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
1,2or3codes 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.NULLby default.- ties
Picks
'most_recent'or'oldest'version when overlap is identical across multiple NUTS versions.'most_recent'by default.
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.
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.
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)
#> # A tibble: 6 × 4
#> indic_ag geo time values
#> <chr> <chr> <dbl> <dbl>
#> 1 I07A1_EQ_Y AT 2010 97401
#> 2 I07A1_EQ_Y AT1 2010 21388
#> 3 I07A1_EQ_Y AT11 2010 2110
#> 4 I07A1_EQ_Y AT111 2010 270
#> 5 I07A1_EQ_Y AT112 2010 455
#> 6 I07A1_EQ_Y AT113 2010 1385
# 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')
#>
#> ── Classifying version of NUTS codes ───────────────────────────────────────────
#> Within groups defined by country:
#> ! These NUTS codes cannot be identified or classified: DEZZ.
#> ✔ Unique NUTS version classified.
#> ✖ Missing NUTS codes detected. See the tibble 'missing_data' in the output.
#> $data
#> # A tibble: 36 × 5
#> from_code from_version from_level country values
#> <chr> <chr> <dbl> <chr> <dbl>
#> 1 DE11 2006 2 Germany 11320
#> 2 DE12 2006 2 Germany 3710
#> 3 DE13 2006 2 Germany 9710
#> 4 DE14 2006 2 Germany 11220
#> 5 DE21 2006 2 Germany 22760
#> 6 DE22 2006 2 Germany 16390
#> 7 DE23 2006 2 Germany 12500
#> 8 DE24 2006 2 Germany 8440
#> 9 DE25 2006 2 Germany 10380
#> 10 DE26 2006 2 Germany 6150
#> # ℹ 26 more rows
#>
#> $versions_data
#> # A tibble: 6 × 3
#> from_version country overlap_perc
#> <chr> <chr> <dbl>
#> 1 2006 Germany 100
#> 2 2021 Germany 88.6
#> 3 2016 Germany 88.6
#> 4 2013 Germany 88.6
#> 5 2010 Germany 88.6
#> 6 NA NA NA
#>
#> $missing_data
#> # A tibble: 4 × 4
#> from_code from_version from_level country
#> <chr> <chr> <dbl> <chr>
#> 1 DE30 2006 2 Germany
#> 2 DE50 2006 2 Germany
#> 3 DE60 2006 2 Germany
#> 4 DEC0 2006 2 Germany
#>
#> attr(,"groups")
#> [1] "country"
#> attr(,"class")
#> [1] "nuts.classified" "list"
# 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')
#>
#> ── Classifying version of NUTS codes ───────────────────────────────────────────
#> Within groups defined by country and time:
#> ! These NUTS codes cannot be identified or classified: ME000 and NOZZZ.
#> ✔ Unique NUTS version classified.
#> ✖ Missing NUTS codes detected. See the tibble 'missing_data' in the output.
#> $data
#> # A tibble: 1,902 × 6
#> from_code from_version from_level country time values
#> <chr> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 AT111 2021 3 Austria 2010 276
#> 2 AT112 2021 3 Austria 2010 482
#> 3 AT113 2021 3 Austria 2010 1422
#> 4 AT121 2021 3 Austria 2010 6889
#> 5 AT122 2021 3 Austria 2010 3133
#> 6 AT123 2021 3 Austria 2010 2027
#> 7 AT124 2021 3 Austria 2010 5870
#> 8 AT125 2021 3 Austria 2010 905
#> 9 AT126 2021 3 Austria 2010 1103
#> 10 AT127 2021 3 Austria 2010 506
#> # ℹ 1,892 more rows
#>
#> $versions_data
#> # A tibble: 312 × 4
#> from_version country time overlap_perc
#> <chr> <chr> <dbl> <dbl>
#> 1 2021 Austria 2010 100
#> 2 2016 Austria 2010 100
#> 3 2013 Austria 2010 100
#> 4 2010 Austria 2010 100
#> 5 2006 Austria 2010 100
#> 6 2016 Belgium 2000 100
#> 7 2013 Belgium 2000 100
#> 8 2010 Belgium 2000 100
#> 9 2006 Belgium 2000 100
#> 10 2021 Belgium 2000 81.8
#> # ℹ 302 more rows
#>
#> $missing_data
#> # A tibble: 324 × 5
#> from_code from_version from_level country time
#> <chr> <chr> <dbl> <chr> <dbl>
#> 1 CZ010 2021 3 Czechia 2003
#> 2 DK011 2021 3 Denmark 2003
#> 3 DK012 2021 3 Denmark 2003
#> 4 EE001 2016 3 Estonia 2003
#> 5 EE007 2016 3 Estonia 2003
#> 6 EL111 2010 3 Greece 2003
#> 7 EL112 2010 3 Greece 2003
#> 8 EL113 2010 3 Greece 2003
#> 9 EL114 2010 3 Greece 2003
#> 10 EL115 2010 3 Greece 2003
#> # ℹ 314 more rows
#>
#> attr(,"groups")
#> [1] "country" "time"
#> attr(,"class")
#> [1] "nuts.classified" "list"