Create Different Kinds of Data in `{peacesciencer}`
Source:vignettes/different-data-types.Rmd
different-data-types.RmdThis tutorial is a companion to the manuscript, which shows how to create different kinds of data in peacesciencer. However, space considerations (for ideal publication in a peer-reviewed journal) preclude the full “knitting” experience (i.e. giving the user a preview of what the data look like). What follows is a brief guide that expands on the tutorial section of the manuscript for creating different kinds of data in peacesciencer.
This vignette will lean on the tidyverse package, which will be included in almost anything you should do (optimally) with peacesciencer. I will also load lubridate. Internal functions in peacesciencer use lubridate—it is a formal dependency of peacesciencer—but users may want to load it for doing some additional stuff outside of peacesciencer.
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.1 ✔ readr 2.1.4
#> ✔ forcats 1.0.0 ✔ stringr 1.5.0
#> ✔ ggplot2 3.4.1 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
#> ✔ purrr 1.0.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(peacesciencer)
#> {peacesciencer} includes additional remote data for separate download. Please type ?download_extdata() for more information.
#> This message disappears on load when these data are downloaded and in the package's `extdata` directory.
library(lubridate)State-Year Data
The most basic form of data peacesciencer creates is
state-year, by way of create_stateyears().
create_stateyears() has two arguments: system
and mry. system takes either “cow” or “gw”,
depending on whether the user wants Correlates of War state years or
Gleditsch-Ward state-years. It defaults to “cow” in the absence of a
user-specified override given the prominence of Correlates of War data
in the peace science ecosystem. mry takes a logical
(TRUE or FALSE), depending on whether the user
wants the function to extend to the most recently concluded calendar
year (2022). The Correlates of War state system data extend to the end
of 2016 while the Gleditsch-Ward state system extend to the end of the
2017. This argument will allow the researcher to extend the data a few
years, under the (reasonable) assumption there have been no fundamental
composition changes to the state system since these data sets were last
updated. mry defaults to TRUE in the absence
of a user-specified override.
This will create Correlates of War state-year data from 1816 to 2022.
create_stateyears()
#> # A tibble: 17,121 × 3
#> ccode statenme year
#> <dbl> <chr> <int>
#> 1 2 United States of America 1816
#> 2 2 United States of America 1817
#> 3 2 United States of America 1818
#> 4 2 United States of America 1819
#> 5 2 United States of America 1820
#> 6 2 United States of America 1821
#> 7 2 United States of America 1822
#> 8 2 United States of America 1823
#> 9 2 United States of America 1824
#> 10 2 United States of America 1825
#> # ℹ 17,111 more rowsThis will create Gleditsch-Ward state-year data from 1816 to 2017.
create_stateyears(system = "gw", mry = FALSE)
#> # A tibble: 17,767 × 3
#> gwcode statename year
#> <dbl> <chr> <int>
#> 1 2 United States of America 1816
#> 2 2 United States of America 1817
#> 3 2 United States of America 1818
#> 4 2 United States of America 1819
#> 5 2 United States of America 1820
#> 6 2 United States of America 1821
#> 7 2 United States of America 1822
#> 8 2 United States of America 1823
#> 9 2 United States of America 1824
#> 10 2 United States of America 1825
#> # ℹ 17,757 more rowsDyad-Year Data
create_dyadyears() is one of the most useful functions
in peacesciencer, transforming the raw Correlates of War
state system data (cow_states in
peacesciencer) or Gleditsch-Ward state system data
(gw_states) into all possible dyad-years. It has three
arguments. system and mry operate the same as
they do in create_stateyears(). There is an additional
argument—directed—that also takes a logical
(TRUE or FALSE). The default here is
TRUE, returning directed dyad-year data (useful
for dyadic conflict analyses where the initiator/target distinction
matters). FALSE returns non-directed dyad-year
data, useful for cases where the initiator/target distinction does not
matter and the researcher cares more about the presence or absence of a
conflict. The convention for non-directed dyad-year data is that
ccode2 > ccode1 and the underlying code of
create_dyadyears() simply takes the directed dyad-year data
and chops it in half with that rule.
