surveycore gives you a (mostly) complete workflow for
survey data analysis. This vignette is designed to give you a quick
overview of the main functionality present in this package. It is
comprised of three main sections:
Creating survey objects
Conducting simple analysis
Using
surveytidyfor data manipulation
Quick PSA before jumping in:
surveycore was built as an alternative to
survey and srvyr. However, the code powering
the variance estimation and analysis is vendored from the
survey package. Every aspect of this package that
calculates anything has been tested to ensure it provides the same
numerical results. This means every estimate surveycore
produces has been numerically verified to match survey
output. Without Thomas Lumley’s work on that package, surveycore would
not be possible.
Creating the survey object
The first step when conducting survey analysis is creating the right survey object where we specify the sampling design, weights, and whatever other information is needed. Without this information, point estimates may be biased and standard errors are almost certainly wrong (Lumley 2010; Lohr 2022).
Fortunately, we don’t have to calculate that uncertainty ourselves! That’s what the survey objects are for. They tell the analysis functions how to conduct their analysis so they can properly take into account the variance and bias from the survey design.
surveycore has five different survey object
constructors:
Rather than going into detail on each constructor, I’m just going to
provide a quick overview of each. For more information visit
vignette("creating-survey-objects").
as_survey()
You want to use this if you used a probability sample and the data you have has cluster IDs, strata, and/or design weights. In this example we’ll use the General Social Survey which has variables for clustering, strata, and design weights.
gss_svy <- as_survey(
gss_2024,
ids = vpsu,
strata = vstrat,
weights = wtssps
)#> Warning: ! Some PSUs appear in more than one stratum: "1" and "2". If PSUs are nested
#> within strata, set `nest = TRUE`.
gss_svy#> #> ── Survey Design ───────────────────────────────────────────────────────────────#> <survey_taylor> (Taylor series linearization)#> Sample size: 3309#> #> # A tibble: 3,309 × 27
#> vpsu vstrat wtssps wtssnrps ballot year id age sex race hispanic
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 3401 1.89 2.37 2 2024 1 33 1 2 1
#> 2 1 3401 1.15 1.39 2 2024 2 64 1 1 1
#> 3 1 3401 0.916 1.17 3 2024 3 69 2 1 1
#> 4 1 3401 2.29 2.74 1 2024 4 19 1 3 1
#> 5 1 3401 1.01 1.27 3 2024 5 70 2 1 1
#> 6 1 3401 1.17 1.34 2 2024 6 53 1 3 5
#> 7 1 3401 3.21 4.07 2 2024 7 48 2 1 1
#> 8 1 3401 0.424 0.533 3 2024 8 30 2 1 1
#> 9 1 3401 2.15 2.75 3 2024 9 60 2 2 1
#> 10 1 3401 2.24 2.94 1 2024 10 25 1 2 1
#> # ℹ 3,299 more rows
#> # ℹ 16 more variables: educ <dbl>, degree <dbl>, income16 <dbl>, marital <dbl>,
#> # wrkstat <dbl>, hrs1 <dbl>, adults <dbl>, partyid <dbl>, polviews <dbl>,
#> # happy <dbl>, health <dbl>, trust <dbl>, natfare <dbl>, abany <dbl>,
#> # attend <dbl>, relig <dbl>
as_survey_rep()
Use this when the data you have comes with pre-built replicate weight
columns like repwt_1, repwt_2. For example,
Pew’s Jewish American study from 2020 uses replicate weights.
pew_jewish_svy <- as_survey_rep(
pew_jewish_2020,
weights = extweight,
repweights = extweight1:extweight100,
type = "JK1"
)
pew_jewish_svy#> #> ── Survey Design ───────────────────────────────────────────────────────────────#> <survey_replicate> (JK1, 100 replicates)#> Sample size: 5881#> #> # A tibble: 5,881 × 130
#> extweight extweight1 extweight2 extweight3 extweight4 extweight5 extweight6
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 271. 267. 272. 271. 272. 269. 265.
#> 2 186. 183. 236. 186. 189. 185. 182.
#> 3 182. 181. 185. 188. 184. 181. 189.
#> 4 308. 307. 312. 324. 308. 305. 320.
#> 5 165. 165. 167. 170. 166. 163. 164.
#> 6 173. 170. 175. 173. 174. 173. 168.
#> 7 352. 347. 353. 351. 358. 353. 338.
#> 8 314. 312. 318. 316. 314. 314. 309.
