Package 'peacesciencer'

Description

add_archigos() allows you to add some information about leaders to dyad-year or state-year data. The function leans on an abbreviated version of the data, which also comes in this package.

Usage

add_archigos(data)

Arguments

Details

The function leans on attributes of the data that are provided by the create_dyadyear() or create_stateyear() function. Make sure that function (or data created by that function) appear at the top of the proverbial pipe.

Value

add_archigos() takes a dyad-year or state-year data frame and adds a few summary variables based off the leader-level data. These include whether there was a leader transition in the state-year (or first/second state in the dyad-year), whether there was an "irregular" leader transition, the number of leaders in the state-year, the unique leader ID for Jan. 1 of the year, and the unique leader ID for Dec. 31 of the year.

Author(s)

Steven V. Miller

References

Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. "Introducing Archigos: A Dataset of Political Leaders" Journal of Peace Research 46(2): 269–83.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_archigos()

create_stateyears() %>% add_archigos()

Description

add_atop_alliance() allows you to add Alliance Treaty Obligations and Provisions (ATOP) data to a (dyad-year, leader-dyad-year) data frame.

Usage

add_atop_alliance(data)

Arguments

Details

Data are from version 5.0 of ATOP.

This function will also work with leader-dyad-years, though users should be careful with leader-level applications of alliance data. Alliance data are primarily communicated yearly, making it possible—even likely—that at least one leader-dyad in a given year is credited with an alliance that was not active in the particular leader-dyad. The ATOP alliance data are not communicated with time measurements more granular than the year, at least for dyad-years. The alliance-level data provided by ATOP do have termination dates, but I am unaware how well these start and termination dates coincide with particular members joining after the fact or exiting early. The alliance phase data appear to communicate that "phases" are understood as beginning/ending when the underlying document is amended in such a way that it affects one of their variable codings, but this may or may not be because of a signatory joining after the fact or exiting early. More guidance will be useful going forward, but use these data for leader-level analyses with that in mind.

Value

add_atop_alliance() takes a (dyad-year, leader-dyad-year) data frame and adds information about the alliance pledge in that given dyad-year from the ATOP data. These include whether there was an alliance with a defense pledge, an offense pledge, neutrality pledge, non-aggression pledge, or pledge for consultation in time of crisis.

Author(s)

Steven V. Miller

References

Leeds, Brett Ashley, Jeffrey M. Ritter, Sara McLaughlin Mitchell, and Andrew G. Long. 2002. Alliance Treaty Obligations and Provisions, 1815-1944. International Interactions 28: 237-60.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_atop_alliance()

Description

add_capital_distance() allows you to add capital-to-capital distance to a (dyad-year, leader-year, leader-dyad-year, state-year) data frame. The capitals are coded in the cow_capitals and gw_capitals data frames, along with their latitudes and longitudes. The distance variable that emerges capdist is calculated using the "Vincenty" method (i.e. "as the crow flies") and is expressed in kilometers.

Usage

add_capital_distance(data)

Arguments

Details

The function leans on attributes of the data that are provided by one of the "create" functions in this package (e.g. create_dyadyear() or create_stateyear()).

Value

add_capital_distance() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds the capital-to-capital distance between the first state and the second state (in dyad-year data) or the minimum capital-to-capital distance for a given state in a given year. A minor note about this function: cases of capital transition are recorded in the underlying data but, in the conversion to capital-years (and eventual merging into a dyad-year data frame), the Jan. 1 capital is used for calculating distances.

Author(s)

Steven V. Miller

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>% add_capital_distance()

create_stateyears() %>% add_capital_distance()

Description

add_ccode_to_gw() allows you to match, as well as one can, Correlates of War system membership data with Gleditsch-Ward system data.

Usage

add_ccode_to_gw(data)

Arguments

Details

The data-raw directory on the project's Github contains more information about the underlying data that assists in merging in these codes.

The user will invariably need to be careful and ask why they want these data included. The issue here is that both have a different composition and the merging process will not (and cannot) be perfect. We can note that a case like Gran Colombia is not too difficult to handle (i.e. CoW does not have this entity and none of the splinter states conflict with CoW's coding). However, there is greater weirdness with a case like the unification of West Germany and East Germany. Herein, Correlates of War treats the unification as the reappearance of the original Germany whereas Gleditsch-Ward treat the unification as an incorporation of East Germany into West Germany. The script will not create state-year or dyad-year duplicates for the Gleditsch-Ward codes. The size of the original data remain unchanged. However, there will be some year duplicates for various Correlates of War codes (prominently Serbia and Yugoslavia in 2006). Use with care. You can also use the countrycode package. Whether you use this function or the countrycode package, do not do this kind of merging without assessing the output.

Value

add_ccode_to_gw() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame that already has Gleditsch-Ward state system codes and adds their corollary Correlates of War codes.

Author(s)

Steven V. Miller

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

create_dyadyears(system = "gw") %>% add_ccode_to_gw()

create_stateyears(system = 'gw') %>% add_ccode_to_gw()

Description

add_contiguity() allows you to add Correlates of War contiguity data to a dyad-year, leader-year, or leader-dyad-year, or state-year data frame.

Usage

add_contiguity(data)

Arguments

Details

The contiguity codes in the dyad-year data range from 0 to 5. 1 = direct land contiguity. 2 = separated by 12 miles of water or fewer (a la Stannis Baratheon). 3 = separated by 24 miles of water or fewer (but more than 12 miles). 4 = separated by 150 miles of water or fewer (but more than 24 miles). 5 = separated by 400 miles of water or fewer (but more than 150 miles).

Importantly, 0 are the dyads that are not contiguous at all in the CoW contiguity data. This is a conscious decision on my part as I do not think of the CoW's contiguity data as exactly ordinal. Cross-reference CoW's contiguity data with the minimum distance data in this exact package to see how some dyads that CoW codes as not contiguous are in fact very close to each other, sometimes even land-contiguous. For example, Zimbabwe and Namibia are separated by only about a few hundred feet of water at that peculiar intersection of the Zambezi River where the borders of Zambia, Botswana, Namibia, and Zimbabwe meet. There is no contiguity record for this in the CoW data. There are other cases where contiguity records are situationally missing (e.g. India-Bangladesh, and Bangladesh-Myanmar in 1971) or other cases where states are much closer than CoW's contiguity data imply (e.g. Pakistan and the Soviet Union were separated by under 30 kilometers of Afghani territory). The researcher is free to recode these 0s to be, say, 6s, but this is why peacesciencer does not do this.

For additional clarity, the "master records" produce duplicates for cases when the contiguity relationship changed in a given year. This function returns the minimum contiguity relationship observed in that given year. There should be no duplicates in the returned output.

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

Value

add_contiguity() takes a data frame and adds information about the contiguity relationship based on the "master records" for the Correlates of War direct contiguity data (v. 3.2). If the data are dyad-year (or leader dyad-year), the function returns the lowest contiguity type observed in the dyad-year (if contiguity is observed at all). If the data are state-year (or leader-year), the data return the total number of land and sea borders calculated from these master records.

Author(s)

Steven V. Miller

References

Stinnett, Douglas M., Jaroslav Tir, Philip Schafer, Paul F. Diehl, and Charles Gochman (2002). "The Correlates of War Project Direct Contiguity Data, Version 3." Conflict Management and Peace Science 19 (2):58-66.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_contiguity()

create_stateyears() %>% add_contiguity()

Description

add_cow_alliance() allowed you to add Correlates of War alliance data to a dyad-year data frame. However, this function is deprecated at the request of the data set's maintainer and any use of the Correlates of War's alliance data will have to be done manually. The function now returns a stop communicating this development.

Usage

add_cow_alliance(data)

Arguments

Details

Duplicates in the original directed dyad-year alliance data were pre-processed. Check cow_alliance in the package's data-raw directory on Github for more information.

This function will also work with leader-dyad-years, though users should be careful with leader-level applications of alliance data. Alliance data are primarily communicated yearly, making it possible—even likely—that at least one leader-dyad in a given year is credited with an alliance that was not active in the particular leader-dyad. The Correlates of War's alliance data are not communicated with time measurements more granular than the year. Apply these data to leader-level analyses with that in mind.

Value

add_cow_alliance() now returns a stop communicating the maintainer's request to reject all software that facilitates the use of the data in this fashion. add_cow_alliance() previously took a dyad-year data frame and added information about the alliance pledge in that given dyad-year. These include whether there was an alliance with a defense pledge, neutrality pledge, non-aggression pledge, or pledge for consultation in time of crisis (entente).

Author(s)

Steven V. Miller

References

Gibler, Douglas M. 2009. International Military Alliances, 1648-2008. Congressional Quarterly Press.

Examples

## Not run: 
# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_cow_alliance()

## End(Not run)

Description

add_cow_majors() allows you to add Correlates of War major power variables to a dyad-year, leader-year, leader dyad-year, or state-year data frame.

Usage

add_cow_majors(data)

Arguments

Details

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

Value

add_cow_majors() takes a data frame and adds information about major power status for the given state or dyad in that year. If the data are dyad-year (or leader dyad-year), the function returns two columns for whether the first state (i.e. ccode1) or the second state (i.e. ccode2) are major powers in the given year, according to the Correlates of War. 1 = is a major power. 0 = is not a major power. If the data are state-year (or leader-year), the functions returns just one column (cowmaj) for whether the state was a major power in a given state-year.

Author(s)

Steven V. Miller

References

Correlates of War Project. 2017. "State System Membership List, v2016." Online, https://correlatesofwar.org/data-sets/state-system-membership/

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_cow_majors()

Description

add_cow_mids() merges in CoW's MID data to a dyad-year data frame. The version of the CoW-MID data in this package is version 5.0.

Usage

add_cow_mids(data, keep)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. This merging process employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame.

The function will also return a message to the user about the case-exclusion rules that went into this process. Users who are interested in implementing their own case-exclusion rules should look up the "whittle" class of functions also provided in this package.

Value

add_cow_mids() takes a dyad-year data frame and adds dyad-year dispute information from the CoW-MID data.

Author(s)

Steven V. Miller

References

Palmer, Glenn, and Roseanne W. McManus and Vito D'Orazio and Michael R. Kenwick and Mikaela Karstens and Chase Bloch and Nick Dietrich and Kayla Kahn and Kellan Ritter and Michael J. Soules. 2021. "The MID5 Dataset, 2011–2014: Procedures, coding rules, and description" Conflict Management and Peace Science.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>% add_cow_mids()

# keep just the dispute number and Side A/B identifiers
cow_ddy %>% add_cow_mids(keep=c("dispnum","sidea1", "sidea2"))

Description

add_cow_trade() allows you to add Correlates of War trade data to your (dyad-year, leader-year, leader-dyad-year, state-year) data frame

Usage

add_cow_trade(data)

Arguments

Details

For the dyad-year (and leader-dyad-year) data, there must be some kind of information loss in order to work within the limited space available to this package. This package loads a truncated version of the data in which the trade values are rounded to three decimal points in order to greatly reduce the disk space for this package. I do not think this to be terribly problematic, though I admit I do not like it. If this is a problem for your research question, you may want to consider not using this function for dyad-year or leader-dyad-year data.

Be mindful that the data are fundamentally state-year or dyad-year and that extensions to leader-level data should be understood as approximations for leaders (leader-dyads) in a given state-year (dyad-year).

Value

add_cow_trade() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds information about the volume of trade in that given dyad-year or state-year. For the state-year (leader-year) data, these are minimally the sum of all imports and the sum of all exports. For dyad-year (leader-dyad-year) data, this function returns the value of imports in current million USD in the first country from the second country (and vice-versa) along with their "smooth" equivalents.

Author(s)

Steven V. Miller

References

Barbieri, Katherine, Omar M. G. Keshk, and Brian Pollins. 2009. "TRADING DATA: Evaluating our Assumptions and Coding Rules." Conflict Management and Peace Science. 26(5): 471-491.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
# The function below works, but depends on running `download_extdata()` beforehand.
# cow_ddy %>% add_cow_trade()

create_stateyears() %>% add_cow_trade()

Description

add_cow_wars() allows you to Correlates of War data to a dyad-year or state-year data frame

Usage

add_cow_wars(data, type, intratype = "all")

Arguments

Details

Intra-state war data are coerced into true state-year data by first selecting the duplicate state-years on unique onsets, then whichever war was the deadliest. The inter-state war data work functionally the same way.

On intra-state wars: the primary_state is used to identify the government principally fighting the domestic non-state actor over central control over local issues. Internationalized civil wars are included in the data, but not for outside actors that intervene on behalf of the government or rebel group.

Extra-state war functionality is not available right now as I try to figure out the demand for its use.

Value

add_cow_wars() takes a dyad-year or state-year data frame and returns information about wars from either the inter-state or intra-state war data set from the Correlates of War. The function works for state-year data when the user wants information about extra-state wars or intra-state wars. The function works for dyad-year data when the user wants information about inter-state wars.

Author(s)

Steven V. Miller

References

Dixon, Jeffrey, and Meredith Sarkees. 2016. A Guide to Intra-State Wars: An Examination of Civil Wars, 1816-2014. Thousand Oaks, CA: Sage.

Sarkees, Meredith Reid, and Frank Wheldon Wayman. 2010. Resort to War: A Data Guide to Inter-State, Extra-State, Intra-State, and Non-State Wars, 1816-2007. Washington DC: CQ Press.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

create_stateyears(system = "cow") %>%
add_cow_wars(type = "intra", intratype = "central control")

create_stateyears(system = "cow") %>%
add_cow_wars(type = "intra", intratype = "local issues")

cow_ddy %>% add_cow_wars(type = "inter")

Description

add_creg_fractionalization() allows you to add information about the fractionalization/polarization of a state's ethnic and religious groups to your data.

Usage

add_creg_fractionalization(data)

Arguments

Details

Please see the information for the underlying data creg, and the associated R script in the data-raw directory, to see how these data are generated.

The creg data have a few duplicates. When standardizing to true CoW codes, the duplicates concern Serbia/Yugoslavia in 1991 and 1992 as well as Russia/the Soviet Union in 1991. When standardizing to true Gleditsch-Ward codes, the duplicates concern Serbia/Yugoslavia in 1991 and Russia/Soviet Union in 1991. In those cases, the function does a group-by arrange for the more fractionalized/polarized estimate under the (reasonable, I think) assumption that these are estimates prior to the dissolution of those states. If this is problematic, feel free to consult the underlying data and merge those in manually.

The underlying data have both Gleditsch-Ward codes and Correlates of War codes. The merge it makes depends on what you declare as the "master" system at the top of the pipe (i.e. in create_dyadyears() or create_stateyears()). If, for example, you run create_stateyears(system="cow") and follow it with add_gwcode_to_cow(), the merge will be on the Correlates of War codes and not the Gleditsch-Ward codes. You can see the script mechanics to see how this is achieved.

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

Value

add_creg_fractionalization() takes a dyad-year, leader-year, leader-dyad-year, or state-data frame, whether the primary state identifiers are from the Correlates of War system or the Gleditsch-Ward system, and returns information about the fractionalization and polarization of the state(s) in a given year. The function returns four additional columns when the data are state-year and returns eight additional columns when the data are state-year (or leader-year). The columns returned are the fractionalization of ethnic groups, the polarization of ethnic groups, the fractionalization of religious groups, and the polarization of religious groups. When the data are dyad-year (or leader-dyad-year), the return doubles because it provides information for both states in the dyad.

Author(s)

Steven V. Miller

References

Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Wacziarg. 2003. "Fractionalization". Journal of Economic Growth 8: 155-194.

Montalvo, Jose G. and Marta Reynal-Querol. 2005. "Ethnic Polarization, Potential Conflict, and Civil Wars" American Economic Review 95(3): 796–816.

Nardulli, Peter F., Cara J. Wong, Ajay Singh, Buddy Petyon, and Joseph Bajjalieh. 2012. The Composition of Religious and Ethnic Groups (CREG) Project. Cline Center for Democracy.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_creg_fractionalization()

create_stateyears() %>% add_creg_fractionalization()

create_stateyears(system = "gw") %>% add_creg_fractionalization()

Description

add_democracy() allows you to add estimates of democracy to your data.

Usage

add_democracy(data)

Arguments

Details

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

A vignette on the package's website talks about how these data are here primarily to encourage you to maximize the number of observations in the analysis to follow. Xavier Marquez' QuickUDS estimates have the best coverage. If democracy is ultimately a control variable, or otherwise a variable not of huge concern for the analysis (i.e. the user has no particular stake on the best measurement of democracy or the best conceptualization and operationalization of "democracy"), please use Marquez' estimates instead of Polity or V-dem. If the user is doing an analysis of inter-state conflict, and across the standard post-1816 domain in conflict studies, definitely don't use the Polity data because the extent of its missingness is both large and unnecessary. Please read the vignette describing these issues here: http://svmiller.com/peacesciencer/articles/democracy.html

Value

add_democracy() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds information about the level of democracy for the state or two states in the dyad in a given year. If the data are dyad-year or leader-dyad-year, the function adds six total columns for the first state (i.e. ccode1 or gwcode1) and the second state (i.e. ccode2 or gwcode2) about the level of democracy measured by the Varieties of Democracy project (v2x_polyarchy), the Polity project (polity2), and Xavier Marquez' QuickUDS extensions/estimates. If the data are state-year or leader-year, the function returns three additional columns to the original data that contain that same information for a given state in a given year.

Author(s)

Steven V. Miller

References

Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Luhrmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Agnes Cornell, Lisa Gastaldi, Haakon Gjerlow, Valeriya Mechkova, Johannes von Romer, Aksel Sundtrom, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2020. "V-Dem Codebook v10" Varieties of Democracy (V-Dem) Project.

