de_county
Description
This data contains 2010 Census data for each of the three counties in DE.
Usage
data("de_county")
Format
An sf dataframe with 3 observations
Examples
data('de_county')
de_tract
Description
This data contains 2010 Census data for each of the 218 tracts in DE.
Usage
data("de_tract")
Format
An sf dataframe with 218 observations
Examples
data('de_tract')
Compute Absolute Centralization
Description
Compute Absolute Centralization
Usage
ds_abs_cent(.data, .cols, .name)
abs_cent(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with absolute centralization. Leave missing to return a vector.
...
arguments to forward to ds_abs_cent from abs_cent
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_abs_cent(de_county, c(pop_white, starts_with('pop_')))
ds_abs_cent(de_county, c(pop_white, starts_with('pop_')), 'abs_cent')
Compute Absolute Clustering
Description
Compute Absolute Clustering
Usage
ds_abs_clust(.data, .cols, .name)
abs_clust(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with absolute clustering. Leave missing to return a vector.
...
arguments to forward to ds_abs_clust from abs_clust
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_abs_clust(de_county, c(pop_white, starts_with('pop_')))
ds_abs_clust(de_county, c(pop_white, starts_with('pop_')), 'abs_clust')
Compute Absolute Concentration
Description
Compute Absolute Concentration
Usage
ds_abs_conc(.data, .cols, .name)
abs_conc(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with absolute concentration. Leave missing to return a vector.
...
arguments to forward to ds_abs_conc from abs_conc
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_abs_conc(de_county, c(pop_black, starts_with('pop_')))
ds_abs_conc(de_county, c(pop_black, starts_with('pop_')), 'abs_conc')
Compute Atkinson b Index
Description
Compute Atkinson b Index
Usage
ds_atkinson(.data, .cols, .name, b = 0.5)
atkinson(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with Atkinson b index. Leave missing to return a vector.
b
Default 0.5. Exponent parameter b, where 0 <= b <= 1.
...
arguments to forward to ds_atkinson from atkinson
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_atkinson(de_county, c(pop_white, starts_with('pop_')))
ds_atkinson(de_county, starts_with('pop_'), 'atkinson')
Compute Blau's Index
Description
Compute Blau's Index
Usage
ds_blau(.data, .cols, .name)
blau(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with.
.name
name for column with Blau index. Leave missing to return a vector.
...
arguments to forward to ds_blau from blau
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_blau(de_county, starts_with('pop_'))
ds_blau(de_county, starts_with('pop_'), 'blau')
Compute Correlation Index
Description
Compute Correlation Index
Usage
ds_correlation(.data, .cols, .name)
correlation(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with Correlation index. Leave missing to return a vector.
...
arguments to forward to ds_correlation from correlation
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_correlation(de_county, c(pop_white, starts_with('pop_')))
ds_correlation(de_county, starts_with('pop_'), 'correlation')
Compute Distance Decay Interaction
Description
Compute Distance Decay Interaction
Usage
ds_dd_interaction(.data, .cols, .name, .comp = FALSE)
dd_interaction(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with distance decay interaction. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_dd_interaction from dd_interaction
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_dd_interaction(de_county, c(pop_black, starts_with('pop_')))
ds_dd_interaction(de_county, c(pop_black, starts_with('pop_')), 'dd_interaction')
Compute Distance Decay Isolation
Description
Compute Distance Decay Isolation
Usage
ds_dd_isolation(.data, .cols, .name, .comp = FALSE)
dd_isolation(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with distance decay isolation. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_dd_isolation from dd_isolation
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_dd_isolation(de_county, c(pop_black, starts_with('pop_')))
ds_dd_isolation(de_county, c(pop_black, starts_with('pop_')), 'dd_isolation')
Compute Delta Index
Description
Compute Delta Index
Usage
ds_delta(.data, .cols, .name, .comp = FALSE)
delta(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with delta index. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_delta from delta
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_delta(de_county, c(pop_white, starts_with('pop_')))
ds_delta(de_county, starts_with('pop_'), 'delta')
Compute Dissimilarity Index
Description
Compute Dissimilarity Index
Usage
ds_dissim(.data, .cols, .name, .comp = FALSE)
dissim(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with dissimilarity index. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_dissim from dissim
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_dissim(de_county, c(pop_white, starts_with('pop_')))
ds_dissim(de_county, c(pop_white, starts_with('pop_')), .comp = TRUE)
ds_dissim(de_county, starts_with('pop_'), 'dissim')
Compute Diversity
Description
This is equivalent to perplexity.
