Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

A sample of the raw 2016 CPS dataset

Description

This is a 10,000 row sample of the data that comes out of cps_read(years = 2016).

Usage

cps_2016_10k

Format

A tibble with 10,000 rows and 17 columns:

FILE

Which default file the case came from

YEAR

Year of interview

STATE

State postal abbreviation

AGE

Person's age as of the end of survey week; topcoded at 80 and 85

SEX

Binary sex

EDUCATION

Highest level of school completed or degree received

RACE

Race

HISPANIC

Hispanic status

WEIGHT

Original CPS survey weight

VRS_VOTE

Whether respondent voted in the election; self-reported

VRS_REG

Whether respondent was registered to vote in the election; self-reported

VRS_REG_WHYNOT

Reason for not being registered to vote

VRS_VOTE_WHYNOT

Reason for not voting

VRS_VOTEMODE_2004toPRESENT

Whether respondent voted by mail

VRS_VOTEWHEN_2004toPRESENT

Whether respondent voted on election day or before

VRS_REG_METHOD

Method of registration

VRS_RESIDENCE

Duration of time living at current address

A sample of the full CPS dataset

Description

This is a 10,000 row sample of the data that comes out of cpsvote::cps_load_basic.

Usage

cps_allyears_10k

Format

A tibble with 10,000 rows and 25 columns:

FILE

Which default file the case came from

YEAR

Year of interview

STATE

State postal abbreviation

AGE

Person's age as of the end of survey week; topcoded at 90 until 2002, 80 in 2004, and 80/85 after

SEX

Binary sex

EDUCATION

Highest level of school completed or degree received

RACE

Race

HISPANIC

Hispanic status

WEIGHT

Original CPS survey weight

VRS_VOTE

Whether respondent voted in the election; self-reported

VRS_REG

Whether respondent was registered to vote in the election; self-reported

VRS_VOTE_TIME

What time of day respondent voted

VRS_RESIDENCE

Duration of time living at current address

VRS_VOTE_WHYNOT

Reason for not voting

VRS_VOTEMETHOD_1996to2002

Method of voting, pre-2004

VRS_REG_SINCE95

Whether respondent had registered to vote since 1995

VRS_REG_DMV

Whether respondent registered at the DMV

VRS_REG_METHOD

Method of registration

VRS_REG_WHYNOT

Reason for not being registered to vote

VRS_VOTEMODE_2004toPRESENT

Whether respondent voted by mail, 2004 on

VRS_VOTEWHEN_2004toPRESENT

Whether respondent voted on election day or before, 2004 on

VRS_VOTEMETHOD_CON

A consolidation of VRS_VOTEMETHOD_1996to2002, VRS_VOTEMODE_2004toPRESENT, and VRS_VOTEWHEN_2004toPRESENT

cps_turnout

Recode of VRS_VOTE for CPS turnout calculation

hurachen_turnout

Recode of VRS_VOTE for adjusted Hur & Achen turnout calculation

turnout_weight

Adjusted weight for calculating voter turnout (per Hur & Achen)

Sample column specifications for reading CPS data

Description

Because the CPS is a fixed-width file that changes data locations (and variable names) across years, to correctly read the data you have to specify which start/end positions correspond to which column names in each year. This is one such specification. To add extra data or change column names, see the Vignette.

Usage

cps_cols

Format

A data frame with 204 rows and 8 columns:

year

year

cps_name

original column name as given by the CPS

new_name

a new name, which tries to describe the variable and join sensibly across multiple years

start_pos

which character of a line the variable starts with

end_pos

which character of a line the variable ends with

col_type

whether the column is character, numeric, or a factor

description

the question text/description from the CPS

notes

any notes for question administration or analysis

Download CPS microdata

Description

Download CPS microdata

Usage

cps_download_data(
  path = "cps_data",
  years = seq(1994, 2018, 2),
  overwrite = FALSE
)

Arguments

Details

• File names will be written in the style "cps_nov2018.zip", with the appropriate years.

• The Voting and Registration Supplement is only conducted in even-numbered years (since 1964), so any entry in years outside of this will be skipped.

• Currently the package only supports downloads from 1994 onwards, so any entry in years before 1994 will be skipped.

Examples

## Not run: 
cps_download_data(path = "cps_docs", years = 2016, overwrite = TRUE)

## End(Not run)

Download CPS technical documentation

Description

Download CPS technical documentation

Usage

cps_download_docs(
  path = "cps_docs",
  years = seq(1994, 2018, 2),
  overwrite = FALSE
)

Arguments

Details

• File names will be written in the style "cps_nov2018.pdf", with the appropriate years.

• The Voting and Registration Supplement is only conducted in even-numbered years (since 1964), so any entry in years outside of this will be skipped.

• Currently the package only supports downloads from 1994 onwards, so any entry in years before 1994 will be skipped.

Examples

## Not run: 
cps_download_docs(path = "cps_docs", years = 2016, overwrite = TRUE)

## End(Not run)

Sample factor specifications for reading CPS data

Description

Because the CPS changes factor levels across years, to correctly read the data you have to specify which numeric codes correspond to which character values in each year. This is one such specification. To add extra data, see the Vignette.

Usage

cps_factors

Format

A data frame with 204 rows and 8 columns:

year

year

cps_name

original column name as given by the CPS

new_name

a new name, which tries to describe the variable and join sensibly across multiple years

code

the numeric code contained in the raw CPS data

value

the character value corresponding to each numeric code

Details

These match the exact specifications from the CPS, including NA codes and any typos that occur (e.g., "Hipsanic" is common in older years).