Marshall, Monty G., Ted Robert Gurr, and Keith Jaggers. 2017. "Polity IV Project: Political Regime Characteristics and Transitions, 1800-2017." Center for Systemic Peace.

Marquez, Xavier, "A Quick Method for Extending the Unified Democracy Scores" (March 23, 2016). doi:10.2139/ssrn.2753830

Pemstein, Daniel, Stephen Meserve, and James Melton. 2010. "Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type." Political Analysis 18(4): 426-449.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_democracy()

create_stateyears(system="gw") %>% add_democracy()
create_stateyears(system="cow") %>% add_democracy()

Description

add_fpsim() allows you to add a variety of dyadic foreign policy similarity measures to your (dyad-year, leader-dyad-year) data frame

Usage

add_fpsim(data, keep)

Arguments

Details

For the dyad-year (and leader-dyad-year) data, there must be some kind of information loss in order to reduce the disk space data like these command. In this case, all calculations are rounded to three decimal spots. I do not think this to be terribly problematic, though I admit I do not like it. If this is a problem for your research question (though I can't imagine it would be), you may want to consider not using this function for dyad-year or leader-dyad-year data.

Be mindful that the data are fundamentally dyad-year and that extensions to leader-level data should be understood as approximations for leaders-dyads in a given dyad-year.

The data this function uses are directed dyad-year and the merge is a left-join, making this function agnostic about whether your dyad-year (or leader-dyad-year) data are directed or non-directed.

Haege's (2011) article reads at first glance as agnostic about which of these particular measures you should consider a "preferred" or "default" measure of dyadic foreign policy similarity. Indeed, the 2011 publication in Political Analysis mostly drives the point home that S has important limitations and the multiple variants Haege calculates are not substitutable. This means a user interested in measuring dyadic foreign policy similarity might have to cycle through all of them to assess their varying effects whereas a user interested in this as just a control variable for the model can (probably) get by with picking just one and not belaboring the measure any further.

Suggested Defaults

An evaluation of the data, the article, and an email exchange with the author leads to the following points the user should consider. What follows is a rationale for why users should think of kappa as a default measure for dyadic foreign policy similarity, though why the "valued" equivalent for the alliance data is an inadvisable default. The example at the end of the document offers the operational "nudge" for what the user should want from this function.

• The choice of measure will in part depend on the temporal domain. If the user has just a post-WWII sample, the UN voting measures offer better coverage. We're all partial to the alliance data, though, because of its 19th century coverage.

• Haege implores the use of chance-corrected measures, like Cohen's (1960) kappa or Scott's (1955) pi. Of the two, Haege suggests kappa over pi. The rationale is the user would need to build in a very strong assumption that the baseline propensity of forming a tie in the dyad is the same for both members of the dyad to make Scott's (1955) pi as appropriate an estimate as Cohen's (1960) kappa even as both have the important chance correction.

• The choice of squared versus absolute distances is arbitrary. Users probably do not think about the differences, or know about the differences. S was usually calculated with absolute differences in software packages, though this was never usually belabored to the user. Comparability with S might be an argument in favor of absolute distance as a default, but keep in mind that squared distances are much more commonly used in most other types of distance and association metrics.

• The choice of binary or valued is also a design choice for the user to consider on the full merits, though the practice of valuing alliance ties on a quantitative scale builds in strong assumptions about the scale of alliance strength as presented in something like the Correlates of War or ATOP typology. S has traditionally done this by default, which is another reason its application in a lot of quantitative peace science research is suspect.

Value

add_fpsim() takes a (dyad-year, leader-dyad-year) data frame and adds information about the dyadic foreign policy similarity, based on several measures calculated and offered by Frank Haege.

Author(s)

Steven V. Miller

References

The Main Source of the Data

For any use of these data whatsoever (except for Tau-b), please cite Haege (2011). Data are version 2.0.

• Haege, Frank M. 2011. "Choice or Circumstance? Adjusting Measures of Foreign Policy Similarity for Chance Agreement." Political Analysis 19(3): 287-305.

Tau-b is calculated by me and not Haege, and no additional citation (beyond citing the package) is necessary.

Citations for the Particular Similarity Measure You Choose

Additional citations depend on what particular measure of similarity you're using, whether Kendall's (1938) Tau-b, Signorino and Ritter's (1999) S, Cohen's (1960) kappa and Scott's (1955) pi. Haege (2011) is part of a chorus arguing against the use of S, though S measures are included in these data if you elect to ignore the chorus and use this measure. Likewise, Tau-b is in here, though it is not a good measure of dyadic foreign policy similarity for reasons that Signorino and Ritter (1999) mention. Haege (2011) argues for a chance-corrected measure of dyadic foreign policy similarity, either Cohen's (1960) kappa or Scott's (1955) pi.

• Cohen, Jacob. 1960. "A Coefficient of Agreement for Nominal Scales." Educational and Psychological Measurement 20(1): 37-46.

• Kendall, M.G. 1938. "A New Measure of Rank Correlation." Biometrika 30(1/2): 81–93.

• Scott, William A. 1955. "Reliability of Content Analysis: The Case of Nominal Scale Coding." Public Opinion Quarterly 19(3): 321–5.

• Signorino, Curtis S. and Jeffrey M. Ritter. "Tau-b or Not Tau-B: Measuring the Similarity of Foreign Policy Positions." 43(1): 115–44.

Citations for the Underlying Data Informing the Similarity Measure

Haege (2011) also suggests you cite the underlying data informing the similarity measure, whether it is UN voting or alliances. In his case, he recommended a Voeten citation from 2013 and the alliance data proper. In the case of the alliances, I know Gibler's (2009) book is recommended even if the alliance data have since been updated (and reflected in this measure). In the UN voting data, my understanding is the 2017 paper in Journal of Conflict Resolution is also the preferred citation.

• Bailey, Michael A., Anton Strezhnev, and Erik Voeten. 2017. "Estimating the Dynamic State Preferences from United Nations Voting Data." Journal of Conflict Resolution 61(2): 430–456.

• Gibler, Douglas M. 2009. International Military Alliances, 1648-2008. Washington DC: CQ Press.

Examples

## Not run: 
# just call `library(tidyverse)` at the top of the your script.
library(magrittr)
# The function below works, but depends on
# running `download_extdata()` beforehand.
cow_ddy %>% add_fpsim()

# Select just the two kappa measures that are suggested defaults.
# `kappaba`: kappa for binary alliance data if you have pre-WWII data.
# `kappavv`: kappa for UN voting data if you just post-WWII data.
cow_ddy %>% add_fpsim(keep=c("kappaba", "kappavv"))


## End(Not run)

Description

add_gml_mids() merged in GML's MID data to a (dyad-year, leader-year, leader-dyad-year, state-year) data frame. The current version of the GML MID data is 2.2.1. However, this function is deprecated at the request of one of the data set's maintainers, insisting that the use of the data be done manually with design decisions made 100% at the discretion of the researcher. The function now returns a stop communicating this development.

Usage

add_gml_mids(data, keep, init = "sidea-all-joiners")

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. This merging process employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame.

The function will also return a message to the user about the case-exclusion rules that went into this process. Users who are interested in implementing their own case-exclusion rules should look up the "whittle" class of functions also provided in this package.

Determining "initiation" for state-year summaries of inter-state disputes is possible since there is an implied directionality of "initiation." In about half of all cases, this is straightforward. You can use the participant summaries and determine that if the dispute was bilateral and the dispute did not escalate beyond an attack, the state on Side A initiated the dispute. For multilateral MIDs, these conditions still hold at least for originators. However, there is considerable difficulty for cases where 1) participant-level summaries suggested actions at the level of clash or higher, 2) the participant was a joiner and not an originator. The effort required to flesh this out is enormous, and perhaps forthcoming in a future update.

add_gml_mids() allows you to make one of three judgment calls here (see the arguments section of the documentation). If it were my call to make, I would say you should probably use the option "sidea-all-joiners". My review of the MID data with Doug Gibler suggests most states that join a dispute are not roped into a conflict (i.e. targeted by some other state) after the first incident. They routinely initiate their entry into the conflict, which is what this concept of "initiation" is supposed to capture in the literature. There are no doubt cases where some third state is brought into the dispute by the actions of some other state even as the original MID coding rules place a high barrier on coding that type of dispute entry. However, the time required to individually assess whether a state initiated their entry into a MID under something other than the simplest of cases (e.g. bilateral cases where the highest participant action fell short of a clash) would be too time-consuming. It would require an audit of almost half of all participant-level summaries in the data. In a forthcoming publication, Gibler and Miller offer excellent coverage here with a new data set on militarized events. However, this would include only confrontations after World War II.

Value

add_gml_mids() now returns a stop communicating one of the maintainer's requests to reject all software that facilitates the use of the data in this fashion. Before, it took a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds dispute information from the GML MID data. If the data are dyad-year, the return is a laundry list of information about onsets, ongoing conflicts, and assorted participant- and dispute-level summaries. If the data are leader-dyad-year, these are carefully matched to leaders as well. If the data are state-year or leader-year, the function returns information about ongoing disputes (and onsets) and whether there were any ongoing disputes (and onsets) the state (or leader) initiated.

Author(s)

Steven V. Miller

References

Gibler, Douglas M., Steven V. Miller, and Erin K. Little. 2016. “An Analysis of the Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.

Examples

## Not run: 
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>% add_gml_mids()

# keep just the dispute number and Side A/B identifiers
cow_ddy %>% add_gml_mids(keep=c("dispnum","sidea1", "sidea2"))

## End(Not run)

Description

add_gwcode_to_cow() allows you to match, as well as one can, Gleditsch-Ward system membership data with Correlates of War state system membership data.

Usage

add_gwcode_to_cow(data)

Arguments

Details

The data-raw directory on the project's Github contains more information about the underlying data that assists in merging in these codes.

The user will invariably need to be careful and ask why they want these data included. The issue here is that both have a different composition and the merging process will not (and cannot) be perfect. We can note that a case like Serbia/Yugoslavia is not too difficult to handle (since "Serbia" never overlaps with "Yugoslavia" in the Gleditsch-Ward data and Correlates of War understands Serbia as the predecessor state, dominant state, and successor state to Yugoslavia). However, there is greater weirdness with a case like Yemen/Yemen Arab Republic. The script will not create state-year or dyad-year duplicates for the Correlates of War codes. The size of the original data remain unchanged. However, there will be some year duplicates for various Gleditsch-Ward codes (e.g. Yemen, again). Use with care. You can also use the countrycode package. Whether you use this function or the countrycode package, do not do this kind of merging without assessing the output.

Value

add_gwcode_to_cow() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame that already has Correlates of War state system codes and adds their corollary Gleditsch-Ward codes.

Author(s)

Steven V. Miller

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_gwcode_to_cow()

create_stateyears() %>% add_gwcode_to_cow()

Description

add_igos() allows you to add information from the Correlates of War International Governmental Organizations data to dyad-year or state-year data, matching on Correlates of War system codes.

Usage

add_igos(data)

Arguments

Details

The function leans on attributes of the data that are provided by the create_dyadyear() or create_stateyear() function. Make sure that function (or data created by that function) appear at the top of the proverbial pipe.

Value

add_igos() takes a dyad-year data frame or state-year data frame and adds information available from the Correlates of War International Governmental Organizations data. If the data are dyad-year, the function returns the original data with just one additional column for the total number of mutual IGOs for which both members of the dyad are full members. If the data are state-year, the function returns the original data with four additional columns. These are the number of IGOs for which the state is a full member, the number of IGOs for which the state is an associate member, the number of IGOs for which the state is an observer, and the number of IGOs for which the state is involved in any way (i.e. the sum of the other three columns).

Author(s)

Steven V. Miller

References

Pevehouse, Jon C.W., Timothy Nordstron, Roseanne W McManus, and Anne Spencer Jamison. 2020. "Tracking Organizations in the World: The Correlates of War IGO Version 3.0 datasets." Journal of Peace Research 57(3): 492-503.

Wallace, Michael, and J. David Singer. 1970. "International Governmental Organization in the Global System, 1815-1964." International Organization 24: 239-87.

Examples

# just call library(tidyverse) at the top of the pipe
library(magrittr)

cow_ddy %>% add_igos()

create_stateyears() %>% add_igos()

Description

add_lead() allows you to add some data recorded in the LEAD data to your leader-year or leader-dyad-year data.

Usage

add_lead(data, keep)

Arguments

Value

add_lead() takes a leader-year or leader-dyad-year data frame and adds some data recorded in the LEAD data to it. For leader-dyad-year data, suffices of "1" and "2" are added to the data to indicate attributes of the first leader (obsid1) or the second leader (obsid2), respectively.

Author(s)

Steven V. Miller

References

Ellis, Carli Mortenson, Michael C. Horowitz, and Allan C. Stam. 2015. "Introducing the LEAD Data Set." International Interactions 41(4): 718–741.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

create_leaderyears() %>% add_lead()

create_leaderyears() %>% add_lead(keep = c("yrsexper"))

Description

add_lwuf() allows you to add estimates of leader willingness to use force to leader-year data or leader-dyad-year data.

Usage

add_lwuf(data, keep)

Arguments

Details

See lwuf for more information, but I'll copy-paste it here too.

The letter published by Carter and Smith (2020) contains more information as to what these thetas refer. The "M1" theta is a variation of the standard Rasch model from the boilerplate information in the LEAD data. The authors consider this to be "theoretically relevant" or "risk-related" as these all refer to conflict or risk-taking. The "M2" theta expands on "M1" by including political orientation and psychological characteristics. "M3" and "M4" expand on "M1" and "M2" by considering all 36 variables in the LEAD data.

The authors construct and include all these measures, though their analyses suggest "M2" is the best-performing measure. You should probably consider using theta2_mean as your default estimate of leader willingness to use force in leader-year analyses.

Value

add_lwuf() takes a leader-year or leader-dyad-year data frame and adds estimates of leader willingness to use force, as generated by Carter and Smith (2020).

Author(s)

Steven V. Miller

References

Carter, Jeff and Charles E. Smith, Jr. 2020. "A Framework for Measuring Leaders' Willingness to Use Force." American Political Science Review 114(4): 1352–1358.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

create_leaderyears() %>% add_lwuf()

Description

add_minimum_distance() allows you to add the minimum distance (in kilometers) to a (dyad-year, leader-year, leader-dyad-year, state-year) data frame. These estimates are recorded in the cow_mindist and gw_mindist data that come with this package. The data are current as of the end of 2019.

Usage

add_minimum_distance(data)

Arguments

Details

The function leans on attributes of the data that are provided by one of the "create" functions in this package (e.g. create_dyadyear() or create_stateyear()).

Value

add_minimum_distance() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds the minimum distance between the first state and the second state (in dyad-year data) or the minimum minimum (sic) distance for a given state in a given year.

Author(s)

Steven V. Miller

References

Schvitz, Guy, Luc Girardin, Seraina Ruegger, Nils B. Weidmann, Lars-Erik Cederman, and Kristian Skrede Gleditsch. 2022. "Mapping The International System, 1886-2017: The CShapes 2.0 Dataset." Journal of Conflict Resolution. 66(1): 144-161.

Weidmann, Nils B. and Kristian Skrede Gleditsch. 2010. "Mapping and Measuring Country Shapes: The cshapes Package." The R Journal 2(1): 18-24.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>% add_minimum_distance()

create_dyadyears(system = "gw") %>% add_minimum_distance()

create_stateyears(system ="gw") %>% add_minimum_distance()

Description

add_nmc() allows you to add the Correlates of War National Material Capabilities data to your data.

Usage

add_nmc(data)

Arguments

Details

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

Value

add_nmc() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds information about the national material capabilities for the state or two states in the dyad in a given year. If the data are dyad-year (or leader-dyad-year), the function adds 12 total columns for the first state (i.e. ccode1) and the second state (i.e. ccode2) for all estimates of national military capabilities provided by the Correlates of War project. If the data are state-year (or leader-year), the function returns six additional columns to the original data that contain that same information for a given state in a given year.

Author(s)

Steven V. Miller

References

Singer, J. David, Stuart Bremer, and John Stuckey. (1972). "Capability Distribution, Uncertainty, and Major Power War, 1820-1965." in Bruce Russett (ed) Peace, War, and Numbers, Beverly Hills: Sage, 19-48.

Singer, J. David. 1987. "Reconstructing the Correlates of War Dataset on Material Capabilities of States, 1816-1985." International Interactions 14(1): 115-32.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_nmc()

create_stateyears() %>% add_nmc()

Description

add_peace_years() calculates peace years for your ongoing conflicts. The function works for both dyad-year and state-year data generated in peacesciencer. As of the forthcoming v. 0.7.0, add_peace_years() will be deprecated for the more generic and versatile add_spells(). Users are free to continue with the function, though I recommend it only for more balanced panels (like state-year or dyad-year), and less for imbalanced panels (like leader-years, or leader-dyad-years). As the change in name implies, add_spells() will have greater flexibility with both cross-sectional units and time.

Usage

add_peace_years(data, pad = FALSE)

Arguments

Details

The function internally uses sbtscs() from stevemisc. In the interest of full disclosure, sbtscs() leans heavily on btscs() from DAMisc. I optimized some code for performance.

Importantly, the underlying function (sbtscs() in stevemisc, by way of btscs() in DAMisc) has important performance issues if you're trying to run it when your event data are sandwiched by observations without any event data. Here's what I mean. Assume you got the full Gleditsch-Ward state-year data from 1816 to 2020 and then added the UCDP armed conflict data to it. If you want the peace-years for this, the function will fail because every year from 1816 to 1945 (along with 2020, as of writing) have no event data. You can force the function to "not fail" by setting pad = TRUE as an argument, but it's not clear this is advisable for this reason. Assume you wanted event data in UCDP for just the extrasystemic onsets. The data start in 1946 and, in 1946, the United Kingdom, Netherlands, and France had extrasystemic conflicts. For all years before 1946, the events are imputed as 1 for those countries that had 1s in the first year of observation and everyone else is NA and implicitly assumed to be a zero. For those NAs, the function runs a sequence resulting in some wonky spells in 1946 that are not implied by (the absence of) the data. In fact, none of those are implied by the absence of data before 1946.