Usage
ds_diversity(.data, .cols, .name, q = 1)
diversity(..., .data = dplyr::across(everything()))
ds_perplexity(.data, .cols, .name, q = 1)
perplexity(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with.
.name
name for column with diversity. Leave missing to return a vector.
q
exponent parameter. Default 0. Can not be 1.
...
arguments to forward to ds_diversity from diversity
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_diversity(de_county, starts_with('pop_'))
ds_diversity(de_county, starts_with('pop_'), 'diversity')
Compute Entropy Index
Description
Compute Entropy Index
Usage
ds_entropy(.data, .cols, .name, .comp = FALSE)
entropy(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with entropy index. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_entropy from entropy
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_entropy(de_county, c(pop_white, starts_with('pop_')))
ds_entropy(de_county, starts_with('pop_'), 'entropy')
Compute Gini Index
Description
Compute Gini Index
Usage
ds_gini(.data, .cols, .name, .comp = FALSE)
gini(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with gini index. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_gini from gini
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_gini(de_county, c(pop_white, starts_with('pop_')))
ds_gini(de_county, starts_with('pop_'), 'gini')
Compute Herfindahl-Hirshman Index
Description
This is equivalent to the Simpson Index.
Usage
ds_hhi(.data, .cols, .name)
hhi(..., .data = dplyr::across(everything()))
ds_simpson(.data, .cols, .name)
simpson(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with.
.name
name for column with HHI. Leave missing to return a vector.
...
arguments to forward to ds_hhi from hhi
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_hhi(de_county, starts_with('pop_'))
ds_hhi(de_county, starts_with('pop_'), 'blau')
Compute Interaction Index
Description
Compute Interaction Index
Usage
ds_interaction(.data, .cols, .name, .comp = FALSE)
interaction(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with Interaction index. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_interaction from interaction
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_interaction(de_county, c(pop_white, starts_with('pop_')))
ds_interaction(de_county, starts_with('pop_'), 'interaction')
Compute Simpson Index
Description
Compute Simpson Index
Usage
ds_inv_simpson(.data, .cols, .name)
inv_simpson(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with.
.name
name for column with Simpson Index Leave missing to return a vector.
...
arguments to forward to ds_inv_simpson from inv_simpson
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_inv_simpson(de_county, starts_with('pop_'))
ds_inv_simpson(de_county, starts_with('pop_'), 'blau')
Compute Isolation Index
Description
Compute Isolation Index
Usage
ds_isolation(.data, .cols, .name, .comp = FALSE)
isolation(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with Isolation index. Leave missing to return a vector.
.comp
Default is FALSE. FALSE returns the sum, TRUE returns the components.
...
arguments to forward to ds_isolation from isolation
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_isolation(de_county, c(pop_white, starts_with('pop_')))
ds_isolation(de_county, starts_with('pop_'), 'isolation')
Compute Relative Centralization
Description
Compute Relative Centralization
Usage
ds_rel_cent(.data, .cols, .name)
rel_cent(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with relative centralization. Leave missing to return a vector.
...
arguments to forward to ds_rel_cent from rel_cent
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_rel_cent(de_county, c(pop_white, starts_with('pop_')))
ds_rel_cent(de_county, c(pop_white, starts_with('pop_')), 'rel_cent')
Compute Relative Clustering
Description
Compute Relative Clustering
Usage
ds_rel_clust(.data, .cols, .name)
rel_clust(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with relative clustering. Leave missing to return a vector.
...
arguments to forward to ds_rel_clust from rel_clust
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_rel_clust(de_county, c(pop_black, starts_with('pop_')))
ds_rel_clust(de_county, c(pop_black, starts_with('pop_')), 'rel_clust')
Compute Relative Concentration
Description
Compute Relative Concentration
Usage
ds_rel_conc(.data, .cols, .name)
rel_conc(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with relative concentration. Leave missing to return a vector.
...
arguments to forward to ds_rel_conc from rel_conc
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_rel_conc(de_county, c(pop_black, starts_with('pop_')))
ds_rel_conc(de_county, c(pop_black, starts_with('pop_')), 'rel_conc')
Compute Reyni Entropy
Description
Compute Reyni Entropy
Usage
ds_reyni(.data, .cols, .name, q = 0)
reyni(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with.
