The srvyexploR package provides datasets used in the book Exploring Complex Survey Data Analysis Using R: A Tidy Introduction with {srvyr} and {survey}. This will help readers follow along with the examples and work through the exercises.
Installation
To install the development version from GitHub, use:
# install.packages("pak")
pak::pak("tidy-survey-r/srvyrexploR")To load the package, use:
About the data
This package includes data from three surveys including the American National Election Studies (ANES), the National Crime Victimization Survey (NCVS), and the Residential Energy Consumption Survey (RECS).
ANES
The ANES data is based on the publicly available 2020 ANES data with additional derived variables and is subset to people who completed both pre and post-election interviews. The ANES Times Series Studies collect data on political polling in the United States and has been conducted since 1948.For more information about the 2020 study, see the American National Election Studies website. On the ANES website, you can learn more about the study, see codebooks and methodology reports, and download the data (after registering). We received permission to distribute this data for the purpose of the book. Once the package is loaded, you can use the data immediately as follows:
head(anes_2020)
#> # A tibble: 6 × 65
#> V200001 CaseID V200002 InterviewMode V200010b Weight V200010c VarUnit V200010d
#> <dbl> <dbl> <hvn_l> <fct> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 200015 200015 3 Web 1.01 1.01 2 2 9
#> 2 200022 200022 3 Web 1.16 1.16 2 2 26
#> 3 200039 200039 3 Web 0.769 0.769 1 1 41
#> 4 200046 200046 3 Web 0.521 0.521 2 2 29
#> 5 200053 200053 3 Web 0.966 0.966 1 1 23
#> 6 200060 200060 3 Web 0.235 0.235 2 2 37
#> # ℹ 56 more variables: Stratum <fct>, V201006 <hvn_lbll>,
#> # CampaignInterest <fct>, V201023 <hvn_lbll>, EarlyVote2020 <fct>,
#> # V201024 <hvn_lbll>, V201025x <hvn_lbll>, V201028 <hvn_lbll>,
#> # V201029 <hvn_lbll>, V201101 <hvn_lbll>, V201102 <hvn_lbll>,
#> # VotedPres2016 <fct>, V201103 <hvn_lbll>, VotedPres2016_selection <fct>,
#> # V201228 <hvn_lbll>, V201229 <hvn_lbll>, V201230 <hvn_lbll>,
#> # V201231x <hvn_lbll>, PartyID <fct>, V201233 <hvn_lbll>, …See ?anes_2020 for more information about the data.
Also, included in the package is a Stata version of the ANES data with a subset of the columns and is subset to people who completed both pre and post-election interviews. To load this dataset, we recommend using the {haven} package as follows:
anes_stata <- haven::read_dta(system.file("extdata", "anes_2020_stata_example.dta", package = "srvyrexploR"))NCVS
The NCVS data is based off of publicly available data for the 2021 NCVS. The NCVS is a survey conducted by the Bureau of Justice Statistics and asks people age 12 and over about their crime victimizations. The study has been conducted continuously since 1992. This package includes three datasets - one for household-level data (ncvs_2021_household), one for person-level data (ncvs_2021_person), and one for incident-level data (ncvs_2021_incident) where each includes a subset of the columns of the full data available from 2021 at ICPSR. This data is reproduced here with permission from ICPSR.