The function works just fine if you truncate your temporal domain to reflect the nature of your event data. Basically, if you want to use this function more generally, filter your dyad-year or state-year data to make sure there are no years without any event data recorded (e.g. why would you have a CoW-MID analyses of dyad-years with observations before 1816?). This is less a problem when years with all-NAs succeed (and do not precede) the event data. For example, the UCDP conflict data run from 1946 to 2019 (as of writing). Having 2020 observations in there won't compromise the function output when pad = TRUE is included as an argument.

Finally, add_peace_years() will only calculate the peace years and will leave the temporal dependence adjustment to the taste of the researcher. Importantly, I do not recommend manually creating splines or square/cube terms because it creates more problems in adjusting for temporal dependence in model predictions. In a regression formula in R, you can specify the Carter and Signorino (2010) approach as ... + gmlmidspell + I(gmlmidspell^2) + I(gmlmidspell^3) (assuming you ran add_peace_years() on a dyad-year data frame including the Gibler-Miller-Little conflict data). The Beck et al. cubic splines approach is ... + splines::bs(gmlmidspell, 4). This function includes the spell and three splines (hence the 4 in the command). Either approach makes for easier model predictions, given R's functionality.

Value

add_peace_years() takes a dyad-year or state-year data frame and adds peace years for ongoing conflicts. Dyadic conflict data supported include the Correlates of War (CoW) Militarized Interstate Dispute (MID) data set and the Gibler-Miller-Little (GML) corrections to CoW-MID. State-level conflict data supported in this function include the UCDP armed conflict data and the CoW intra-state war data.

Author(s)

Steven V. Miller

References

Armstrong, Dave. 2016. “DAMisc: Dave Armstrong's Miscellaneous Functions.” R package version 1.4-3.

Beck, Nathaniel, Jonathan N. Katz, and Richard Tucker. 1998. "Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable." American Journal of Political Science 42(4): 1260–1288.

Carter, David B. and Curtis S. Signorino. 2010. "Back to the Future: Modeling Time Dependence in Binary Data." Political Analysis 18(3): 271–292.

Miller, Steven V. 2017. “Quickly Create Peace Years for BTSCS Models with sbtscs in stevemisc.” http://svmiller.com/blog/2017/06/quickly-create-peace-years-for-btscs-models-with-stevemisc/

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>%
add_gml_mids(keep = NULL) %>%
add_cow_mids(keep = NULL) %>%
add_contiguity() %>%
add_cow_majors() %>%
filter_prd()  %>%
add_peace_years()

Description

add_rugged_terrain() allows you to add information, however crude, about the "ruggedness" of a state's terrain to your (dyad-year, leader-year, leader-dyad-year, state-year) data.

Usage

add_rugged_terrain(data)

Arguments

Details

Please see the information for the underlying data rugged, and the associated R script in the data-raw directory, to see how these data are generated. Importantly, these data are time-agnostic and move slowly. We're talking about geography here. Both data sets benchmark around 1999-2000 and it's a leap of faith to use these data for comparisons across the entirety of the Correlates of War or Gleditsch-Ward system membership. Every use of data of these types have been either cross-sectional snapshots or for making state-to-state comparisons after World War II (think of your prominent civil war studies here). Be mindful about what you expect to get from these data.

The underlying data have both Gleditsch-Ward codes and Correlates of War codes. The merge it makes depends on what you declare as the "master" system at the top of the pipe (e.g.. in create_dyadyears() or create_stateyears()). If, for example, you run create_stateyears(system="cow") and follow it with add_gwcode_to_cow(), the merge will be on the Correlates of War codes and not the Gleditsch-Ward codes. You can see the script mechanics to see how this is achieved.

Value

add_rugged_terrain() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame, whether the primary state identifiers are from the Correlates of War system or the Gleditsch-Ward system, and returns information about the "ruggedness" of the state's terrain. The two indicators returned are the "terrain ruggedness index" calculated by Nunn and Puga (2012) and a logarithmic transformation of how mountainous the state is (as calculated by Gibler and Miller, 2014). The dyad-year (leader-dyad-year) data get four additional columns (i.e. both indicators for both states in the dyad) whereas the state-year data get just the two additional columns.

Author(s)

Steven V. Miller

References

Fearon, James D., and David Laitin, "Ethnicity, Insurgency, and Civil War" American Political Science Review 97: 75–90.

Gibler, Douglas M. and Steven V. Miller. 2014. "External Territorial Threat, State Capacity, and Civil War." Journal of Peace Research 51(5): 634-646.

Nunn, Nathan and Diego Puga. 2012. "Ruggedness: The Blessing of Bad Geography in Africa." Review of Economics and Statistics. 94(1): 20-36.

Riley, Shawn J., Stephen D. DeGloria, and Robert Elliot. 1999. "A Terrain Ruggedness Index That Quantifies Topographic Heterogeneity,” Intermountain Journal of Sciences 5: 23–27.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_rugged_terrain()

create_stateyears() %>% add_rugged_terrain()

create_stateyears(system = "gw") %>% add_rugged_terrain()

Description

add_sdp_gdp() allows you to add estimated GDP and "surplus" domestic product data from a 2020 analysis published in International Studies Quarterly by Anders, Fariss, and Markowitz.

Usage

add_sdp_gdp(data)

Arguments

Details

The function leans on attributes of the data that are provided by one of the "create" functions. Make sure a recognized function (or data created by that function) appear at the top of the proverbial pipe. Users will also want to note that the underlying function access two different data sets. It appears that the results published in the International Studies Quarterly used Correlates of War classification, but a follow-up repository on Github uses Gleditsch-Ward classification. The extent to which these estimates are generated by simulation, it does mean the estimates will be slightly different across both data sets even for common observations (e.g. the United States in 1816).

Because these are large nominal numbers, the estimates have been log-transformed. Users can always exponentiate these if they choose. Researchers can use these data to construct reasonable estimates of surplus GDP per capita, but must exponentiate the underlying variables before doing this.

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

Value

add_sdp_gdp() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame and adds information about the estimated gross domestic product (in 2011 USD) for that year, the estimated population in that year, the GDP per capita in that year, and what Anders, Fariss and Markowitz term the "surplus domestic product" in that year. If the data are dyad-year (leader-dyad-year), the function adds eight total columns for the first state (i.e. ccode1) and the second state (i.e. ccode2) for all these estimates. If the data are state-year (or leader-year), the function returns four additional columns to the original data that contain that same information for a given state in a given year.

Author(s)

Steven V. Miller

References

Anders, Therese, Christopher J. Fariss, and Jonathan N. Markowitz. 2020. "Bread Before Guns or Butter: Introducing Surplus Domestic Product (SDP)" International Studies Quarterly 64(2): 392–405.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_sdp_gdp()

create_stateyears() %>% add_sdp_gdp()

create_stateyears(system = "gw") %>% add_sdp_gdp()

Description

add_spells() calculates "spells" in your state-year, leader-year, or dyad-year data. The application here is mostly concerned with things like "peace spells" between conflicts in a given cross-sectional unit (e.g. a state or dyad).

Usage

add_spells(data, conflict_event_type = "ongoing", ongo = FALSE)

Arguments

Details

The function internally uses ps_spells() from stevemisc. In the interest of full disclosure, ps_spells() leans heavily on add_duration() from spduration. I optimized some code for performance.

Thinking of an application like peace-years, add_spells() will only calculate the peace years and will leave the temporal dependence adjustment to the taste of the researcher. Importantly, I do not recommend manually creating splines or square/cube terms because it creates more problems in adjusting for temporal dependence in model predictions. In a regression formula in R, you can specify the Carter and Signorino (2010) approach as ... + gmlmidspell + I(gmlmidspell^2) + I(gmlmidspell^3) (assuming you ran add_spells() on a dyad-year data frame including the Gibler-Miller-Little conflict data). The Beck et al. cubic splines approach is ... + splines::bs(gmlmidspell, 4). This function includes the spell and three splines (hence the 4 in the command). Either approach makes for easier model predictions, given R's functionality.

Thinking of our dyadic analyses of conflict, I've always understood that something like "peace-years" should be calculated on the ongoing event and not the onset of the event. Think of something like the Iran-Iraq War (MID#2115) as illustrative here. The MID (which became a war) started in 1980 and ended in 1988. There are no other bilateral incidents between Iran-Iraq independent of the war, per Correlates of War coding rules. If peace years are calculated at the "onset" of the event, it would list peace-years between the two countries from 1981 to 1988. I've never understood that to make sense, but still I've seen others insist this is the correct way to do it. add_peace_years() would force the calculation on the ongoing event, which I still maintain is correct. add_spells() will allow you to calculate on onsets, even if ongoing events are the default.

The underlying function for add_spells() will stop without a return if there are NAs bracketing observed events. The surest way this will happen is if you're doing something like a dyad-year analysis of inter-state conflicts from 1816 to 2010, but create_dyadyears() created observations from 2011 to 2020 for you as well. Remove those before using this function and confine the temporal domain to just those time-units (e.g. years) for which there is observed event data. See what I do in the example below.

Value

add_spells() takes a dyad-year, leader-year, or state-year data frame and adds spells for ongoing conflicts. Dyadic conflict data supported include the Correlates of War (CoW) Militarized Interstate Dispute (MID) data set and the Gibler-Miller-Little (GML) corrections to CoW-MID. State-level conflict data supported in this function include the UCDP armed conflict data and the CoW intra-state war data. Leader-year conflict data supported include the GML MID data.

Author(s)

Steven V. Miller

References

Beger, Andreas, Daina Chiba, Daniel W. Hill, Jr, Nils W. Metternich, Shahryar Minhas and Michael D. Ward. 2018. “spduration: Split-Population and Duration (Cure) Regression.” R package version 0.17.1.

Beck, Nathaniel, Jonathan N. Katz, and Richard Tucker. 1998. "Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable." American Journal of Political Science 42(4): 1260–1288.

Carter, David B. and Curtis S. Signorino. 2010. "Back to the Future: Modeling Time Dependence in Binary Data." Political Analysis 18(3): 271–292.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

aaa <- subset(cow_ddy, year <= 2010)

aaa %>%
add_gml_mids(keep = NULL) %>%
add_cow_mids(keep = NULL) %>%
add_contiguity() %>%
add_cow_majors() %>%
filter_prd()  %>%
add_spells()

Description

add_strategic_rivalries() merges in Thompson and Dreyer's (2012) strategic rivalry data to a dyad-year data frame. The right-bound, as of right now, are bound at 2010.

Usage

add_strategic_rivalries(data, across_types = 1)

Arguments

Details

add_strategic_rivalries() will include some other information derived from the rivalry data that the user may not want (e.g. start year of the rivalry). Feel free to select those out after the fact. Function includes an on-the-fly adjustment for Austria for rivalry #79. In this case, the Austria-Serbia rivalry continues for two years after Austria-Hungary (ccode: 300) became Austria (ccode: 305).

The across_types argument is optional and observed for only state-year calls. It defaults to 1. At the default, the function looks into the rivalry data (in td_rivalries) and focuses on the type1 column. If, say, a state has an ongoing rivalry and it is primarily spatial, it codes that as a spatial rivalry. Assume you input across_types = 2, the function then looks across both the type1 and type2 columns to see if there is a spatial component to the rivalry as either its primary or secondary dimension. If so, it codes that as a 1. across_types must be 1, 2, or 3.

Value

add_strategic_rivalries() takes a dyad-year data frame and adds information about ongoing strategic rivalries. It will also include a simple dummy variable for whether there was an ongoing rivalry in the year or not. For state-year data, it returns the count of ongoing strategic rivalries for the state in the year meeting a certain criteria (i.e. whether the state has an interventionary, ideological, positional, or spatial rivalry in an ongoing year, and how many).

Author(s)

Steven V. Miller

References

Miller, Steven V. 2019. "Create and Extend Strategic (International) Rivalry Data in R". URL: http://svmiller.com/blog/2019/10/create-extend-strategic-rivalry-data-r/

Thompson, William R. and David Dreyer. 2012. Handbook of International Rivalries. CQ Press.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
cow_ddy %>% add_strategic_rivalries()

# across_types defaults to 1
create_stateyears() %>% add_strategic_rivalries()

Description

add_ucdp_acd() allows you to add UCDP Armed Conflict data to a state-year data frame

Usage

add_ucdp_acd(data, type, issue, only_wars = FALSE)

Arguments

Details

Right now, only state-year data are supported. It's worth saying that "both" in the issue argument should not be understood as equivalent to c("territory","government"). The former is a kind of "AND" (in boolean speak) and is an explicit category in the data. The latter is an "OR" (in boolean speak) and is in all likelihood what you want if you are tempted to specify "both" in the issue argument.

Value

add_ucdp_acd() takes a state-year data frame and returns state-year information from the UCDP Armed Conflict data set (v. 20.1). The variables returned are whether there is an ongoing armed conflict in that year, whether there was an armed conflict episode onset that year, what was the maximum intensity observed that year (if an armed conflict was observed), and a character vector of the associated conflict IDs that year.

Author(s)

Steven V. Miller

References

Gleditsch, Nils Petter; Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Havard Strand (2002) Armed Conflict 1946–2001: A New Dataset. Journal of Peace Research 39(5): 615–637.

Pettersson, Therese; Stina Hogbladh & Magnus Oberg (2019). Organized violence, 1989-2018 and peace agreements. Journal of Peace Research 56(4): 589-603.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
library(dplyr)

create_stateyears(system = "gw") %>%
filter(between(year, 1946, 2019)) %>%
add_ucdp_acd()

create_stateyears(system = "gw") %>%
filter(between(year, 1946, 2019)) %>%
add_ucdp_acd(type = "intrastate", issue = "government")

Description

add_ucdp_onsets() allows you to add information about conflict episode onsets from the UCDP data program to state-year data.

Usage

add_ucdp_onsets(data)

Arguments

Details

The function leans on attributes of the data that are provided by the create_dyadyear() or create_stateyear() function. Make sure that function (or data created by that function) appear at the top of the proverbial pipe. The underlying data are version 19.1. Importantly, the UCDP yearly onset data are nominally state-year, but technically state-dyad-episode-year for cases of onsets. For example, there are four France-1946 observations because of four new conflict episodes with Cambodia, Laos, Thailand, and Vietnam. There are two Panama-1989 episodes, one for the invasion by the United States and another for a failed coup attempt. That means the are duplicates in the original data that I process into summaries. The user will probably want to consider some kind of recoding here.

Value

add_ucdp_onsets() takes a state-year data frame and adds a few summary variables based off armed conflict onsets data provided by UCDP. The variables returned are the sum of new conflict dyads (should they exist) in a given state-year, and the sum of new onset episodes (or new conflicts) that are separated by one, two, three, five, or 10 years since the last conflict episode.

Author(s)

Steven V. Miller

References

Gleditsch, Nils Petter; Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Havard Strand (2002) Armed Conflict 1946–2001: A New Dataset. Journal of Peace Research 39(5): 615–637.

Pettersson, Therese; Stina Hogbladh & Magnus Oberg (2019). Organized violence, 1989-2018 and peace agreements. Journal of Peace Research 56(4): 589-603.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
library(dplyr)

create_stateyears(system="gw") %>% add_ucdp_onsets()

create_stateyears() %>%
  add_gwcode_to_cow() %>% add_ucdp_onsets()

# Recall, these are summaries. You'll need to post-process to what you want.

create_stateyears(system="gw") %>%
  add_ucdp_onsets() %>%
  mutate(onset = ifelse(sumonset1 > 0, 1, 0))

Description

These are leader-level data drawn from the Archigos data. Space considerations mean I offer here just a few columns based on these data. Data are version 4.1.

Usage

archigos

Format

A data frame with 3409 observations on the following 11 variables.

gwcode

a numeric vector for the Gleditsch-Ward state code

obsid

a character vector for observation ID

leadid

the unique leader identifier

leader

the leader name

yrborn

the year the leader was born

gender

a categorical variable for leader gender ("M" for men, "W" for women)

startdate

a date for the leader start date

enddate

a date for the leader end date

entry

a character vector for the leader's entry type

exit

a character vector for the leader's exit type

exitcode

a character vector for more information about the leader's exit type

Details

Space considerations mean I can only offer a few columns from the overall data. Archigos data are rich with information. Consult the raw data available on Hein Goeman's website for more.

To best conform with data requirements on CRAN, a few leader names were renamed if they included irregular characters (e.g. umlauts or accents). These leaders, in these particular applications, hav been renamed to "(Juan Orlando) Hernandez" (HON-2014), "(Antonio) Saca Gonzalez" (SAL-2004), "Julian Trujillo Largacha" (COL-1878), "Cesar Gaviria Trujillo" (COL-1990), "Gabriel Garcia Moreno" (ECU-1869), "Marcos A. Morinigo" (PAR-1894-1), "Higinio Morinigo" (PAR-1940), "Sebastian Pinera" (CHL-2010), "Sauli Niinisto" (FIN-2012), "Louis Gerhard De Geer" (SWD-1876), "Stefan Lofven" (SWD-2014), "Lars Lokke Rasmussen" (DEN-2009, DEN-2015), and "Fernando de Araujo" (ETM-2008-1). None of these names contain these special characters in the data here.

