Package 'synthACS'

Description

Add a new constraint to the mapping between a given macro dataset (class "macroACS") and a matching micro dataset (class "micro_synthetic). May be called repeatedly to create a set of constraints.

Usage

add_constraint(
  attr_name = "variable",
  attr_totals,
  micro_data,
  constraint_list = NULL
)

Arguments

Value

A list of constraints.

Examples

## Not run: 
## assumes that you have a micro_synthetic dataset named test_micro and attribute counts
## named a,e,g respectively 
c_list <- add_constraint(attr_name= "age", attr_totals= a, micro_data= test_micro)
c_list <- add_constraint(attr_name= "edu_attain", attr_totals= e, micro_data= test_micro,
                        constraint_list= c_list)
c_list <- add_constraint(attr_name= "gender", attr_totals= g, micro_data= test_micro,
                         constraint_list= c_list)

## End(Not run)

Description

A dataset containing age-adjusted death rate data by race and gender of the deceased. Data is provided for 1980-2013.

Usage

adjDR

Format

A data.frame with 612 observations and 4 variables.

year

The year for which data was was recorded.

race

The racial group of the deceased One of all all races; white whites; black_aa black / African-American; nat_amer American Indian or Native Alaskan; asian_isl Asian or Pacific Islander; hisp_lat Hispanic.

gender

The gender of the deceased. One of c(both, male, female)

adj_death_rate

The age-adjusted death rate. See details.

Details

• The age-adjusted death rates are used to compare relative mortality risks among groups and over time. They were computed by the direct method, which is defined

  $R'= \sum_{i} \frac{P_{si}}{P_{s}}R_i$

  where $P_{si}$ is the standard population for age group i, $P_s$ is the total US standard population and $R_i$ is the raw death rate for age group i.

• Populations are based on census counts enumerated as of April 1 of the census year and estimated as of July 1 for non-census years.

Source

https://www.cdc.gov/nchs/nvss/deaths.htm

References

Xu, J. Q., S. L. Murphy, and K. D. Kochanek. "Deaths: final data for 2013." National Vital Statistics Reports 64.2 (2015).

Description

A dataset containing death rates for individuals by age group and race for the United States, 2013.

Usage

AgeRaceDR

Format

A data.frame with 360 observations and 4 variables.

age

The exact age, in years, at which life expectany is calculated.

race

The racial group of the deceased One of all all races; white whites; black black / African-American; hispanic Hispanic; asian.isl Asian and Pacific Islander; nat.amer Native American or Alaska Native.

gender

The gender of the deceased. One of c(both, male, female)

death_rate

The raw death rate. See details.

Details

• The death rate is defined as deaths per 100,000 population.

Source

https://www.cdc.gov/nchs/nvss/deaths.htm

References

Xu, J. Q., S. L. Murphy, and K. D. Kochanek. "Deaths: final data for 2013." National Vital Statistics Reports 64.2 (2015).

Description

Create a new age constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_age(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 a1 <- all_geog_constraint_age(obj, "synthetic")
 a2 <- all_geog_constraint_age(obj, "macro_table")

## End(Not run)

Description

Create a new educational attainment constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_edu(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 e1 <- all_geog_constraint_edu(obj, "synthetic")
 e2 <- all_geog_constraint_edu(obj, "macro_table")

## End(Not run)

Description

Create a new employment status constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_employment(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 e1 <- all_geog_constraint_employment(obj, "synthetic")
 e2 <- all_geog_constraint_employment(obj, "macro_table")

## End(Not run)

Description

Create a new gender constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_gender(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 g1 <- all_geog_constraint_gender(obj, "synthetic")
 g2 <- all_geog_constraint_gender(obj, "macro_table")

## End(Not run)

Description

Create a new geographic mobility constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_geog_mob(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 gm1 <- all_geog_constraint_geog_mob(obj, "synthetic")
 gm2 <- all_geog_constraint_geog_mob(obj, "macro_table")

