# Example: Residential Care Community Services User (NSLTCP RCC SU) report

This example uses the National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) to replicate the estimates from a report called Residential Care Community Resident Characteristics: United States, 2018. “The survey used a sample of residential care community residents, obtained from a frame that was constructed from lists of licensed residential care communities acquired from the state licensing agencies in each of the 50 states and the District of Columbia.”

The RCC SU 2018 survey comes with the `surveytable` package, for use in examples, in an object called `rccsu2018`.

# Begin

Begin by loading the `surveytable` package.

    library(surveytable)

Now, specify the survey that you’d like to analyze.

    set_survey(rccsu2018)

Survey info {RCC SU 2018 PUF}
Variables | Observations | Design
--- | --- | ---
81 | 904 | Stratified Independent Sampling design svydesign(ids = ~1, strata = ~pufstrata2 + su_facid, fpc = ~pufpopfac2, weights = ~suwt, data = d1)

Check the survey name, survey design variables, and the number of observations to verify that it all looks correct.

For this example, we do want to turn on certain NCHS-specific options, such as identifying low-precision estimates. If you do not care about identifying low-precision estimates, you can skip this command. To turn on the NCHS-specific options:

    set_opts(mode = "NCHS")
    ## * Mode: NCHS.

Alternatively, you can combine these two commands into a single command, like so:

    set_survey(rccsu2018, mode = "NCHS")
    ## * Mode: NCHS.

Survey info {RCC SU 2018 PUF}
Variables | Observations | Design
--- | --- | ---
81 | 904 | Stratified Independent Sampling design svydesign(ids = ~1, strata = ~pufstrata2 + su_facid, fpc = ~pufpopfac2, weights = ~suwt, data = d1)

# Figure 1

This figure shows the percentage of residents by sex, race / ethnicity, and age group.

Sex.

    tab("sex")

Resident’s gender (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Male | 272 | 299 | 24 | 255 | 352 | 32.6 | 2.5 | 27.7 | 37.7
Female | 632 | 619 | 26 | 570 | 673 | 67.4 | 2.5 | 62.3 | 72.3
N = 904. Checked NCHS presentation standards. Nothing to report.

Race / ethnicity.

    var_list("race")

Variables beginning with ‘race’ {RCC SU 2018 PUF}
Variable | Class | Long name
--- | --- | ---
raceeth2 | factor | Resident’s race/ethnicity

    tab("raceeth2")

Resident’s race/ethnicity (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
White | 816 | 821 | 23 | 776 | 868 | 89.3 | 1.8 | 85.4 | 92.6 |
Black | 40 | 54 | 14 | 31 | 95 | 5.9 | 1.5 | 3.3 | 9.6 |
Hispanic | 23 | 18 | 5 | 9 | 34 | 1.9 | 0.6 | 1.0 | 3.4 |
Other | 25 | 26 | 8 | 12 | 55 | 2.8 | 0.9 | 1.3 | 5.3 | Cx
N = 904. Checked NCHS presentation standards: Cx: suppress count (and rate).

In the published figure, the Hispanic and Other categories have been merged into a single category called “Another race or ethnicity”. We can do that using the `var_collapse()` function.

    var_collapse("raceeth2"
                 , "Another race or ethnicity"
                 , c("Hispanic", "Other"))
    tab("raceeth2")

Resident’s race/ethnicity (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
White | 816 | 821 | 23 | 776 | 868 | 89.3 | 1.8 | 85.4 | 92.6
Black | 40 | 54 | 14 | 31 | 95 | 5.9 | 1.5 | 3.3 | 9.6
Another race or ethnicity | 48 | 44 | 10 | 27 | 70 | 4.8 | 1.1 | 2.9 | 7.3
N = 904. Checked NCHS presentation standards. Nothing to report.

Age group.

    var_list("age")

Variables beginning with ‘age’ {RCC SU 2018 PUF}
Variable | Class | Long name
--- | --- | ---
age2 | numeric | Resident’s age

`age2` is a numeric variable. We need to create a categorical variable based on this numeric variable. This is done using the `var_cut()` function.

    var_cut("Age", "age2"
            , c(-Inf, 64, 74, 84, Inf)
            , c("Under 65", "65-74", "75-84", "85 and over") )
    tab("Age")

Age (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Under 65 | 75 | 69 | 11 | 49 | 96 | 7.5 | 1.2 | 5.2 | 10.3
65-74 | 98 | 111 | 17 | 82 | 151 | 12.1 | 1.8 | 8.8 | 16.1
75-84 | 221 | 235 | 22 | 195 | 282 | 25.5 | 2.2 | 21.3 | 30.2
85 and over | 510 | 504 | 26 | 456 | 557 | 54.9 | 2.6 | 49.7 | 60.0
N = 904. Checked NCHS presentation standards. Nothing to report.