For clarity's sake, I renamed the ccode column in the raw data to be gwcode. This is because it may deceive the user peeking into the data that these are not Correlates of War state codes, but Gleditsch-Ward state codes.

References

Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. "Introducing Archigos: A Dataset of Political Leaders" Journal of Peace Research 46(2): 269–83.

Description

These are directed dyad-year-level data for alliance obligations and provisions from the ATOP project

Usage

atop_alliance

Format

A data frame with 272,046 observations on the following eight variables.

ccode1

a numeric vector for the Correlates of War state code for the first state

ccode2

a numeric vector for the Correlates of War state code for the second state

year

a numeric vector for the year

atop_defense

a numeric vector that equals 1 if there was an alliance observed with a defense pledge

atop_offense

a numeric vector that equals 1 if there was an alliance observed with a offense pledge

atop_neutral

a numeric vector that equals 1 if there was an alliance observed with a neutrality pledge

atop_nonagg

a numeric vector that equals 1 if there was an alliance observed with a non-aggression pledge

atop_consul

a numeric vector that equals 1 if there was an alliance observed with a consultation pledge

Details

The data-raw directory on the project's Github shows how the data were processed.

References

Leeds, Brett Ashley, Jeffrey M. Ritter, Sara McLaughlin Mitchell, and Andrew G. Long. 2002. Alliance Treaty Obligations and Provisions, 1815-1944. International Interactions 28: 237-60.

Description

These are democracy data for all Correlates of War state system members.

Usage

ccode_democracy

Format

A data frame with 16536 observations on the following 5 variables.

ccode

the Correlates of War system code

year

a numeric vector for the year

v2x_polyarchy

the Varieties of Democracy "polyarchy" estimate

polity2

the the polity2 score from the Polity project

xm_qudsest

an extension of the Unified Democracy Scores (UDS) estimates, made possibly by the QuickUDS package from Xavier Marquez.

Details

Missing data connote data that are unavailable for various reasons. Either there is no democracy data to code or, in the case of the Polity project, the state system member is outright not evaluated for the variable.

The Polity data are from 2017. The Varieties of Democracy data are version 10. Xavier Marquez' QuickUDS estimates (i.e. extensions of Pemstein et al. (2010)) come from a package Marquez makes available on his Github (https://github.com/xmarquez/QuickUDS).

References

Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Luhrmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Agnes Cornell, Lisa Gastaldi, Haakon Gjerlow, Valeriya Mechkova, Johannes von Romer, Aksel Sundtrom, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2020. "V-Dem Codebook v10" Varieties of Democracy (V-Dem) Project.

Marshall, Monty G., Ted Robert Gurr, and Keith Jaggers. 2017. "Polity IV Project: Political Regime Characteristics and Transitions, 1800-2017." Center for Systemic Peace.

Marquez, Xavier, "A Quick Method for Extending the Unified Democracy Scores" (March 23, 2016). doi:10.2139/ssrn.2753830

Pemstein, Daniel, Stephen Meserve, and James Melton. 2010. "Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type." Political Analysis 18(4): 426-449.

Description

This is a complete list of capitals and capital transitions for Correlates of War state system members. I use it internally for calculating capital-to-capital distances in the add_capital_distances() function.

Usage

cow_capitals

Format

A data frame with 252 observations on the following 7 variables.

ccode

a numeric vector for the Correlates of War state code

statenme

a character vector for the state

capital

a character vector for the name of the capital

styear

a character vector for the start year. See details section for more information.

endyear

a character vector for the end year. See details section for more information.

lat

a numeric vector of the latitude coordinates for the capital

lng

a numeric vector of the longitude coordinates for the capital

Details

For convenience, the start year for most states is 1816. Samoa, for example, was not a state in 1816. However, the functions that use the cow_capitals data will not create observations for states that did not exist at a given point in time.

The data should be current as of the end of 2020.

Cases where a start year is not 1816 indicate a capital transition. For example, Brazil's capital moved from Rio de Janeiro to Brasilia (a planned capital) in 1960. Only 25 states in the data experienced a capital transition. The most recent was Burundi in 2018. Indonesia, as of writing, is planning on a capital transition, but this has not been completed yet.

Kazakhstan renamed its capital for the state leader in 2019. These data retain the name of Astana. This will be changed in the event the software I use records this change.

The capitals data are not without some peculiarities. Prominently, Portugal transferred the Portuguese court from Lisbon to Rio de Janeiro from 1808 to 1821. This is recorded in the data. A knowledge of the inter-state conflict data will note there was no war or dispute between, say, Portugal and Spain (or Portugal and any other country) at any point during this time, but it does create some weirdness that would suggest a massive distance between two countries, like Portugal and Spain, that are otherwise land-contiguous.

On Spain: the republican government moved the capital at the start of the civil war (in 1936) to Valencia. However, it abandoned this capital by 1937. I elect to not record this capital transition.

The data also do some (I think) reasonable back-dating of capitals to coincide with states in transition without necessarily formal capitals by the first appearance in the state system membership data. These concern Lithuania, Kazakhstan, and the Philippines. Kaunas is the initial post-independence capital of Lithuania. Almaty is the initial post-independence capital of Kazakhstan. Quezon City is the initial post-independence capital of the Philippines. This concerns, at the most, one or two years for each of these three countries.

Description

These contain an abbreviated version of the "master records" for the Correlates of War direct contiguity data. Data contain a few cosmetic changes to assist with some functions downstream from it.

Usage

cow_contdir

Format

A data frame with 2025840 observations on the following 4 variables.

ccode1

a numeric vector for the Correlates of War state code for the first state

ccode2

a numeric vector for the Correlates of War state code for the second state

conttype

a numeric vector for the contiguity relationship

begin

the year-month when this contiguity relationship begins (YYYYMM)

end

the year-month when this contiguity relationship ends (YYYYMM)

Details

The "master record" provided by the Correlates of War is "non-directed." I make these data "directed" for convenience.

For clarity, the contiguity codes range from 1 to 5. 1 = direct land contiguity. 2 = separated by 12 miles of water or fewer (a la Stannis Baratheon). 3 = separated by 24 miles of water or fewer (but more than 12 miles). 4 = separated by 150 miles of water or fewer (but more than 24 miles). 5 = separated by 400 miles of water or fewer (but more than 150 miles). Cases of separation by more than 400 miles of water are not included in the master record (but are easily discerned based on complete dyad-year data).

References

Stinnett, Douglas M., Jaroslav Tir, Philip Schafer, Paul F. Diehl, and Charles Gochman (2002). "The Correlates of War Project Direct Contiguity Data, Version 3." Conflict Management and Peace Science 19 (2):58-66.

Description

This is a complete directed dyad-year data frame of Correlates of War state system members. I offer it here as a shortcut for various other functions when I am working on new additions and don't want to invest time in waiting for create_dyadyears() to run. As a general rule, this data frame is updated after every calendar year to include the most recently concluded calendar year.

Usage

cow_ddy

Format

A data frame with the following 3 variables.

ccode1

a numeric vector for the Correlates of War state code for the first state

ccode2

a numeric vector for the Correlates of War state code for the second state

year

a numeric vector for the year

Details

Data are a quick generation from the create_dyadyears() function in this package.

Description

This is a complete (I believe) data set on Correlates of War states and Gleditsch-Ward states, a byproduct of a full_join() between gw_states and cow_states that leans largely on the state abbreviation variable.

Usage

cow_gw_years

Format

A data frame with 16936 observations on the following 6 variables.

gwcode

a Gleditsch-Ward state code

stateabb

the state abbreviation, which was the greatest source of agreement between both data sets

gw_statename

the state name as it appears in the Gleditsch-Ward data

ccode

a Correlates of War state code

cow_statename

the state name as it appears in the Correlates of War data

year

a numeric vector for the year

Details

The data-raw directory on the project's Github contains more information about how these data were created. I'm going to use it for internal stuff. The workflow is going to treat the Correlates of War state system membership codes as more of the "master" codes, for which the user can add Gleditsch-Ward identifiers as they see fit. Data are extended to 2020, assuming no changes to state system membership for either data set.

Description

This is a non-directed dyad-year version of the Correlates of War IGOs data. I use it internally for merging IGOs data into dyad-year data.

Usage

cow_igo_ndy

Format

A data frame with 917695 observations on the following 4 variables.

ccode1

the Correlates of War state system code for the first state

ccode2

the Correlates of War state system code for the second state

year

the year

dyadigos

the sum of mutual IGOs for which each state appears as a full member in a given year

Details

The data-raw directory on the project's Github contains additional information about how these data were generated from the otherwise enormous dyad-year IGOs data provided by the Correlates of War project. Given the size of that data, and the size limitations of R packages for CRAN, the data I provide here can only be simpler summaries. If you want specifics, you'll need to consult the underlying raw data provided on the Correlates of War project.

References

Pevehouse, Jon C.W., Timothy Nordstrom, Roseanne W McManus, Anne Spencer Jamison, 2020. “Tracking Organizations in the World: The Correlates of War IGO Version 3.0 datasets”, Journal of Peace Research 57(3): 492-503.

Wallace, Michael, and J. David Singer. 1970. "International Governmental Organization in the Global System, 1815-1964." International Organization 24: 239-87.

Description

This is a state-year version of the Correlates of War IGOs data. I use it internally for merging IGOs data into state-year data.

Usage

cow_igo_sy

Format

A data frame with 1557 observations on the following 5 variables.

ccode

the Correlates of War state system code for the state

year

the year

sum_igo_full

the sum of IGOs for which the state is a full member in a given year

sum_igo_associate

the sum of IGOs for which the state is just an associate member in a given year

sum_igo_observer

the sum of IGOs for which the state is just an observer in a given year

sum_igo_anytype

the sum of IGOs for which the state is a member of any kind in a given year.

Details

The data-raw directory on the project's Github contains additional information about how these data were generated from the otherwise enormous dyad-year IGOs data provided by the Correlates of War project. Given the size of that data, and the size limitations of R packages for CRAN, the data I provide here can only be simpler summaries. If you want specifics, you'll need to consult the underlying raw data provided on the Correlates of War project.

References

Pevehouse, Jon C.W., Timothy Nordstrom, Roseanne W McManus, Anne Spencer Jamison. 2020. “Tracking Organizations in the World: The Correlates of War IGO Version 3.0 datasets”, Journal of Peace Research 57(3): 492-503.

Wallace, Michael, and J. David Singer. 1970. "International Governmental Organization in the Global System, 1815-1964." International Organization 24: 239-87.

Description

These are the Correlates of War major powers data.

Usage

cow_majors

Format

A data frame with 14 observations on the following 8 variables.

ccode

a numeric vector for the Correlates of War country code

styear

the start year as a major power

stmonth

the start month as a major power

stday

the start day as a major power

endyear

the end year as a major power

endmonth

the end month as a major power

endday

the end day as a major power

version

a version identifier

Details

Data are provided "as-is" with no additional re-cleaning before inclusion into this data set (beyond eliminating the state abbreviation).

References

Correlates of War Project. 2017. "State System Membership List, v2016." Online, https://correlatesofwar.org/data-sets/state-system-membership/

Description

These are directed dyadic dispute year data derived from the Correlates of War (CoW) Militarized Interstate Dispute (MID) project. Data are from version 5.0. These were whittled to where there is no duplicate dyad-years. Its primary aim here is merging into a dyad-year data frame.

Usage

cow_mid_ddydisps

Format

A data frame with 10234 observations on the following 25 variables.

dispnum

a numeric vector for the CoW-MID dispute number

ccode1

a numeric vector for the focal state in the dyad

ccode2

a numeric vector for the target state in the dyad

year

a numeric vector for the dispute-year

cowmidongoing

a numeric vector for whether there was a dispute ongoing in that year

cowmidonset

a numeric vector for whether it was the onset of a new dispute (or new participant-entry into a recurring dispute)

sidea1

is ccode1 on side A of the dispute?

sidea2

is ccode2 on side A of the dispute?

fatality1

a numeric vector for the overall fatality level of ccode1 in the dispute

fatality2

a numeric vector for the overall fatality level of ccode2 in the dispute

fatalpre1

a numeric vector for the known fatalities (with precision) for ccode1 in the dispute

fatalpre2

a numeric vector for the known fatalities (with precision) for ccode2 in the dispute

hiact1

a numeric vector for the highest action of ccode1 in the dispute

hiact2

a numeric vector for the highest action of ccode2 in the dispute

hostlev1

a numeric vector for the hostility level of ccode1 in the dispute

hostlev2

a numeric vector for the hostility level of ccode2 in the dispute

orig1

is ccode1 an originator of the dispute?

orig2

is ccode2 an originator of the dispute?

fatality

a numeric vector for the fatality level of the dispute

hostlev

a numeric vector for the hostility level of the MID

mindur

a numeric vector for the minimum duration of the MID

maxdur

a numeric vector for the maximum duration of the MID

recip

a numeric vector for whether a MID was reciprocated

stmon

a numeric vector for the start month of the MID

Details

The process of creating these is described at one of the references below. Importantly, these data are somewhat "naive." That is: they won't tell you, for example, that Brazil and Japan never directly fought each other during World War II. Instead, it will tell you that there were two years of overlap for the two on different sides of the conflict and that the highest action for both was a war. The data are thus similar to what the EUGene program would create for users back in the day. Use these data with that limitation in mind.

References

Miller, Steven V. 2021. "How to (Meticulously) Convert Participant-Level Dispute Data to Dyadic Dispute-Year Data in R." URL: http://svmiller.com/blog/2021/05/convert-cow-mid-data-to-dispute-year/

Palmer, Glenn, and Roseanne W. McManus and Vito D'Orazio and Michael R. Kenwick and Mikaela Karstens and Chase Bloch and Nick Dietrich and Kayla Kahn and Kellan Ritter and Michael J. Soules. 2021. "The MID5 Dataset, 2011–2014: Procedures, coding rules, and description" Conflict Management and Peace Science.

Description

These are directed dyadic dispute year data derived from the Correlates of War (CoW) Militarized Interstate Dispute (MID) project. Data are from version 5.0.

Usage

cow_mid_dirdisps

Format

A data frame with 11390 observations on the following 18 variables.

dispnum

a numeric vector for the CoW-MID dispute number

ccode1

a numeric vector for the focal state in the dyad

ccode2

a numeric vector for the target state in the dyad

year

a numeric vector for the dispute-year

dispongoing

a numeric vector for whether there was a dispute ongoing in that year

disponset

a numeric vector for whether it was the onset of a new dispute (or new participant-entry into a recurring dispute)

sidea1

is ccode1 on side A of the dispute?

sidea2

is ccode2 on side A of the dispute?

fatality1

a numeric vector for the overall fatality level of ccode1 in the dispute

fatality2

a numeric vector for the overall fatality level of ccode2 in the dispute

fatalpre1

a numeric vector for the known fatalities (with precision) for ccode1 in the dispute

fatalpre2

a numeric vector for the known fatalities (with precision) for ccode2 in the dispute

hiact1

a numeric vector for the highest action of ccode1 in the dispute

hiact2

a numeric vector for the highest action of ccode2 in the dispute

hostlev1

a numeric vector for the hostility level of ccode1 in the dispute

hostlev2

a numeric vector for the hostility level of ccode2 in the dispute

orig1

is ccode1 an originator of the dispute?

orig2

is ccode2 an originator of the dispute?

Details

The process of creating these is described at one of the references below. Importantly, these data are somewhat "naive." That is: they won't tell you, for example, that Brazil and Japan never directly fought each other during World War II. Instead, it will tell you that there were two years of overlap for the two on different sides of the conflict and that the highest action for both was a war. The data are thus similar to what the EUGene program would create for users back in the day. Use these data with that limitation in mind.

References

Miller, Steven V. 2021. "How to (Meticulously) Convert Participant-Level Dispute Data to Dyadic Dispute-Year Data in R." URL: http://svmiller.com/blog/2021/05/convert-cow-mid-data-to-dispute-year/

Palmer, Glenn, and Roseanne W. McManus and Vito D'Orazio and Michael R. Kenwick and Mikaela Karstens and Chase Bloch and Nick Dietrich and Kayla Kahn and Kellan Ritter and Michael J. Soules. 2021. "The MID5 Dataset, 2011–2014: Procedures, coding rules, and description" Conflict Management and Peace Science.

Description

This is an abbreviated version of the dispute-level CoW-MID data.

Usage

cow_mid_disps

Format

A data frame with 2436 observations on the following 7 variables.

dispnum

a numeric vector for the CoW-MID dispute number

outcome

a numeric vector for the outcome of the MID

styear

a numeric vector for the start year of the MID

stmon

a numeric vector for the start month of the MID

settle

a numeric vector for the how dispute was settled

fatality

a numeric vector for the fatality level of the dispute

mindur

a numeric vector for the minimum duration of the MID

maxdur

a numeric vector for the maximum duration of the MID

hiact

a numeric vector for the highest action of the MID

hostlev

a numeric vector for the hostility level of the MID

recip

a numeric vector for whether a MID was reciprocated

Details

These data are purposely light on information; they're not intended to be used for dispute-level analyses, per se. They're intended to augment the directed dyadic dispute-year data by adding in variables that serve as exclusion rules to whittle the data from dyadic dispute-year to just dyad-year data.

References

Palmer, Glenn, and Roseanne W. McManus and Vito D'Orazio and Michael R. Kenwick and Mikaela Karstens and Chase Bloch and Nick Dietrich and Kayla Kahn and Kellan Ritter and Michael J. Soules. 2021. "The MID5 Dataset, 2011–2014: Procedures, coding rules, and description" Conflict Management and Peace Science.

Description

These are non-directed dyad-year data for the minimum distance between states in the Correlates of War state system from 1886 to 2019. The data are generated from the cshapes package.