head(ncvs_2021_household)
#> # A tibble: 6 × 12
#> YEARQ IDHH WGTHHCY V2117 V2118 V2015 V2143 SC214A V2122 V2126B V2127B V2129
#> <dbl> <chr> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 2021. 171005… 0 139 1 <NA> 3 12 33 0 2 3
#> 2 2021. 171005… 1072. 63 2 2 2 8 32 17 2 1
#> 3 2021. 171005… 0 140 1 <NA> 2 5 33 13 2 3
#> 4 2021. 171005… 0 139 1 <NA> 3 13 33 0 2 3
#> 5 2021. 171005… 1200. 138 1 1 2 11 29 18 2 1
#> 6 2021. 171005… 1254. 138 1 1 2 8 24 13 2 2
head(ncvs_2021_person)
#> # A tibble: 6 × 11
#> YEARQ IDHH IDPER WGTPERCY V3014 V3015 V3018 V3023A V3024 V3084 V3086
#> <dbl> <chr> <chr> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 2021. 1710051365368… 1710… 1216. 84 3 2 1 2 6 2
#> 2 2021. 1710053925458… 1710… 1362. 70 5 2 1 2 2 2
#> 3 2021. 1710053925458… 1710… 0 43 5 1 1 2 <NA> <NA>
#> 4 2021. 1710053925458… 1710… 0 15 5 1 1 2 <NA> <NA>
#> 5 2021. 1710053965345… 1710… 1422. 89 1 2 1 2 2 2
#> 6 2021. 1710053965345… 1710… 0 90 1 1 1 2 <NA> <NA>
head(ncvs_2021_incident)
#> # A tibble: 6 × 60
#> YEARQ IDHH IDPER V4012 WGTVICCY V4016 V4017 V4018 V4019 V4021B V4022 V4024
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 2021. 1710071… 1710… 1 1780. 1 1 <NA> <NA> 9 3 6
#> 2 2021. 1710071… 1710… 1 1990. 2 1 <NA> <NA> 8 3 7
#> 3 2021. 1710071… 1710… 2 1990. 2 1 <NA> <NA> 8 3 7
#> 4 2021. 1710073… 1710… 1 4653. 1 1 <NA> <NA> 1 3 5
#> 5 2021. 1710074… 1710… 1 2302. 1 1 <NA> <NA> 2 3 21
#> 6 2021. 1710074… 1710… 1 2308. 1 1 <NA> <NA> 8 3 5
#> # ℹ 48 more variables: V4049 <fct>, V4050 <fct>, V4051 <fct>, V4052 <fct>,
#> # V4053 <fct>, V4054 <fct>, V4055 <fct>, V4056 <fct>, V4057 <fct>,
#> # V4058 <fct>, V4234 <fct>, V4235 <fct>, V4241 <fct>, V4242 <fct>,
#> # V4243 <fct>, V4244 <fct>, V4245 <fct>, V4248 <dbl>, V4256 <fct>,
#> # V4257 <fct>, V4258 <fct>, V4259 <fct>, V4260 <fct>, V4261 <fct>,
#> # V4262 <fct>, V4263 <fct>, V4264 <fct>, V4265 <fct>, V4266 <fct>,
#> # V4267 <fct>, V4268 <fct>, V4269 <fct>, V4270 <fct>, V4271 <fct>, …RECS
Three files are included associated with RECS - a dataset with the 2015 data with some derived variables created for the book (recs_2015), the 2020 data with some derived variables created for the book (recs_2020), and the 2020 data with the original variables (recs_2020_raw). RECS is a survey about energy consumption and expenditure among residential households in the United States and has been conducted since 1979 by the Energy Information Administration. More information about the original data is available at the RECS website.