## End(Not run)

Description

Create a new individual income constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_income(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 i1 <- all_geog_constraint_income(obj, "synthetic")
 i2 <- all_geog_constraint_income(obj, "macro_table")

## End(Not run)

Description

Create a new marital status constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_marital_status(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 m1 <- all_geog_constraint_marital_status(obj, "synthetic")
 m2 <- all_geog_constraint_marital_status(obj, "macro_table")

## End(Not run)

Description

Create a new nativity status constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_nativity(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 n1 <- all_geog_constraint_nativity(obj, "synthetic")
 n2 <- all_geog_constraint_nativity(obj, "macro_table")

## End(Not run)

Description

Create a new poverty status constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_poverty(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 p1 <- all_geog_constraint_poverty(obj, "synthetic")
 p2 <- all_geog_constraint_poverty(obj, "macro_table")

## End(Not run)

Description

Create a new race constraint list to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'synthACS').

Usage

all_geog_constraint_race(obj, method = c("synthetic", "macro.table"))

Arguments

See Also

all_geogs_add_constraint

Examples

## Not run: 
 # assumes that obj of class 'synthACS' already exists in your environment
 r1 <- all_geog_constraint_race(obj, "synthetic")
 r2 <- all_geog_constraint_race(obj, "macro_table")

## End(Not run)

Description

Optimize the candidate micro datasets such that the lowest loss against the macro dataset constraints are obtained. Loss is defined here as total absolute error (TAE) and constraints are defined by the constraint_list_list. Optimization is done by simulated annealing and geographies are run in parallel.

Usage

all_geog_optimize_microdata(
  macro_micro,
  prob_name = "p",
  constraint_list_list,
  p_accept = 0.4,
  max_iter = 10000L,
  seed = sample.int(10000L, size = 1, replace = FALSE),
  leave_cores = 1L,
  verbose = TRUE
)

Arguments

See Also

optimize_microdata

Examples

## Not run: 
 # assumes that micro_synthetic and cll already exist in your environment
 # see: examples for derive_synth_datasets() and all_geogs_add_constraint()
 optimized_la <- all_geog_optimize_microdata(micro_synthetic, prob_name= "p", 
     constraint_list_list= cll, p_accept= 0.01, max_iter= 1000L)

## End(Not run)

Description

Add a new attribute to a set (ie list) of synthetic_micro datasets using conditional relationships between the new attribute and existing attributes (eg. wage rate conditioned on age and education level). The same attribute is added to *each* synthetic_micro dataset, where each dataset is supplied a distinct relationship for attribute creation.

Usage

all_geog_synthetic_new_attribute(
  df_list,
  prob_name = "p",
  attr_name = "variable",
  conditional_vars = NULL,
  st_list = NULL,
  leave_cores = 1L
)

Arguments

Value

A list of new synthetic_micro datasets each with class "synthetic_micro".

See Also

synthetic_new_attribute

Examples

## Not run: 
 set.seed(567L)
 df <- data.frame(gender= factor(sample(c("male", "female"), size= 100, replace= TRUE)),
                 age= factor(sample(1:5, size= 100, replace= TRUE)),
                 pov= factor(sample(c("lt_pov", "gt_eq_pov"),
                                    size= 100, replace= TRUE, prob= c(.15,.85))),
                 p= runif(100))
df$p <- df$p / sum(df$p)
class(df) <- c("data.frame", "micro_synthetic")

# and example test elements
cond_v <- c("gender", "pov")
levels <- c("employed", "unemp", "not_in_LF")
sym_tbl <- data.frame(gender= rep(rep(c("male", "female"), each= 3), 2),
                      pov= rep(c("lt_pov", "gt_eq_pov"), each= 6),
                      cnts= c(52, 8, 268, 72, 12, 228, 1338, 93, 297, 921, 105, 554),
                      lvls= rep(levels, 4))



df_list <- replicate(10, df, simplify= FALSE)
st_list <- replicate(10, sym_tbl, simplify= FALSE)

# run
library(parallel)
syn <- all_geog_synthetic_new_attribute(df_list, prob_name= "p", attr_name= "variable",
                                        conditional_vars= cond_v,st_list= st_list)

## End(Not run)

Description

Add a new constraint to the mapping between a a set of macro datasets and a matching set of micro dataset (supplied as class 'macro_micro'). May be called repeatedly to create a set of constraints across the sub-geographies.