Usage

cow_mindist

Format

A data frame with 817053 observations on the following 4 variables.

ccode1

the Correlates of War state system code for the first state

ccode2

the Correlates of War state system code for the second state

year

the year

mindist

the minimum distance between states on Jan. 1 of the year, in kilometers

Details

The data are generated from the cshapes package. Data are automatically generated (by default) as directed dyad-years. I elect to make them non-directed for space considerations. Making non-directed dyad-year data into directed dyad-year data isn't too difficult in R. It just looks weird to see the code that does it.

Previous versions of these data were for the minimum distance as of Dec. 31 of the referent year. These are now Jan. 1. Most of the data I provide elsewhere in this package are to be understood as the data as they were at the start of the year. This is how I process, for example, the capitals data as they get merged in the add_capital_distance() function. However, the script that generates these data are set at Jan. 1 of the year and not Dec. 31. Right now, the cshapes does not appear to work on my system and I do not know why. Fortunately, the package authors made these data available.

References

Schvitz, Guy, Luc Girardin, Seraina Ruegger, Nils B. Weidmann, Lars-Erik Cederman, and Kristian Skrede Gleditsch. 2022. "Mapping The International System, 1886-2017: The CShapes 2.0 Dataset." Journal of Conflict Resolution. 66(1): 144-161.

Weidmann, Nils B. and Kristian Skrede Gleditsch. 2010. "Mapping and Measuring Country Shapes: The cshapes Package." The R Journal 2(1): 18-24

Description

These are version 6.0 of the Correlates of War National Military Capabilities data. Data omit the state abbreviation and version identifier for consideration.

Usage

cow_nmc

Format

A data frame with 15171 observations on the following 9 variables.

ccode

a numeric vector for the Correlates of War country code

year

the year

milex

an estimate of military expenditures (in thousands). See details section for more.

milper

an estimate of the size of military personnel (in thousands) for the state

irst

an estimate of iron and steel production (in thousands of tons)

pec

an estimate of primary energy consumption (thousands of coal-ton equivalents)

tpop

an estimate of the total population size of the state (in thousands)

upop

an estimate of the urban population size of the state (in thousands). See details section for more.

cinc

The Composite Index of National Capability ("CINC") score. See details section for more.

Details

The user will want to be a little careful with how some of these data are used, beyond the typical caveat about how difficult it is to pin-point how many thousands of coal-tons a state like Baden was producing in the 19th century.

First, military expenditures are denominated in British pounds sterling for observations between 1816 and 1913. The observations from 1914 and beyond are denominated in current United States dollars. This is according to the manual.

Second, urban population size is an estimate based on, well, an estimate of the size of the population living in an area with 100,000 or more people.

Third, the Composite Index of National Capability score is calculated as each state's world share of each of the six composite indicators also included in the data in a given year. It theoretically is bound between 0 and 1. A state with a 1 is 100% responsible for 1) all of the military expenditures in the world, 2) is the only state with a military, 3) does all the iron and steel production, 4) all the world's primary energy consumption, and 5) is the only state in the world with a population and an urban population. Incidentally, the maximum scores observed in the data belong to the United States in 1945.

References

Singer, J. David, Stuart Bremer, and John Stuckey. (1972). "Capability Distribution, Uncertainty, and Major Power War, 1820-1965." in Bruce Russett (ed) Peace, War, and Numbers, Beverly Hills: Sage, 19-48.

Singer, J. David. 1987. "Reconstructing the Correlates of War Dataset on Material Capabilities of States, 1816-1985" International Interactions, 14: 115-32.

Description

These are state-year level data for surplus and gross domestic product for Correlates of War state system members. Data also include population estimates for per capita standardization.

Usage

cow_sdp_gdp

Format

A data frame with 27753 observations on the following five variables.

ccode

a numeric vector for the Correlates of War state code

year

a numeric vector for the year

wbgdp2011est

a numeric vector for the estimated natural log of GDP in 2011 USD (log-transformed)

wbpopest

a numeric vector for the estimated population size (log-transformed)

sdpest

a numeric vector for the estimated surplus domestic product (log-transformed)

wbgdppc2011est

a numeric vector for the estimated GDP per capita (log-transformed)

Details

These were extracted from the actual replication files from International Studies Quarterly. Because these data are ultimately being simulated, a user can expect some slight differences between the Correlates of War version of these data (which Anders et al. published) and the Gleditsch-Ward version of these data (which appear to be the one the authors will more vigorously support going forward).

Space considerations compel me to round these data to three decimal points. These "economic" data are routinely the biggest in the package, and it's because of the decimal points. The justification for this is these data are estimated/simulated anyways and the information loss is at the 1/1000th decimal point. This procedure basically cuts the size of the data to be less than 25% of its original size. The original simulations are available for remote download if you'd like. Type ?download_extdata() for more information.

References

Anders, Therese, Christopher J. Fariss, and Jonathan N. Markowitz. 2020. "Bread Before Guns or Butter: Introducing Surplus Domestic Product (SDP)" International Studies Quarterly 64(2): 392–405.

Description

These are the Correlates of War state system membership data.

Usage

cow_states

Format

A data frame with 243 observations on the following 10 variables.

stateabb

a character vector for the state abbreviation

ccode

a numeric vector for the Correlates of War country code

statenme

a character vector for the state name

styear

the start year in the system

stmonth

the start month in the system

stday

the start day in the system

endyear

the end year in the system

endmonth

the end month in the system

endday

the end day in the system

version

a version identifier

Details

Data are provided "as-is" with no additional re-cleaning before inclusion into this data set.

References

Correlates of War Project. 2017. "State System Membership List, v2016." Online, https://correlatesofwar.org/data-sets/state-system-membership/

Description

These are state-year-level data for national trade from the Correlates of War project.

Usage

cow_trade_sy

Format

A data frame with 14410 observations on the following four variables.

ccode

the Correlates of War state system code

year

the year

imports

total imports of the state in current million USD

exports

total exports of the state in current million USD

Details

The data-raw directory on the project's Github shows how the data were processed.

References

Barbieri, Katherine and Omar M.G. Keshk. 2016. Correlates of War Project Trade Data Set Codebook, Version 4.0. Online: https://correlatesofwar.org

Barbieri, Katherine, Omar M.G. Keshk, and Brian Pollins. 2009. "TRADING DATA: Evaluating Our Assumptions and Coding Rules." Conflict Management and Peace Science, 26(5): 471-491.

Description

These are a modified version of the inter-state war data from the Correlates of War project. Data are version 4.0. The temporal domain is 1816-2007. Data are functionally directed dyadic war-year.

Usage

cow_war_inter

Format

A data frame with 1932 observations on the following 15 variables.

warnum

the Correlates of War war number

ccode1

the Correlates of War state code for side1

ccode2

the Correlates of War state code for side2

year

a numeric vector for the year

cowinteronset

a dummy variable for whether this is an inter-state war onset (i.e. either the year in StartYear1 or StartYear2 in the raw data)

cowinterongoing

a numeric constant of 1

sidea1

a numeric vector for the side in the war for ccode1, either 1 or 2

sidea2

a numeric vector for the side in the war for ccode2, either 1 or 2

initiator1

a dummy variable that equals 1 if ccode1 initiated the war

initiator2

a dummy variable that equals 1 if ccode2 initiated the war

outcome1

the outcome for ccode1 as numeric vector. Outcomes are 1 (winner), 2 (loser), 3 (compromise/tied), 4 (transformed into another type of war), 5 (ongoing at end of 2007, which is not observed in these data), 6 (stalemate), 7 (conflict continues below severity of war), and 8 (changed sides)

outcome2

the outcome for ccode2 as numeric vector. Outcomes are 1 (winner), 2 (loser), 3 (compromise/tied), 4 (transformed into another type of war), 5 (ongoing at end of 2007, which is not observed in these data), 6 (stalemate), 7 (conflict continues below severity of war), and 8 (changed sides)

batdeath1

the estimated deaths for ccode1 (-9 = unknown)

batdeath2

the estimated deaths for ccode2 (-9 = unknown)

resume

a dummy variable that equals 1 if this is a conflict resumption episode

Details

See data-raw directory for how these data were generated. These data are here if you want it, but I caution against using them as gospel. There are a few problems here. One: -9s proliferate the data for battle deaths on either side, which is unhelpful. There are 10 cases where the sum of battle deaths is exactly 1,000 or 1,001. This is suspicious. The "side" variables are not well-explained—in fact they're not explained at all in the codebook— and this can lead a user astray if they want to interpret them analogous to the sidea variables in the Correlates of War Militarized Interstate Dispute data. You probably want to use the initiator variables for this. Further, the war data routinely betray the MID data and the two do not speak well to each other. The language Sarkees and Wayman (2010) use in their book talk about how MIDs "precede" a war or are "associated" with a war, which forgets the war data are supposed to be a subset of the MID data. In one case (Gulf War), they get the associated dispute number wrong and, in one prominent case (War of Bosnian Independence), they argue no MID exists at all (it's actually MID#3557).

References

Sarkees, Meredith Reid, and Frank Wheldon Wayman. 2010. Resort to War: A Data Guide to Inter-State, Extra-State, Intra-State, and Non-State Wars, 1816-2007. Washington DC: CQ Press.

Description

These are a modified version of the intra-state war data from the Correlates of War project. Data are version 4.1. The temporal domain is 1816-2007.

Usage

cow_war_intra

Format

A data frame with 1361 observations on the following 17 variables.

warnum

the Correlates of War war number

warname

the Correlates of War war name

wartype

a character vector for the type of war, either "local issues" or "central control"

year

a numeric vector for the year

cowintraonset

a dummy variable for whether this is a civil war onset (i.e. either the year in StartYear1 or StartYear2 in the raw data)

cowintraongoing

a numeric constant of 1

resume_combat

a dummy variable for whether this is a resumption of a conflict (i.e. StartYear2 is not -8)

primary_state

a dummy variable for whether the state is the primary state having the civil war

ccodea

the Correlates of War state code for the participant on Side A. -8 = not applicable (participant is not a state)

sidea

the name of the participant on Side A. -8 = not applicable (no additional party on this side)

ccodeb

the Correlates of War state code for the participant on Side B. -8 = not applicable (participant is not a state)

sideb

the name of the participant on Side B. -8 = not applicable (no additional party on this side)

intnl

a dummy variable for if this is an internationalized civil war

outcome

an unordered-categorical variable for the outcome of the civil war. Values include 1 (Side A wins), 2 (Side B wins), 3 (Compromise), 4 (war transformed into another type of war), 5 (war is ongoing at the end of 2007), 6 (stalemate), 7 (conflict continues below severity of war)

sideadeaths

the estimated deaths for the Side A participant (-9 = unknown, -8 = not applicable)

sidebdeaths

the estimated deaths for the Side B participant (-9 = unknown, -8 = not applicable)

ongo2007

a dummy variable for if this war is ongoing as of the end of 2007

Details

See data-raw directory for how these data were generated. In the Guinnea-Bissau Civil War (1998, 1999), the "Mane Junta" have the accented-e scrubbed to coincide with CRAN's character requirements.

References

Dixon, Jeffrey, and Meredith Sarkees. 2016. A Guide to Intra-State Wars: An Examination of Civil Wars, 1816-2014. Thousand Oaks, CA: Sage.

Sarkees, Meredith Reid, and Frank Wheldon Wayman. 2010. Resort to War: A Data Guide to Inter-State, Extra-State, Intra-State, and Non-State Wars, 1816-2007. Washington DC: CQ Press.

Description

create_dyadyears() allows you to dyad-year data from either the Correlates of War (CoW) state system membership data or the Gleditsch-Ward (gw) system membership data. The function leans on internal data provided in the package.

Usage

create_dyadyears(system = "cow", mry = TRUE, directed = TRUE, subset_years)

Arguments

Value

create_dyadyears() takes state system membership data provided by either Correlates of War or Gleditsch-Ward and returns a dyad-year data frame.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2019. “Create Country-Year and (Non)-Directed Dyad-Year Data With Just a Few Lines in R” http://svmiller.com/blog/2019/01/create-country-year-dyad-year-from-country-data/

Examples

# CoW is default, will include years beyond 2016 (most recent CoW update)
create_dyadyears()

# Gleditsch-Ward, include most recent years
create_dyadyears(system="gw")

# Gleditsch-Ward, don't include most recent years
create_dyadyears(system="gw", mry=FALSE)

# Gleditsch-Ward, don't include most recent years, directed = FALSE
create_dyadyears(system="gw", mry=FALSE, directed = FALSE)

Description

create_leaderdays() allows you to generate leader-day data from leader-level data provided in peacesciencer.

Usage

create_leaderdays(system = "archigos", standardize = "none")

Arguments

Details

create_leaderdays(), as of writing, only supports the Archigos data set of leaders. I envision this function being mostly for internal uses. Basically, create_leaderyears() effectively starts by first running a version of create_leaderdays(). So, why not have this function too?

Value

create_leaderdays() takes leader-level data available in peacesciencer and returns a leader-day-level data frame.

Author(s)

Steven V. Miller

References

Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. "Introducing Archigos: A Dataset of Political Leaders" Journal of Peace Research 46(2): 269–83.

Examples

create_leaderdays()

create_leaderdays(standardize = "gw")

Description

create_leaderdyadyears() allows you to created leader dyad-year data from the Archigos data first introduced and described by Goemans et al. (2009).

Usage

create_leaderdyadyears(directed = TRUE, system = "gw")

Arguments

Details

This is a complete and universal leader dyad-year data frame for all possible dyadic leader pairings from 1870 to 2015. This has several implications. First: these data are enormous. The output is over 2 million rows long! Second: the time required to create these data from scratch would take too long for a normal function call. This amounts to an unholy combination of data that are too large for CRAN's disk space restrictions (5 MB) and too time-consuming to do from scratch every time. Thus, the data are pre-generated and stored remotely. Check download_extdata() for more information.

Value

create_leaderdyadyears() takes remote data available for separate download and returns a complete leader dyad-year data frame for all leaders, and all possible dyads, from 1870 to 2015.

Author(s)

Steven V. Miller

References

Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. "Introducing Archigos: A Dataset of Political Leaders" Journal of Peace Research 46(2): 269–83.

Examples

## Not run: 
# download_extdata()
# ^ make sure you've run this first.
# default is directed
create_leaderdyadyears()

# non-directed
create_leaderdyadyears(directed = FALSE)

## End(Not run)

Description

create_leaderyears() allows you to generate leader-year data from leader-level data provided in peacesciencer

Usage

create_leaderyears(system = "archigos", standardize = "none", subset_years)

Arguments

Details

create_leaderyears(), as of writing, only supports the Archigos data set of leaders.

Many leader ages are known with precision. Many are not recorded in the Archigos data. Knowing well that years are aggregates of days, the leader age variable that gets returned in this output should be treated as an approximation of the leader's age.

Be mindful that leader tenure is calculated before any standardization argument. Archigos has some leader entries that precede the state system entry for the state, or otherwise do not coincide with state system dates. For example, Lynden Pindling was in his seventh year as leader of The Bahamas (in various titles) before independence in 1973 (in which he became prime minister). Leader tenure is not tethered to state system dates in situations like this (only the dates recorded in the Archigos data).

The leader tenure variable returned here does have the odd effect of potentially misstating leader tenure, or at least making it seem unusual. For example, Jimmy Carter (USA-1877) was president in 1977 (year 1), 1978 (year 2), 1979 (year 3), 1980 (year 4), and exited in January 1981 (year 5). Again: years are aggregates of days and it's not evident how else this information should be perfectly communicated with that in mind. Users with some R skills can extract the underlying information from the archigos data and, perhaps, calculate something like the maximum leader tenure (in days) on either Dec. 31 of the referent year, or leader exit before Dec. 31 that year, or something to that effect. No matter, I think this to at least be a defensible variable to present to the user with those limitations in mind. If the user is interested in leader tenure in a leader-year analysis, this variable should be fine. If the user is interested in something like the effect of a fifth year on some kind of leader behavior, they will want to figure out something else.

Value

create_leaderyears() takes leader-level data available in peacesciencer and returns a leader-year-level data frame. This minimal output contains the observation ID from Archigos, the year, the state code for the leader (i.e. either Correlates of War or Gleditsch-Ward, depending on the standardize argument), the leader's name in Archigos (if it may help the reader to have that), an approximation of the leader's age, and the year in office for the leader (as a running count, starting at 1).

Author(s)

Steven V. Miller

References

Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. "Introducing Archigos: A Dataset of Political Leaders" Journal of Peace Research 46(2): 269–83.

Examples

# standardize = 'none' is default
create_leaderyears()

create_leaderyears(standardize = 'gw')

Description

create_statedays() allows you to create state-day data from either the Correlates of War (CoW) state system membership data or the Gleditsch-Ward (gw) system membership data. The function leans on internal data provided in the package.

Usage

create_statedays(system = "cow", mry = TRUE)

Arguments

Value

create_statedays() takes state system membership data provided by either Correlates of War or Gleditsch-Ward and returns a simple state-day data frame.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2019. “Create Country-Year and (Non)-Directed Dyad-Year Data With Just a Few Lines in R” http://svmiller.com/blog/2019/01/create-country-year-dyad-year-from-country-data/

Examples

# CoW is default, will include years beyond 2016 (most recent CoW update)
create_statedays()

# Gleditsch-Ward, include most recent years
create_statedays(system="gw")

# Gleditsch-Ward, don't include most recent years
create_statedays(system="gw", mry=FALSE)

Description

create_stateyears() allows you to generate state-year data from either the Correlates of War (CoW) state system membership data or the Gleditsch-Ward (gw) system membership data. The function leans on internal data provided in the package.