Here are all Correlates of War dyad-years from 1816 to 2022.
create_dyadyears()
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> # A tibble: 2,139,270 × 3
#> ccode1 ccode2 year
#> <dbl> <dbl> <int>
#> 1 2 20 1920
#> 2 2 20 1921
#> 3 2 20 1922
#> 4 2 20 1923
#> 5 2 20 1924
#> 6 2 20 1925
#> 7 2 20 1926
#> 8 2 20 1927
#> 9 2 20 1928
#> 10 2 20 1929
#> # ℹ 2,139,260 more rowsHere are all Gleditsch-Ward dyad-years with the same temporal domain.
create_dyadyears(system = "gw")
#> Joining with `by = join_by(gwcode1, gwcode2, year)`
#> # A tibble: 2,089,826 × 3
#> gwcode1 gwcode2 year
#> <dbl> <dbl> <int>
#> 1 2 20 1867
#> 2 2 20 1868
#> 3 2 20 1869
#> 4 2 20 1870
#> 5 2 20 1871
#> 6 2 20 1872
#> 7 2 20 1873
#> 8 2 20 1874
#> 9 2 20 1875
#> 10 2 20 1876
#> # ℹ 2,089,816 more rowsMajor vs. Major Dyad-Years
Consider this section of the vignette as a comparison to the kind of
dyad-year data that EUGene would create for a user, apparently on
request. EUGene would apparently create these types of dyad-years as
specific dyad-year types whereas peacesciencer treats
them as case exclusions you can do after the fact given other
functionality in the package. For example, here are just major vs. major
dyads. For simplicity’s sake, these will all be directed dyad-years at
their core (and captured with cow_ddy in the package as a
shortcut).
cow_ddy %>% add_cow_majors() %>%
filter(cowmaj1 == 1 & cowmaj2 == 1)
#> # A tibble: 6,140 × 5
#> ccode1 ccode2 year cowmaj1 cowmaj2
#> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 2 200 1898 1 1
#> 2 2 200 1899 1 1
#> 3 2 200 1900 1 1
#> 4 2 200 1901 1 1
#> 5 2 200 1902 1 1
#> 6 2 200 1903 1 1
#> 7 2 200 1904 1 1
#> 8 2 200 1905 1 1
#> 9 2 200 1906 1 1
#> 10 2 200 1907 1 1
#> # ℹ 6,130 more rowsMajor vs. Any State Dyad-Years
These are all dyad-years where any state is a major power.
cow_ddy %>% add_cow_majors() %>%
filter(cowmaj1 == 1 | cowmaj2 == 1)
#> # A tibble: 183,722 × 5
#> ccode1 ccode2 year cowmaj1 cowmaj2
#> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 2 20 1920 1 0
#> 2 2 20 1921 1 0
#> 3 2 20 1922 1 0
#> 4 2 20 1923 1 0
#> 5 2 20 1924 1 0
#> 6 2 20 1925 1 0
#> 7 2 20 1926 1 0
#> 8 2 20 1927 1 0
#> 9 2 20 1928 1 0
#> 10 2 20 1929 1 0
#> # ℹ 183,712 more rowsAll Contiguous Dyad-Years
These are all dyad-years separated by 400 miles of water or fewer,
though the documentation for add_contiguity() cautions that
users should be at least a little critical of the contiguity data.
cow_ddy %>% add_contiguity() %>%
filter(conttype %in% c(1:5))
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> # A tibble: 82,440 × 4
#> ccode1 ccode2 year conttype
#> <dbl> <dbl> <int> <dbl>
#> 1 2 20 1920 1
#> 2 2 20 1921 1
#> 3 2 20 1922 1
#> 4 2 20 1923 1
#> 5 2 20 1924 1
#> 6 2 20 1925 1
#> 7 2 20 1926 1
#> 8 2 20 1927 1
#> 9 2 20 1928 1
#> 10 2 20 1929 1
#> # ℹ 82,430 more rowsAll Dyad-Years Within a Set Distance
These are all dyad-years with a minimum distance of some
user-specified threshold (in kilometers). This function will lean on
add_minimum_distance(), which does have the side effect of
truncating the left bound of the temporal domain to—as of right
now—1886. These are all Correlates of War dyad-years from 1886 to 2019
separated by 1,000 kilometers or fewer.
