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peacesciencer:
Tools and Data for Quantitative Peace Science
peacesciencer is an R package including various
functions and data sets to allow easier analyses in the field of
quantitative peace science. The goal is to provide an R package that
reasonably approximates what made EUGene so attractive to scholars
working in the field of quantitative peace science in the early 2000s.
EUGene shined because it encouraged replications of conflict models
while having the user also generate data from scratch. Likewise, this R
package will offer tools to approximate what EUGene did within the R
environment (i.e. not requiring Windows for installation).
You can install this on CRAN, as follows:
install.packages("peacesciencer")You can install the development version of this package through the
devtools package. The development version of the package
invariably has more goodies, but may or may not be at various levels of
stress-testing.
devtools::install_github("svmiller/peacesciencer"){peacesciencer}New users should read two things to get started. The package’s
website has an exhaustive list and description of all the
functions and data included in the package. {peacesciencer}
has a user’s guide that is worth reading. The user’s guide points to
its potential uses and benefits while also offering some encouragement
for those completely new to the R programming language. The package is
designed to be accessible to those with no prior experience with R,
though completely new users who feel lost or overwhelmed should learn
about the “tidy” approach to R
to help them get started.
The workflow is going to look something like this. First, start with
one of two processes to create either dyad-year or state-year data. The
dyad-year data are created with the create_dyadyears()
function. It has a few optional parameters with hidden defaults. The
user can specify what kind of state system (system) data
they want to use—either Correlates of War ("cow") or
Gleditsch-Ward ("gw"), whether they want to extend the data
to the most recently concluded calendar year (mry)
(i.e. Correlates of War state system membership data are current as of
Dec. 31, 2016 and the script can extend that to the end of the most
recently concluded calendar year), and whether the user wants directed
or non-directed dyad-year data (directed).
The create_stateyears() works much the same way, though
“directed” and “non-directed” make no sense in the state-year context.
Both functions default to Correlates of War state system membership data
to the most recently concluded calendar year.
Thereafter, the user can specify what additional variables they want added to these dyad-year or state-year data. Do note: the additional functions lean primarily on Correlates of War state code identifiers. Indeed, the bulk of the quantitative peace science data ecosystem is built around the Correlates of War project. The variables the user wants are added in a “pipe” in a process like this. Do note that the user may want to break up the data-generating process into a few manageable “chunks” (e.g. first generating dyad-year data and saving to an object, adding to it piece by piece).
Here’s what this will look like in operation. Assume you want to
create some data for something analogous to a “dangerous dyads” design
for all non-directed dyad-years. Here’s how you’d do it in
{peacesciencer}, which is going to be lifted from the
source R scripts for the user’s guide. The first part of this code chunk
will lean on core {peacesciencer} functionality whereas the
other stuff is some post-processing and, as a bonus, some modeling.
# library(tidyverse) # load this first for most/all things
# library(peacesciencer) # the package of interest
# library(stevemisc) # a dependency, but also used for standardizing variables for better interpretation
library(tictoc)
tic()