Usage

create_stateyears(system = "cow", mry = TRUE, subset_years)

Arguments

Value

create_stateyears() takes state system membership data provided by either Correlates of War or Gleditsch-Ward and returns a simple state-year data frame.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2019. “Create Country-Year and (Non)-Directed Dyad-Year Data With Just a Few Lines in R” http://svmiller.com/blog/2019/01/create-country-year-dyad-year-from-country-data/

Examples

# CoW is default, will include years beyond 2016 (most recent CoW update)
create_stateyears()

# Gleditsch-Ward, include most recent years
create_stateyears(system="gw")

# Gleditsch-Ward, don't include most recent years
create_stateyears(system="gw", mry=FALSE)

Description

This is a data set with state-year estimates for ethnic and religious fractionalization/polarization, by way of the Composition of Religious and Ethnic Groups (CREG) project at the University of Illinois. I-L-L.

Usage

creg

Format

A data frame with 11523 observations on the following 9 variables.

ccode

a Correlates of War state code

gwcode

a Gleditsch-Ward state code

creg_ccode

a numeric code for the state, mostly patterned off Correlates of War codes but with important differences. See details section for more.

year

the year

ethfrac

an estimate of the ethnic fractionalization index. See details for more.

ethpol

an estimate of the ethnic polarization index. See details for more.

relfrac

an estimate of the religious fractionalization index. See details for more.

relpol

an estimate of the religious polarization index. See details for more.

Details

The data-raw directory on the project's Github contains more information about how these data were created. Pay careful attention to how I assigned CoW/G-W codes. The underlying data are version 1.02.

The state codes provided by the CREG project are mostly Correlates of War codes, but with some differences. Summarizing these differences: the state code for Serbia from 1992 to 2013 is actually the Gleditsch-Ward code (340). Russia after the dissolution of the Soviet Union (1991-onward) is 393 and not 365. The Soviet Union has the 365 code. Yugoslavia has the 345 code. The code for Yemen (678) is effectively the Gleditsch-Ward code because it spans the entire post-World War II temporal domain. Likewise, the code for post-unification Germany is the Gleditsch-Ward code (260) as well. The codebook actually says it's 265 (which would be East Germany's code), but this is assuredly a typo based on the data.

The codebook cautions there are insufficient data for ethnic group estimates for Cameroon, France, India, Kosovo, Montenegro, Mozambique, and Papua New Guinea. The French case is particularly disappointing but the missing data there are a function of both France's constitution and modelling issues for CREG (per the codebook). There are insufficient data to make religious group estimates for China, North Korea, and the short-lived Republic of Vietnam.

The fractionalization estimates are the familiar Herfindahl-Hirschman concentration index. The polarization formula comes by way of Montalvo and Reynal-Querol (2000), though this book does not appear to be published beyond its placement online. I recommend Montalvo and Reynal-Querol (2005) instead. You can cite Alesina (2003) for the fractionalization measure if you'd like.

In the most literal sense of "1", the group proportions may not sum to exactly 1 because of rounding in the data. There were only two problem cases in these data worth mentioning. First, in both data sets, there would be the occasional duplicates of group names by state-year (for example: Afghanistan in 1951 in the ethnic group data and the United States in 1948 in the religious group data). In those cases, the script I make available in the data-raw directory just select distinct values and that effectively fixes the problem of duplicates, where they do appear. Finally, Costa Rica had a curious problem for most years in the religious group data. All Costa Rica years have group data for Protestants, Roman Catholics, and "others." Up until 1964 or so, the "others" are zero. Afterward, there is some small proportion of "others". However, the sum of Protestants, Roman Catholics, and "others" exceeds 1 (pretty clearly) and the difference between the sum and 1 is entirely the "others." So, I drop the "others" for all years. I don't think that's terribly problematic, but it's worth saying that's what I did.

References

Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Wacziarg. 2003. "Fractionalization". Journal of Economic Growth 8: 155-194.

Montalvo, Jose G. and Marta Reynal-Querol. 2005. "Ethnic Polarization, Potential Conflict, and Civil Wars" American Economic Review 95(3): 796–816.

Nardulli, Peter F., Cara J. Wong, Ajay Singh, Buddy Petyon, and Joseph Bajjalieh. 2012. The Composition of Religious and Ethnic Groups (CREG) Project. Cline Center for Democracy.

Description

declare_attributes() allows the user to declare peacesciencer-specific attributes to data they bring from outside the package. This allows the user to use package functions as shortcuts, where appropriate.

Usage

declare_attributes(data, data_type, system, conflict_type)

Arguments

Details

The function's documentation will include what attributes are available to be declared. No doubt, the list of potential attributes will grow in time, but the attributes that can be declared are limited to just what I've built into the package to this point. Users cannot declare more than one attribute of a given type (i.e. a user cannot declare the system to be both Correlates of War and Gleditsch-Ward).

The idea here is, basically, to allow the user to use functions in peacesciencer for data they have created or have acquired from elsewhere. However, this functions provides no assurances about quality control in the various merges built elsewhere into this package. This package aggressively tests functions for data generated in-house. If your outside data have merges, the various "add" functions may not perfectly perform. There is no real way I can control for this since the data are coming from outside the package and not through one of the "create" functions. In your particular case, that may not be much of a problem. However, it's the user's responsibility to do their own quality control in this situation.

Value

declare_attributes() takes a data frame and adds peacesciencer-specific attributes to the data frame. This will allow the user to take advantage of many of the functions in this package without starting the process with one of the "create" functions. If nothing is declared in the function, no attribute is added and the function just returns the original data without any change.

Author(s)

Steven V. Miller

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

data.frame(ccode = 2, year = c(1816:1830)) -> usa_years

usa_years %>% declare_attributes(data_type = 'state_year', system = 'cow')

Description

download_extdata() leverages R's inst directory flexibility to allow you to download some extra data and store it in the package.

Usage

download_extdata(overwrite = FALSE)

Arguments

Value

download_extdata() downloads some extra data stored on my website (http://svmiller.com) and sticks them in the extdata directory in the package.

A Description of Various Data Sets This Will Download

Running download_extdata() returns the following data that will be stored in the package's extdata directory.

Correlates of War Dyadic Trade Data Set (v. 4.0)

These are directed dyad-year-level data for dyadic trade from the Correlates of War project. The trade values presented here have been rounded to three decimal points to conserve space. The data downloaded by this function are about 4.1 megabytes in size.

Directed Leader Dyad-Year Data, 1870-2015 (CoW States)

These are all directed leader dyad-year data from 1870-2015. Data come from the Archigos data (version 4.1). The data are standardized to just those observations where both leaders and states appear in the CoW state system data. The data downloaded by this function are about 2 megabytes in size.

Directed Leader Dyad-Year Data, 1870-2015 (Gleditsch-Ward States)

These are all directed leader dyad-year data from 1870-2015. Data come from the Archigos data (version 4.1). The data represent every possible dyadic leader-pairing in the Archigos data (which is denominated in the Gleditsch-Ward system), but standardizes leader dyad-years to Gleditsch-Ward state system dates. The data downloaded by this function are about 2.2 megabytes in size.

Chance-Corrected Measures of Foreign Policy Similarity (FPSIM, v. 2)

The FPSIM data set provides measures of foreign policy similarity of dyads based on alliance ties (Correlates of War, version 4.1) and UN General Assembly voting (Voeten, version 17) for all members of the Correlates of War state system. The alliance data cover the time period from 1816 to 2012, and the UN voting data from 1946 to 2015. The similarity measures include various versions of Ritter and Signorino's S (weighted/non-weighted by material capabilities; squared/absolute distance metrics) as well as the chance-corrected measures Cohen's (1960) kappa and Scott's (1955) pi. The measures based on alliance data come in two versions: one is based on valued alliance ties and the other is based on binary alliance ties. Data were last updated on December 7, 2017, and this description was effectively plagiarized (with his blessing) from Frank Haege's Dataverse.

These data are directed dyad-years with 17 columns and 1,872,198 observations. They will almost certainly be the largest data set I nudge/ask you to download remotely. The file containing this information is 18.6 MB in size. To reduce size further, these decimal points have also been rounded to three spots.

Haege generated all estimates of dyadic foreign policy similarity, except for the taub column. That was generated separately, by me.

(Non-Directed) Dyadic Minimum Distance Data Plus (CoW States)

These are non-directed dyadic minimum distance data from Schvitz et al. (2022) for all Correlates of War states from the start of 1886 to the end of 2019. Note that I call these "data plus", with the idea of informally branding these as a kind of augmentation of what you might otherwise do with the cshapes package. This data set has over 4.4 million rows for each dyadic minimum distance for all available years. Within each year, there is a recorded minimum distance for Jan. 1, June 30, Dec. 31 and, in addition, any day within the year where the composition of the international system (or shape of a state) changed, as recorded in cshapes. Sometimes these changes concern the dyadic minimum distance; sometimes they don't. For example, the League of Nations is responsible for a lot shape changes (i.e. system entry) in the CoW state system data in the year 1920. That obviously won't change the dyadic minimum distance between the U.S. and Canada, which will always be zero. Sometimes the start of the year (Jan. 1), the midpoint of the year (June 30), or the end of the year (Dec. 31) coincides with a system change. Often it doesn't. Note that a referent day (Jan. 1, June 30, Dec. 31) may not appear in a given year for a given dyad if that date exists outside CoW state system membership. For example, Canada doesn't appear as a state system member until Jan. 10, 1920. The goal of this data set is allow you to more quickly generate dyadic minimum distances within peacesciencer's functionality if you are proficient in tidyverse verbs. You could also use it to highlight how often the dyadic minimum distance may vary within a year for a given dyad.

Despite the dimensions of the data set, it's not too big of a download. The data are about 1.7 MB in size.

(Non-Directed) Dyadic Minimum Distance Data Plus (G-W States)

These are non-directed dyadic minimum distance data from Schvitz et al. (2022) for all Gleditsch-Ward states from the start of 1886 to the end of 2019. Note that I call these "data plus", with the idea of informally branding these as a kind of augmentation of what you might otherwise do with the cshapes package. This data set has over 3.7 million rows for each dyadic minimum distance for all available years. Within each year, there is a recorded minimum distance for Jan. 1, June 30, Dec. 31 and, in addition, any day within the year where the composition of the international system (or shape of a state) changed, as recorded in cshapes. Sometimes these changes concern the dyadic minimum distance; sometimes they don't. For example, the dissolution of the Soviet Union is responsible for a lot shape changes (i.e. system entry) in 1991. That obviously won't change the dyadic minimum distance between the U.S. and Canada, which will always be zero. Sometimes the start of the year (Jan. 1), the midpoint of the year (June 30), or the end of the year (Dec. 31) coincides with a system change. Often it doesn't. Note that a referent day (Jan. 1, June 30, Dec. 31) may not appear in a given year for a given dyad if that date exists outside G-W state system membership. For example, Haiti disappears from the state system on July 4, 1915 and reappears on Aug. 15, 1934. That means there won't be any dyadic minimum distance observations with the U.S., for example, on Dec. 31, 1915 or June 30, 1934. The goal of this data set is allow you to more quickly generate dyadic minimum distances within peacesciencer's functionality if you are proficient in tidyverse verbs. You could also use it to highlight how often the dyadic minimum distance may vary within a year for a given dyad.

Despite the dimensions of the data set, it's not too big of a download. The data are about 1.4 MB in size.

Author(s)

Steven V. Miller

References

Barbieri, Katherine, Omar M. G. Keshk, and Brian Pollins. 2009. "TRADING DATA: Evaluating our Assumptions and Coding Rules." Conflict Management and Peace Science. 26(5): 471-491.

Goemans, Henk E., Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. "Introducing Archigos: A Dataset of Political Leaders" Journal of Peace Research 46(2): 269–83.

Haege, Frank. 2011. "Choice or Circumstance? Adjusting Measures of Foreign Policy Similarity for Chance Agreement." Political Analysis 19(3): 287-305.

Schvitz, Guy, Luc Girardin, Seraina Ruegger, Nils B. Weidmann, Lars-Erik Cederman, and Kristian Skrede Gleditsch. 2022. "Mapping The International System, 1886-2017: The CShapes 2.0 Dataset." Journal of Conflict Resolution. 66(1): 144-161.

Weidmann, Nils B. and Kristian Skrede Gleditsch. 2010. "Mapping and Measuring Country Shapes: The cshapes Package." The R Journal 2(1): 18-24.

Examples

## Not run: 
# Here's where the data are going to be downloaded.
system.file("extdata", package="peacesciencer")
# Now, let's download the data.
download_extdata()

## End(Not run)

Description

This is a simple data set that communicates directed dyads in the Correlates of War data that appear in the same year, but not in any particular day in the year. They are used in an anti-join in the create_dyadyears() function in this package.

Usage

false_cow_dyads

Format

A data frame the following four variables.

ccode1

a numeric vector for the Correlates of War state code for the first state

ccode2

a numeric vector for the Correlates of War state code for the second state

year

a numeric vector for the year

in_ps

a constant that equals 1 if these data would appear in create_dyadyears() if you were not careful to remove them.

Details

Think of the directed Suriname and Republic of Vietnam dyad here as illustrative here. The Republic of Vietnam exits the Correlates of War state system on April 30, 1975 whereas Suriname enters the state system on November 25, 1975. Both appear in the same year, but not at the same time.

Description

This is a simple data set that communicates directed dyads in the Gleditsch-Ward data that appear in the same year, but not in any particular day in the year. They are used in an anti-join in the create_dyadyears() function in this package.

Usage

false_gw_dyads

Format

A data frame the following four variables.

gwcode1

a numeric vector for the Gleditsch-Ward state code for the first state

gwcode2

a numeric vector for the Gleditsch-Ward state code for the second state

year

a numeric vector for the year

in_ps

a constant that equals 1 if these data would appear in create_dyadyears() if you were not careful to remove them.

Details

Think of the directed Suriname and Republic of Vietnam dyad here as illustrative here. The Republic of Vietnam exits the Correlates of War state system on April 30, 1975 whereas Suriname enters the state system on November 25, 1975. Both appear in the same year, but not at the same time.

Description

filter_prd() filters a dyad-year data frame to just those that are "politically relevant." This is useful for discarding unnecessary (and unwanted) observations that just consume space in memory.

Usage

filter_prd(data)

Arguments

Details

"Political relevance" can be calculated a few ways. Right now, the function considers only "direct" contiguity and Correlates of War major power status. You can employ maximalist definitions of "direct contiguity" to focus on just the land-contiguous. This function is inclusive of any type of contiguity relationship.

As of version 0.5, filter_prd() is a shortcut for add_contiguity() and/or add_cow_majors() if the function is executed in the absence of the data needed to create politically relevant dyads. See the example below for what this means.

Value

filter_prd() takes a dyad-year data frame, assuming it has columns for major power status and contiguity type, calculates whether the dyad is "politically relevant", and subsets the data frame to just those observations.

Author(s)

Steven V. Miller

References

Weede, Erich. 1976. "Overwhelming preponderance as a pacifying condition among contiguous Asian dyads." Journal of Conflict Resolution 20: 395-411.

Lemke, Douglas and William Reed. 2001. "The Relevance of Politically Relevant Dyads." Journal of Conflict Resolution 45(1): 126-144.

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

A <- cow_ddy %>% add_contiguity() %>% add_cow_majors() %>% filter_prd()

A

# you can also use it as a shortcut for the other functions required
# to calculate politically relevant dyads.
B <- cow_ddy %>% filter_prd()

B

identical(A,B)

Description

This is a simple data set of 71 arms races reported by Gibler et al. in their 2005 article in Journal of Peace Research.

Usage

grh_arms_races

Format

A data frame the following five variables.

race_id

the arms race identifier

ccode1

a numeric vector for the Correlates of War state code for the first state

ccode2

a numeric vector for the Correlates of War state code for the second state

styear

the start year for the arms race

endyear

the end year for the arms race

Details

Data are taken from the appendix of Gibler, Rider, and Hutchison's 2005 article in Journal of Peace Research. Read the article and appendix for more information about coding procedures.

References

Gibler, Douglas M., Toby J. Rider, and Marc L. Hutchison. 2005. "Taking Arms Against a Sea of Troubles: Conventional Arms Races during Periods of Rivalry" Journal of Peace Research 42(2): 131–47.

Description

This is a complete list of capitals and capital transitions for Gleditsch-Ward state system members. I use it internally for calculating capital-to-capital distances in the add_capital_distances() function.

Usage

gw_capitals

Format

A data frame with 248 observations on the following 7 variables.

gwcode

a numeric vector for the Gleditsch-Ward state code

statenme

a character vector for the state

capital

a character vector for the name of the capital

styear

a character vector for the start year. See details section for more information.

endyear

a character vector for the end year. See details section for more information.

lat

a numeric vector of the latitude coordinates for the capital

lng

a numeric vector of the longitude coordinates for the capital

Details

For convenience, the start year for most states is 1816. Samoa, for example, was not a state in 1816. However, the functions that use the gw_capitals data will not create observations for states that did not exist at a given point in time.

The data should be current as of the end of 2020.

Cases where a start year is not 1816 indicate a capital transition. For example, Brazil's capital moved from Rio de Janeiro to Brasilia (a planned capital) in 1960. Only 25 states in the data experienced a capital transition. The most recent was Burundi in 2018. Indonesia, as of writing, is planning on a capital transition, but this has not been completed yet.

Kazakhstan renamed its capital for the state leader in 2019. These data retain the name of Astana. This will be changed in the event the software I use records this change.

The capitals data are not without some peculiarities. Prominently, Portugal transferred the Portuguese court from Lisbon to Rio de Janeiro from 1808 to 1821. This is recorded in the data. A knowledge of the inter-state conflict data will note there was no war or dispute between, say, Portugal and Spain (or Portugal and any other country) at any point during this time, but it does create some weirdness that would suggest a massive distance between two countries, like Portugal and Spain, that are otherwise land-contiguous.