# Figure 2

This figure shows the percentage of residents with Medicaid, overall and by age group.

    tab("medicaid2")

Used Medicaid to pay for services (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 674 | 674 | 24 | 628 | 723 | 80.8 | 2.1 | 76.4 | 84.7
TRUE | 143 | 160 | 18 | 128 | 201 | 19.2 | 2.1 | 15.3 | 23.6
N = 817. Checked NCHS presentation standards. Nothing to report.

As we can see, for some observations, the value of this variable is unknown (it’s missing or `NA`). The above command calculates percentages based on all observations, including the ones with missing (`NA`) values. However, in the published figure, the percentages are based on the knowns only. To exclude the `NA`’s from the calculation, use the `drop_na` argument:

    tab("medicaid2", drop_na = TRUE)

Used Medicaid to pay for services (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 674 | 674 | 24 | 628 | 723 | 80.8 | 2.1 | 76.4 | 84.7
TRUE | 143 | 160 | 18 | 128 | 201 | 19.2 | 2.1 | 15.3 | 23.6
N = 817. Checked NCHS presentation standards. Nothing to report.

Note that the table title alerts you to the fact that you are using known values only.

By age group:

    tab_subset("medicaid2", "Age", drop_na = TRUE)

Used Medicaid to pay for services (Age = Under 65) (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 31 | 30 | 8 | 17 | 56 | 49.8 | 9.5 | 30.3 | 69.4 | Px
TRUE | 35 | 31 | 8 | 18 | 52 | 50.2 | 9.5 | 30.6 | 69.7 | Px
N = 66. Checked NCHS presentation standards: Px: suppress percent.

Used Medicaid to pay for services (Age = 65-74) (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 53 | 55 | 11 | 37 | 83 | 62 | 7.6 | 45.4 | 76.7 | Px
TRUE | 30 | 34 | 8 | 20 | 58 | 38 | 7.6 | 23.3 | 54.6 | Px
N = 83. Checked NCHS presentation standards: Px: suppress percent.

Used Medicaid to pay for services (Age = 75-84) (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 163 | 167 | 18 | 134 | 208 | 79.1 | 4.5 | 68.6 | 87.3
TRUE | 33 | 44 | 11 | 26 | 75 | 20.9 | 4.5 | 12.7 | 31.4
N = 196. Checked NCHS presentation standards. Nothing to report.

Used Medicaid to pay for services (Age = 85 and over) (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 427 | 421 | 23 | 378 | 469 | 89.1 | 2.2 | 83.8 | 93.1
TRUE | 45 | 52 | 11 | 33 | 81 | 10.9 | 2.2 | 6.9 | 16.2
N = 472. Checked NCHS presentation standards. Nothing to report.

Note that according to the NCHS presentation criteria, some of the percentages should be suppressed.

# Figure 4

(Figure 3 is slightly more involved, so we’ll do it next.)

- This figure shows the percentage of residents who have one of a select set of chronic conditions.
- In addition, it shows the distribution of residents by the number of conditions.

Here’s a table for high blood pressure.

    tab("hbp")

Resident diagnosed with high blood pressure (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 397 | 404 | 25 | 357 | 457 | 44.8 | 2.6 | 39.7 | 50.0
TRUE | 481 | 498 | 26 | 449 | 552 | 55.2 | 2.6 | 50.0 | 60.3
N = 878. Checked NCHS presentation standards. Nothing to report.