create_dyadyears(directed = FALSE, mry = FALSE) %>%
filter_prd() %>%
add_gml_mids(keep = NULL) %>%
add_peace_years() %>%
add_nmc() %>%
add_democracy() %>%
add_cow_alliance() %>%
add_sdp_gdp() -> Data
Data %>%
mutate(landcontig = ifelse(conttype == 1, 1, 0)) %>%
mutate(cowmajdyad = ifelse(cowmaj1 == 1 | cowmaj2 == 1, 1, 0)) %>%
# Create estimate of militarization as milper/tpop
# Then make a weak-link
mutate(milit1 = milper1/tpop1,
milit2 = milper2/tpop2,
minmilit = ifelse(milit1 > milit2,
milit2, milit1)) %>%
# create CINC proportion (lower over higher)
mutate(cincprop = ifelse(cinc1 > cinc2,
cinc2/cinc1, cinc1/cinc2)) %>%
# create weak-link specification using Quick UDS data
mutate(mindemest = ifelse(xm_qudsest1 > xm_qudsest2,
xm_qudsest2, xm_qudsest1)) %>%
# Create "weak-link" measure of jointly advanced economies
mutate(minwbgdppc = ifelse(wbgdppc2011est1 > wbgdppc2011est2,
wbgdppc2011est2, wbgdppc2011est1)) -> Data
# r2sd() is in {stevemisc}, a {peacesciencer} dependency.
# This is just for a more readable regression output.
Data %>%
mutate_at(vars("cincprop", "mindemest", "minwbgdppc", "minmilit"),
~r2sd(.)) -> Data
broom::tidy(modDD <- glm(gmlmidonset ~ landcontig + cincprop + cowmajdyad + cow_defense +
mindemest + minwbgdppc + minmilit +
gmlmidspell + I(gmlmidspell^2) + I(gmlmidspell^3), data= Data,
family=binomial(link="logit")))
#> # A tibble: 11 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -3.06 0.0635 -48.2 0
#> 2 landcontig 1.06 0.0568 18.7 4.21e- 78
#> 3 cincprop 0.455 0.0363 12.5 6.63e- 36
#> 4 cowmajdyad 0.144 0.0575 2.51 1.20e- 2
#> 5 cow_defense -0.119 0.0580 -2.04 4.09e- 2
#> 6 mindemest -0.499 0.0525 -9.51 1.93e- 21
#> 7 minwbgdppc 0.293 0.0511 5.72 1.06e- 8
#> 8 minmilit 0.255 0.0226 11.3 2.02e- 29
#> 9 gmlmidspell -0.147 0.00505 -29.0 5.33e-185
#> 10 I(gmlmidspell^2) 0.00247 0.000135 18.4 2.74e- 75
#> 11 I(gmlmidspell^3) -0.0000116 0.000000891 -13.0 1.16e- 38
toc()
#> 7.35 sec elapsedHere is how you might do a standard civil conflict analysis using Gleditsch-Ward states and UCDP conflict data.
tic()
create_stateyears(system = 'gw') %>%
filter(year %in% c(1946:2019)) %>%
add_ucdp_acd(type=c("intrastate"), only_wars = FALSE) %>%
add_peace_years() %>%
add_democracy() %>%
add_creg_fractionalization() %>%
add_sdp_gdp() %>%
add_rugged_terrain() -> Data
create_stateyears(system = 'gw') %>%
filter(year %in% c(1946:2019)) %>%
add_ucdp_acd(type=c("intrastate"), only_wars = TRUE) %>%
add_peace_years() %>%
rename_at(vars(ucdpongoing:ucdpspell), ~paste0("war_", .)) %>%
left_join(Data, .) -> Data
Data %>%
arrange(gwcode, year) %>%
group_by(gwcode) %>%
mutate_at(vars("xm_qudsest", "wbgdppc2011est",
"wbpopest"), list(l1 = ~lag(., 1))) %>%
rename_at(vars(contains("_l1")),
~paste("l1", gsub("_l1", "", .), sep = "_") ) -> Data
modCW <- list()
broom::tidy(modCW$"All UCDP Conflicts" <- glm(ucdponset ~ l1_wbgdppc2011est + l1_wbpopest +
l1_xm_qudsest + I(l1_xm_qudsest^2) +
newlmtnest + ethfrac + relfrac +
ucdpspell + I(ucdpspell^2) + I(ucdpspell^3), data=subset(Data),
family = binomial(link="logit")))
#> # A tibble: 11 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -5.10 1.35 -3.77 0.000160
#> 2 l1_wbgdppc2011est -0.285 0.110 -2.59 0.00953
#> 3 l1_wbpopest 0.229 0.0672 3.41 0.000644
#> 4 l1_xm_qudsest 0.257 0.181 1.43 0.154
#> 5 I(l1_xm_qudsest^2) -0.726 0.211 -3.44 0.000574
#> 6 newlmtnest 0.0549 0.0666 0.824 0.410
#> 7 ethfrac 0.442 0.358 1.23 0.217
#> 8 relfrac -0.389 0.402 -0.969 0.333
#> 9 ucdpspell -0.0738 0.0393 -1.88 0.0601
#> 10 I(ucdpspell^2) 0.00443 0.00205 2.16 0.0304
#> 11 I(ucdpspell^3) -0.0000602 0.0000280 -2.15 0.0316
broom::tidy(modCW$"Wars Only" <- glm(war_ucdponset ~ l1_wbgdppc2011est + l1_wbpopest +
l1_xm_qudsest + I(l1_xm_qudsest^2) +
newlmtnest + ethfrac + relfrac +
war_ucdpspell + I(war_ucdpspell^2) + I(war_ucdpspell^3), data=subset(Data),
family = binomial(link="logit")))
#> # A tibble: 11 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -6.59 2.08 -3.16 0.00156
#> 2 l1_wbgdppc2011est -0.343 0.172 -1.99 0.0463
#> 3 l1_wbpopest 0.272 0.106 2.56 0.0105
#> 4 l1_xm_qudsest -0.0847 0.270 -0.313 0.754
#> 5 I(l1_xm_qudsest^2) -0.761 0.352 -2.16 0.0307
#> 6 newlmtnest 0.342 0.112 3.05 0.00226
#> 7 ethfrac 0.333 0.554 0.601 0.548
#> 8 relfrac -0.281 0.593 -0.474 0.635
#> 9 war_ucdpspell -0.111 0.0562 -1.98 0.0478
#> 10 I(war_ucdpspell^2) 0.00466 0.00252 1.85 0.0643
#> 11 I(war_ucdpspell^3) -0.0000499 0.0000302 -1.65 0.0982
toc()
#> 2.315 sec elapsed{peacesciencer}You can (and should) cite what you do in
{peacesciencer}. The package includes a data frame of a
BibTeX file (ps_bib) and a function for
finding and returning BibTeX entries that you can include
in your projects. This is the ps_cite() function. The
ps_cite() function takes a string and does a partial match
for relevant keywords (as KEYWORDS) associated with entries
in the ps_bib file. For example, you can (and should) cite
the package itself.
