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)
#> V200001 CaseID V200002 InterviewMode V200010b Weight V200010c VarUnit
#> 1 200015 200015 3 Web 1.0057375 1.0057375 2 2
#> 2 200022 200022 3 Web 1.1634731 1.1634731 2 2
#> 3 200039 200039 3 Web 0.7686811 0.7686811 1 1
#> 4 200046 200046 3 Web 0.5210195 0.5210195 2 2
#> 5 200053 200053 3 Web 0.9657892 0.9657892 1 1
#> 6 200060 200060 3 Web 0.2347078 0.2347078 2 2
#> V200010d Stratum V201006 CampaignInterest V201023 EarlyVote2020 V201024
#> 1 9 9 2 Somewhat interested -1 <NA> -1
#> 2 26 26 3 Not much interested -1 <NA> -1
#> 3 41 41 2 Somewhat interested -1 <NA> -1
#> 4 29 29 3 Not much interested -1 <NA> -1
#> 5 23 23 2 Somewhat interested -1 <NA> -1
#> 6 37 37 1 Very much interested -1 <NA> -1
#> V201025x V201028 V201029 V201101 V201102 VotedPres2016 V201103
#> 1 3 -1 -1 -1 1 Yes 2
#> 2 3 -1 -1 -1 1 Yes 5
#> 3 3 -1 -1 -1 1 Yes 1
#> 4 3 -1 -1 -1 1 Yes 1
#> 5 3 -1 -1 -1 1 Yes 2
#> 6 3 -1 -1 -1 2 No -1
#> VotedPres2016_selection V201228 V201229 V201230 V201231x
#> 1 Trump 2 1 -1 7
#> 2 Other 5 -1 2 4
#> 3 Clinton 3 -1 3 3
#> 4 Clinton 2 2 -1 6
#> 5 Trump 3 -1 2 4
#> 6 <NA> 3 -1 3 3
#> PartyID V201233 TrustGovernment V201237
#> 1 Strong republican 5 Never 3
#> 2 Independent 5 Never 4
#> 3 Independent-democrat 4 Some of the time 4
#> 4 Not very strong republican 3 About half the time 2
#> 5 Independent 5 Never 4
#> 6 Independent-democrat 4 Some of the time 2
#> TrustPeople V201507x Age AgeGroup V201510 Education V201546
#> 1 About half the time 46 46 40-49 6 Bachelor's 1
#> 2 Some of the time 37 37 30-39 3 Post HS 2
#> 3 Some of the time 40 40 40-49 2 High school 2
#> 4 Most of the time 41 41 40-49 4 Post HS 2
#> 5 Some of the time 72 72 70 or older 8 Graduate 2
#> 6 Most of the time 71 71 70 or older 3 Post HS 2
#> V201547a V201547b V201547c V201547d V201547e V201547z V201549x RaceEth
#> 1 -3 -3 -3 -3 -3 -3 3 Hispanic
#> 2 -3 -3 -3 -3 -3 -3 4 Asian, NH/PI
#> 3 -3 -3 -3 -3 -3 -3 1 White
#> 4 -3 -3 -3 -3 -3 -3 4 Asian, NH/PI
#> 5 -3 -3 -3 -3 -3 -3 5 AI/AN
#> 6 -3 -3 -3 -3 -3 -3 1 White
#> V201600 Gender V201607 V201610 V201611 V201613 V201615 V201616 V201617x
#> 1 1 Male -3 -3 -3 -3 -3 -3 21
#> 2 2 Female -3 -3 -3 -3 -3 -3 13
#> 3 2 Female -3 -3 -3 -3 -3 -3 17
#> 4 1 Male -3 -3 -3 -3 -3 -3 7
#> 5 1 Male -3 -3 -3 -3 -3 -3 22
#> 6 2 Female -3 -3 -3 -3 -3 -3 3
#> Income Income7 V202051 V202066 V202072 VotedPres2020
#> 1 $175,000-249,999 $125k or more -1 1 -1 <NA>
#> 2 $70,000-74,999 $60k to < 80k -1 4 1 Yes
#> 3 $100,000-109,999 $100k to < 125k -1 4 1 Yes
#> 4 $35,000-39,999 $20k to < 40k -1 4 1 Yes
#> 5 $250,000 or more $125k or more -1 4 1 Yes
#> 6 $15,000-19,999 Under $20k -1 4 1 Yes
#> V202073 V202109x V202110x VotedPres2020_selection
#> 1 -1 0 -1 <NA>
#> 2 3 1 3 Other
#> 3 1 1 1 Biden
#> 4 1 1 1 Biden
#> 5 2 1 2 Trump
#> 6 1 1 1 BidenSee ?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>, …NSDUH
The National Survey on Drug Use and Health (NSDUH) is an annual survey of the civilian, non-institutionalized population in the United States who are at least 12 years old. Topics include substance use (tobacco, alcohol, and illicit drugs including marijuana), mental health, and general health. This package provides a subset of the variables from the 2023 Public Use File. For more details about the study and the data, refer to the Methodological Summary and Definitions, Data User’s Guide, and Codebook.