#> 9 395. 394. 395. 394. 392. 392. 392.
#> 10 176. 177. 178. 181. 177. 175. 172.
#> # ℹ 5,871 more rows
#> # ℹ 123 more variables: extweight7 <dbl>, extweight8 <dbl>, extweight9 <dbl>,
#> # extweight10 <dbl>, extweight11 <dbl>, extweight12 <dbl>, extweight13 <dbl>,
#> # extweight14 <dbl>, extweight15 <dbl>, extweight16 <dbl>, extweight17 <dbl>,
#> # extweight18 <dbl>, extweight19 <dbl>, extweight20 <dbl>, extweight21 <dbl>,
#> # extweight22 <dbl>, extweight23 <dbl>, extweight24 <dbl>, extweight25 <dbl>,
#> # extweight26 <dbl>, extweight27 <dbl>, extweight28 <dbl>, …
as_survey_srs()
Use this when your survey was a pure simple random sample, meaning each respondent had equal probability of selection and there are neither clustering nor strata.
I don’t have a specific dataset example to use, so I’ll just make a fake one. This fake dataset is meant to replicate a school district survey.
set.seed(101)
N <- 400 # total schools in district
n <- 80 # schools sampled
school_survey <- data.frame(
school_id = sample(seq_len(N), n),
avg_score = round(rnorm(n, mean = 72, sd = 11), 1),
pct_frpl = round(runif(n, 0.10, 0.85), 2), # % free/reduced price lunch
enrollment = round(runif(n, 180, 850)),
sw = N / n, # equal sampling weight = 400/80 = 5.0
fpc = N # population size for FPC
)
school_svy <- as_survey_srs(
school_survey,
weights = sw, # each sampled school represents 5 schools in the population
fpc = fpc # reduces SEs: we sampled 20% of the population
)
school_svy#> #> ── Survey Design ───────────────────────────────────────────────────────────────#> <survey_srs> (simple random sample)#> Sample size: 80#> #> # A tibble: 80 × 6
#> school_id avg_score pct_frpl enrollment sw fpc
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 329 72.3 0.55 610 5 400
#> 2 313 75.2 0.36 294 5 400
#> 3 95 60.1 0.17 187 5 400
#> 4 209 73.4 0.24 729 5 400
#> 5 351 81.6 0.18 324 5 400
#> 6 317 71.3 0.38 296 5 400
#> 7 315 57.4 0.11 188 5 400
#> 8 246 68.3 0.16 545 5 400
#> 9 355 66.2 0.32 531 5 400
#> 10 128 71.5 0.54 656 5 400
#> # ℹ 70 more rows
as_survey_calibrated()
Use this when you used a non-probability panel (e.g., Qualtrics Panels, Cint/Lucid, Dynata, YouGov, etc.) and have created weights designed to ensure the sample is representative of the population you are interested in.
ns_wave1_svy <- as_survey_calibrated(ns_wave1, weights = weight)
ns_wave1_svy#> #> ── Survey Design ───────────────────────────────────────────────────────────────#> <survey_calibrated> (calibrated / non-probability) [experimental]#> Sample size: 6422#> #> # A tibble: 6,422 × 171
#> response_id start_date right_track economy_better interest
#> <chr> <dttm> <dbl> <dbl> <dbl>
#> 1 00100002 2019-07-18 08:11:41 2 2 2
#> 2 00100003 2019-07-18 08:12:31 1 3 1
#> 3 00100004 2019-07-18 08:12:04 2 3 2
#> 4 00100005 2019-07-18 08:12:05 2 2 2
#> 5 00100007 2019-07-18 08:11:43 1 1 1
#> 6 00100008 2019-07-18 08:12:24 2 2 2
#> 7 00100009 2019-07-18 08:13:15 2 2 4
#> 8 00100010 2019-07-18 08:13:06 1 1 1
#> 9 00100011 2019-07-18 08:11:47 2 2 3
#> 10 00100012 2019-07-18 08:12:25 2 3 2
#> # ℹ 6,412 more rows
#> # ℹ 166 more variables: registration <dbl>, news_sources_facebook <dbl>,
#> # news_sources_cnn <dbl>, news_sources_msnbc <dbl>, news_sources_fox <dbl>,
#> # news_sources_network <dbl>, news_sources_localtv <dbl>,
#> # news_sources_telemundo <dbl>, news_sources_npr <dbl>,
#> # news_sources_amtalk <dbl>, news_sources_new_york_times <dbl>,
#> # news_sources_local_newspaper <dbl>, news_sources_other <dbl>, …
as_survey_twophase()
This is very rare, so it’s unlikely you have this. But, if you took a large sample of a population and then resampled a subset of them, then you might have a two-phase design. Some common contexts are: case-cohort studies, medical validation studies, or surveys with a screening phase.
