Retrieves PolarCAP data for defined countries and years. Returns data in wide format. For tidy
format, use melt.PolarCAP().
Usage
get.PolarCAP(
countries = NA,
years = NA,
type = c("ideology", "affect"),
value.only = FALSE,
include.se = FALSE
)Arguments
- countries
a character vector of countries to be retrieved. See Details.
- years
a numeric vector of years to be retrieved.
- type
a character vector indicating which polarization estimates should be returned. Must be
"ideology","affect", or both.- value.only
a logical indicating whether
get.PolarCAP()should return a data frame of results (FALSE, the default) or a single estimate as a scalar (TRUE).- include.se
a logical indicating whether standard errors should be returned. Defaults to
FALSE.
Value
If value.only = FALSE, get.PolarCAP() returns a data frame with columns
corresponding to country names, country ISO3 codes, years, polarization estimates for the
polarization type(s) given in type, and associated standard errors (if
include.se = TRUE). If value.only = TRUE, get.PolarCAP() returns a scalar
polarization estimate for the polarization type given in type.
Details
Ideally, country names passed to countries would be ISO 3166-1 alpha-3 country codes
(case-insensitive). However, get.PolarCAP() will accept country names in almost any language or
format and attempt to convert them to ISO3 codes by calling to.ISO3().
get.PolarCAP() will alert the user to any country names still unrecognized after this
conversion and return results only for those which are recognized.
Examples
get.PolarCAP("USA", c(2018, 2019), "ideology", include.se = TRUE)
#> # A tibble: 2 × 6
#> country country_code year ideology ideology_se notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 United States USA 2018 0.682 0.00970 NA
#> 2 United States USA 2019 0.761 0.0102 NA
get.PolarCAP("USA", c(2018, 2019), c("ideology", "affect"), include.se = TRUE)
#> # A tibble: 2 × 8
#> country country_code year ideology affect ideology_se affect_se notes
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 United States USA 2018 0.682 0.773 0.00970 0.0114 NA
#> 2 United States USA 2019 0.761 0.728 0.0102 0.0118 NA
countries <- rep(c("MEX", "USA"), each = 2)
years <- rep(c(2018, 2019), 2)
data <- as.data.frame(cbind(countries, years))
data$ideology1 <- apply(data, 1, function(x) get.PolarCAP(x[1], x[2], type = "ideology",
value.only = TRUE))
data
#> countries years ideology1
#> 1 MEX 2018 0.6114545
#> 2 MEX 2019 0.6389439
#> 3 USA 2018 0.6819618
#> 4 USA 2019 0.7611854