Retrieve the categories of a country-specific variable in LIS/LWS for a given country.
get_country_specifc_categories.RdRetrieve the categories of a country-specific variable in LIS/LWS for a given country.
Usage
get_country_specifc_categories(
variable,
iso2,
from = NULL,
to = NULL,
lws = FALSE,
n_categories = FALSE
)Arguments
- variable
A unit-length character vector containing a LIS/LWS country-specific (with the "_c" suffix) variable.
- iso2
A character vector with a valid iso2 code for countries present in LIS/LWS.
- from
A numeric value representing the year (inclusive) after which the LIS/LWS datasets should be considered.
- to
A numeric value representing the year (inclusive) up to which the LIS/LWS datasets should be considered.
- lws
A logical value, that guides the tool to search in the LIS or LWS database. The argument is FALSE by default, taking LIS as the databse to be investigated if nothing is specified.
- n_categories
A logical value indicating whether to output the number of categories of a single country-specific variable, across the entire time series for a given country.
Examples
# In years where no data is recorded for a given variable, it is automatically hidden from the output
variable_exists(variable = "health_c", iso2 = "it")
#> $it
#> 1977 1978 1979 1980 1981 1982 1983 1984 1986 1987 1989 1991 1993
#> "No" "No" "No" "No" "No" "No" "No" "No" "No" "No" "No" "No" "No"
#> 1995 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2020
#> "Yes" "No" "No" "No" "No" "Yes" "Yes" "Yes" "No" "No" "No" "No"
#>
get_country_specifc_categories(variable = "health_c", iso2 = "it", from = 1995, to = 2020)
#> $`it10 - health status as described by the respondent`
#> 1 2 3 4 5
#> "very good" "good" "fair" "bad" "very bad"
#>
#> $`it08 - health status as described by the respondent`
#> 1 2 3 4 5
#> "very good" "good" "fair" "bad" "very bad"
#>
#> $`it06 - health status as described by the respondent`
#> 1 2 3 4 5
#> "very good" "good" "fair" "bad" "very bad"
#>
#> $`it95 - health status as described by the respondent`
#> 1 2 3 4 5
#> "very good" "good" "fair" "bad" "very bad"
#>
# To retrieve information on LWS datasets
get_country_specifc_categories(variable = "bus1_c", iso2 = "fi", lws = TRUE)
#> $`fi19 - legal form of non-traded self-employment businesses with an active role, main bu`
#> 0
#> "does not own a business or has no active role in running the business"
#> 111
#> "sole proprietorship / independent professional"
#> 112
#> "partnership"
#> 113
#> "limited liability companies"
#>
#> $`fi16 - legal form of non-traded self-employment businesses with an active role, main bu`
#> 0
#> "does not own a business or has no active role in running the business"
#> 111
#> "sole proprietorship / independent professional"
#> 112
#> "partnership"
#> 113
#> "limited liability companies"
#>
#> $`fi13 - legal form of the business, main business`
#> 0
#> "does not own a business"
#> 111
#> "sole proprietorship / independent professional"
#> 112
#> "partnership"
#> 113
#> "limited liability companies"
#> 120
#> "no active role in running the business"
#>
#> $`fi09 - owns a business (or part of it) that is not publicly traded`
#> 0 1
#> "does not own a business" "owns a business"
#>
# Using the `n_categories` argument
get_country_specifc_categories(variable = "region_c", iso2 = "es", n_categories = TRUE)
#> $`LIS database. Number of distinct categories in variable: region_c.`
#> es22 es21 es20 es19 es18 es17 es16 es15 es14 es13 es12 es11 es10 es09 es08 es07
#> 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
#> es06 es05 es04 es00 es99 es98 es97 es96 es95 es94 es93 es90 es80
#> 19 19 18 7 7 7 7 7 7 7 7 18 18
#>
# To use this function acroos multiples countries one could make use of the `purrr::map()` function
purrr::map(lissyrtools::show_countries_lws(), ~get_country_specifc_categories(variable = "bus1_c", iso2 = .x, lws = TRUE , n_categories = TRUE))
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> Warning: The selected variable: bus1_c, does not have values other than zeros or missings for the selectd years.
#> $Australia
#> $Australia$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Austria
#> $Austria$`LWS database. Number of distinct categories in variable: bus1_c.`
#> at21 at17 at14 at11
#> 5 5 6 5
#>
#>
#> $Canada
#> $Canada$`LWS database. Number of distinct categories in variable: bus1_c.`
#> ca19 ca16 ca12 ca05 ca99
#> 2 2 2 2 2
#>
#>
#> $Chile
#> $Chile$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Denmark
#> $Denmark$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Estonia
#> $Estonia$`LWS database. Number of distinct categories in variable: bus1_c.`
#> ee21 ee17 ee13
#> 6 6 5
#>
#>
#> $Finland
#> $Finland$`LWS database. Number of distinct categories in variable: bus1_c.`
#> fi19 fi16 fi13 fi09
#> 4 4 5 2
#>
#>
#> $France
#> $France$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Germany
#> $Germany$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Greece
#> $Greece$`LWS database. Number of distinct categories in variable: bus1_c.`
#> gr21 gr18 gr14 gr09
#> 7 5 6 6
#>
#>
#> $Italy
#> $Italy$`LWS database. Number of distinct categories in variable: bus1_c.`
#> it20 it16 it14 it12 it10 it08 it06 it04 it02 it00 it98 it95
#> 2 7 6 7 6 7 7 7 2 2 2 2
#>
#>
#> $Japan
#> $Japan$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Luxembourg
#> $Luxembourg$`LWS database. Number of distinct categories in variable: bus1_c.`
#> lu21 lu18 lu14 lu10
#> 2 6 7 5
#>
#>
#> $Norway
#> $Norway$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Slovakia
#> $Slovakia$`LWS database. Number of distinct categories in variable: bus1_c.`
#> sk21 sk17 sk14 sk10
#> 4 5 6 6
#>
#>
#> $Slovenia
#> $Slovenia$`LWS database. Number of distinct categories in variable: bus1_c.`
#> si21 si17 si14
#> 5 5 6
#>
#>
#> $`South Africa`
#> $`South Africa`$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $`South Korea`
#> $`South Korea`$`LWS database. Number of distinct categories in variable: bus1_c.`
#> named integer(0)
#>
#>
#> $Spain
#> $Spain$`LWS database. Number of distinct categories in variable: bus1_c.`
#> es21 es17 es14 es11 es08
#> 6 6 6 5 5
#>
#>
#> $Sweden
#> $Sweden$`LWS database. Number of distinct categories in variable: bus1_c.`
#> se02
#> 2
#>
#>
#> $`United Kingdom`
#> $`United Kingdom`$`LWS database. Number of distinct categories in variable: bus1_c.`
#> uk19 uk17 uk15 uk13 uk11 uk09 uk07
#> 2 59 51 52 56 52 54
#>
#>
#> $`United States`
#> $`United States`$`LWS database. Number of distinct categories in variable: bus1_c.`
#> us22 us19 us16 us13 us10 us07 us04 us01 us98 us95
#> 8 8 8 8 9 7 8 7 7 8
#>
#>