Package 'pollster' reference manual

Title:	Calculate Crosstab and Topline Tables of Weighted Survey Data
Description:	Calculate common types of tables for weighted survey data. Options include topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as well as summary tables of weighted numeric variables. Optionally, include the margin of error at selected confidence intervals including the design effect. The design effect is calculated as described by Kish (1965) <doi:10.1002/bimj.19680100122> beginning on page 257. Output takes the form of tibbles (simple data frames). This package conveniently handles labelled data, such as that commonly used by 'Stata' and 'SPSS.' Complex survey design is not supported at this time.
Authors:	John D. Johnson [aut, cre]
Maintainer:	John D. Johnson <[email protected]>
License:	CC0
Version:	0.1.6
Built:	2025-02-24 04:03:32 UTC
Source:	https://github.com/jdjohn215/pollster

weighted crosstabs

Description

crosstab returns a tibble containing a weighted crosstab of two variables

Usage

crosstab(
  df,
  x,
  y,
  weight,
  remove = "",
  n = TRUE,
  pct_type = "row",
  format = "wide",
  unwt_n = FALSE
)
crosstab(
  df,
  x,
  y,
  weight,
  remove = "",
  n = TRUE,
  pct_type = "row",
  format = "wide",
  unwt_n = FALSE
)

Arguments

`df`	The data source
`x`	The independent variable
`y`	The dependent variable
`weight`	The weighting variable
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE numeric totals are included. They are included in a separate column for row and cell percentages, but in a separate row for wide format column percentages.
`pct_type`	Controls the kind of percentage values returned. One of "row," "cell," or "column."
`format`	one of "long" or "wide"
`unwt_n`	logical, if TRUE a column "unweighted_n" is included containing the unweighted frequency count. It is not available when pct_type is "column"

Details

Options include row, column, or cell percentages. The tibble can be in long or wide format.

Value

a tibble

Examples

crosstab(df = illinois, x = voter, y = raceethnic, weight = weight)
crosstab(df = illinois, x = voter, y = raceethnic, weight = weight, n = FALSE)
crosstab(df = illinois, x = voter, y = raceethnic, weight = weight)
crosstab(df = illinois, x = voter, y = raceethnic, weight = weight, n = FALSE)

weighted 3-way crosstabs

Description

crosstab_3way returns a tibble containing a weighted crosstab of two variables by a third variable

Usage

crosstab_3way(
  df,
  x,
  y,
  z,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "wide",
  unwt_n = FALSE
)
crosstab_3way(
  df,
  x,
  y,
  z,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "wide",
  unwt_n = FALSE
)

Arguments

`df`	The data source
`x`	The independent variable
`y`	The dependent variable
`z`	The second control variable
`weight`	The weighting variable
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE numeric totals are included.
`pct_type`	Controls the kind of percentage values returned. One of "row" or "cell."
`format`	one of "long" or "wide"
`unwt_n`	logical, if TRUE a column is added containing unweighted frequency counts

Details

Options include row or cell percentages. The tibble can be in long or wide format. These tables are ideal for use with small multiples created with ggplot2::facet_wrap.

Value

a tibble

Examples

crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight)
crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight,
format = "wide")
crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight)
crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight,
format = "wide")

Calculate the design effect of a sample

Description

deff_calc returns a single number

Usage

deff_calc(w)
deff_calc(w)

Arguments

`w`	a vector of weights

Details

This function returns the design effect of a given sample using the formula length(w)*sum(w^2)/(sum(w)^2). It is designed for use in the moe family of functions. If any weights are equal to 0, they are removed prior to calculation.

Value

A number

Examples

deff_calc(illinois$weight)

deff_calc(illinois$weight)

Illinois respondents to the Voting and Registration Supplement for the Current Population Survey

Description

A dataset containing the responses of 36,207 Illinois respondents to the Current Population Survey's biennial Voting and Registration Supplement for the Current Population Survey, 1996-2018.