Usage

all_geogs_add_constraint(
  attr_name = "variable",
  attr_total_list,
  macro_micro,
  constraint_list_list = NULL
)

Arguments

Value

A list of constraint lists.

See Also

add_constraint

Examples

## Not run: 
# assumes that micro_synthetic already exists in your environment

# 1. build constraints for gender and age
g <- all_geog_constraint_gender(micro_synthetic, method= "macro.table")

a <- all_geog_constraint_age(micro_synthetic, method= "macro.table")

# 2. bind constraints to geographies and macro-data
cll <- all_geogs_add_constraint(attr_name= "age", attr_total_list= a, 
          macro_micro= micro_synthetic)
cll <- all_geogs_add_constraint(attr_name= "gender", attr_total_list= g, 
          macro_micro= micro_synthetic, constraint_list_list= cll)


## End(Not run)

Description

A dataset containing birth rate data in the United States by age and race of the mother. Data for all races is provided for 1970-2014 and for individual races from 1989-2014.

Usage

BR2014

Format

A data.frame with 1,750 observations and 4 variables.

year

The year for which data was was recorded.

race

The racial group of the mothers. One of all all races; white non-hispanic whites; black_aa black / African-American; nat_amer American Indian or Native Alaskan; asian_isl Asian or Pacific Islander; hisp_lat Hispanic or Latin American.

age_group

The age group of the mother.

birth_rate

The birth rate. See Details.

Details

• The birth rate is defined as births per 1,000 women in the specified group (age and race).

• Populations are based on census counts enumerated as of April 1 of the census year and estimated as of July 1 for non-census years.

• Beginning in 1997, birth rates for age group 45up by relating births to all women age 45 or older to this group. Prior to 1997, only births to women age 45-49 were included.

Source

https://www.cdc.gov/nchs/nvss/births.htm

References

Hamilton, Brady E., et al. "Births: final data for 2014." National Vital Statistics Reports 64.12 (2015): 1-64.

Description

Calculates the total absolute error (TAE) between sample micro data and constraining totals from the matching macro data. Allows for updating of prior TAE instead of re-calculating to improve speed in iterating. The updating feature is particularly helpful for optimizing micro data fitting via simulated annealing (see optimize_microdata).

Usage

calculate_TAE(
  sample_data,
  constraint_list,
  prior_sample_totals = NULL,
  dropped_obs_totals = NULL,
  new_obs = NULL
)

Arguments

Examples

## Not run: 
## assumes that you have a micro_synthetic dataset named test_micro and attribute count
## named g respectively 
c_list <- add_constraint(attr_name= "gender", attr_totals= g, micro_data= test_micro,
            constraint_list= c_list)
calculate_TAE(test_micro, c_list)

## End(Not run)

Description

Combine objects of class "smsm_set" into a single object of class "smsm_set"

Usage

combine_smsm(...)

Arguments

See Also

split, all_geog_optimize_microdata

Examples

## Not run: 
 combined <- combine_smsm(smsm1, smsm2, smsm3)

## End(Not run)

Description

Derive synthetic micro datasets for each sub-geography of a given set of geographic macro data constraining tabulations. See Details... By default, micro dataset generation is run in parallel with load balancing. Macro data is assumed to have been pulled from the US Census API via the acs package.

Usage

derive_synth_datasets(macro_data, parallel = TRUE, leave_cores = 2)

Arguments

Value

A list of the input macro datasets produced by pull_synth_data and a list of synthetic micro datasets for each geographical subset within the specified macro geography.

