Data Details

Variable Descriptions

Variable Name Description
release The release. Current version is V0.1 (Preprint).
release_date The release date. Latest version is 2023-12-18. This is updated manually with changes to the data or model that create changes in the estimates.
data_source The source of the events data. We have IDEA and ICEWS versions.
created_date The date that the dissent scores were created. This updates with every fit of the model.
country_name The name of the country.
ccode The numeric COW code for the country.
stateabb The 3-digit alpha COW code for the country.
year The year.
n_events The total number of events (dissent and not-dissent).
n_dissent_events The total number of dissent events in the country-year.
avg_pi The posterior average of the parameter \(\pi\) from the statistical model.
avg_eta The posterior average of the parameter \(\eta\) from the statistical model.
dissent_score The dissent score. This is \(\eta\) rescaled to have mean = 0 and SD = 0.5.
se_dissent_score The posterior SD of the dissent score.

Summary Using glimpse()

# load packages
library(tidyverse)
library(dataverse)

# get dissent scores from dataverse
dissent <- get_dataframe_by_name(
  filename = "dissent-scores.tab",
  dataset  = "doi:10.7910/DVN/CL4CA8",
  server   = "dataverse.harvard.edu", 
  original = TRUE, 
  .f = readr::read_csv)

# quick look
glimpse(dissent)
Rows: 2,775
Columns: 15
$ release             <chr> "V0.1 (Preprint)", "V0.1 (Preprint)", "V0.1 (Prepr…
$ release_date        <date> 2023-12-18, 2023-12-18, 2023-12-18, 2023-12-18, 2…
$ data_source         <chr> "IDEA", "IDEA", "IDEA", "IDEA", "IDEA", "IDEA", "I…
$ created_date        <date> 2023-12-18, 2023-12-18, 2023-12-18, 2023-12-18, 2…
$ country_name        <chr> "United States", "United States", "United States",…
$ ccode               <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 20, 2…
$ stateabb            <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "…
$ year                <dbl> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 19…
$ n_events            <dbl> 135537, 140735, 151180, 177717, 194368, 195981, 20…
$ n_dissent_events    <dbl> 118, 109, 124, 138, 127, 117, 123, 130, 147, 121, …
$ frac_dissent_events <dbl> 0.0008706110, 0.0007745053, 0.0008202143, 0.000776…
$ avg_pi              <dbl> 0.0008690543, 0.0007750752, 0.0008211722, 0.000779…
$ avg_eta             <dbl> -7.051493, -7.166315, -7.107983, -7.160229, -7.331…
$ dissent_score       <dbl> -0.62807636, -0.67911036, -0.65318394, -0.67640508…
$ se_dissent_score    <dbl> 0.04107096, 0.04241524, 0.03997557, 0.03736980, 0.…

Summary Using skim()

# generate numerical summaries
skimr::skim(dissent)
Data summary
Name dissent
Number of rows 2775
Number of columns 15
_______________________
Column type frequency:
character 4
Date 2
numeric 9
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
release 0 1 15 15 0 1 0
data_source 0 1 4 4 0 1 0
country_name 0 1 4 32 0 195 0
stateabb 0 1 3 3 0 195 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
release_date 0 1 2023-12-18 2023-12-18 2023-12-18 1
created_date 0 1 2023-12-18 2023-12-18 2023-12-18 1

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
ccode 0 1 458.42 257.16 2.00 235.00 450.00 666.00 990.00 ▆▇▇▇▃
year 0 1 1997.13 4.29 1990.00 1993.00 1997.00 2001.00 2004.00 ▇▇▇▇▇
n_events 0 1 3443.06 14685.37 0.00 105.00 517.00 2451.50 236686.00 ▇▁▁▁▁
n_dissent_events 0 1 9.82 25.01 0.00 0.00 2.00 9.00 652.00 ▇▁▁▁▁
frac_dissent_events 0 1 0.01 0.01 0.00 0.00 0.00 0.01 0.33 ▇▁▁▁▁
avg_pi 0 1 0.01 0.01 0.00 0.00 0.00 0.01 0.11 ▇▁▁▁▁
avg_eta 0 1 -5.64 1.12 -8.92 -6.40 -5.62 -4.86 -2.08 ▁▅▇▅▁
dissent_score 0 1 0.00 0.50 -1.46 -0.34 0.01 0.35 1.58 ▁▅▇▅▁
se_dissent_score 0 1 0.23 0.12 0.02 0.14 0.22 0.29 0.56 ▅▇▆▂▂