We will use the nwtco data from the survival
package.
nwtco <- survival::nwtco
# in.subcohort is stored as 0/1 — must be logical for as_survey_twophase()
nwtco$in.subcohort <- as.logical(nwtco$in.subcohort)
# Phase 1: all 4,028 enrolled patients (each patient is their own unit)
phase1 <- as_survey(nwtco, ids = seqno)#> Warning: ! No weights provided.
#> ℹ Treating as equal-probability sampling within clusters (unknown population
#> size).
#> ℹ Population totals will equal sample totals, not estimated population totals.
# Phase 2: subcohort, with Phase 2 sampling stratified by relapse status
nwtco_svy <- as_survey_twophase(
phase1,
strata2 = rel, # Phase 2 strata: cases (rel=1) vs. non-cases (rel=0)
subset = in.subcohort, # Logical column: TRUE = selected into Phase 2
method = "full"
)
nwtco_svy#> #> ── Survey Design ───────────────────────────────────────────────────────────────#> <survey_twophase> (method: full)#> Phase 1 sample size: 4028#> Phase 2 sample size: 668#> #> # A tibble: 4,028 × 10
#> seqno instit histol stage study rel edrel age in.subcohort
#> <int> <int> <int> <int> <int> <int> <int> <int> <lgl>
#> 1 1 2 2 1 3 0 6075 25 FALSE
#> 2 2 1 1 2 3 0 4121 50 FALSE
#> 3 3 2 2 1 3 0 6069 9 FALSE
#> 4 4 2 1 4 3 0 6200 28 TRUE
#> 5 5 2 2 2 3 0 1244 55 FALSE
#> 6 6 1 1 2 3 0 2932 32 FALSE
#> 7 7 1 1 4 3 1 324 45 FALSE
#> 8 8 1 1 2 3 0 5408 44 FALSE
#> 9 9 1 1 1 3 0 5215 123 FALSE
#> 10 10 2 1 2 3 0 1381 31 FALSE
#> # ℹ 4,018 more rows
#> # ℹ 1 more variable: ..surveycore_wt.. <int>As you may have noticed, each survey object has a print method that shows the first 10 rows of each data set, similar to a tibble, but also includes a brief description of the survey design.
Conducting Simple Analysis
In addition to creating survey objects, surveycore has
several functions designed to make analysis easier:
Frequency tables — get_freqs()
get_freqs() calculates weighted frequencies (aka
proportions). The first argument is the survey design, the second is the
variable you want to get the frequencies for. Here’s a simple example
where we calculate whether or not people are willing to consider voting
for Trump.
get_freqs(ns_wave1_svy, consider_trump)#> # A tibble: 3 × 3
#> consider_trump pct n
#> <fct> <dbl> <int>
#> 1 Yes 0.321 2087
#> 2 No 0.555 3615
#> 3 Don't know 0.125 705Analyzing multiple variables at once
A key piece of survey research involves select-all-that-apply style
questions. For example, the Nationscape data asked people: “We’re
interested in where you might have heard news about politics in the last
week. Please indicate which of the following sources you used.” Rather
than looking at each one individually, get_freqs() allows
you to pass in multiple variables (it uses tidy-select
under the hood to do this). Let’s look at an example:
#> # A tibble: 24 × 4
#> name value pct n
#> <fct> <chr> <dbl> <int>
#> 1 Social media (e.g., Facebook, Twitter) Yes 0.615 4187
#> 2 Social media (e.g., Facebook, Twitter) No 0.385 2235
#> 3 CNN Yes 0.400 2532
#> 4 CNN No 0.600 3890
#> 5 MSNBC Yes 0.266 1667
#> 6 MSNBC No 0.734 4755
#> 7 Fox News (cable) Yes 0.361 2360
#> 8 Fox News (cable) No 0.639 4062
#> 9 Network news (ABC, CBS, NBC) or PBS Yes 0.580 3711
#> 10 Network news (ABC, CBS, NBC) or PBS No 0.420 2711
#> # ℹ 14 more rowsThe name column identifies which variable each row
belongs to; value holds the response code. You can also
change the name of the columns if you want. For example:
ns_wave1_svy |>
get_freqs(
c(news_sources_facebook:news_sources_other),
names_to = "news_source",
values_to = "choice"
)#> # A tibble: 24 × 4
#> news_source choice pct n
#> <fct> <chr> <dbl> <int>
#> 1 Social media (e.g., Facebook, Twitter) Yes 0.615 4187
#> 2 Social media (e.g., Facebook, Twitter) No 0.385 2235
#> 3 CNN Yes 0.400 2532
#> 4 CNN No 0.600 3890
#> 5 MSNBC Yes 0.266 1667
#> 6 MSNBC No 0.734 4755
#> 7 Fox News (cable) Yes 0.361 2360
#> 8 Fox News (cable) No 0.639 4062
#> 9 Network news (ABC, CBS, NBC) or PBS Yes 0.580 3711
#> 10 Network news (ABC, CBS, NBC) or PBS No 0.420 2711
#> # ℹ 14 more rowsWeighted means — get_means()
get_means() estimates the survey-weighted mean of a
continuous variable.