Details

In the absence of true micro level datasets for a given geographic area, synthetic datasets can be used. This function uses conditional and marginal probability distributions (at the aggregate level) to generate synthetic micro population datasets, which are built one constraint at a time. Taking as input the macro level data (class "macroACS"), this function builds synthetic micro datasets for each lower level geographical area within the area of study.

In simplest terms, the goal is to generate a joint probability distribution for an attribute vector; and, to create synthetic individuals from this distribution. However, note that information for the full joint distribution is typically not available, so we construct it as a product of conditional and marginal probabilities. This is done one attribute at a time; where it is assumed that there is some sort of continuum of attribute dependence. That is, some attributes are more important (eg. gender, age) in 'determining' others (eg. educational attainment, marital status, etc). These more important attributes need to be assigned first, whereas less important attributes may be assigned later. Most of these distinctions are largely intuitive, but care must be taken in choosing the order of constructed attributes.

This function provides a synthetic population with the following characteristics as well as each synthetic individual's probability of inclusion. The included characteristics are: age, gender, marital status, educational attainment, employment status, nativity, poverty status, geographic mobility in the prior year, individual income, and race. Additional attributes which interest the user may be added in a similar manner via synthetic_new_attribute.

**Note:** INDIVIDUAL, not HOUSEHOLD level, synthetic population datasets are created.

References

Birkin, Mark, and M. Clarke. "SYNTHESIS-a synthetic spatial information system for urban and regional analysis: methods and examples." Environment and planning A 20.12 (1988): 1645-1671.

See Also

pull_synth_data, acs.fetch, geo.make

Examples

## Not run: 
# make geography
la_geo <- acs::geo.make(state= "CA", county= "Los Angeles", tract= "*")
# pull data elements for creating synthetic data
la_dat <- pull_synth_data(2014, 5, la_geo)
# derive synthetic data
la_synthetic <- derive_synth_datasets(la_dat, leave_cores= 0)

## End(Not run)

Description

Gets aggregate, macro, data, either estimate or standard error, for a specified geography and specified dataset.

Usage

fetch_data(acs, geography, dataset = c("estimate", "st.err"), choice = NULL)

Arguments

Description

Generate a list of attribute vectors for new synthetic attribute creation from a "macroACS" object.

Usage

gen_attr_vectors(acs, choice)

Arguments

See Also

all_geog_synthetic_new_attribute, synthetic_new_attribute

Description

Extract the best fit micro population (resulting from the simulated annealing algorithm) for a given geography.

Usage

get_best_fit(obj, geography)

Arguments

Description

Get the names of the datasets in a given "macroACS" object.

Usage

get_dataset_names(acs)

Arguments

See Also

fetch_data

Description

Get the data collection endyear from a "macroACS" object

Usage

get_endyear(acs)

Arguments

Description

Extract the final TAE (resulting from the simulated annealing algorithm) for a given geography.

Usage

get_final_tae(obj, geography)

Arguments

Description

Get the summary information of the geography selected from a "macroACS" object

Usage

get_geography(acs)

Arguments

Description

Get the data collection span from a "macroACS" object

Usage

get_span(acs)

Arguments

Description

Function that checks if the target object is a macro_micro object.

Usage

is.macro_micro(x)

Arguments

Value

Returns TRUE if its argument has class "macro_micro" among its classes and FALSE otherwise.

Description

Function that checks if the target object is a macroACS object.

Usage

is.macroACS(x)

Arguments

Value

Returns TRUE if its argument has class "macroACS" among its classes and FALSE otherwise.

Description

Function that checks if the target object is a micro_synthetic object.

Usage

is.micro_synthetic(x)

Arguments

Value

Returns TRUE if its argument has class "micro_synthetic" among its classes and FALSE otherwise.

Description

Function that checks if the target object is a smsm_set object.