Usage

illinois
illinois

Format

A data frame with 36207 rows and 9 variables:

year: year of survey
fips: the state fips code
sex: sex of the respondent, labelled value
educ6: highest level of education for respondent, labelled values
raceethnic: one of white, black, Hispanic, or other, labelled values
maritalstatus: one of Married, Widowed/divorced/Sep, or Never Married, labelled values
rv: indicates if the respondent is registered to vote, labelled values
voter: indicates if the respondent voted, labelled values
age: the age of the respondent, numeric values
weight: the number of people each respondent is calculated to represent

Source

https://www.census.gov/topics/public-sector/voting.html

weighted crosstabs with margin of error

Description

moe_crosstab returns a tibble containing a weighted crosstab of two variables with margin of error

Usage

moe_crosstab(
  df,
  x,
  y,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)
moe_crosstab(
  df,
  x,
  y,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)

Arguments

`df`	The data source
`x`	The independent variable
`y`	The dependent variable
`weight`	The weighting variable, defaults to zwave_weight
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE numeric totals are included.
`pct_type`	Controls the kind of percentage values returned. One of "row" or "cell." Column percents are not supported.
`format`	one of "long" or "wide"
`zscore`	defaults to 1.96, consistent with a 95% confidence interval
`unwt_n`	logical, if TRUE it adds a column with unweighted frequency values

Details

Options include row or cell percentages. The tibble can be in long or wide format. The margin of error includes the design effect of the weights.

Value

a tibble

Examples

moe_crosstab(df = illinois, x = voter, y = raceethnic, weight = weight)
moe_crosstab(df = illinois, x = voter, y = raceethnic, weight = weight, n = FALSE)
moe_crosstab(df = illinois, x = voter, y = raceethnic, weight = weight)
moe_crosstab(df = illinois, x = voter, y = raceethnic, weight = weight, n = FALSE)

weighted 3-way crosstabs with margin of error

Description

moe_crosstab_3way returns a tibble containing a weighted crosstab of two variables by a third variable with margin of error

Usage

moe_crosstab_3way(
  df,
  x,
  y,
  z,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)
moe_crosstab_3way(
  df,
  x,
  y,
  z,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)

Arguments

`df`	The data source
`x`	The independent variable
`y`	The dependent variable
`z`	The second control variable
`weight`	The weighting variable
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE numeric totals are included.
`pct_type`	Controls the kind of percentage values returned. One of "row" or "cell."
`format`	one of "long" or "wide"
`zscore`	defaults to 1.96, consistent with a 95% confidence interval
`unwt_n`	logical, if TRUE it adds a column with unweighted frequency values

Details

Options include row or cell percentages. The tibble can be in long or wide format. These tables are ideal for use with small multiples created with ggplot2::facet_wrap.

Value

a tibble

Examples

moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight)
moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight,
format = "wide")
moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight)
moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = maritalstatus, weight = weight,
format = "wide")

weighted topline with margin of error

Description

moe_topline returns a tibble containing a weighted topline of one variable with margin of error

Usage

moe_topline(
  df,
  variable,
  weight,
  remove = c(""),
  n = TRUE,
  pct = TRUE,
  valid_pct = TRUE,
  cum_pct = TRUE,
  zscore = 1.96
)
moe_topline(
  df,
  variable,
  weight,
  remove = c(""),
  n = TRUE,
  pct = TRUE,
  valid_pct = TRUE,
  cum_pct = TRUE,
  zscore = 1.96
)

Arguments

`df`	The data source
`variable`	the variable name
`weight`	The weighting variable, defaults to zwave_weight
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE a frequency column is included percentages, but in a separate row for column percentages.
`pct`	logical, if TRUE a column of percents is included
`valid_pct`	logical, if TRUE a column of valid percents is included
`cum_pct`	logical, if TRUE a column of cumulative percents is included
`zscore`	defaults to 1.96, consistent with a 95% confidence interval

Details

By default the table includes a column for frequency count, percent, valid percent, and cumulative percent.