# Mean discrimination against blacks
get_means(ns_wave1_svy, discrimination_blacks)#> # A tibble: 1 × 4
#> mean ci_low ci_high n
#> <dbl> <dbl> <dbl> <int>
#> 1 2.41 2.36 2.47 6364Population totals — get_totals()
get_totals() estimates the weighted sum: the total count
or aggregate for the target population. It’s important to note that the
weights typically used in non-probability samples are calibration
weights which are designed to ensure the sample is representative of the
population it is sampled from. These weights will not give you accurate
population totals. However, design weights scaled to represent the
target population will give estimated population size.
To show the difference, we will use Pew’s Jewish-Americans Study from 2020 to show a study with weights that show population totals and the Nationscape data to show how calibrated weights do not.
First, we will look at the Nationscape data. Since these are calibration weights, the totals will add up to the number of rows (6,422).
get_totals(ns_wave1_svy)#> # A tibble: 1 × 3
#> total ci_low ci_high
#> <dbl> <dbl> <dbl>
#> 1 6422 6192. 6652.However, the Pew Jewish-Americans study shows us the estimated total population of Jews in the US:
get_totals(pew_jewish_svy)#> # A tibble: 1 × 3
#> total ci_low ci_high
#> <dbl> <dbl> <dbl>
#> 1 9971358. 9971322. 9971394.Note how we did not use any variables. That’s because we are
interested in the sample’s population size. However, if we are
interested in the estimated total income or age, we can do that by
specifying it in the x argument.
If we wanted to understand how many estimated people selected a
specific response option, we use the group argument. For
example: if we wanted to know how many Jews are in each age category we
would calculate it like this:
get_totals(pew_jewish_svy, group = age4cat)#> # A tibble: 5 × 4
#> age4cat total ci_low ci_high
#> <fct> <dbl> <dbl> <dbl>
#> 1 18-29 1916708. 1752196. 2081221.
#> 2 30-49 3129220. 2977800. 3280639.
#> 3 50-64 2300014. 2181724. 2418305.
#> 4 65+ 2406454. 2293478. 2519431.
#> 5 No Answer 218962. 163386. 274537.Weighted correlations — get_corr()
get_corr() estimates survey-weighted Pearson
correlations between two or more continuous variables. Confidence
intervals use the Fisher Z transformation, guaranteeing bounds in (−1,
1).
Let’s look at approval for Trump and Biden, but first we’ll do some
quick data cleaning using the surveytidy package (I’ll go
more in-depth later showing what this package can do).
library(surveytidy)
ns_wave1_svy |>
# drop NAs
drop_na(cand_favorability_trump, cand_favorability_biden) |>
# remove "Not sure" responses (coded 999)
filter_out(cand_favorability_trump == 999 | cand_favorability_biden == 999) |>
# calculate correlations
get_corr(c(cand_favorability_trump, cand_favorability_biden))Next, let’s look at favorability across multiple variables.
ns_wave1_svy |>
drop_na(c(cand_favorability_trump:cand_favorability_pence)) |>
# filter out any respondents who said "Not sure"
filter_out(
dplyr::if_any(
c(cand_favorability_trump:cand_favorability_pence),
~ .x == 999
)
) |>
get_corr(c(cand_favorability_trump:cand_favorability_pence))The output defaults to a long version where each row is a unique
variable pair. It shows the correlation in r, the
confidence intervals, p-values, and other relevant information.
