Nate P
Reputation: 23
'INDEX' is of length zero error in gtsummary::tbl_svysummary
I have created a survey object via tidyverse manipulations and the srvyr package that has the following characteristics:
> dat_svy
Stratified 1 - level Cluster Sampling design (with replacement)
With (306) clusters.
Called via srvyr
Sampling variables:
- ids: region
- strata: year
- weights: weights
Data variables:
- year (dbl), region (dbl), weights (dbl), suicide1 (dbl), suicide2 (dbl),
suicide3 (dbl), suicide4 (dbl), white (dbl), college_degree (dbl), male (dbl),
birth_cohort (dbl), not_married (dbl), no_children (dbl), no_siblings (dbl),
lives_alone (dbl), no_church (dbl), socially_isolated (dbl), no_afterlife
(dbl), unhappy (dbl)
When I call the tbl_svysummary function from the gtsummary package on it, I get the error: "Error in tapply(y, by, sum, na.rm = na.rm, default = 0L) : 'INDEX' is of length zero".
I can't find anything on this error anywhere. I don't have any factor or character variables to worry about - tbl_svysummary just refuses to work. What do I need to change or what am I doing wrong here?
Simple dput of the top hundred rows - the dataframe is around 72,000 responses in full:
dat_dput <- structure(list(year = c(1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972), region = c(3, 3, 3, 3, 3, 3, 3,
3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
1, 1, 1, 1, 1, 1, 1, 1, 4), weights = c(0.663196271930943, 0.917370028585327,
0.897412512251031, 1.06634082743438, 0.94432371066466, 0.526887241987567,
0.526887241987567, 0.546578869586901, 0.283198307048893, 0.494322145606406,
0.494322145606406, 0.514302430419033, 0.342881139044641, 0.737740988776798,
0.325847822011128, 0.344758822112228, 0.69963910150137, 0.775624523191156,
1.15320013722553, 2.11211800992106, 0.64273816062543, 0.64273816062543,
0.69963910150137, 0.848913248628709, 1.16102275743865, 0.94432371066466,
0.739610183680864, 0.897412512251031, 0.847172098766174, 0.842558088614268,
0.842558088614268, 1.02792370661946, 1.63985954597833, 1.11309898130309,
0.847172098766174, 0.847172098766174, 0.94432371066466, 1.44345531723148,
0.626115859108271, 1.22820170398128, 0.900385775275192, 1.01223292017645,
1.18963602327628, 1.22151246676606, 1.40871806128592, 1.02792370661946,
1.5807802341143, 1.10937369168099, 0.735581995192448, 1.10333163542922,
1.10333163542922, 0.919680751859048, 0.735581995192448, 1.02792370661946,
2.34789863846679, 0.919680751859048, 1.11590271768198, 1.11590271768198,
0.914145070725301, 0.76937467894537, 0.753400464602581, 1.15401876109887,
0.956302790407163, 0.602587164960281, 0.753400464602581, 0.939180577180875,
0.919680751859048, 0.897412512251031, 1.38819734003634, 0.878833896874097,
0.735581995192448, 1.11590271768198, 0.917370028585327, 0.939180577180875,
2.10778598425346, 1.09328067639143, 1.33873713023916, 1.16835272322354,
2.10778598425346, 1.17475084745831, 1.4711639903849, 0.897412512251031,
0.459990562003962, 1.02792370661946, 1.05389299212673, 0.878833896874097,
1.15760792327927, 1.98973798274558, 0.878833896874097, 0.685909878201486,
0.878833896874097, 1.3267263503737, 1.3267263503737, 0.7289825566487,
1.08429379105826, 0.692768867456301, 1.09343284866041, 0.884517387650749,
0.861435127509909, 0.842072856888276), suicide1 = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), suicide2 = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), suicide3 = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), suicide4 = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), white = c(1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), college_degree = c(1,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0), male = c(0, 1,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,
0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1,
0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0), birth_cohort = c(2,
1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1,
2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2,
2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2), not_married = c(1,
0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), no_children = c(1,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), no_siblings = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), lives_alone = c(1,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), no_church = c(0,
0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), socially_isolated = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), no_afterlife = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), unhappy = c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0)), row.names = c(NA, 100L), class = "data.frame")
dat_svy <- dat_dput |>
as_survey_design(
ids = region,
strata = year,
weights = weights,
nest = TRUE)
tbl_svysummary(dat_svy)
Upvotes: 0
Views: 41
Answers (2)
Alex Strashny
Reputation: 96
I suggest using surveytable. It's easy to use and does not cause the error.
I create the survey object using survey, to avoid messing around with tibbles. In your original data, categorical variables should be stored as factor. I convert all the variables (other than weights) to factor on the fly.