Usage

is.smsm_set(x)

Arguments

Value

Returns TRUE if its argument has class "macroACS" among its classes and FALSE otherwise.

Description

Function that checks if the target object is a synthACS object.

Usage

is.synthACS(x)

Arguments

Value

Returns TRUE if its argument has class "synthACS" among its classes and FALSE otherwise.

Description

An anonymized dataset containing the geographic information of hospitals in Los Angeles County California, USA.

Usage

la_hospitals

Format

A data.frame with 631 observations and 7 variables

geo_long

The hospital's longitude.

geo_lat

The hospital's lattitude.

city

The hospital's postal city.

state_fips

The hospital's alpha FIPS code.

zip

The hospital's five digit postal ZIP code.

census_tract

The census tract in which the hospital is located.

county_name

The hospital's county – "LOS ANGELES".

Description

A dataset containing life expectancy at certain ages by race, hispanic origin and sex for the United States, 2013.

Usage

LifeExp

Format

A data.frame with 396 observations and 4 variables.

age

The exact age, in years, at which life expectany is calculated.

race

The racial group of the deceased One of all all races; white whites; black black / African-American; hispanic Hispanic; non.hisp.white non Hispanic whites; non.hispanic.black non Hispanic blacks.

gender

The gender of the deceased. One of c(both, male, female)

life_expectancy

The life expectancy for an individual at the exact age with the given race and gender.

Source

https://www.cdc.gov/nchs/nvss/deaths.htm

References

Xu, J. Q., S. L. Murphy, and K. D. Kochanek. "Deaths: final data for 2013." National Vital Statistics Reports 64.2 (2015).

Description

Marginalize, (ie- reduce in number), attributes of a synthetic dataset of class 'micro_synthetic' or a list of synthetic datasets of class 'synthACS'. This is done by marginalizing the joint distribution based on a set of specified attributes (see Arguments below).

Usage

marginalize_attr(obj, varlist, marginalize_out = FALSE)

Arguments

Examples

{
# dummy data setup
set.seed(567L)
df <- data.frame(gender= factor(sample(c("male", "female"), size= 100, replace= TRUE)),
                 age= factor(sample(1:5, size= 100, replace= TRUE)),
                 pov= factor(sample(c("below poverty", "at above poverty"), 
                                   size= 100, replace= TRUE, prob= c(.15,.85))),
                 p= runif(100))
df$p <- df$p / sum(df$p)
class(df) <- c("data.frame", "micro_synthetic")

df2 <- marginalize_attr(df, varlist= "gender")
df3 <- marginalize_attr(df, varlist= c("gender", "age"))
df4 <- marginalize_attr(df, varlist= c("gender", "age"), marginalize_out= TRUE)

df_list <- replicate(10, df, simplify= FALSE)
dummy_list <- replicate(10, list(NULL), simplify= FALSE)
df_list <- mapply(function(a,b) {return(list(a, b))}, a= dummy_list, b= df_list, SIMPLIFY = FALSE)
class(df_list) <- c("list", "synthACS")

# run the function
df_list2 <- marginalize_attr(df_list, varlist= c("gender", "age"))
}

Description

A dataset containing multiple birth rate data by race of the mother. Data for all races is provided for 1980-2014 and for individual races from 1990-2014.

Usage

MBR

Format

A data.frame with 110 observations and 8 variables.

year

The year for which data was was recorded.

race

The racial group of the mothers. One of all all races; white non-hispanic whites; black_aa non Hispanic black / African-American; hisp_lat Hispanic.

births

Total births for the year and racial group in the United States.

twin_births

Total twin births for the year and racial group in the United States.

triplet_more_births

Total triplet or higher order births for the year and racial group in the United States.

MBRate

The number of live births in all multiple deliveries per 1,000 live births.

twinBR

The number of live births in all twin deliveries per 1,000 live births.

twinBR

The number of live births in all triplet or higher order deliveries per 100,000 live births.

