit administrative unit will return an entry of 1, while each additional administrative unit-district adds 1.

Usage

splits_count(plans, shp, admin)

Arguments

Value

numeric matrix

Examples

data(nh)
data(nh_m)
# For a single plan:
splits_count(plans = nh$r_2020, shp = nh, admin = county)

# Or many plans:
splits_count(plans = nh_m[, 3:5], shp = nh, admin = county)

Fuzzy Splits by District (Experimental)

Description

Not all relevant geographies nest neatly into Census blocks, including communities of interest or neighborhood. For these cases, this provides a tabulation by district of the number of splits. As some geographies can be split multiple times, the sum of these splits may not reflect the total number of splits.

Usage

splits_district_fuzzy(plans, shp, nbr, thresh = 0.01, epsg)

Arguments

Details

Beware, this requires a nbr shape input and will be slower than checking splits in cases where administrative unit nests cleanly into the geographies represented by shp.

Value

numeric matrix

Examples

data(nh)
data(nh_m)

# toy example,
# suppose we care about the splits of the counties and they don't nest
nh_cty <- nh %>% dplyr::group_by(county) %>% dplyr::summarize()

# For a single plan:
splits_district_fuzzy(plans = nh$r_2020, shp = nh, nbr = nh_cty)

# Or many plans:
splits_district_fuzzy(plans = nh_m[, 3:5], shp = nh, nbr = nh_cty)

Compute Number of Administrative Units Split More than Once

Description

Compute Number of Administrative Units Split More than Once

Usage

splits_multi(plans, shp, admin)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)
# For a single plan:
splits_multi(plans = nh$r_2020, shp = nh, admin = county)

# Or many plans:
splits_multi(plans = nh_m[, 3:5], shp = nh, admin = county)

Compute Number of Sub-Administrative Units Split

Description

Compute Number of Sub-Administrative Units Split

Usage

splits_sub_admin(plans, shp, sub_admin)

Arguments

Value

A numeric vector. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)
# For a single plan:
splits_sub_admin(plans = nh$r_2020, shp = nh, sub_admin = county)

# Or many plans:
splits_sub_admin(plans = nh_m[, 3:5], shp = nh, sub_admin = county)

Count the Number of Splits in Each Sub-Administrative Unit

Description

Tallies the number of unique sub-administrative unit-districts. An unsplit administrative unit will return an entry of 1, while each additional sub-administrative unit-district adds 1.

Usage

splits_sub_count(plans, shp, sub_admin)

Arguments

Value

numeric matrix

Examples

data(nh)
data(nh_m)
# For a single plan:
splits_sub_count(plans = nh$r_2020, shp = nh, sub_admin = county)

# Or many plans:
splits_sub_count(plans = nh_m[, 3:5], shp = nh, sub_admin = county)

Count the Total Splits in Each Plan

Description

Counts the total number of administrative splits.

Usage

splits_total(plans, shp, admin)

Arguments

Value

numeric matrix

Examples

data(nh)
data(nh_m)
# For a single plan:
splits_total(plans = nh$r_2020, shp = nh, admin = county)

# Or many plans:
splits_total(plans = nh_m[, 3:5], shp = nh, admin = county)

Tally a Column by District

Description

Helper function to aggregate a vector by district. Can be used to calculate total population, group percentages, and more.

Usage

tally(plans, shp, x)

Arguments

Value

A numeric vector with the tallies. Can be shaped into a district-by-plan matrix.

Examples

data(nh)
data(nh_m)

tally(nh_m, nh, pop) # total population
tally(nh_m, nh, vap_hisp) / tally(nh_m, nh, vap) # HVAP