cow_ddy %>% add_minimum_distance() %>%
filter(mindist <= 1000)
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> # A tibble: 167,532 × 4
#> ccode1 ccode2 year mindist
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2 20 1921 0
#> 2 2 20 1922 0
#> 3 2 20 1923 0
#> 4 2 20 1924 0
#> 5 2 20 1925 0
#> 6 2 20 1926 0
#> 7 2 20 1927 0
#> 8 2 20 1928 0
#> 9 2 20 1929 0
#> 10 2 20 1930 0
#> # ℹ 167,522 more rowsDyadic Dispute-Year Data
Dyadic dispute-year data come pre-processed in
peacesciencer. Another
vignette show how these are transformed to true dyad-year data, but
they are also available for analysis. For example, the (directed) dyadic
dispute-year Gibler-Miller-Little (GML) MID data are available as
gml_dirdisp. Here, we can add information to these dyadic
dispute-years to identify contiguity relationships and Correlates of War
major status.
gml_dirdisp %>% add_contiguity() %>% add_cow_majors()
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> # A tibble: 10,276 × 42
#> dispnum ccode1 ccode2 year midongoing midonset sidea1 sidea2 revstate1
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 2 200 1902 1 1 1 0 1
#> 2 2 200 2 1902 1 1 0 1 1
#> 3 3 300 345 1913 1 1 1 0 1
#> 4 3 345 300 1913 1 1 0 1 0
#> 5 4 200 339 1946 1 1 0 1 0
#> 6 4 339 200 1946 1 1 1 0 0
#> 7 7 200 651 1951 1 1 1 0 0
#> 8 7 200 651 1952 1 0 1 0 0
#> 9 7 651 200 1951 1 1 0 1 1
#> 10 7 651 200 1952 1 0 0 1 1
#> # ℹ 10,266 more rows
#> # ℹ 33 more variables: revstate2 <dbl>, revtype11 <dbl>, revtype12 <dbl>,
#> # revtype21 <dbl>, revtype22 <dbl>, fatality1 <dbl>, fatality2 <dbl>,
#> # fatalpre1 <dbl>, fatalpre2 <dbl>, hiact1 <dbl>, hiact2 <dbl>,
#> # hostlev1 <dbl>, hostlev2 <dbl>, orig1 <dbl>, orig2 <dbl>, hiact <dbl>,
#> # hostlev <dbl>, mindur <dbl>, maxdur <dbl>, outcome <dbl>, settle <dbl>,
#> # fatality <dbl>, fatalpre <dbl>, stmon <dbl>, endmon <dbl>, recip <dbl>, …Users interested in the Correlates of War MID data will have this
available for use as cow_mid_dirdisps. Future updates may
change the object names for better standardization, but this is how it
is now.
State-Day Data
peacesciencer comes with a
create_statedays() function. This is admittedly more proof
of concept as it is really difficult to conjure too many
daily data sets in peace science, certainly with coverage into
the 19th century. No matter, create_statedays() will create
these data. It too has the same system and mry
arguments (and same defaults) as create_stateyears().
Here are all Correlates of War state-days from 1816 to 2022.
create_statedays()
#> # A tibble: 6,203,441 × 3
#> ccode statenme date
#> <dbl> <chr> <date>
#> 1 2 United States of America 1816-01-01
#> 2 2 United States of America 1816-01-02
#> 3 2 United States of America 1816-01-03
#> 4 2 United States of America 1816-01-04
#> 5 2 United States of America 1816-01-05
#> 6 2 United States of America 1816-01-06
#> 7 2 United States of America 1816-01-07
#> 8 2 United States of America 1816-01-08
#> 9 2 United States of America 1816-01-09
#> 10 2 United States of America 1816-01-10
#> # ℹ 6,203,431 more rowsHere are all Gleditsch-Ward state-days with the same temporal domain.