head(nsduh_2023)
#> # A tibble: 6 × 22
#> QUESTID2 ANALWT2_C VESTR_C VEREP NICVAPMON TOBMON ALCMON ILLMON ILTOBVAPALC
#> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int>
#> 1 10000053 3276. 40031 2 0 0 1 0 1
#> 2 10000679 15630. 40021 2 0 1 1 0 1
#> 3 10001208 4018. 40043 1 0 1 0 1 1
#> 4 10001260 10712. 40030 2 0 0 0 0 0
#> 5 10001588 8195. 40023 2 0 0 1 0 1
#> 6 10004996 3771. 40048 1 1 1 1 0 1
#> # ℹ 13 more variables: BNGDRKMON <int>, IRPYUD5ALC <int>, UD5ILLANY <int>,
#> # UD5ILALANY <int>, YMDELT <fct>, YMDEYR <fct>, MDEIMPY <fct>, AMIPY <int>,
#> # SMIPY <int>, AGE3 <fct>, NEWRACE2 <fct>, IRSEX <fct>, POVERTY3 <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>, …CHIS
The CHIS data is a subset of variables from the 2023 California Health Interview Survey Adult Public Use File. CHIS is an annual survey of people in households in California with several topics related to health and social determinants of health. For more information about the study, refer to the CHIS website. To download a full version of the data with all variables or view codebooks, create an account and download the public use files. See a snippet of the data below:
head(chis_2023)
#> # A tibble: 6 × 98
#> PUF1Y_ID AH1V2 AH22 SMKCUR30 AB1 DIABETES BMI_P RBMI AB17 DSTRS12 AB29V2
#> <chr> <fct> <fct> <fct> <fct> <fct> <dbl> <fct> <fct> <fct> <fct>
#> 1 23021436 Yes No No Very … No 35.6 Obes… No No No
#> 2 23009146 Yes No No Excel… No 23.0 Norm… No No No
#> 3 23005039 Yes No No Good No 25.6 Over… Yes No Borde…
#> 4 23025815 Yes Yes No Fair No 42.5 Obes… No No Borde…
#> 5 23010158 Yes No No Good Yes 24.7 Norm… No No Yes
#> 6 23006250 Yes No No Excel… No 19.1 Norm… No No No
#> # ℹ 87 more variables: SPK_ENG <fct>, POVLL2_P1V2 <dbl>, POVLL <fct>,
#> # SRAGE_P1 <ord>, SRSEX <fct>, OMBSRR_P1 <fct>, RAKEDW0 <dbl>, RAKEDW1 <dbl>,
#> # RAKEDW2 <dbl>, RAKEDW3 <dbl>, RAKEDW4 <dbl>, RAKEDW5 <dbl>, RAKEDW6 <dbl>,
#> # RAKEDW7 <dbl>, RAKEDW8 <dbl>, RAKEDW9 <dbl>, RAKEDW10 <dbl>,
#> # RAKEDW11 <dbl>, RAKEDW12 <dbl>, RAKEDW13 <dbl>, RAKEDW14 <dbl>,
#> # RAKEDW15 <dbl>, RAKEDW16 <dbl>, RAKEDW17 <dbl>, RAKEDW18 <dbl>,
#> # RAKEDW19 <dbl>, RAKEDW20 <dbl>, RAKEDW21 <dbl>, RAKEDW22 <dbl>, …See ?chis_2023 for more information about the data.
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
CHIS:
- California Health Interview Survey. CHIS 2023 Adult Public Use Files. [Computer file]. UCLA Center for Health Policy Research, Los Angeles, CA. February 2025 version
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
NSDUH:
- Center for Behavioral Health Statistics and Quality, 2025. 2023 National Survey on Drug Use and Health: Public use file data users’ guide. https://www.samhsa.gov/data/data-wecollect/nsduh/datafiles
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