.name
name for column with Reyni entropy. Leave missing to return a vector.
q
exponent parameter. Default 0. Can not be 1.
...
arguments to forward to ds_reyni from reyni
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_reyni(de_county, starts_with('pop_'))
ds_reyni(de_county, starts_with('pop_'), 'reyni')
Compute Shannon Index
Description
Compute Shannon Index
Usage
ds_shannon(.data, .cols, .name)
shannon(..., .data = dplyr::across(everything()))
Arguments
.data
.cols
tidy-select
Columns to compute the measure with.
.name
name for column with Shannon index. Leave missing to return a vector.
...
arguments to forward to ds_shannon from shannon
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_shannon(de_county, starts_with('pop_'))
ds_shannon(de_county, starts_with('pop_'), 'shannon')
Compute Spatial Proximity
Description
Compute Spatial Proximity
Usage
ds_spat_prox(.data, .cols, .name)
spat_prox(..., .data = dplyr::across(everything()))
Arguments
.data
tibble with sf geometry
.cols
tidy-select
Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
first column as first group and sum of other columns as second.
.name
name for column with spatial proximity. Leave missing to return a vector.
...
arguments to forward to ds_spat_prox from spat_prox
Value
a tibble or numeric vector if .name missing
Examples
data('de_county')
ds_spat_prox(de_county, c(pop_black, starts_with('pop_')))
ds_spat_prox(de_county, c(pop_black, starts_with('pop_')), 'spat_prox')
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- tidyselect
all_of,any_of,contains,ends_with,everything,last_col,matches,num_range,one_of,starts_with
Tidy eval helpers
Description
This page lists the tidy eval tools reexported in this package from rlang. To learn about using tidy eval in scripts and packages at a high level, see the dplyr programming vignette and the ggplot2 in packages vignette. The Metaprogramming section of Advanced R may also be useful for a deeper dive.
The tidy eval operators
{{,!!, and!!!are syntactic constructs which are specially interpreted by tidy eval functions. You will mostly need{{, as!!and!!!are more advanced operators which you should not have to use in simple cases.The curly-curly operator
{{allows you to tunnel data-variables passed from function arguments inside other tidy eval functions.{{is designed for individual arguments. To pass multiple arguments contained in dots, use...in the normal way.my_function <- function(data, var, ...) { data |> group_by(...) |> summarise(mean = mean({{ var }})) }-
enquo()andenquos()delay the execution of one or several function arguments. The former returns a single expression, the latter returns a list of expressions. Once defused, expressions will no longer evaluate on their own. They must be injected back into an evaluation context with!!(for a single expression) and!!!(for a list of expressions).my_function <- function(data, var, ...) { # Defuse var <- enquo(var) dots <- enquos(...) # Inject data |> group_by(!!!dots) |> summarise(mean = mean(!!var)) }In this simple case, the code is equivalent to the usage of
{{and...above. Defusing withenquo()orenquos()is only needed in more complex cases, for instance if you need to inspect or modify the expressions in some way. The
.datapronoun is an object that represents the current slice of data. If you have a variable name in a string, use the.datapronoun to subset that variable with[[.my_var <- "disp" mtcars |> summarise(mean = mean(.data[[my_var]]))
Another tidy eval operator is
:=. It makes it possible to use glue and curly-curly syntax on the LHS of=. For technical reasons, the R language doesn't support complex expressions on the left of=, so we use:=as a workaround.my_function <- function(data, var, suffix = "foo") { # Use `{{` to tunnel function arguments and the usual glue # operator `{` to interpolate plain strings. data |> summarise("{{ var }}_mean_{suffix}" := mean({{ var }})) }Many tidy eval functions like
dplyr::mutate()ordplyr::summarise()give an automatic name to unnamed inputs. If you need to create the same sort of automatic names by yourself, useas_label(). For instance, the glue-tunnelling syntax above can be reproduced manually with:my_function <- function(data, var, suffix = "foo") { var <- enquo(var) prefix <- as_label(var) data |> summarise("{prefix}_mean_{suffix}" := mean(!!var)) }Expressions defused with
enquo()(or tunnelled with{{) need not be simple column names, they can be arbitrarily complex.as_label()handles those cases gracefully. If your code assumes a simple column name, useas_name()instead. This is safer because it throws an error if the input is not a name as expected.