head(recs_2015)
#> # A tibble: 6 × 141
#> DOEID REGIONC Region Division MSAStatus Urbanicity HousingUnitType YearMade
#> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <ord>
#> 1 10001 4 West Pacific Metropol… Urban Area Single-family … 2000-20…
#> 2 10002 3 South West So… None Rural Single-family … 1980-19…
#> 3 10003 3 South East So… Metropol… Urban Area Single-family … 1970-19…
#> 4 10004 2 Midwest West No… Micropol… Urban Clu… Single-family … 1950-19…
#> 5 10005 1 Northeast Middle … Metropol… Urban Area Single-family … 1970-19…
#> 6 10006 1 Northeast New Eng… None Urban Clu… Apartment: 5 o… 1980-19…
#> # ℹ 133 more variables: SpaceHeatingUsed <lgl>, HeatingBehavior <fct>,
#> # WinterTempDay <dbl>, WinterTempAway <dbl>, WinterTempNight <dbl>,
#> # ACUsed <lgl>, ACBehavior <fct>, SummerTempDay <dbl>, SummerTempAway <dbl>,
#> # SummerTempNight <dbl>, TOTCSQFT <dbl>, TOTHSQFT <dbl>, TOTSQFT_EN <dbl>,
#> # TOTUCSQFT <dbl>, TOTUSQFT <dbl>, NWEIGHT <dbl>, BRRWT1 <dbl>, BRRWT2 <dbl>,
#> # BRRWT3 <dbl>, BRRWT4 <dbl>, BRRWT5 <dbl>, BRRWT6 <dbl>, BRRWT7 <dbl>,
#> # BRRWT8 <dbl>, BRRWT9 <dbl>, BRRWT10 <dbl>, BRRWT11 <dbl>, BRRWT12 <dbl>, …
head(recs_2020)
#> # A tibble: 6 × 100
#> DOEID ClimateRegion_BA Urbanicity Region REGIONC Division STATE_FIPS
#> <dbl> <fct> <fct> <fct> <chr> <fct> <chr>
#> 1 100001 Mixed-Dry Urban Area West WEST Mountain So… 35
#> 2 100002 Mixed-Humid Urban Area South SOUTH West South … 05
#> 3 100003 Mixed-Dry Urban Area West WEST Mountain So… 35
#> 4 100004 Mixed-Humid Urban Area South SOUTH South Atlan… 45
#> 5 100005 Mixed-Humid Urban Area Northeast NORTHEAST Middle Atla… 34
#> 6 100006 Hot-Humid Urban Area South SOUTH West South … 48
#> # ℹ 93 more variables: state_postal <fct>, state_name <fct>, HDD65 <dbl>,
#> # CDD65 <dbl>, HDD30YR <dbl>, CDD30YR <dbl>, HousingUnitType <fct>,
#> # YearMade <ord>, TOTSQFT_EN <dbl>, TOTHSQFT <dbl>, TOTCSQFT <dbl>,
#> # SpaceHeatingUsed <lgl>, ACUsed <lgl>, HeatingBehavior <fct>,
#> # WinterTempDay <dbl>, WinterTempAway <dbl>, WinterTempNight <dbl>,
#> # ACBehavior <fct>, SummerTempDay <dbl>, SummerTempAway <dbl>,
#> # SummerTempNight <dbl>, NWEIGHT <dbl>, NWEIGHT1 <dbl>, NWEIGHT2 <dbl>, …
head(recs_2020_raw)
#> # A tibble: 6 × 789
#> DOEID REGIONC DIVISION STATE_FIPS state_postal state_name BA_climate
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 100001 WEST Mountain South 35 NM New Mexico Mixed-Dry
#> 2 100002 SOUTH West South Cen… 05 AR Arkansas Mixed-Hum…
#> 3 100003 WEST Mountain South 35 NM New Mexico Mixed-Dry
#> 4 100004 SOUTH South Atlantic 45 SC South Car… Mixed-Hum…
#> 5 100005 NORTHEAST Middle Atlantic 34 NJ New Jersey Mixed-Hum…
#> 6 100006 SOUTH West South Cen… 48 TX Texas Hot-Humid
#> # ℹ 782 more variables: IECC_climate_code <chr>, UATYP10 <chr>, HDD65 <dbl>,
#> # CDD65 <dbl>, HDD30YR_PUB <dbl>, CDD30YR_PUB <dbl>, TYPEHUQ <dbl>,
#> # CELLAR <dbl>, CRAWL <dbl>, CONCRETE <dbl>, BASEOTH <dbl>, BASEFIN <dbl>,
#> # ATTIC <dbl>, ATTICFIN <dbl>, STORIES <dbl>, PRKGPLC1 <dbl>,
#> # SIZEOFGARAGE <dbl>, KOWNRENT <dbl>, YEARMADERANGE <dbl>, BEDROOMS <dbl>,
#> # NCOMBATH <dbl>, NHAFBATH <dbl>, OTHROOMS <dbl>, TOTROOMS <dbl>,
#> # STUDIO <dbl>, WALLTYPE <dbl>, ROOFTYPE <dbl>, HIGHCEIL <dbl>, …Examples
To analyze the survey data, we recommend using the {srvyr} package as follows:
# install.packages("pak")
pak::pak("gergness/srvyr")
library(srvyr)
recs_des <- recs_2020 %>%
as_survey_rep(
weights = NWEIGHT, repweights = NWEIGHT1:NWEIGHT60,
type = "JK1", scale = 59 / 60, mse = TRUE,
variables = c(ACUsed, Region)
)
recs_des
#> Call: Called via srvyr
#> Unstratified cluster jacknife (JK1) with 60 replicates and MSE variances.