create_statedays(system = "gw")
#> # A tibble: 6,765,801 × 3
#> gwcode statename date
#> <dbl> <chr> <date>
#> 1 2 United States of America 1816-01-01
#> 2 2 United States of America 1816-01-02
#> 3 2 United States of America 1816-01-03
#> 4 2 United States of America 1816-01-04
#> 5 2 United States of America 1816-01-05
#> 6 2 United States of America 1816-01-06
#> 7 2 United States of America 1816-01-07
#> 8 2 United States of America 1816-01-08
#> 9 2 United States of America 1816-01-09
#> 10 2 United States of America 1816-01-10
#> # ℹ 6,765,791 more rowsI can conjure an application where a user may want to think of daily conflict episodes within the Gleditsch-Ward domain. The UCDP armed conflict data have more precise dates than, say, the Correlates of War MID data, making such an analysis possible. However, there are no conflict data before 1946 and you should reflect that with peacesciencer with something like this. This will require lubridate.
create_statedays(system = "gw") %>%
filter(year(date) >= 1946)
#> # A tibble: 3,998,000 × 3
#> gwcode statename date
#> <dbl> <chr> <date>
#> 1 2 United States of America 1946-01-01
#> 2 2 United States of America 1946-01-02
#> 3 2 United States of America 1946-01-03
#> 4 2 United States of America 1946-01-04
#> 5 2 United States of America 1946-01-05
#> 6 2 United States of America 1946-01-06
#> 7 2 United States of America 1946-01-07
#> 8 2 United States of America 1946-01-08
#> 9 2 United States of America 1946-01-09
#> 10 2 United States of America 1946-01-10
#> # ℹ 3,997,990 more rowsState-Month Data
State-months are simple aggregations of state-days. You can
accomplish this with a few more extra commands after
create_statedays().
create_statedays(system = "gw") %>%
mutate(year = year(date),
month = month(date)) %>%
distinct(gwcode, statename, year, month)
#> # A tibble: 222,370 × 4
#> gwcode statename year month
#> <dbl> <chr> <dbl> <dbl>
#> 1 2 United States of America 1816 1
#> 2 2 United States of America 1816 2
#> 3 2 United States of America 1816 3
#> 4 2 United States of America 1816 4
#> 5 2 United States of America 1816 5
#> 6 2 United States of America 1816 6
#> 7 2 United States of America 1816 7
#> 8 2 United States of America 1816 8
#> 9 2 United States of America 1816 9
#> 10 2 United States of America 1816 10
#> # ℹ 222,360 more rowsState-Quarter Data
There is some assumption worth belaboring about what a “quarter”
would look like in a more general context, but it might look something
like this. Again, this is an aggregation of
create_statedays().
create_statedays(system = "gw") %>%
mutate(year = year(date),
month = month(date)) %>%
filter(month %in% c(1, 4, 7, 10)) %>%
mutate(quarter = case_when(
month == 1 ~ "Q1",
month == 4 ~ "Q2",
month == 7 ~ "Q3",
month == 10 ~ "Q4"
)) %>%
distinct(gwcode, statename, year, quarter)
#> # A tibble: 74,079 × 4
#> gwcode statename year quarter
#> <dbl> <chr> <dbl> <chr>
#> 1 2 United States of America 1816 Q1
#> 2 2 United States of America 1816 Q2
#> 3 2 United States of America 1816 Q3
#> 4 2 United States of America 1816 Q4
#> 5 2 United States of America 1817 Q1
#> 6 2 United States of America 1817 Q2
#> 7 2 United States of America 1817 Q3
#> 8 2 United States of America 1817 Q4
#> 9 2 United States of America 1818 Q1
#> 10 2 United States of America 1818 Q2
#> # ℹ 74,069 more rowsLeader-Day (Leader-Month, Leader-Year) Data
peacesciencer has leader-level units of analysis as
well, which can be easily created with the modified Archigos
(archigos) data in peacesciencer. The data
are version 4.1.
