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

Description

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.

Usage

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.

Examples

Run this code

# NOT RUN {
# call decon dataset
get_decon()

# preview decon
head(decon)
# }

Run the code above in your browser using DataLab

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