ps_cite("peacesciencer")
#> @ARTICLE{peacesciencer-package,
#> AUTHOR = {Steven V. Miller},
#> JOURNAL = {Conflict Management and Peace Science},
#> TITLE = {peacesciencer}: An R Package for Quantitative Peace Science Research},
#> YEAR = {2022},
#> KEYWORDS = {peacesciencer, add_capital_distance(), add_ccode_to_gw(), add_gwcode_to_cow(), capitals},
#> URL = {http://svmiller.com/peacesciencer/}}You can see what are the relevant citations to consider using for the
data returned by add_democracy()
ps_cite("add_democracy()")
#> @UNPUBLISHED{coppedgeetal2020vdem,
#> AUTHOR = {Michael Coppedge and John Gerring and Carl Henrik Knutsen and Staffan I. Lindberg and Jan Teorell and David Altman and Michael Bernhard and M. Steven Fish and Adam Glynn and Allen Hicken and Anna Luhrmann and Kyle L. Marquardt and Kelly McMann and Pamela Paxton and Daniel Pemstein and Brigitte Seim and Rachel Sigman and Svend-Erik Skaaning and Jeffrey Staton and Agnes Cornell and Lisa Gastaldi and Haakon Gjerl{\o}w and Valeriya Mechkova and Johannes von R{\"o}mer and Aksel Sundtr{\"o}m and Eitan Tzelgov and Luca Uberti and Yi-ting Wang and Tore Wig and Daniel Ziblatt},
#> NOTE = {Varieties of Democracy ({V}-{D}em) Project},
#> TITLE = {V-Dem Codebook v10},
#> YEAR = {2020},
#> KEYWORDS = {add_democracy(), v-dem, varieties of democracy}}
#>
#> @UNPUBLISHED{marquez2016qme,
#> AUTHOR = {Xavier Marquez},
#> NOTE = {Available at SSRN: http://ssrn.com/abstract=2753830},
#> TITLE = {A Quick Method for Extending the {U}nified {D}emocracy {S}cores},
#> YEAR = {2016},
#> KEYWORDS = {add_democracy(), UDS, Unified Democracy Scores},
#> URL = {http://dx.doi.org/10.2139/ssrn.2753830}}
#>
#> @UNPUBLISHED{marshalletal2017p,
#> AUTHOR = {Monty G. Marshall and Ted Robert Gurr and Keith Jaggers},
#> NOTE = {University of Maryland, Center for International Development and Conflict Management},
#> TITLE = {Polity {IV} Project: Political Regime Characteristics and Transitions, 1800-2016},
#> YEAR = {2017},
#> KEYWORDS = {add_democracy(), polity}}
#>
#> @ARTICLE{pemsteinetal2010dc,
#> AUTHOR = {Pemstein, Daniel and Stephen A. Meserve and James Melton},
#> JOURNAL = {Political Analysis},
#> NUMBER = {4},
#> PAGES = {426--449},
#> TITLE = {Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type},
#> VOLUME = {18},
#> YEAR = {2010},
#> KEYWORDS = {add_democracy(), UDS, Unified Democracy Scores},
#> OWNER = {steve},
#> TIMESTAMP = {2011.01.30}}You can also return partial matches to see what citations are associated with, say, alliance data in this package.
ps_cite("alliance")
#> @BOOK{gibler2009ima,
#> AUTHOR = {Douglas M. Gibler},
#> PUBLISHER = {Washington DC: CQ Press},
#> TITLE = {International Military Alliances, 1648-2008},
#> YEAR = {2009},
#> KEYWORDS = {add_cow_alliance()}}
#>
#> @ARTICLE{leedsetal2002atop,
#> AUTHOR = {Bretty Ashley Leeds and Jeffrey M. Ritter and Sara McLaughlin Mitchell and Andrew G. Long},
#> JOURNAL = {International Interactions},
#> PAGES = {237--260},
#> TITLE = {Alliance Treaty Obligations and Provisions, 1815-1944},
#> VOLUME = {28},
#> YEAR = {2002},
#> KEYWORDS = {add_atop_alliance()}}This function might expand in complexity in future releases, but you
can use it right now for finding appropriate citations. You an also scan
the ps_bib data to see what is in there.
{peacesciencer} is already more than capable to meet a
wide variety of needs in the peace science community. Users are free to
raise an issue on the project’s Github if some feature is not performing
as they think it should or if there are additions they would like to
see.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.