nuts_aggregate() transforms regional NUTS data between NUTS levels.
Usage
nuts_aggregate(
data,
to_level,
variables,
weight = NULL,
missing_rm = FALSE,
missing_weights_pct = FALSE,
multiple_versions = c("error", "most_frequent")
)Arguments
- data
A nuts.classified object returned by
nuts_classify().- to_level
Number corresponding to the desired NUTS level to be aggregated to:
1or2.- variables
Named character specifying variable names and variable type (
'absolute'or'relative'), e.g.c('var_name' = 'absolute').- weight
String with name of the weight used for conversion. Can be area size
'areaKm'(default), population in 2011'pop11'or 2018'pop18', or artificial surfaces in 2012'artif_surf12'and 2018'artif_surf18'.- missing_rm
Boolean that is FALSE by default. TRUE removes regional flows that depart from missing NUTS codes.
- missing_weights_pct
Boolean that is FALSE by default. TRUE computes the percentage of missing weights due to missing departing NUTS regions for each variable.
- multiple_versions
By default equal to
'error', when providing multiple NUTS versions within groups. If set to'most_frequent'data is converted using the best-matching NUTS version.
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)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
# 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
# Aggregate from NUTS 3 to 2 by indicator x year
manure %>%
filter(nchar(geo) == 5) %>%
nuts_classify(nuts_code = "geo",
group_vars = c('indic_ag','time')) %>%
# Group vars are automatically passed on
nuts_aggregate(to_level = 2,
variables = c('values'= 'absolute'),
weight = 'pop18')
#>
#> ── Classifying version of NUTS codes ───────────────────────────────────────────
#> Within groups defined by country, indic_ag, and time:
#> ! These NUTS codes cannot be identified or classified: ME000, ME000, ME000,
#> ME000, ME000, ME000, ME000, ME000, NOZZZ, NOZZZ, NOZZZ, NOZZZ, SIZZZ, and
#> NOZZZ.
#> ✔ Unique NUTS version classified.
#> ✖ Missing NUTS codes detected. See the tibble 'missing_data' in the output.
#>
#> ── Aggregating level of NUTS codes ─────────────────────────────────────────────
#> Within groups defined by country, indic_ag, and time:
#> ℹ Aggregate from NUTS regional level 3 to 2.
#> ✖ These NUTS codes cannot be converted and are dropped: ME000, NOZZZ, and
#> SIZZZ.
#> ✔ Version is unique.
#> ✖ Missing NUTS codes in data. No values are calculated for regions associated
#> with missing NUTS codes. Ensure that the input data is complete.
#> # A tibble: 3,486 × 5
#> to_code country indic_ag time values
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 AT11 Austria I07A1_EQ_Y 2010 2110
#> 2 AT11 Austria I07A1_EQ_YC 2010 337
#> 3 AT11 Austria I07A2_EQ_Y 2010 802
#> 4 AT11 Austria I07A2_EQ_YC 2010 756
#> 5 AT11 Austria I07A31_EQ_Y 2010 278
#> 6 AT11 Austria I07A32_EQ_Y 2010 31
#> 7 AT11 Austria I07A3_EQ_YC 2010 261
#> 8 AT11 Austria I07A_EQ_Y 2010 2180
#> 9 AT12 Austria I07A1_EQ_Y 2010 19250
#> 10 AT12 Austria I07A1_EQ_YC 2010 1853
#> # ℹ 3,476 more rows