Convert a survey design object to a data frame with weights stored as columns
Source:R/as_data_frame_with_weights.R
as_data_frame_with_weights.RdConvert a survey design object to a data frame with weights stored as columns
Usage
as_data_frame_with_weights(
design,
full_wgt_name = "FULL_SAMPLE_WGT",
rep_wgt_prefix = "REP_WGT_",
vars_to_keep = NULL
)Arguments
- design
A survey design object, created with either the
surveyorsrvyrpackages.- full_wgt_name
The column name to use for the full-sample weights
- rep_wgt_prefix
For replicate design objects, a prefix to use for the column names of the replicate weights. The column names will be created by appending the replicate number after the prefix.
- vars_to_keep
By default, all variables in the data will be kept. To select only a subset of the non-weight variables, you can supply a character vector of variable names to keep.
Examples
data("lou_vax_survey", package = 'svrep')
library(survey)
# Create a survey design object
survey_design <- svydesign(data = lou_vax_survey,
weights = ~ SAMPLING_WEIGHT,
ids = ~ 1)
rep_survey_design <- as.svrepdesign(survey_design,
type = "boot",
replicates = 10)
# Adjust the weights for nonresponse
nr_adjusted_design <- redistribute_weights(
design = rep_survey_design,
reduce_if = RESPONSE_STATUS == "Nonrespondent",
increase_if = RESPONSE_STATUS == "Respondent",
by = c("RACE_ETHNICITY", "EDUC_ATTAINMENT")
)
# Save the survey design object as a data frame
nr_adjusted_data <- as_data_frame_with_weights(
nr_adjusted_design,
full_wgt_name = "NR_ADJUSTED_WGT",
rep_wgt_prefix = "NR_ADJUSTED_REP_WGT_"
)
head(nr_adjusted_data)
#> RESPONSE_STATUS RACE_ETHNICITY
#> 1 Nonrespondent White alone, not Hispanic or Latino
#> 2 Nonrespondent Black or African American alone, not Hispanic or Latino
#> 3 Respondent White alone, not Hispanic or Latino
#> 4 Nonrespondent White alone, not Hispanic or Latino
#> 5 Nonrespondent White alone, not Hispanic or Latino
#> 6 Respondent White alone, not Hispanic or Latino
#> SEX EDUC_ATTAINMENT VAX_STATUS SAMPLING_WEIGHT NR_ADJUSTED_WGT
#> 1 Female Less than high school <NA> 596.702 0.000
#> 2 Female High school or beyond <NA> 596.702 0.000
#> 3 Female Less than high school Vaccinated 596.702 1223.239
#> 4 Female Less than high school <NA> 596.702 0.000
#> 5 Female High school or beyond <NA> 596.702 0.000
#> 6 Female High school or beyond Vaccinated 596.702 1059.068
#> NR_ADJUSTED_REP_WGT_1 NR_ADJUSTED_REP_WGT_2 NR_ADJUSTED_REP_WGT_3
#> 1 0 0.000 0
#> 2 0 0.000 0
#> 3 0 2572.449 0
#> 4 0 0.000 0
#> 5 0 0.000 0
#> 6 0 0.000 0
#> NR_ADJUSTED_REP_WGT_4 NR_ADJUSTED_REP_WGT_5 NR_ADJUSTED_REP_WGT_6
#> 1 0.000 0.000 0.000
#> 2 0.000 0.000 0.000
#> 3 1260.888 0.000 0.000
#> 4 0.000 0.000 0.000
#> 5 0.000 0.000 0.000
#> 6 2058.492 3243.364 1056.924
#> NR_ADJUSTED_REP_WGT_7 NR_ADJUSTED_REP_WGT_8 NR_ADJUSTED_REP_WGT_9
#> 1 0 0.000 0
#> 2 0 0.000 0
#> 3 0 1219.633 0
#> 4 0 0.000 0
#> 5 0 0.000 0
#> 6 0 1024.285 0
#> NR_ADJUSTED_REP_WGT_10
#> 1 0.000
#> 2 0.000
#> 3 1202.584
#> 4 0.000
#> 5 0.000
#> 6 2074.098
# Check the column names of the result
colnames(nr_adjusted_data)
#> [1] "RESPONSE_STATUS" "RACE_ETHNICITY" "SEX"
#> [4] "EDUC_ATTAINMENT" "VAX_STATUS" "SAMPLING_WEIGHT"
#> [7] "NR_ADJUSTED_WGT" "NR_ADJUSTED_REP_WGT_1" "NR_ADJUSTED_REP_WGT_2"
#> [10] "NR_ADJUSTED_REP_WGT_3" "NR_ADJUSTED_REP_WGT_4" "NR_ADJUSTED_REP_WGT_5"
#> [13] "NR_ADJUSTED_REP_WGT_6" "NR_ADJUSTED_REP_WGT_7" "NR_ADJUSTED_REP_WGT_8"
#> [16] "NR_ADJUSTED_REP_WGT_9" "NR_ADJUSTED_REP_WGT_10"