Details

• Data for race cateogry "all" includes races other than white and black and origin not stated.

• Race and Hispanic origin are reported separately on birth certificates. Persons of Hispanic origin may be of any race.

Source

https://www.cdc.gov/nchs/nvss/births.htm

References

Hamilton, Brady E., et al. "Births: final data for 2014." National Vital Statistics Reports 64.12 (2015): 1-64.

Description

Optimize the candidate micro dataset such that the lowest loss against the macro dataset constraints is obtained. Loss is defined here as total absolute error (TAE) and constraints are defined by the constraint_list. Optimization is done by simulated annealing–see details.

Usage

optimize_microdata(
  micro_data,
  prob_name = "p",
  constraint_list,
  tolerance = round(sum(constraint_list[[1]])/2000 * length(constraint_list), 0),
  resample_size = min(sum(constraint_list[[1]]), max(500,
    round(sum(constraint_list[[1]]) * 0.005, 0))),
  p_accept = 0.4,
  max_iter = 10000L,
  seed = sample.int(10000L, size = 1, replace = FALSE),
  verbose = TRUE
)

Arguments

Details

Spatial microsimulation involves the study of individual-level phenomena within a specified set of geographies in which these individuals act. It involves the creation of synthetic data to model, via simulation, these phenomena. As a first step to simulation, an appropriate micro-level (ie. individual) dataset must be generated. This function creates such appropriate micro-level datasets given a set of candidate observations and macro-level constraints.

Optimization is done via simulated annealing, where we wish to minimize the total absolute error (TAE) between the micro-data and the macro-constraints. The annealing procedure is controlled by the parameters tolerance, resample_size, p_accept, and max_iter. Specifically, tolerance indicates the maximum allowable TAE between the output micro-data and the macro-constraints within a given max_iter allowable iterations to converge. resample_size and p_accept control movement about the candidate space. Specfically, resample_size controls the jump size between neighboring candidates and p_accept controls the hill-climbing rate for exiting local minima.

Please see the references for a more detailed discussion of the simulated annealing procedure.

References

Ingber, Lester. "Very fast simulated re-annealing." Mathematical and computer modelling 12.8 (1989): 967-973.

Metropolis, Nicholas, et al. "Equation of state calculations by fast computing machines." The journal of chemical physics 21.6 (1953): 1087-1092.

Szu, Harold, and Ralph Hartley. "Fast simulated annealing." Physics letters A 122.3 (1987): 157-162.

Examples

## Not run: 
## assumes you have micro_synthetic object named test_micro and constraint_list named c_list
opt_data <- optimize_microdata(test_micro, "p", c_list, max_iter= 10, resample_size= 500, 
              p_accept= 0.01, verbose= FALSE)

## End(Not run)

Description

Plot the path TAE in the simulated annealing algorithm for a given geography

Usage

plot_TAEpath(object, geography, ...)

Arguments

Description

A wrapper function to pull multiple base tables from ACS API via acs.fetch.

Usage

pull_acs_basetables(endyear, span, geography, table_vec)

Arguments

Value

A 'macroACS' class object

References

https://data.census.gov/cedsci/

Examples

## Not run: 
# make geography
la_geo <- acs::geo.make(state= "CA", county= "Los Angeles")
# pull data 
la_dat <- pull_acs_basetables(endyear= 2015, span= 1, geography= la_geo, 
  table_vec= c("B01001", "B01002", "B01003"))

## End(Not run)

Description

Pull ACS data for a specified geography from base tables B15011 and B15012. Note: only 2014 data is supplied by ACS

Usage

pull_bachelors(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B14001, B14003, B15001, B15002. Not currently implemented: B15010, B28006 Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_edu(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B07001, B07003, B07008, B07009, B07010, and B07012. These tables provide data on geographic mobility in the past year by a number of slices. Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_geo_mobility(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B09019, B11011, B19081, B25002, B25003, B25004, B25010, B25024, B25056, B25058, B25071, and B27001. Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_household(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make B28001 - TYPES OF COMPUTERS IN HOUSEHOLD B28002 - PRESENCE AND TYPES OF INTERNET SUBSCRIPTIONS IN HOUSEHOLD