Value

a tibble

Examples

moe_topline(df = illinois, variable = educ6, weight = weight)
moe_topline(df = illinois, variable = educ6, weight = weight, remove = c("LT HS"))
moe_topline(df = illinois, variable = educ6, weight = weight)
moe_topline(df = illinois, variable = educ6, weight = weight, remove = c("LT HS"))

weighted crosstabs with margin of error, where the x-variable identifies different survey waves

Description

moe_wave_crosstab returns a tibble containing a weighted crosstab of two variables with margin of error. Use this function when the x-variable indicates different survey waves for which weights were calculated independently.

Usage

moe_wave_crosstab(
  df,
  x,
  y,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)
moe_wave_crosstab(
  df,
  x,
  y,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)

Arguments

`df`	The data source
`x`	The independent variable, which uniquely identifies survey waves
`y`	The dependent variable
`weight`	The weighting variable, defaults to zwave_weight
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE numeric totals are included.
`pct_type`	Controls the kind of percentage values returned. One of "row" or "cell." Column percents are not supported.
`format`	one of "long" or "wide"
`zscore`	defaults to 1.96, consistent with a 95% confidence interval
`unwt_n`	logical, if TRUE it adds a column with unweighted frequency values

Details

Options include row or cell percentages. The tibble can be in long or wide format. The margin of error includes the design effect of the weights, calculated separately for each survey wave.

Value

a tibble

Examples

moe_wave_crosstab(df = illinois, x = year, y = maritalstatus, weight = weight)
moe_wave_crosstab(df = illinois, x = year, y = maritalstatus, weight = weight, format = "wide")
moe_wave_crosstab(df = illinois, x = year, y = maritalstatus, weight = weight)
moe_wave_crosstab(df = illinois, x = year, y = maritalstatus, weight = weight, format = "wide")

weighted 3-way crosstabs with margin of error, where the z-variable identifies different survey waves

Description

moe_wave_crosstab_3way returns a tibble containing a weighted crosstab of two variables by a third variable with margin of error. Use this function when the z-variable indicates different survey waves for which weights were calculated independently.

Usage

moe_wave_crosstab_3way(
  df,
  x,
  y,
  z,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)
moe_wave_crosstab_3way(
  df,
  x,
  y,
  z,
  weight,
  remove = c(""),
  n = TRUE,
  pct_type = "row",
  format = "long",
  zscore = 1.96,
  unwt_n = FALSE
)

Arguments

`df`	The data source
`x`	The independent variable
`y`	The dependent variable
`z`	The second control variable, uniquely identifies survey waves
`weight`	The weighting variable
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE numeric totals are included.
`pct_type`	Controls the kind of percentage values returned. One of "row" or "cell."
`format`	one of "long" or "wide"
`zscore`	defaults to 1.96, consistent with a 95% confidence interval
`unwt_n`	logical, if TRUE it adds a column with unweighted frequency values

Details

Options include row or cell percentages. The tibble can be in long or wide format. These tables are ideal for use with small multiples created with ggplot2::facet_wrap.

Value

a tibble

Examples

moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = year, weight = weight)
moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = year, weight = weight, format = "wide")
moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = year, weight = weight)
moe_crosstab_3way(df = illinois, x = sex, y = educ6, z = year, weight = weight, format = "wide")

Calculate the margin of error (including design effect) of a sample

Description

moedeff_calc returns a single number. It is designed for use in the moe family of functions.

Usage

moedeff_calc(pct, deff, n, zscore = 1.96)
moedeff_calc(pct, deff, n, zscore = 1.96)

Arguments

`pct`	a proportion
`deff`	a design effect
`n`	the sample size
`zscore`	defaults to 1.96, consistent with a 95% confidence interval.

Details

This function returns the margin of error including design effect of a given sample of weighted data using the formula sqrt(deff)*zscore*sqrt((pct*(1-pct))/(n-1))*100

Value

A percentage

Examples

moedeff_calc(pct = 0.515, deff = 1.6, n = 214)
moedeff_calc(pct = 0.515, deff = 1.6, n = 214)

weighted summary table

Description

summary_table returns a tibble containing a weighted summary table of a single variable.