On Spain: the republican government moved the capital at the start of the civil war (in 1936) to Valencia. However, it abandoned this capital by 1937. I elect to not record this capital transition.

On Myanmar: the Gleditsch-Ward system stands out as having Myanmar entered for the bulk of the 19th century. The capitals recorded for Myanmar (Burma) coincide with capitals of the Konbaung dynasty.

The data also do some (I think) reasonable back-dating of capitals to coincide with states in transition without necessarily formal capitals by the first appearance in the state system membership data. These concern Lithuania, Kazakhstan, and the Philippines. Kaunas is the initial post-independence capital of Lithuania. Almaty is the initial post-independence capital of Kazakhstan. Quezon City is the initial post-independence capital of the Philippines. This concerns, at the most, one or two years for each of these three countries.

Description

This is a complete (I believe) data set on Gleditsch-Ward states and Correlates of War states, a byproduct of a full_join() between gw_states and cow_states that leans largely on the state abbreviation variable.

Usage

gw_cow_years

Format

A data frame with 18425 observations on the following 6 variables.

gwcode

a Gleditsch-Ward state code

stateabb

the state abbreviation, which was the greatest source of agreement between both data sets

gw_statename

the state name as it appears in the Gleditsch-Ward data

ccode

a Correlates of War state code

cow_statename

the state name as it appears in the Correlates of War data

year

a numeric vector for the year

Details

The data-raw directory on the project's Github contains more information about how these data were created. I'm going to use it for internal stuff. The workflow is going to treat the Gleditsch-Ward state system membership codes as more of the "master" codes, for which the user can add Correlates of War identifiers as they see fit. Data are extended to 2020, assuming no changes to state system membership for either data set.

Description

This is a complete directed dyad-year data frame of Gleditsch-Ward state system members. I offer it here as a shortcut for various other functions. As a general rule, this data frame is updated after every calendar year to include the most recently concluded calendar year.

Usage

gw_ddy

Format

A data frame with the following 3 variables.

gwcode1

a numeric vector for the Correlates of War state code for the first state

gwcode2

a numeric vector for the Correlates of War state code for the second state

year

a numeric vector for the year

Details

Data are a quick generation from the create_dyadyears(system="gw") function in this package.

Description

These are non-directed dyad-year data for the minimum distance between states in the Gleditsch-Ward state system from 1886 to 2018. The data are generated from the cshapes package.

Usage

gw_mindist

Format

A data frame with 868813 observations on the following 4 variables.

gwcode1

the Gleditsch-Ward state system code for the first state

gwcode2

the Gleditsch-Ward state system code for the second state

year

the year

mindist

the minimum distance between states on Jan. 1 of the year, in kilometers

Details

The data are generated from the cshapes package. The package authors purport that the data are generated to be compatible with the Gleditsch-Ward system. I trust them on this; indeed, Gleditsch is one of the authors of the cshapes package.

Data are automatically generated (by default) as directed dyad-years. I elect to make them non-directed for space considerations. Making non-directed dyad-year data into directed dyad-year data isn't too difficult in R. It just looks weird to see the code that does it.

Previous versions of these data were for the minimum distance as of Dec. 31 of the referent year. These are now Jan. 1. Most of the data I prove elsewhere in this package are to be understood as the data as they were at the start of the year. This is how I process, for example, the capitals data as they get merged in the add_capital_distance() function. However, the script that generates these data are set at Jan. 1 of the year and not Dec. 31. Right now, the cshapes does not appear to work on my system and I do not know why. Fortunately, the package authors made these data available.

References

Schvitz, Guy, Luc Girardin, Seraina Ruegger, Nils B. Weidmann, Lars-Erik Cederman, and Kristian Skrede Gleditsch. 2022. "Mapping The International System, 1886-2017: The CShapes 2.0 Dataset." Journal of Conflict Resolution. 66(1): 144-161.

Weidmann, Nils B. and Kristian Skrede Gleditsch. 2010. "Mapping and Measuring Country Shapes: The cshapes Package." The R Journal 2(1): 18-24

Description

These are state-year level data for surplus and gross domestic product for Correlates of War state system members. Data also include population estimates for per capita standardization.

Usage

gw_sdp_gdp

Format

A data frame with 27387 observations on the following five variables.

gwcode

a numeric vector for the Gleditsch-Ward state code

year

a numeric vector for the year

wbgdp2011est

a numeric vector for the estimated natural log of GDP in 2011 USD (log-transformed)

wbpopest

a numeric vector for the estimated population size (log-transformed)

sdpest

a numeric vector for the estimated surplus domestic product (log-transformed)

wbgdppc2011est

a numeric vector for the estimated GDP per capita (log-transformed)

Details

These were provided by Anders on a separate Github repository for this project. Because these data are ultimately being simulated, a user can expect some slight differences between the Correlates of War version of these data (which Anders et al. published) and the Gleditsch-Ward version of these data (which appear to be the one the authors will more vigorously support going forward).

Space considerations compel me to round these data to three decimal points. These "economic" data are routinely the biggest in the package, and it's because of the decimal points. The justification for this is these data are estimated/simulated anyways and the information loss is at the 1/1000th decimal point. This procedure basically cuts the size of the data to be less than 25% of its original size. The original simulations are available for remote download if you'd like. Type ?download_extdata() for more information.

References

Anders, Therese, Christopher J. Fariss, and Jonathan N. Markowitz. 2020. "Bread Before Guns or Butter: Introducing Surplus Domestic Product (SDP)" International Studies Quarterly 64(2): 392–405.

Description

These are the independent states in Gleditsch and Ward's data.

Usage

gw_states

Format

A data frame with 216 observations on the following 5 variables.

gwcode

a numeric vector for the Gleditsch-Ward country code

stateabb

a character vector for state abbreviation

statename

a character vector for the state name

startdate

the start date in the data

enddate

the end date in the data

Details

Data originally provided by Gleditsch with no column names. Column names were added before some light re-cleaning in order to generate these data. "Wuerttemberg" and "Cote D'Ivoire" in the statename column needed to be renamed to ensure maximal compliance with CRAN, which raises notes for every non-ASCII character that appears in its package. I do not think this to be problematic at all and, after all, state names should never be a basis for something like a match or merge you would do in countrycode.

References

Gleditsch, Kristian S. and Michael D. Ward. 1999. "A Revised List of Independent States since the Congress of Vienna." International Interactions 25(4): 393–413.

Description

These are democracy data for all Correlates of War state system members.

Usage

gwcode_democracy

Format

A data frame with 18289 observations on the following 5 variables.

gwcode

the Gleditsch-Ward system code

year

a numeric vector for the year

v2x_polyarchy

the Varieties of Democracy "polyarchy" estimate

polity2

the the polity2 score from the Polity project

xm_qudsest

an extension of the Unified Democracy Scores (UDS) estimates, made possibly by the QuickUDS package from Xavier Marquez.

Details

Missing data connote data that are unavailable for various reasons. Either there is no democracy data to code or, in the case of the Polity project, the state system member is outright not evaluated for the variable.

The Polity data are from 2017. The Varieties of Democracy data are version 10. Xavier Marquez' QuickUDS estimates (i.e. extensions of Pemstein et al. (2010)) come from a package Marquez makes available on his Github (https://github.com/xmarquez/QuickUDS).

References

Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Luhrmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Agnes Cornell, Lisa Gastaldi, Haakon Gjerlow, Valeriya Mechkova, Johannes von Romer, Aksel Sundtrom, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2020. "V-Dem Codebook v10" Varieties of Democracy (V-Dem) Project.

Marshall, Monty G., Ted Robert Gurr, and Keith Jaggers. 2017. "Polity IV Project: Political Regime Characteristics and Transitions, 1800-2017." Center for Systemic Peace.

Marquez, Xavier, "A Quick Method for Extending the Unified Democracy Scores" (March 23, 2016). doi:10.2139/ssrn.2753830

Pemstein, Daniel, Stephen Meserve, and James Melton. 2010. "Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type." Political Analysis 18(4): 426-449.

Description

This is a data set with state-year estimates for ethnic fractionalization.

Usage

hief

Format

A data frame with 8808 observations on the following 5 variables.

ccode

a Correlates of War state code

gwcode

a Gleditsch-Ward state code

year

the year

efindex

a numeric vector for the estimate of ethnic fractionalization

Details

The data-raw directory on the project's Github contains more information about how these data were created.

References

Drazanova, Lenka. 2020. "Introducing the Historical Index of Ethnic Fractionalization (HIEF) Dataset: Accounting for Longitudinal Changes in Ethnic Diversity." Journal of Open Humanities Data 6:6 doi:10.5334/johd.16

Description

These are an abbreviated version of the LEAD Data Set, incorporating variables that I think are most interesting or potentially useful from these data.

Usage

LEAD

Format

A data frame with 3409 observations on the following 12 variables.

obsid

an observational ID from archigos

leveledu

0 = primary, 1 = secondary, 2 = university, 3 = graduate

milservice

did leader have prior military service?

combat

did leader have prior combat experience in military service?

rebel

was leader previously part of a rebel group?

warwin

was leader previously part of a winning war effort as part of military service?

warloss

was leader previously part of a losing war effort as part of military service?

rebelwin

was leader previously part of a winning war effort as part of a rebel group?

rebelloss

was leader previously part of a losing war effort as part of a rebel group?

yrsexper

previous years of experience in politics before becoming a leader

physhealth

does leader have physical health issues?

mentalhealth

does leader have mental health issues?

Details

Data are ported from Ellis et al. (2015). Users who want more of these variables included in peacesciencer should raise an issue on Github.

References

Ellis, Carli Mortenson, Michael C. Horowitz, and Allan C. Stam. 2015. "Introducing the LEAD Data Set." International Interactions 41(4): 718–741.

Description

This is a simple data set that matches, as well as one can, leader codes across Archigos 4.1, Archigos 2.9, and the LEAD data set.

Usage

leader_codes

Format

A data frame the following four variables.

obsid

the observation ID in the Archigos data

leadid

the leader ID in version 4.1 of the Archigos data

leadid29

the leader ID in version 2.9 of the Archigos data

leaderid

the leader ID in the LEAD data

Details

These data treat version 4.1 of the Archigos data as the gospel leader data (if you will) for which the observation ID (obsid) is the master code indicating a leader tenure period. It also builds in an assumption that various observations that duplicate in the LEAD data should not have duplicated. This concerns Francisco Aguilar Barquer (who appears twice), Emile Reuter (who appears twice), and Gunnar Thoroddsen (who appears three times) in the LEAD data despite having uninterrupted tenures in office. None of the covariates associated with these leaders change in the LEAD data, which is why I assume they were duplicates.

Description

These are the estimates of leader willingness to use force as estimated by Carter and Smith (2020).

Usage

lwuf

Format

A data frame with 3409 observations on the following 9 variables.

obsid

an observational ID from archigos

theta1_mean

the mean simulated M1 theta, as estimated by Carter and Smith (2020)

theta1_sd

the standard deviation of simulated M1 thetas

theta2_mean

the mean simulated M2 theta, as estimated by Carter and Smith (2020)

theta2_sd

the standard deviation of simulated M2 thetas

theta3_mean

the mean simulated M3 theta, as estimated by Carter and Smith (2020)

theta3_sd

the standard deviation of simulated M3 thetas

theta4_mean

the mean simulated M4 theta, as estimated by Carter and Smith (2020)

theta4_sd

the standard deviation of simulated M4 thetas

Details

The letter published by the authors contains more information as to what these thetas refer. The "M1" theta is a variation of the standard Rasch model from the boilerplate information in the LEAD data. The authors consider this to be "theoretically relevant" or "risk-related" as these all refer to conflict or risk-taking. The "M2" theta expands on "M1" by including political orientation and psychological characteristics. "M3" and "M4" expand on "M1" and "M2" by considering all 36 variables in the LEAD data.

The authors construct and include all these measures, though their analyses suggest "M2" is the best-performing measure.

References

Carter, Jeff and Charles E. Smith, Jr. 2020. "A Framework for Measuring Leaders' Willingness to Use Force." American Political Science Review 114(4): 1352–1358.

Description

These are Zeev Maoz' data for what states are regional or global powers at a given point time. They are extensions of the Correlates of War major power data, which only codes "major" power without consideration of regional or global distinctions. Think of Austria-Hungary as intuitive of the issue here. Austria-Hungary is a major power in the Correlates of War data, but there is good reason to treat Austria-Hungary as a major power only within Europe. That is what Zeev Maoz tries to do here.

Usage

maoz_powers

Format

A data frame with 20 observations on the following 5 variables.

ccode

a numeric vector for the Correlates of War country code

regstdate

the start date for regional power status

regenddate

the end date for regional power status

globstdate

the start date for global power status

globenddate

the end date for global power status

References

Maoz, Zeev. 2010. Network of Nations: The Evolution, Structure, and Impact of International Networks, 1816-2001. Cambridge University Press.

Description

This is a BibTeX file, loaded as a data frame, to assist the user in properly citing the source material that is used in this package.

Usage

ps_bib

Format

A data frame with the following columns.

CATEGORY

the BibTeX entry type

BIBTEXKEY

the BibTeX unique entry key

ADDRESS

another BibTeX field

ANNOTE

another BibTeX field

AUTHOR

a list of authors for this entry

BOOKTITLE

another BibTeX field, for book title (if appropriate)

CHAPTER

another BibTeX field, for chapter (if appropriate)

CROSSREF

another BibTeX field

EDITION

another BibTeX field, for edition of book (if appropriate)

EDITOR

another BibTeX field, for book editor (if appropriate)

HOWPUBLISHED

another BibTeX field

INSTITUTION

another BibTeX field

JOURNAL

another BibTeX field, for the journal name (if appropriate)

KEY

another BibTeX field

MONTH

another BibTeX field

NOTE

another BibTeX field

NUMBER

another BibTeX field, for journal volume number (if appropriate)

ORGANIZATION

another BibTeX field

PAGES

another BibTeX field, for pages of the entry

PUBLISHER

another BibTeX field, for book publisher (if appropriate)

SCHOOL

another BibTeX field

SERIES

another BibTeX field

TITLE

another BibTeX field, for title of the entry

TYPE

another BibTeX field

VOLUME

another BibTeX field, for journal volume (if appropriate)

YEAR

another BibTeX field, for year of publication

KEYWORDS

another BibTeX field, used primarily for selective filtering in this package

URL

another BibTeX field, for website (if appropriate)

OWNER

another BibTeX field

TIMESTAMP

another BibTeX field, used occasionally when I started populating my master file (you will see some old entries here)

DOI

another BibTeX field, for a digital object identifier (used rarely)

EPRINT

another BibTeX field

JOURNALTITLE

another BibTeX field, which I think is actually a BibLaTeX field

ISSN

another BibTeX field

ABSTRACT

another BibTeX field, for entry abstract (if appropriate)

DATE.ADDED

another BibTeX field

DATE.MODIFIED

another BibTeX field

Details

See data-raw directory for how these data were generated. The data were created by bib2df, which is now a package dependency. I assume the user has some familiarity with BibTeX. Some entries were copy-pasted from my master bibliography file that I started in 2008 or so.

Description

ps_cite() allows the user to get citations to scholarship that they should include in their papers that incorporate the functions and data in this package.

Usage

ps_cite(x, column = "keywords")

Arguments

Details

The base functionality here is simple pattern-matching on keywords in ps_bib. This simple pattern-matching is in base R. I assume the user has some familiarity with BibTeX.

Value

ps_cite() takes a character vector and scans the ps_bib data in this package to return a BibTeX citation (or citations) for the researcher to use to properly cite the material they are getting from this package. The citations are returned as a full BibTeX entry (or entries) that they can copy-paste into their own BibTeX file.

Author(s)

Steven V. Miller

Examples

# Cite the package
ps_cite("peacesciencer")

Description

This is a simple data set that communicates the version numbers of data included in this package. It's a companion to the data frame ps_bib, and other information functions like ps_cite() and ps_version(). The latter uses this data set.

Usage

ps_data_version

Format

A data frame the following four variables.

category

a category for the type of data

data

the name of the particular data source coinciding with the category

version

the version number included in peacesciencer for this data source

bibtexkey

a character key for the BibTeX key corresponding with an appropriate citation in ps_bib

Details

Version numbers that are years should be understood as data sources with no formal version numbering system, per se. Instead, they communicate a year of last update. For example, the Correlates of War does not formally version number its state system data as it does its MID data. Likewise, the Anders et al. (2020) simulations of population and surplus/gross domestic product are not formally versioned, per se. Instead, the data were published and last updated in 2020.

Description

ps_version() allows the user to see version information about data included in peacesciencer.

Usage

ps_version(cat)

Arguments

Details

The base functionality here is simple pattern-matching on keywords in ps_data_version. This simple pattern-matching is in base R. I assume the user has some familiarity with the types of data included in this package.

The searching is done by category included in the ps_data_version data. Users may want to just minimally run ps_version() with no argument specified to see for themselves what's in it. Typing unique(ps_data_version$category) may also get them started.

The user can consider this a companion function to ps_cite(). Whereas ps_cite() will return the appropriate citation to use in the bibliography, it may not tell them the version number at all. For example, the classic and suggested citations for the Correlates of War National Material Capabilities data are too Singer et al. (1972) and Singer (1987), though the data included in this package are about 30 years older than the most recent citation of the two.

The information communicated here can/should be included alongside a parenthetical citation. For example, the contiguity data are quite a bit more current than the suggested citation to Stinnett et al. (2002). Thus, a user may want to cite the data in their paper as something like (Stinnett et al. 2002, v. 3.2).