Description

Pull ACS data for a specified geography from base tables B19083, B19301, B19326, B21001, B22001, B23020, B24011. Not yet implemented: B28004 Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_inc_earnings(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B12001, B12006, B12007, 12501 Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_mar_status(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B01001, B01002, B02001, B06007, B06008, B06009, B06010, B06011, AND B06012. These tables reference population counts by a number of slices. Multiple additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_population(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B17001, B17004, B18101, B19001, B19013, B19055, B19057. Not yet implemented: B17002 Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_pov_inc(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B01001B-I and B02001. ' These tables reference population counts by race.

Usage

pull_race_data(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

Pull ACS data for a specified geography from base tables B01001, B02001, B12002, B15001, B06001, B06010, B23001, B17005, and B17005. These tables reference population counts by a number of slices. Multiple additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_synth_data(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a list of data.frames of estimates, a list of data.frames of standard errors, and the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Examples

## Not run: 
# make geography
la_geo <- acs::geo.make(state= "CA", county= "Los Angeles", tract= "*")
# pull data elements for creating synthetic data
la_dat <- pull_synth_data(2014, 5, la_geo)

## End(Not run)

Description

Pull ACS data for a specified geography from base tables B08012, B08101, B08121, B08103, B08124, B08016, B08017. Additional fields, mainly percentages and aggregations, are calculated.

Usage

pull_transit_work(endyear, span, geography)

Arguments

Value

A list containing the endyear, span, a data.frame of estimates, a data.frame of standard errors, a character vector of the original column names, and a data.frame of the geography metadata from acs.fetch.

See Also

acs.fetch, geo.make

Description

A dataset containing raw death rate data by race and gender of the deceased. Data is provided for 1980-2013.

Usage

rawDR

Format

A data.frame with 612 observations and 4 variables.

year

The year for which data was was recorded.

race

The racial group of the deceased One of all all races; white whites; black_aa black / African-American; nat_amer American Indian or Native Alaskan; asian_isl Asian or Pacific Islander; hisp_lat Hispanic.

gender

The gender of the deceased. One of c(both, male, female)

death_rate

The raw death rate. See details.

Details

• The death rate is defined as deaths per 100,000 population.

• Populations are based on census counts enumerated as of April 1 of the census year and estimated as of July 1 for non-census years.

Source

https://www.cdc.gov/nchs/nvss/deaths.htm

References

Xu, J. Q., S. L. Murphy, and K. D. Kochanek. "Deaths: final data for 2013." National Vital Statistics Reports 64.2 (2015).

Description

Split a "macroACS" object into subsets. This may be helpful for users who have limited memory available on their machines before proceding to derive sample synthetic micro data.

Usage

split(acs, n_splits)

Arguments

See Also

derive_synth_datasets

Description

A dataset containing birth rate data by US state and age for all US states and territories in 2014.

Usage

stateFR

Format

A data.frame with 612 observations and 3 variables.

state

The state or territory for which data was was recorded.

age_group

The age group of the mother.

birth_rate

The birth rate. See Details.

Details

• The birth rate is defined as births per 1,000 women in the specified group.

• Birth rates for age_group 45_49 are computed by relating births to women aged 45 and over to women aged 45-49

• Data for the "United States" as a whole excludes data for the territories.

• Data is missing (eg. NA) when data does not meet standards of reliability or percision; birth rates based on fewer than 20 births.

Source

https://www.cdc.gov/nchs/nvss/births.htm

References

Hamilton, Brady E., et al. "Births: final data for 2014." National Vital Statistics Reports 64.12 (2015): 1-64.

Description

summary method for class 'smsm_set'.