# Using survey
dat_svy <- dat_dput |>
survey::svydesign(
ids = ~region,
strata = ~year,
weights = ~weights,
data = _,
nest = TRUE)
library(surveytable)
for (nn in names(dat_svy$variables)) {
# Converting to factor on the fly.
if (nn != "weights") dat_svy$variables[,nn] = as.factor(dat_svy$variables[,nn])
set_survey(dat_svy)
print( tab(nn) )
}
The full output is too long to list here. Here are some examples:
year {dat_svy}
┌───────┬─────┬────────┬────┬────┬─────┐
│ Level │ n │ Number │ SE │ LL │ UL │
├───────┼─────┼────────┼────┼────┼─────┤
│ 1972 │ 100 │ 98 │ 56 │ 31 │ 305 │
└───────┴─────┴────────┴────┴────┴─────┘
N = 100.
region {dat_svy}
┌───────┬────┬────────┬────┬────┬─────┬─────────┬──────┬─────┬───────┐
│ Level │ n │ Number │ SE │ LL │ UL │ Percent │ SE │ LL │ UL │
├───────┼────┼────────┼────┼────┼─────┼─────────┼──────┼─────┼───────┤
│ 1 │ 8 │ 8 │ 8 │ 1 │ 88 │ 8.2 │ 10.4 │ 0 │ 72 │
├───────┼────┼────────┼────┼────┼─────┼─────────┼──────┼─────┼───────┤
│ 3 │ 61 │ 64 │ 64 │ 9 │ 477 │ 65.7 │ 28.3 │ 1.4 │ 100 │
├───────┼────┼────────┼────┼────┼─────┼─────────┼──────┼─────┼───────┤
│ 4 │ 8 │ 7 │ 7 │ 1 │ 73 │ 6.9 │ 8.9 │ 0 │ 65.7 │
├───────┼────┼────────┼────┼────┼─────┼─────────┼──────┼─────┼───────┤
│ 6 │ 7 │ 8 │ 8 │ 1 │ 91 │ 7.8 │ 10 │ 0 │ 70.2 │
├───────┼────┼────────┼────┼────┼─────┼─────────┼──────┼─────┼───────┤
│ 7 │ 16 │ 11 │ 11 │ 1 │ 101 │ 11.4 │ 14.1 │ 0 │ 83.2 │
└───────┴────┴────────┴────┴────┴─────┴─────────┴──────┴─────┴───────┘
N = 100.
weights {dat_svy}
┌─────────┬──────┬────────┬───────┐
│ % known │ Mean │ SEM │ SD │
├─────────┼──────┼────────┼───────┤
│ 100 │ 1.12 │ 0.0533 │ 0.439 │
└─────────┴──────┴────────┴───────┘
...
unhappy {dat_svy}
┌───────┬────┬────────┬────┬────┬─────┬─────────┬─────┬──────┬──────┐
│ Level │ n │ Number │ SE │ LL │ UL │ Percent │ SE │ LL │ UL │
├───────┼────┼────────┼────┼────┼─────┼─────────┼─────┼──────┼──────┤
│ 0 │ 88 │ 88 │ 50 │ 29 │ 272 │ 90.4 │ 1.7 │ 84.8 │ 94.4 │
├───────┼────┼────────┼────┼────┼─────┼─────────┼─────┼──────┼──────┤
│ 1 │ 12 │ 9 │ 6 │ 2 │ 41 │ 9.6 │ 1.7 │ 5.6 │ 15.2 │
└───────┴────┴────────┴────┴────┴─────┴─────────┴─────┴──────┴──────┘
N = 100.
Upvotes: 0
Daniel D. Sjoberg
Reputation: 11774
I am not sure exactly what is going on, but the issue is in trying to summarize the weights column: something you most often do not need to summarize is a descriptive table. (Also, the survey pkg is throwing a separate warning about the weights column, and this could be related.)
If you exclude the weights column from the summary, you get the expected table. NOTE: There are many columns that are all missing in your data frame; hence, the errors when trying to summarize those columns.)
library(gtsummary)
dat_svy <- dat_dput |>
srvyr::as_survey_design(
ids = region,
strata = year,
weights = weights,
nest = TRUE)
names(dat_dput)
#> [1] "year" "region" "weights"
#> [4] "suicide1" "suicide2" "suicide3"
#> [7] "suicide4" "white" "college_degree"
#> [10] "male" "birth_cohort" "not_married"
#> [13] "no_children" "no_siblings" "lives_alone"
#> [16] "no_church" "socially_isolated" "no_afterlife"
#> [19] "unhappy"
tbl_svysummary(
dat_svy,
include = -weights
) |>
as_kable()
#> Sample size greater than population size: are weights correctly scaled?
#> The following errors were returned during `as_kable()`:
#> ✖ For variable `no_afterlife` and "median", "p25", and "p75" statistics: error
#> in evaluating the argument 'x' in selecting a method for function 't':
#> missing value where TRUE/FALSE needed
| Characteristic | N = 98 |
|---|---|
| year | |
| 1972 | 98 (100%) |
| region | |
| 1 | 8 (8.2%) |
| 3 | 64 (66%) |
| 4 | 7 (6.9%) |
| 6 | 8 (7.8%) |
| 7 | 11 (11%) |
| suicide1 | NA (NA, NA) |
| Unknown | 98 |
| suicide2 | NA (NA, NA) |
| Unknown | 98 |
| suicide3 | NA (NA, NA) |
| Unknown | 98 |
| suicide4 | NA (NA, NA) |
| Unknown | 98 |
| white | 93 (96%) |
| college_degree | 16 (16%) |
| male | 45 (46%) |
| birth_cohort | |
| 1 | 72 (73%) |
| 2 | 26 (27%) |
| not_married | 23 (24%) |
| no_children | 30 (31%) |
| no_siblings | 4 (4.2%) |
| lives_alone | 4 (4.4%) |
| no_church | 12 (13%) |
| socially_isolated | |
| 0 | 98 (100%) |
| no_afterlife | NA (NA, NA) |
| Unknown | 98 |
| unhappy | 9 (9.6%) |
Created on 2024-12-02 with reprex v2.1.1
Upvotes: 2
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