archigos
#> # A tibble: 3,409 × 11
#> obsid gwcode leadid leader yrborn gender startdate enddate entry exit
#> <chr> <dbl> <chr> <chr> <dbl> <chr> <date> <date> <chr> <chr>
#> 1 USA-1869 2 81dcc… Grant 1822 M 1869-03-04 1877-03-04 Regu… Regu…
#> 2 USA-1877 2 81dcc… Hayes 1822 M 1877-03-04 1881-03-04 Regu… Regu…
#> 3 USA-188… 2 81dcf… Garfi… 1831 M 1881-03-04 1881-09-19 Regu… Irre…
#> 4 USA-188… 2 81dcf… Arthur 1829 M 1881-09-19 1885-03-04 Regu… Regu…
#> 5 USA-1885 2 34fb1… Cleve… 1837 M 1885-03-04 1889-03-04 Regu… Regu…
#> 6 USA-1889 2 81dcf… Harri… 1833 M 1889-03-04 1893-03-04 Regu… Regu…
#> 7 USA-1893 2 34fb1… Cleve… 1837 M 1893-03-04 1897-03-04 Regu… Regu…
#> 8 USA-1897 2 81dcf… McKin… 1843 M 1897-03-04 1901-09-14 Regu… Irre…
#> 9 USA-1901 2 81dd2… Roose… 1858 M 1901-09-14 1909-03-04 Regu… Regu…
#> 10 USA-1909 2 81dd2… Taft 1857 M 1909-03-04 1913-03-04 Regu… Regu…
#> # ℹ 3,399 more rows
#> # ℹ 1 more variable: exitcode <chr>create_leaderdays() will create leader-day data from
archigos.
create_leaderdays()
#> # A tibble: 5,298,380 × 5
#> obsid gwcode leader date yrinoffice
#> <chr> <dbl> <chr> <date> <dbl>
#> 1 USA-1869 2 Grant 1869-03-04 1
#> 2 USA-1869 2 Grant 1869-03-05 1
#> 3 USA-1869 2 Grant 1869-03-06 1
#> 4 USA-1869 2 Grant 1869-03-07 1
#> 5 USA-1869 2 Grant 1869-03-08 1
#> 6 USA-1869 2 Grant 1869-03-09 1
#> 7 USA-1869 2 Grant 1869-03-10 1
#> 8 USA-1869 2 Grant 1869-03-11 1
#> 9 USA-1869 2 Grant 1869-03-12 1
#> 10 USA-1869 2 Grant 1869-03-13 1
#> # ℹ 5,298,370 more rowsI do want to note one thing about the leader-level functions in this
package. Whereas Correlates of War state system membership is often the
default system for a lot of functions (prominently
create_stateyears() and create_dyadyears()),
the Gleditsch-Ward system is the default system because that is the
state system around which the Archigos project created its leader data.
Moreover, the leader data aren’t exactly tethered to the Gleditsch-Ward
state system for dates either (e.g. there are leader entries for
Gleditsch-Ward states that aren’t in the system yet). In a case like
this, you can standardize these leader data to either the Correlates of
War system or the Gleditsch-Ward system with the
standardize argument. By default, the option here is “none”
(i.e. return all available leader days recorded in the Archigos data).
“cow” or “gw” standardizes the leader data to Correlates of War state
system membership or Gleditsch-Ward state system membership,
respectively.
create_leaderdays(standardize = "cow")
#> Joining with `by = join_by(gwcode, year)`
#> Joining with `by = join_by(ccode, date)`
#> # A tibble: 4,824,967 × 5
#> obsid ccode leader date yrinoffice
#> <chr> <dbl> <chr> <date> <dbl>
#> 1 USA-1869 2 Grant 1869-03-04 1
#> 2 USA-1869 2 Grant 1869-03-05 1
#> 3 USA-1869 2 Grant 1869-03-06 1
#> 4 USA-1869 2 Grant 1869-03-07 1
#> 5 USA-1869 2 Grant 1869-03-08 1
#> 6 USA-1869 2 Grant 1869-03-09 1
#> 7 USA-1869 2 Grant 1869-03-10 1
#> 8 USA-1869 2 Grant 1869-03-11 1
#> 9 USA-1869 2 Grant 1869-03-12 1
#> 10 USA-1869 2 Grant 1869-03-13 1
#> # ℹ 4,824,957 more rowsThe user may want to think about some additional post-processing on top of this, but this is enough to get started. From there, the same process that creates state-months can create something like leader-months.
