Creates a non-exhaustive dataframe of 21 variables with renamed columns.

get_decon() creates a non-exhaustive dataset under the name decon consisting of 21 variables with renamed columns from the demographics, ideology, and economy sections of the 2019 CES online survey.

get_decon(pos = 1)

Arguments

pos

Environment assignment. Defaults to 1, which is an assignment to the global environment. decon Variables citizenship Canadian citizenship status yob year of birth gender identified gender of the respondent province_territory Province or Territory of current residence education highest level of education completed vote_likely likelihood of voting on election day vote_likely_ifable likelihood to vote in first election for which respondent is eligible votechoice party most likely to vote for votechoice_text party most likely to vote for - text answers votechoice_couldvote party most likely to vote for if eligible to vote votechoice_couldvote_text party most likely to vote for if eligible to vote - text answers votechoice_unlikely party least likely to vote for votechoice_unlikely_text party least likely to vote for - text answers votechoice_unlikely_couldvote party least likely to vote for if eligible to vote votechoice_unlikely_couldvote_text party least likely to vote for if eligible to vote - text answers vote_advancevote_choice party voted for in the advanced ballot vote_advancevote_choice_text party voted for in the advanced ballot - text vote_partylean party toward which the respondent leans vote_partylean_text party toward which the respondent leans - text answers vote_partylean_couldvote party toward which the respondent leans if eligible vote_partylean_couldvote_text party toward which the respondent leans if eligible - text answers votechoice_secondchoice second choice party of respondent votechoice_secondchoice_text second choice party of respondent - text answers votechoice_couldvote_secondchoice second choice party of respondent if eligible votechoice_couldvote_secondchoice_text second choice party of respondent if eligible - text answers votechoice_partynotvote_1 party respondent would note vote for - first ranking votechoice_partynotvote_2 party respondent would note vote for - second ranking votechoice_partynotvote_3 party respondent would note vote for - third ranking votechoice_partynotvote_4 party respondent would note vote for - fourth ranking votechoice_partynotvote_5 party respondent would note vote for - fifth ranking votechoice_partynotvote_6 party respondent would note vote for - sixth ranking votechoice_partynotvote_7 party respondent would note vote for - seventh ranking votechoice_partynotvote_8 party respondent would note vote for - eighth ranking votechoice_partynotvote_9 party respondent would note vote for - ninth ranking votechoice_partynotvote_text party respondent would note vote for - text answers lr_scale united column of lr_bef and lr_aft values; whether the respondent identifies on the political spectrum lr_scale_bef where the respondent identifies on the political spectrum; asked before party identification questions lr_scale_aft where the respondent identifies on the political spectrum; asked after party identification questions religion religion of respondent sexuality_selected sexual identity sexuality_text sexual identity; written answers language_eng language learned as child and still understand; selected response English language_fr language learned as a child and still understand; selected response French language_abgl language learned as a child and still understand; specified Aboriginal language employment employment status income total household income, before taxes, for the year 2018 income_cat selected household income category marital marital status econ_retro response to question, 'over the past year, has Canada's economy:' econ_fed response to question, 'have the policies of the federal government made Canada's economy...' econ_self response to question, have the policies of the federal government made your financial situation...'

Value

The designed dataframe as a 'tbl_df' object under the name decon.

Details

NAs have not been removed. The politically left/right question variables (lr_bef and lr_aft) have also been joined into one column under the name lr_scale. All variables have been converted to factor type using labelled::to_factor and are listed below.

See also

For further details, see https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DUS88V Stephenson, Laura B; Harell, Allison; Rubenson, Daniel; Loewen, Peter John, 2020, "2019 Canadian Election Study - Online Survey", doi: 10.7910/DVN/DUS88V , Harvard Dataverse, V1

Examples

if (FALSE) {
# call decon dataset
get_decon()

# preview decon
head(decon)
}