Value

ps_version() takes a character vector and scans the ps_data_version data in this package to return information about the particular data versions included in peacesciencer as well as a suggested citation key for scanning ps_cite(). If no category is specified for searching, it just returns all version information for all data included in functions in this package.

Author(s)

Steven V. Miller

Examples

# What can you search for...
unique(ps_data_version$category)

# will show the data versions for everything
ps_version()

# will show data versions for particular categories of data
ps_version("democracy")

ps_version("leaders")

Description

This is a data set on state-level estimates for the "ruggedness" of a state's terrain.

Usage

rugged

Format

A data frame with 192 observations on the following 6 variables.

ccode

a Correlates of War state code

gwcode

a Gleditsch-Ward state code

rugged

the terrain ruggedness index

newlmtnest

the (natural log) percentage estimate of the state's terrain that is mountainous

Details

The data-raw directory on the project's Github contains more information about how these data were created. It goes without saying that these data move slowly so the data are really only applicable for making state-to-state comparisons and not states-in-time comparisons. The terrain ruggedness index is originally introduced by Riley et al. (1999) but is amended by Nunn and Puga (2012). The mountain terrain data was originally created by Fearon and Laitin (2003) but extended and amended by Gibler and Miller (2014). The data are functionally time-agnostic—use with caution in your state-year analyses—but all data sets seem to benchmark around 1999-2000. I'm not sure it matters that much, but it matters a little at the margins, I suppose, if you suspect there are major differences in interpretation of how much more "rugged" the Soviet Union was than Russia, or Yugoslavia than Serbia.

References

Fearon, James D., and David Laitin, "Ethnicity, Insurgency, and Civil War" American Political Science Review 97: 75–90.

Gibler, Douglas M. and Steven V. Miller. 2014. "External Territorial Threat, State Capacity, and Civil War." Journal of Peace Research 51(5): 634-646.

Nunn, Nathan and Diego Puga. 2012. "Ruggedness: The Blessing of Bad Geography in Africa." Review of Economics and Statistics. 94(1): 20-36.

Riley, Shawn J., Stephen D. DeGloria, and Robert Elliot. 1999. "A Terrain Ruggedness Index That Quantifies Topographic Heterogeneity,” Intermountain Journal of Sciences 5: 23–27.

Description

show_duplicates() shows which data are duplicated in data generated in peacesciencer. It's a useful diagnostic tool for users doing some do-it-yourself functions with peacesciencer.

Usage

show_duplicates(data)

Arguments

Details

The function leans on attributes of the data that are provided by the create_dyadyear() or create_stateyear() function. Make sure that function (or data created by that function) appear at the top of the proverbial pipe.

The data returned will also have a new column called duplicated. Thus, an implicit assumption in this function is the user does not have a column in the data with this name that is of interest to the user. It will be overwritten.

Value

show_duplicates() takes a dyad-year data frame or state-year data frame generated in peacesciencer and shows what observations are duplicated by unique combination of dyad-year or state-year, contingent on what was supplied to it.

Author(s)

Steven V. Miller

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

gml_dirdisp %>% show_duplicates()
cow_mid_dirdisps %>% show_duplicates()

Description

A simple summary of all strategic (inter-state) rivalries from Thompson and Dreyer (2012).

Usage

td_rivalries

Format

A data frame with 197 observations on the following 10 variables.

rivalryno

a numeric vector for the rivalry number

rivalryname

a character vector for the rivalry name

ccode1

the Correlates of War state code for the state with the lowest Correlates of War state code in the rivalry

ccode2

the Correlates of War state code for the state with the highest Correlates of War state code in the rivalry

styear

a numeric vector for the start year of the rivalry

endyear

a numeric vector for the end year of the rivalry

region

a character vector for the region of the rivalry, per Thompson and Dreyer (2012)

type1

a character vector for the primary type of the rivalry (spatial, positional, ideological, or interventionary)

type2

a character vector for the secondary type of the rivalry, if applicable (spatial, positional, ideological, or interventionary)

type3

a character vector for the tertiary type of the rivalry, if applicable (spatial, positional, ideological, or interventionary)

Details

Information gathered from the appendix of Thompson and Dreyer (2012). Ongoing rivalries are right-bound at 2010, the date of publication for Thompson and Dreyer's handbook. Users are free to change this if they like. Data are effectively identical to strategic_rivalries in stevemisc, but include some behind-the-scenes processing (described in a blog post on http://svmiller.com) that is available to see on the project's Github repository. The data object is also renamed to avoid a conflict.

References

Miller, Steven V. 2019. "Create and Extend Strategic (International) Rivalry Data in R". URL: http://svmiller.com/blog/2019/10/create-extend-strategic-rivalry-data-r/

Thompson, William R. and David Dreyer. 2012. Handbook of International Rivalries. CQ Press.

Description

A simple summary of all strategic (inter-state) rivalries from Thompson et al. (2021). This is a simple spreadsheet entry job (with some light cleaning) based on information provided from pages 34 to 46 in their book.

Usage

tss_rivalries

Format

A data frame with 264 observations on the following 12 variables.

tssr_id

a numeric vector for the rivalry number

rivalry

a character vector for the rivalry name

ccode1

the Correlates of War state code for the state with the lowest Correlates of War state code in the rivalry

ccode2

the Correlates of War state code for the state with the highest Correlates of War state code in the rivalry

start

a numeric vector for the start year of the rivalry

end

a numeric vector for the end year of the rivalry

positional

a numeric vector that is 1 if Thompson et al. (2021) say the rivalry has a positional element (NA otherwise)

spatial

a numeric vector that is 1 if Thompson et al. (2021) say the rivalry has a spatial element (NA otherwise)

ideological

a numeric vector that is 1 if Thompson et al. (2021) say the rivalry has an ideological element (NA otherwise)

interventionary

a numeric vector that is 1 if Thompson et al. (2021) say the rivalry has an interventionary element (NA otherwise)

principal

a numeric vector that is 1 if Thompson et al. (2021) say the rivalry is the primary (principal) rivalry for the rivals (NA otherwise)

aprin

a numeric vector that is 1 if Thompson et al. (2021) say this is an asymmetric principal rivalry (NA otherwise)

Details

Information gathered from chapter 2 of Thompson et al. (2021). Ongoing rivalries are right-bound at 2020. In several cases, start dates of 1494 and 1816 originally had a "P" attached to them, indicating they were ongoing before that particular year. This is captured in the "raw" spreadsheet included in the "data-raw" directory, though this is adjusted in this finished data product.

This file adjusts for what is assuredly two print errors in Thompson et al. (2021). In print, Thompson et al. (2021) say the Italy-Turkey rivalry extends from 1884-1843 and say the Mauritania-Morocco rivalry extends from 1060-1969. They had meant an end year of 1943 in the first case and a start year of 1960 in the second case. This is fixed in this version.

Venice never appears in any data set in the Correlates of War ecosystem of data, but I gave it a country code of 324 for the sake of these data (and the previous Thompson and Dreyer (2012) version of it). You'll never use this, but it's worth saying out loud that's what I did.

Thompson et al. (2021) dedicate their book to expanding on the various types of rivalry. Users who know the Thompson and Dreyer (2012) version will see a few differences here. First, rivalries no longer have formal primary, secondary, or tertiary types. Instead, rivalries have there/not there markers for whether a particular element of a rivalry type is present in the rivalry. From what I've read so far of Thompson et al. (2021), along with their ordering of the information in Chapter 2, it reads like they've just made informal what was otherwise a more formal classification component to the Thompson and Dreyer (2012) rivalry data. Positional rivalries seem to be an informal "type 1" as Thompson et al. (2021) discuss it, not at all dissimilar to how the classic alliance scholarship treats defense as a "type 1" pledge. No matter, this book is already more explicit that positional and spatial rivalries are clearly different from ideological and interventionary rivalries, and certainly the interventionary rivalries.

"Principal" and "asymmetric principal" rivalries are a new classification in Thompson et al. (2021), relative to Thompson and Dreyer (2012). "Principal" rivalries exist where 1) the two rivals have no other rivalry or 2) the two rivals elevate this rivalry as their primary rivalry among other rivalries. Asymmetric principal rivalries are when only one of the two rivals sees the other as its primary rival. Consider two U.S.-Russian rivalries as illustrative. Tge rivalry with the Soviet Union (tssr_id = 100) was the primary rivalry for the U.S. (and the Soviet Union). However, the U.S. presently sees China as its main rival (tssr_id = 211). The ongoing rivalry with Russia (tssr_id = 246) is one where Russia sees the U.S. as its primary rival but the U.S. does not see Russia the same way.

References

Miller, Steven V. 2019. "Create and Extend Strategic (International) Rivalry Data in R". URL: http://svmiller.com/blog/2019/10/create-extend-strategic-rivalry-data-r/

Thompson, William R., Kentaro Sakuwa, and Prashant Hosur Suhas. 2021. Analyzing Strategic Rivalries in World Politics: Types of Rivalry, Regional Variation, and Escalation/De-escalation. Springer.

Description

These are (kind of) dyadic, but mostly state-level data, used internally for doing stuff with the UCDP armed conflict data

Usage

ucdp_acd

Format

A data frame with 4164 observations on the following 15 variables.

conflict_id

a conflict identifier, not to be confused with an episode identifier (which I don't think UCDP offers)

year

a numeric vector for the year

gwno_a

the Gleditsch-Ward state code for the state on side A of the armed conflict

gwno_a_2nd

the Gleditsch-Ward state code for the state that actively supported side A of the armed conflict with the use of troops

gwno_b

the Gleditsch-Ward state code for the actor on side B of the armed conflict

gwno_b_2nd

the Gleditsch-Ward state code for the state that actively supported side B of the armed conflict with the use of troops

incompatibility

a character vector for the main conflict issue ("territory", "government", "both")

intensity_level

a numeric vector for the intensity level in the calendar year (1 = minor (25-999 deaths), 2 = war (>1,000 deaths))

type_of_conflict

a character vector for the type of conflict ("extrasystemic", "interstate", "intrastate", "II"). "II" is a simple abbreviation of "internationalized intrastate"

start_date

a date of the first battle-related death in the conflict, not to be confused with the first battle-related death of the episode

start_prec

the level of precision for start_date

start_date2

a date of the first battle-related death in the episode, not to be confused with the first battle-related death of the conflict

start_prec2

the level of precision for start_date2

ep_end

a dummy variable for whether the conflict episode ended in the calendar year of observation

ep_end_date

the episode end date, if applicable

Details

The data-raw directory on the project's Github will show how I processed the multiple strings for when there are multiple states on a given side.

References

Gleditsch, Nils Petter; Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Havard Strand (2002) Armed Conflict 1946–2001: A New Dataset. Journal of Peace Research 39(5): 615–637.

Pettersson, Therese; Stina Hogbladh & Magnus Oberg (2019). Organized violence, 1989-2018 and peace agreements. Journal of Peace Research 56(4): 589-603.

Description

These are state-year level data for armed conflict onsets provided by the Uppsala Conflict Data Program (UCDP).

Usage

ucdp_onsets

Format

A data frame with 10142 observations on the following eight variables.

gwcode

a numeric vector for the Gleditsch-Ward state code

year

a numeric vector for the year

sumnewconf

a numeric vector for the sum of new conflicts/conflict-dyads

sumonset1

a numeric vector for the sum of new conflict episodes, whether because this is a new conflict or because there is more than one year since last conflict episode

sumonset2

a numeric vector for the sum of new conflict episodes, whether because this is a new conflict or because there is more than two years since last conflict episode

sumonset3

a numeric vector for the sum of new conflict episodes, whether because this is a new conflict or because there is more than three years since last conflict episode

sumonset5

a numeric vector for the sum of new conflict episodes, whether because this is a new conflict or because there is more than five years since last conflict episode

sumonset10

a numeric vector for the sum of new conflict episodes, whether because this is a new conflict or because there is more than 10 years since last conflict episode

Details

The user will want to note that the data provided by UCDP are technically not country-year observations. They instead duplicate observations for cases of new conflicts or new conflict episodes. Further, the original data do not provide any information about the conflict-dyad in question to which those duplicates pertain. That means the most these data can do for the package's mission is provide summary information. The user should probably recode these variables into something else they may want for a particular application

References

Gleditsch, Nils Petter; Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Havard Strand (2002) Armed Conflict 1946–2001: A New Dataset. Journal of Peace Research 39(5): 615–637.

Pettersson, Therese; Stina Hogbladh & Magnus Oberg (2019). Organized violence, 1989-2018 and peace agreements. Journal of Peace Research 56(4): 589-603.

Description

whittle_conflicts_duration() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will keep the observations with the highest estimated duration.

Usage

whittle_conflicts_duration(data, durtype = "mindur")

wc_duration(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

Some conflicts can be of an unknown length and often come with estimates of a minimum duration and a maximum duration. This will concern the durtype parameter in this function. In many/most conflicts, certainly thinking of the inter-state dispute data, dates are known with precision (to the day) and the estimate of minimum conflict duration is equal to the estimate of maximum conflict duration. For some conflicts, the estimates will vary. This does importantly imply that using this particular whittle function with the default (mindur) will produce different results than using this particular whittle function and asking to retain the highest maximum duration (maxdur). Use the function with that in mind.

wc_duration() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_duration() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have the highest observed dispute-level fatality. This will not eliminate all duplicates, far from it, but it's a sensible cut later into the procedure (after whittling onsets in whittle_conflicts_onsets(), and maybe some other things the extent to which dispute-level duration is a heuristic for dispute-level severity/importance.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_duration()

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_duration()

Description

whittle_conflicts_fatality() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will keep the observations with the highest observed fatality.

Usage

whittle_conflicts_fatality(data)

wc_fatality(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

As of writing, the Correlates of War and Gibler-Miller-Little conflict data record some -9s for fatalities. In those cases, dispute-level fatality is momentarily recoded to be .5 (i.e. fatal, but without too many fatalities). This is a missing data problem that Gibler and Miller correct in a forthcoming publication in Journal of Conflict Resolution. Until then, this function makes that kind of determination about disputes with missing fatalities.

wc_fatality() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_fatality() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have the highest observed dispute-level fatality. This will not eliminate all duplicates, far from it, but it's a sensible second cut (after whittling onsets in whittle_conflicts_onsets() the extent to which dispute-level fatality is a good heuristic for dispute-level severity/importance.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_fatality()

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_fatality()

Description

whittle_conflicts_hostility() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will keep the observations with the highest observed hostility.

Usage

whittle_conflicts_hostility(data)

wc_hostility(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

wc_hostility() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_hostility() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have the highest observed dispute-level fatality. This will not eliminate all duplicates, far from it, but it's a sensible second or third cut (after whittling onsets in whittle_conflicts_onsets() the extent to which dispute-level hostility is a good heuristic for dispute-level severity/importance.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_hostility()

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_hostility()

Description

whittle_conflicts_jds() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will just drop something, as a kind of nuclear option.

Usage

whittle_conflicts_jds(data)

wc_jds(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

This really should be the absolute last exclusion rules a researcher uses. It's a "nuclear option", if you will. Assuming you've run other case exclusion rules to isolate onsets and severe disputes, what remains at the end should be duplicates that are functionally equivalent observations. Your data cannot have duplicates, and these remaining observations are basically the same. Therefore, just drop something.

wc_jds() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_jds() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have the lowest start month.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_jds()

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_jds()

Description

whittle_conflicts_reciprocation() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will drop ongoing conflicts in the presence of unique onsets.

Usage

whittle_conflicts_onsets(data)

wc_onsets(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

wc_onsets() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_onsets() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations with unique onsets where duplicates exist. This will not eliminate all duplicates, far from it, but it's a sensible place to start.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets()

cow_mid_dirdisps %>% whittle_conflicts_onsets()

Description

whittle_conflicts_reciprocation() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will keep the observations that are reciprocated (i.e. have militarized actions on both sides of the conflict).

Usage

whittle_conflicts_reciprocation(data)

wc_recip(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

Scholars are free to use this as a heuristic for whittling conflict-year data to be coerced into true dyad-year data, but I would be remiss if I did not offer a caveat about the reciprocation variable in inter-state dispute data. Namely, it is noisy and is not doing what scholars often think it's doing in the inter-state dispute data. Reciprocation is observed only when there is a militarized action on both sides of the conflict. By definition, someone on Side A will have a militarized action. Not every state on Side B does. However, scholars should not interpret that as the absence of militarized responses. In a forthcoming article in Journal of Conflict Resolution, Doug Gibler and I make the case that reciprocation isn't a useful variable to maintain at all because it can only invite errors (as is often the case in the CoW-MID data) and will obscure the fact that states that are attacked by another side routinely fight back. On many occasions, they also successfully repel the attack. Scholars who uncritically use this variable, certainly for hypothesis-testing on audience costs, are borrowing trouble with this measure.

wc_recip() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_reciprocation() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have militarized actions on both sides of the conflict. This will not eliminate all duplicates, far from it, but it's a sensible cut later into the procedure (after whittling onsets in whittle_conflicts_onsets() the extent to which dispute-level reciprocation is a heuristic for dispute-level severity/importance (after some other considerations).

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_reciprocation()

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_reciprocation()

Description

whittle_conflicts_startmonth() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will keep the observations that have the lowest start month.

Usage

whittle_conflicts_startmonth(data)

wc_stmon(...)

Arguments

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

This really should be one of the last exclusion rules a researcher uses. There is no substantive reason to assume the lower start month matters for the cause of isolating "serious" or "severe" disputes in the presence of duplicates. It's really just a way of isolating which duplicated observation happened first where remaining duplicates are otherwise very similar to each other.

wc_stmon() is a simple, less wordy, shortcut for the same function.

Value

whittle_conflicts_startmonth() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have the lowest start month.

Author(s)

Steven V. Miller

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_startmonth()

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_startmonth()