Switch to wide format for a more familiar correlation-matrix layout:
ns_wave1_svy |>
drop_na(c(cand_favorability_trump:cand_favorability_pence)) |>
# filter out any respondents who said "Not sure"
filter_out(
dplyr::if_any(
c(cand_favorability_trump:cand_favorability_pence),
~ .x == 999
)
) |>
get_corr(c(cand_favorability_trump:cand_favorability_pence), format = "wide")Ratio estimation — get_ratios()
get_ratios() estimates the ratio of two weighted
totals:
Variance is estimated via the delta method (linearization),
equivalent to survey::svyratio() (Lumley 2010).
Ratios are useful when you want an estimate that is invariant to the scale of the weights — for example, wages per hour, spending per household member, or disease prevalence ratios.
A good example uses the Nationscape data. The favorability scale runs from 1 (Very favorable) to 4 (Very unfavorable), so we can estimate the ratio of Trump’s aggregate favorability score to Biden’s. A ratio less than 1 means Trump’s aggregate score is lower — i.e., he is viewed more favorably on average; a ratio greater than 1 means Biden is viewed more favorably.
ns_wave1_svy |>
# drop NAs
drop_na(cand_favorability_trump, cand_favorability_biden) |>
# remove people who don't know enough about Trump or Biden
filter_out(cand_favorability_trump == 999 | cand_favorability_biden == 999) |>
# get the ratio of favorability
get_ratios(
numerator = cand_favorability_trump,
denominator = cand_favorability_biden
)
get_quantiles()
get_quantiles() estimates survey-weighted quantiles
using the Woodruff (1952) confidence interval method. Confidence
intervals are derived by inverting the weighted CDF rather than assuming
normality, so they are generally asymmetric around the
estimate and always respect the range of the data. By default, it
calculates the quantiles at the 25th, 50th, and 75th percentile.
# Quartiles and median of age (default probs = c(0.25, 0.5, 0.75))
get_quantiles(ns_wave1_svy, age)#> # A tibble: 3 × 5
#> quantile estimate ci_low ci_high n
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 p25 32 31 34 6422
#> 2 p50 47 46 49 6422
#> 3 p75 62 62 63 6422Choosing quantiles
Pass any numeric vector to probs. For the median
alone:
get_quantiles(ns_wave1_svy, age, probs = 0.5)#> # A tibble: 1 × 5
#> quantile estimate ci_low ci_high n
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 p50 47 46 49 6422For deciles of age:
get_quantiles(ns_wave1_svy, age, probs = seq(0.1, 0.9, 0.1))#> # A tibble: 9 × 5
#> quantile estimate ci_low ci_high n
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 p10 24 24 25 6422
#> 2 p20 29 29 31 6422
#> 3 p30 35 34 37 6422
#> 4 p40 40 40 42 6422
#> 5 p50 47 46 49 6422
#> 6 p60 53 52 55 6422
#> 7 p70 59 59 61 6422
#> 8 p80 64 63 66 6422
#> 9 p90 71 71 72 6422Subgroup analysis — the group argument
Every analysis function accepts a group argument for
computing estimates separately within levels of a categorical variable.
Pass a bare column name or multiple using c(). For example,
we’ll look at Trump consideration by party identification.
get_freqs(ns_wave1_svy, consider_trump, group = pid3)#> # A tibble: 12 × 4
#> pid3 consider_trump pct n
#> <fct> <fct> <dbl> <int>
#> 1 Democrat Yes 0.0501 136
#> 2 Democrat No 0.887 2042
#> 3 Democrat Don't know 0.0626 111
#> 4 Republican Yes 0.775 1403
#> 5 Republican No 0.128 227
#> 6 Republican Don't know 0.0969 183
#> 7 Independent Yes 0.238 475
#> 8 Independent No 0.566 1071
#> 9 Independent Don't know 0.195 316
#> 10 Something else Yes 0.209 73
#> 11 Something else No 0.570 272
#> 12 Something else Don't know 0.221 91Rows where the grouping variable is NA are excluded from
all groups and do not appear in the output. Responses within each group
sum to 100% for get_freqs().
Controlling uncertainty output
All analysis functions share a common variance argument.