Usage

summary_table(df, variable, weight, name_style = "clean")
summary_table(df, variable, weight, name_style = "clean")

Arguments

`df`	The data source
`variable`	the variable to summarize, it should be numeric
`weight`	The weighting variable
`name_style`	the style of the column names–one of "clean" or "pretty." Clean names are all lower case and words are separated by an underscore. Pretty names begin with a capital letter are words a separated by a space.

Details

The resulting tible includes columns for the variable name, unweighted observations, weighted observations, weighted mean, minimum value, maximum value, unweighted missing values, and weighted missing values

Value

a tibble

Examples

summary_table(illinois, age, weight)
summary_table(illinois, age, weight, name_style = "pretty")
summary_table(illinois, age, weight)
summary_table(illinois, age, weight, name_style = "pretty")

weighted topline

Description

topline returns a tibble containing a weighted topline of one variable

Usage

topline(
  df,
  variable,
  weight,
  remove = c(""),
  n = TRUE,
  pct = TRUE,
  valid_pct = TRUE,
  cum_pct = TRUE
)
topline(
  df,
  variable,
  weight,
  remove = c(""),
  n = TRUE,
  pct = TRUE,
  valid_pct = TRUE,
  cum_pct = TRUE
)

Arguments

`df`	The data source
`variable`	the variable name
`weight`	The weighting variable, defaults to zwave_weight
`remove`	An optional character vector of values to remove from final table (e.g. "refused"). This will not affect any calculations made. The vector is not case-sensitive.
`n`	logical, if TRUE a frequency column is included percentages, but in a separate row for column percentages.
`pct`	logical, if TRUE a column of percents is included
`valid_pct`	logical, if TRUE a column of valid percents is included
`cum_pct`	logical, if TRUE a column of cumulative percents is included

Details

By default the table includes a column for frequency count, percent, valid percent, and cumulative percent.

Value

a tibble

Examples

topline(illinois, sex, weight)
topline(illinois, sex, weight, pct = FALSE)
topline(illinois, sex, weight)
topline(illinois, sex, weight, pct = FALSE)

weighted mean

Description

wtd_mean returns the weighted mean of a variable. It's a tidy-compatible wrapper around stats::weighted.mean().

Usage

wtd_mean(df, variable, weight)
wtd_mean(df, variable, weight)

Arguments

`df`	The data source
`variable`	the variable, it should be numeric
`weight`	The weighting variable

Value

a numeric value

Examples

wtd_mean(illinois, age, weight)

library(dplyr)
illinois %>% wtd_mean(age, weight)
wtd_mean(illinois, age, weight)

library(dplyr)
illinois %>% wtd_mean(age, weight)

Package 'pollster'

Help Index

weighted crosstabs

Description

Usage

Arguments

Details

Value

Examples

weighted 3-way crosstabs

Description

Usage

Arguments

Details

Value

Examples

Calculate the design effect of a sample

Description

Usage

Arguments

Details

Value

Examples

Illinois respondents to the Voting and Registration Supplement for the Current Population Survey

Description

Usage

Format

Source

weighted crosstabs with margin of error

Description

Usage

Arguments

Details

Value

Examples

weighted 3-way crosstabs with margin of error

Description

Usage

Arguments

Details

Value

Examples

weighted topline with margin of error

Description

Usage

Arguments

Details

Value

Examples

weighted crosstabs with margin of error, where the x-variable identifies different survey waves

Description

Usage

Arguments

Details

Value

Examples

weighted 3-way crosstabs with margin of error, where the z-variable identifies different survey waves

Description

Usage

Arguments

Details

Value

Examples

Calculate the margin of error (including design effect) of a sample

Description

Usage

Arguments

Details

Value

Examples

weighted summary table

Description

Usage

Arguments

Details

Value

Examples

weighted topline

Description

Usage