create_leaderdays() %>%
mutate(year = year(date),
month = month(date)) %>%
group_by(gwcode, obsid, year, month) %>%
slice(1)
#> # A tibble: 177,128 × 7
#> # Groups: gwcode, obsid, year, month [177,128]
#> obsid gwcode leader date yrinoffice year month
#> <chr> <dbl> <chr> <date> <dbl> <dbl> <dbl>
#> 1 USA-1869 2 Grant 1869-03-04 1 1869 3
#> 2 USA-1869 2 Grant 1869-04-01 1 1869 4
#> 3 USA-1869 2 Grant 1869-05-01 1 1869 5
#> 4 USA-1869 2 Grant 1869-06-01 1 1869 6
#> 5 USA-1869 2 Grant 1869-07-01 1 1869 7
#> 6 USA-1869 2 Grant 1869-08-01 1 1869 8
#> 7 USA-1869 2 Grant 1869-09-01 1 1869 9
#> 8 USA-1869 2 Grant 1869-10-01 1 1869 10
#> 9 USA-1869 2 Grant 1869-11-01 1 1869 11
#> 10 USA-1869 2 Grant 1869-12-01 1 1869 12
#> # ℹ 177,118 more rowsAnd here are leader-years, which are pre-packaged as a
peacesciencer function. The package also adds some
information about leader gender, an approximation of the leader’s age
that year (i.e. year - yrborn), and a running count
(starting a 1) for the leader’s tenure (in years).
create_leaderyears()
#> # A tibble: 17,686 × 7
#> obsid leader gwcode gender leaderage year yrinoffice
#> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 USA-1869 Grant 2 M 47 1869 1
#> 2 USA-1869 Grant 2 M 48 1870 2
#> 3 USA-1869 Grant 2 M 49 1871 3
#> 4 USA-1869 Grant 2 M 50 1872 4
#> 5 USA-1869 Grant 2 M 51 1873 5
#> 6 USA-1869 Grant 2 M 52 1874 6
#> 7 USA-1869 Grant 2 M 53 1875 7
#> 8 USA-1869 Grant 2 M 54 1876 8
#> 9 USA-1869 Grant 2 M 55 1877 9
#> 10 USA-1877 Hayes 2 M 55 1877 1
#> # ℹ 17,676 more rowsLeader Dyad-Year Data
peacesciencer can also create leader dyad-year data by
way of create_leaderdyadyears(). You can see some
of the underlying code that is creating these data. It’s a lot of
code, it would take a lot of time to run from scratch, and the ensuing
output is too large to store as an R data object in the package because
CRAN hard-caps package size at 5 MB. Instead, users who want these data
should first run download_extdata() when they
first install or update the package. Therein, they can run
create_leaderdyadyears() to create the full universe of
leader dyad-year data.
# create_leaderdyadyears() is effectively doing this.
# Let's do the G-W leader dyad-year data for illustration's sake.
# `download_extdata()` will download these data into the package directory.
# Thus, it is *not* downloading the data fresh each time.
the_url <- "http://svmiller.com/R/peacesciencer/gw_dir_leader_dyad_years.rds"
readRDS(url(the_url)) %>%
declare_attributes(data_type = "leader_dyad_year", system = "gw")
#> # A tibble: 2,336,990 × 11
#> year obsid1 obsid2 gwcode1 gwcode2 gender1 gender2 leaderage1 leaderage2
#> <int> <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1870 AFG-1868 AUH-1848 700 300 M M 45 40
#> 2 1870 AFG-1868 BAV-1864 700 245 M M 45 39
#> 3 1870 AFG-1868 BRA-1840 700 140 M M 45 45
#> 4 1870 AFG-1868 CHN-1861 700 710 M M 45 35
#> 5 1870 AFG-1868 COS-1870 700 94 M M 45 39
#> 6 1870 AFG-1868 ECU-1869 700 130 M M 45 49
#> 7 1870 AFG-1868 GMY-1858 700 255 M M 45 73
#> 8 1870 AFG-1868 GRC-1863 700 350 M M 45 25
#> 9 1870 AFG-1868 IRN-1848 700 630 M M 45 39
#> 10 1870 AFG-1868 JPN-1868 700 740 M M 45 18
#> # ℹ 2,336,980 more rows
#> # ℹ 2 more variables: yrinoffice1 <dbl>, yrinoffice2 <dbl>
# ^ compare with:
# download_extdata()
# create_leaderdyadyears()