nuts_classify {nuts}

Classify version of NUTS codes

Description

nuts_classify() can identify the NUTS version year and level from a variable containing NUTS codes.

Usage

nuts_classify(
  data,
  nuts_code,
  group_vars = NULL,
  ties = c("most_recent", "oldest")
)

Arguments

data

A data frame or tibble that contains a variable with NUTS 1, 2 or 3 codes and possibly other variables. NUTS codes must be of the same level and need to be unique, unless additional grouping variables are specified. No duplicate NUTS codes within groups allowed.

nuts_code

Variable name containing NUTS codes

group_vars

Variable name(s) for classification within groups. nuts_classify() always computes overlap within country. Hence, country variables should not be specified. NULL by default.

ties

Picks 'most_recent' or 'oldest' version when overlap is identical across multiple NUTS versions. 'most_recent' by default.

Details

Console messages can be controlled with rlang::local_options(nuts.verbose = "quiet") to silence messages and nuts.verbose = "verbose" to switch messages back on.

Value

A list of three tibbles. The first tibble contains the original data with the classified NUTS version, level, and country. The second tibble lists the group-specific overlap with each NUTS version. The third tibble shows missing NUTS codes for each group.

The output can be passed to nuts_convert_version() to convert data across NUTS versions and nuts_aggregate() to aggregate across NUTS levels.

Examples

library(dplyr)

# Load EUROSTAT data of manure storage deposits
data(manure)

# Data varies at the NUTS level x indicator x year x country x NUTS code level
head(manure)

# Classify version of NUTS 2 codes in Germany
manure %>%
 filter(nchar(geo) == 4) %>%
 filter(indic_ag == 'I07A_EQ_Y') %>%
 filter(grepl('^DE', geo)) %>%
 filter(time == 2003) %>%
 select(-indic_ag, -time) %>%
 # Data varies at the NUTS code level
 nuts_classify(nuts_code = 'geo')

# Classify version of NUTS 3 codes within country and year
manure %>%
  filter(nchar(geo) == 5) %>%
  filter(indic_ag == 'I07A_EQ_Y') %>%
  select(-indic_ag) %>%
  # Data varies at the year x country x NUTS code level. The country grouping
  # is always used by default.
  nuts_classify(nuts_code = 'geo', group_vars = 'time')