Package 'ags'

Defines a distance metric for the AGS

Description

Defines a distance metric for the AGS

Usage

ags_dist(x, y, landw = 10^6, kreisw = 10^3, gemw = 1, ceiling = 99999999)

Arguments

x, y	vectors of AGS values
landw	weight of the Bundesland (Land) integers
kreisw	weight of the Kreis (district) integers
gemw	weight of the Gemeinde (municipality) integers
ceiling	truncate all distances at this value

Details

The distance metric is defined as

abs(x[1:2]- y[1:2])*landw + abs(x[3:5]- y[3:5])*kreisw + abs(x[6:8]- y[6:8])*gemw,

where z[a:b] means all digits between a and b for integer z.

With the default weights, this sum is the absolute difference between x and y.

Value

A numerical vector.

Examples

ags_dist(14053,14059)

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Number of voters and valid votes in Saxony (1994-2017)

Description

The dataset includes the number of voters and valid votes in all federal elections (Bundestagswahlen) across districts in Saxony.

Usage

btw_sn

Format

A data frame with 155 rows and 4 variables:

district

AGS of the district.

year

Election year.

voters

Number of eligible voters.

valid

Number of valid votes.

Source

https://www.regionalstatistik.de

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Convert the Name or the AGS of a Bundesland

Description

Convert the Name or the AGS of a Bundesland

Usage

code_bundesland(
  sourcevar,
  origin = "ags",
  destination = "name",
  factor = FALSE
)

Arguments

sourcevar	Vector which contains the codes or names to be converted.
origin	The following options are available: ags: AGS (default). name: Bundesland name.
destination	The following options are available: ags: Bundesland AGS (default). iso: The Bundesland two-character abbreviation. name: Bundesland name. name_eng: Bundesland name in English.
factor	If TRUE returns ordered factor.

Details

This function converts a string of Bundesland names into the AGS, the standardized (English) name, or the Bundesland abbreviation.

If origin="AGS", the first two digits will be used to identify a Bundesland. It is therefore important that sourcevar is supplied as a character vector with a leading zeros if applicable.

Value

A character vector.

See Also

format_ags() for formatting AGS.

Examples

library(dplyr)
data(btw_sn)

btw_sn %>% 
 mutate(bl=code_bundesland(district, origin="ags", 
     destination="name"))

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Formats AGS with a Leading Zero

Description

Formats AGS with a Leading Zero

Usage

format_ags(ags, type, verbose = FALSE)

Arguments

ags	Input vector that will be coerced into an integer vector. Factor vectors are first coerced to a character vector and then to an integer vector.
type	Type of AGS supplied as ags. Three options are available: land: Bundesland AGS (Bundeslandschlüssel, 2 digits) district: District AGS (Kreisschlüssel, 5 digits) municipality: Municipality AGS (Gemeindeschlüssel, 8 digits) The abbreviations l, d, and m are also accepted.
verbose	If TRUE the function outputs additional information.

Value

A character vector.

Examples

format_ags(c(1,14), type="land")
format_ags(c(1002,14612), type="district")
format_ags(c(01002000,14612000), type="municipality")

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Crosswalk Municipality or District Statistics

Description

This function constructs time series of counts for Germany's municipalities (Gemeinden) and districts (Kreise).

Usage

xwalk_ags(
  data,
  ags,
  time,
  xwalk,
  variables = NULL,
  strata = NULL,
  weight = NULL,
  fuzzy_time = FALSE,
  verbose = TRUE
)

Arguments

data	A data frame or a data frame extension (e.g. a tibble).
ags	Name of the character variable (quoted) with municipality AGS (Gemeinden, 8 digits) or district AGS (Kreise, 5 digits).
time	Name of the variable (quoted) identifying the year (YYYY format). Values will be coerced to integers.
xwalk	Name of the crosswalk. The following crosswalks are available: xd19, xd20 for district-level data between 1990-2019/2020. xm19, xm20 for municipality-level data between 1990-2019/2020.
variables	Either a vector of names (quoted) for variables to interpolate or NULL to disable interpolation and return the data matched with the xwalk.
strata	Vector of variable names (quoted) or NULL. See details.
weight	Name of the interpolation weight or NULL. The following are available: pop: Population weights. size: Area weights. emp: Weights based on the number of employees (1998 onwards).
fuzzy_time	If FALSE the crosswalk and the data are matched exactly by ags and time. If TRUE they are matched exactly by ags and as best as possible on time. See details below.
verbose	If TRUE the function outputs information on the number of matched and unmatched rows.

Details

This function facilitates the use of crosswalks constructed by the BBSR for municipalities and districts in Germany (Milbert 2010). The crosswalks map one year's set of district/municipality identifiers to later year's identifiers and provide weights to perform area or population weighted interpolation.

All data rows with NAs in either the ags or time variable are excluded. The same applies to all rows with a value in ags or time that never appears in the crosswalk.

Fuzzy matching uses the absolute difference between the year reported in the data and a crosswalk year. If there is a tie, crosswalk years from before the year reported in the data are preferred.

If area or population weighted interpolation is requested (i.e., when variables are supplied), the combination of the variables set in ags, time and strata need to uniquely identify a row in data.

Caution: Data from https://www.regionalstatistik.de/ sometimes includes annual values for merged units (e.g., Städteregion Aachen, 05334)) and for their former parts (Kreis Aachen, 05354 and Stadt Aachen, 05313). When such data is crosswalked with fuzzy_time=TRUE and interpolated, the final counts will be off by approximately factor 2. The reason is that the final output is the sum of the interpolated counts for the parts and the measured count of the merged unit.

Value

If interpolation is requested, the crosswalked and interpolated data are returned. If interpolation is not requested, the data matched with the crosswalk are returned. The following variables are added:

• row_id row number of data before matching.

• ags[*] the crosswalked AGS.

• year_xw the matched year from the crosswalk.

• [*]_conv the interpolation weight.

• diff the absolute difference between year_xw and time.

References

Milbert, Antonia. 2010. "Gebietsreformen–politische Entscheidungen und Folgen für die Statistik." BBSR-Berichte kompakt 6/2010. Bundesinsitut für Bau-, Stadt-und Raumfoschung.

Examples

data(btw_sn)

btw_sn_ags20 <- xwalk_ags(
    data = btw_sn,
    ags = "district",
    time = "year",
    xwalk = "xd20",
    variables = c("voters", "valid"),
    weight = "pop"
)

head(btw_sn_ags20)

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