Usage

## S3 method for class 'smsm_set'
summary(object, ...)

Arguments

Description

Add a new attribute to a synthetic_micro dataset using conditional relationships between the new attribute and existing attributes (eg. wage rate conditioned on age and education level).

Usage

synthetic_new_attribute(
  df,
  prob_name = "p",
  attr_name = "variable",
  conditional_vars = NULL,
  sym_tbl = NULL
)

Arguments

Value

A new synthetic_micro dataset with class "synthetic_micro".

Details

New synthetic variables are introduced to the existing data via conditional probability. Similar to derive_synth_datasets, the goal with this function is to generate a joint probability distribution for an attribute vector; and, to create synthetic individuals from this distribution. Although no limit is placed on the number of variables on which to condition, in practice, data rarely exists which allows more than two or three conditioning variables. Other variables are assumed to be independent from the new attribute.

** There are four different types of conditional/marginal probability models which may be considered for a given new attribute: (1) Independence: it is assumed that each of the variables is independent of the others (2) Pairwise conditional independence: it is assumed that attributes are related to only one other attribute and independent of all others. (3) Conditional independence: Attributes can be depedent on some subset of other attributes and independent of the rest. (4) In the most general case, all attributes are jointly interrelated.

Conditioning is implemented via symbol-tables (sym_tbl) to ensure accurate matching between conditioning variables, new attribute levels, and new attribute probabilities. The symbol table is constructed such that the key in the symbol-table's key-value pair is the specific values for the set of conditioning variables. This key is the first N columns of sym_tbl. A recursive approach is employed to conditionally partition sym_tbl. In this sense, the *order* in which the conditional variables are supplied matters.

The value is final 2 columns of sym_tbl which are a pair of (A) either counts or percentages used to specify the probability for the new attribute and (B) the level that the new attribute takes on.

Examples

{
set.seed(567L)
df <- data.frame(gender= factor(sample(c("male", "female"), size= 100, replace= TRUE)),
                edu= factor(sample(c("LT_college", "BA_degree"), size= 100, replace= TRUE)),
                p= runif(100))
df$p <- df$p / sum(df$p)
class(df) <- c("data.frame", "micro_synthetic")
ST <- data.frame(gender= c(rep("male", 3), rep("female", 3)),
                 attr_pct= c(0.1, 0.8, 0.1, 0.05, 0.7, 0.25),
                 levels= rep(c("low", "middle", "high"), 2))
df2 <- synthetic_new_attribute(df, prob_name= "p", attr_name= "SES", conditional_vars= "gender",
         sym_tbl= ST)

ST2 <- data.frame(gender= c(rep("male", 3), rep("female", 6)),
                  edu= c(rep(NA, 3), rep(c("LT_college", "BA_degree"), each= 3)),
                  attr_pct= c(0.1, 0.8, 0.1, 10, 80, 10, 5, 70, 25),
                  levels= rep(c("low", "middle", "high"), 3))
df2 <- synthetic_new_attribute(df, prob_name= "p", attr_name= "SES",
         conditional_vars= c("gender", "edu"),
         sym_tbl= ST2)
}

Description

A dataset containing total fertility rate data by race of the mother. Data for all races is provided for 1970-2014 and for individual races from 1989-2014.

Usage

TFR

Format

A data.frame with 175 observations and 3 variables.

year

The year for which data was was recorded.

race

The racial group of the mothers. One of all all races; white non-hispanic whites; black_aa black / African-American; nat_amer American Indian or Native Alaskan; asian_isl Asian or Pacific Islander; hisp_lat Hispanic or Latin American.

tfr

The Total Fertility Rate. See Details

Details

The Total Fertility Rate is defined as the sums of the birth rates for the 5-year age groups found in BR2014 multiplied by 5.

Source

https://www.cdc.gov/nchs/nvss/births.htm

References

Hamilton, Brady E., et al. "Births: final data for 2014." National Vital Statistics Reports 64.12 (2015): 1-64.