Apply factor levels to raw CPS data

Description

The CPS publishes their data in a numeric format, with a separate PDF codebook (not machine readable) describing factor values. This function labels the raw numeric CPS data according to a supplied factor key. Codes that appear in a given year and are not included in factors will be recoded as NA.

Usage

cps_label(
  data,
  factors = cpsvote::cps_factors,
  names_col = "new_name",
  na_vals = c("-1", "BLANK", "NOT IN UNIVERSE"),
  expand_year = TRUE,
  rescale_weight = TRUE,
  toupper = TRUE
)

Arguments

Value

CPS data with factor labels in place of the raw numeric data

Examples

cps_label(cps_2016_10k)

load some basic/default CPS data into the environment

Description

This function is a quick starter to working with the CPS, using all of the defaults that are baked into this package. Because the data is so large, it made more sense to ship a "basic" CPS data set as a function rather than as a package data object (which would have been over 10 MB). This function will take you from nothing to having some basic CPS data in your environment, with the option to save this data locally for future ease. A sample of the data that comes out of this function is provided as cpsvote::cps_allyears_10k.

Usage

cps_load_basic(years = seq(1994, 2018, 2), datadir = "cps_data", outdir = NULL)

Arguments

Examples

## Not run: cps_load-basic(years = 2016, outdir = "data")

Read in CPS data

Description

Load multiple years of data from the Current Population Survey. This function will also download the data for you, if it is not present in the given dir.

Usage

cps_read(
  years = seq(1994, 2018, 2),
  dir = "cps_data",
  cols = cpsvote::cps_cols,
  names_col = "new_name",
  join_dfs = TRUE
)

Arguments

Value

a data frame, or list of data frames

Examples

## Not run: cps_read(years = 2016, names_col = "new_name")

Load a single CPS file

Description

Read one year of data from the Current Population Survey

Usage

cps_read_year(
  file,
  cols = cpsvote::cps_cols,
  names_col = "new_name",
  year = as.numeric(stringr::str_extract(file, "\\d{4}"))
)

Arguments

Value

a data frame, with dimensions depending on the year and columns specified

recode the voting variable for turnout calculations

Description

When the CPS calculates voter turnout, they consider the values "Don't know", "Refused", and "No response" to be non-voters, that is they lump these in with "No". With increased levels of survey non-response in recent years, this has caused turnout estimates to artificially deflate when compared to measures of voter turnout from state election offices. This function adds two recodes of the original voting variable, one which applies the CPS recoding where multiple categories map to "No", and one which follows the guidelines from Hur & Achen (2013) of setting these categories to NA. See the Vignette for more information on this process.

Usage

cps_recode_vote(
  data,
  vote_col = "VRS_VOTE",
  items = c("DON'T KNOW", "REFUSED", "NO RESPONSE")
)

Arguments

Value

data with two columns attached, cps_turnout and hurachen_turnout, voting variables recoded according to the process above

Examples

cps_recode_vote(cps_refactor(cps_label(cps_2016_10k)))

combine factor levels across years

Description

The response sets in certain CPS questions change between years. This function consolidates several of these response sets across years (and fixes typos from the CPS documentation), specifically race, Hispanic status, duration of residency, reason for not voting, and method of registration. Additionally, this creates a new column VRS_VOTEMETHOD_CON which consolidates multiple expressions of vote method across years (By Mail, Early, and Election Day) into one variable.

Usage

cps_refactor(data, move_levels = TRUE)

Arguments

Details

While consolidating response sets across multiple surveys can be fraught with peril, this function attempts to combine disparate levels for race and other CPS variable across multiple years. Some of these are relatively straightforward typos fixes ("NON-HIPSANIC" should clearly match "NON-HISPANIC"), but others have differing degrees of subjectivity applied. Take this function with a grain of salt, as it depends on some exact variable names you may or may not be using, and recode variables as needed for your own uses. To explore exactly how these variables were recoded, you can run table(data$RACE, cps_refactor(data)$RACE) in the console, substituting your column of interest in for RACE.

Examples

cps_refactor(cps_label(cps_2016_10k))

Calculations to reweight properly for voter turnout

Description

While the U.S. Census Bureau provides one weight with the CPS, a modified weight is needed to properly calculate voter turnout. This data set provides those calculations, according to Hur and Achen (2013). The comparison data comes from Dr. Michael McDonald's estimates of voter turnout among the voting-eligible population (VEP). It can be joined with CPS data to calculate the new weights needed for analysis, using the function cps_reweight_turnout.

Usage

cps_reweight

Format

A tibble with 1,326 rows and 6 columns:

YEAR

year

STATE

state

response

indicator of turnout in recent election

vep_turnout

proportion of turnout indicator, calculated by McDonald

cps_turnout

proportion of turnout indicator, calculated by CPS

reweight

the factor by which to scale original CPS weights

Source

Turnout data from http://www.electproject.org/home/voter-turnout/voter-turnout-data

apply weight correction for voter turnout

Description

This function applies the turnout correction recommended by Hur & Achen (2013). The data set containing the scaling factor is cpsvote::cps_reweight.

Usage

cps_reweight_turnout(data)

Arguments

Examples

cps_reweight_turnout(cps_recode_vote(cps_refactor(cps_label(cps_2016_10k))))

vectorized na_if

Description

vectorized na_if

Usage

na_ifin(x, y)

Arguments