You can request any combination of:
| Code | What it returns |
|---|---|
"se" |
Standard error |
"ci" |
Confidence interval: ci_low, ci_high
|
"var" |
Variance (square of the SE) |
"cv" |
Coefficient of variation (SE / estimate) |
"moe" |
Margin of error at conf_level
|
"deff" |
Design effect (complex design variance / SRS variance) |
The conf_level argument controls the confidence level
for "ci" and "moe". The default is
0.95; for a 90% CI:
#> # A tibble: 1 × 6
#> mean se ci_low ci_high moe n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 47.2 0.396 46.5 47.8 0.651 6422Set variance = NULL to suppress all uncertainty columns
and return point estimates and sample counts only.
Estimated population counts
Add n_weighted = TRUE to any function to include the
estimated population count — the sum of weights — alongside the
unweighted sample count n. Let’s look at Pew’s
Jewish-Americans data again. Using get_freqs() and
n_weighted = TRUE we can see proportions and estimated
populations. In this example, we’re looking at the proportion
(pct), the number of people from the sample
(n), and the estimated population size
(n_weighted) of Jewish- Americans in each age category.
get_freqs(pew_jewish_svy, age4cat, n_weighted = TRUE)#> # A tibble: 5 × 4
#> age4cat pct n n_weighted
#> <fct> <dbl> <int> <dbl>
#> 1 18-29 0.192 554 1916708.
#> 2 30-49 0.314 1430 3129220.
#> 3 50-64 0.231 1431 2300014.
#> 4 65+ 0.241 2336 2406454.
#> 5 No Answer 0.0220 130 218962.Using surveytidy
pak::pak("jacobdennen/surveytidy")All analysis functions make it easy to pipe in the survey design as
well. This allows you to manipulate the data with dplyr-like verbs from
the surveytidy package. We briefly saw an example of this
earlier when looking at correlations. The surveytidy
package provides dplyr verbs like: filter(),
select(), mutate(), group_by(),
rename(), and a few others that work directly on survey
objects. We’ll show a few of those in play with the Nationscape data
again.
We’ll rename the variable for Biden’s favorability, then create a new one where we add 1 to it, and then select the relevant columns to compare.
ns_wave1_svy |>
# rename the variable
rename(fav_biden = cand_favorability_biden) |>
# let's start with a simple example of adding 1 to favorability of Biden
mutate(fav_biden_1 = fav_biden + 1) |>
# now let's look at the results
select(fav_biden, fav_biden_1)Grouped analysis with group_by()
Now let’s do a slightly more in-depth example. We’re going to look at how people view Trump by age group. First, let’s create the necessary variables.
ns_wave1_svy <- ns_wave1_svy |>
# rename trump favorability
rename(fav_trump = cand_favorability_trump) |>
mutate(
# convert favorability of Trump to a 3 level factor
fav_trump_f3 = dplyr::recode_values(
fav_trump,
c(1:2) ~ "Favorable",
c(3:4) ~ "Unfavorable",
999 ~ "Haven't heard enough"
),
age_f4 = dplyr::case_when(
age < 30 ~ "18-29",
age < 50 ~ "30-49",
age < 65 ~ "50-64",
age > 64 ~ "65+",
.default = NA_character_
)
)
ns_wave1_svy |> select(fav_trump, fav_trump_f3, age, age_f4)Now let’s look at the grouped frequencies. So far, we’ve used the
group argument, but we can also group_by()
from surveytidy. Looking below, we can see that they return identical
results.
Using filter() for domain estimation
It is very common in survey analysis to calculate results among
subgroups. For example, you may be interested in knowing how people
under 30 view Trump. One way is to do what we just did, use
group_by() on an age category. A simpler method is using
the filter() and filter_out() functions from
surveytidy. For example:
ns_wave1_svy |>
# keep only people under 30 in the domain
filter(age < 30) |>
# calculate frequencies
get_freqs(fav_trump_f3)As you can see, the results are the same as those calculated via
group_by() and group. That’s because the
method for estimating subpopulation results is the same in both.
For more information on domain estimation, a vignette on surveytidy’s website will be up shortly.
Summary
| Function | Use for |
|---|---|
get_freqs() |
Categorical variables — weighted distributions, percentages |
get_means() |
Continuous variables — weighted means |
get_totals() |
Population counts or aggregates — weighted sums |
get_ratios() |
Ratios of two weighted totals |
get_corr() |
Pairwise Pearson correlations |
get_quantiles() |
Weighted quantiles and median — Woodruff CIs |
All functions: - Return a tibble subclass ready for further analysis
or display - Accept a group argument for subgroup estimates
- Accept a variance argument to control which uncertainty
columns appear - Handle all five survey design classes:
survey_taylor, survey_replicate,
survey_srs, survey_twophase, and
survey_calibrated