#> Sampling variables:
#> - repweights: `NWEIGHT1 + NWEIGHT2 + NWEIGHT3 + NWEIGHT4 + NWEIGHT5 +
#> NWEIGHT6 + NWEIGHT7 + NWEIGHT8 + NWEIGHT9 + NWEIGHT10 + NWEIGHT11 +
#> NWEIGHT12 + NWEIGHT13 + NWEIGHT14 + NWEIGHT15 + NWEIGHT16 + NWEIGHT17 +
#> NWEIGHT18 + NWEIGHT19 + NWEIGHT20 + NWEIGHT21 + NWEIGHT22 + NWEIGHT23 +
#> NWEIGHT24 + NWEIGHT25 + NWEIGHT26 + NWEIGHT27 + NWEIGHT28 + NWEIGHT29 +
#> NWEIGHT30 + NWEIGHT31 + NWEIGHT32 + NWEIGHT33 + NWEIGHT34 + NWEIGHT35 +
#> NWEIGHT36 + NWEIGHT37 + NWEIGHT38 + NWEIGHT39 + NWEIGHT40 + NWEIGHT41 +
#> NWEIGHT42 + NWEIGHT43 + NWEIGHT44 + NWEIGHT45 + NWEIGHT46 + NWEIGHT47 +
#> NWEIGHT48 + NWEIGHT49 + NWEIGHT50 + NWEIGHT51 + NWEIGHT52 + NWEIGHT53 +
#> NWEIGHT54 + NWEIGHT55 + NWEIGHT56 + NWEIGHT57 + NWEIGHT58 + NWEIGHT59 +
#> NWEIGHT60`
#> - weights: NWEIGHT
#> Data variables:
#> - ACUsed (lgl), Region (fct)
recs_des %>%
group_by(Region) %>%
summarize(
p = survey_mean(ACUsed, vartype = "ci", proportion = TRUE, prop_method = "logit")
)
#> # A tibble: 4 × 4
#> Region p p_low p_upp
#> <fct> <dbl> <dbl> <dbl>
#> 1 Northeast 0.890 0.877 0.901
#> 2 Midwest 0.933 0.922 0.943
#> 3 South 0.942 0.936 0.947
#> 4 West 0.745 0.729 0.760The above example estimates the proportion of residential households that use air-conditioning by region with a 95% confidence interval.
License
Data are available by CC BY 4.0 license. Additionally, re-distributing the ANES or NCVS datasets is subject to their policies.
Additional data use information
Anyone interested in redistributing the NCVS data should refer to ICPSR: Requests for Permission to Redistribute ICPSR Data.
Anyone interested in redistributing the ANES data should refer to the ANES FAQ - disseminate.
References
Data citations:
ANES:
- American National Election Studies. 2021. ANES 2020 Time Series Study Full Release [dataset and documentation]. July 19, 2021 version. https://www.electionstudies.org
NCVS:
- United States. Bureau of Justice Statistics. National Crime Victimization Survey, [United States], 2021. Inter-university Consortium for Political and Social Research [distributor], 2022-09-19. https://doi.org/10.3886/ICPSR38429.v1
RECS:
- U.S. Energy Information Administration. 2024. Residential Energy Consumption 2020 Survey Data. [dataset and documentation]. January 2024 version. https://www.eia.gov/consumption/residential/data/2020/index.php?view=microdata
- U.S. Energy Information Administration. 2018 Residential Energy Consumption 2015 Survey Data. [dataset and documentation]. December 2018 version. https://www.eia.gov/consumption/residential/data/2015/index.php?view=microdata