Once again, unknown values (`NA`) are present, while the figure is based on knowns only. Therefore, we again will use the `drop_na` argument:

    tab("hbp", "alz", "depress", "arth", "diabetes", "heartdise", "osteo"
        , "copd", "stroke", "cancer"
        , drop_na = TRUE)

Resident diagnosed with high blood pressure (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 397 | 404 | 25 | 357 | 457 | 44.8 | 2.6 | 39.7 | 50.0
TRUE | 481 | 498 | 26 | 449 | 552 | 55.2 | 2.6 | 50.0 | 60.3
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with Alzheimer’s/dementia (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 538 | 598 | 26 | 549 | 651 | 66.3 | 2.1 | 62.0 | 70.5
TRUE | 340 | 304 | 19 | 268 | 344 | 33.7 | 2.1 | 29.5 | 38.0
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with depression (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 629 | 654 | 24 | 609 | 703 | 72.5 | 2.1 | 68.1 | 76.6
TRUE | 249 | 248 | 20 | 211 | 292 | 27.5 | 2.1 | 23.4 | 31.9
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with arthritis (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 683 | 717 | 26 | 668 | 770 | 79.5 | 2 | 75.3 | 83.3
TRUE | 195 | 185 | 18 | 152 | 224 | 20.5 | 2 | 16.7 | 24.7
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with diabetes (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 719 | 718 | 23 | 675 | 765 | 79.6 | 2.1 | 75.3 | 83.6
TRUE | 159 | 184 | 20 | 148 | 227 | 20.4 | 2.1 | 16.4 | 24.7
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with heart disease (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 739 | 746 | 25 | 697 | 798 | 82.7 | 1.8 | 78.7 | 86.2
TRUE | 139 | 156 | 17 | 126 | 193 | 17.3 | 1.8 | 13.8 | 21.3
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with osteoporosis (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 766 | 794 | 24 | 749 | 842 | 88 | 1.4 | 84.9 | 90.7
TRUE | 112 | 108 | 13 | 85 | 137 | 12 | 1.4 | 9.3 | 15.1
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with COPD (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 779 | 806 | 24 | 759 | 856 | 89.4 | 1.6 | 85.9 | 92.3
TRUE | 99 | 96 | 14 | 71 | 129 | 10.6 | 1.6 | 7.7 | 14.1
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with stroke (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 789 | 807 | 23 | 764 | 853 | 89.5 | 1.5 | 86.1 | 92.4
TRUE | 89 | 94 | 14 | 70 | 128 | 10.5 | 1.5 | 7.6 | 13.9
N = 878. Checked NCHS presentation standards. Nothing to report.

Resident diagnosed with cancer (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
FALSE | 806 | 824 | 23 | 780 | 871 | 91.4 | 1.6 | 87.7 | 94.2
TRUE | 72 | 78 | 14 | 53 | 114 | 8.6 | 1.6 | 5.8 | 12.3
N = 878. Checked NCHS presentation standards. Nothing to report.

## Advanced variable editing

- `surveytable` provides a number of functions to create or modify survey variables.

- We saw a couple of these above: `var_collapse()` and `var_cut()`.

- Occasionally, you might need to do advanced variable editing. Here’s how:

- Every survey object has an element called `variables`

- This is a data frame where the survey’s variables are located

    class(rccsu2018$variables)
    ## [1] "data.frame"

1. Create a new variable in the `variables` data frame (which is part of the survey object).
2. Call `set_survey()` again. Any time you modify the `variables` data frame, call `set_survey()`.
3. Tabulate the new variable.

We go through these steps to count how many chronic conditions were present.

    rccsu2018$variables$num_cc = 0
    for (vr in c("hbp", "alz", "depress", "arth", "diabetes", "heartdise", "osteo"
                 , "copd", "stroke", "cancer")) {
      idx = which(rccsu2018$variables[,vr])
      rccsu2018$variables$num_cc[idx] = rccsu2018$variables$num_cc[idx] + 1
    }
    set_survey(rccsu2018, mode = "NCHS")
    ## * Mode: NCHS.

Survey info {RCC SU 2018 PUF}
Variables | Observations | Design
--- | --- | ---
82 | 904 | Stratified Independent Sampling design svydesign(ids = ~1, strata = ~pufstrata2 + su_facid, fpc = ~pufpopfac2, weights = ~suwt, data = d1)

`num_cc` is a numeric variable with the number of chronic conditions. The published figure uses a categorical variable which is based on this numeric variable. Use `var_cut()`, which converts numeric variables to categorical (`factor`) variables.

    var_cut("Number of chronic conditions", "num_cc"
            , c(-Inf, 0, 1, 3, 10, Inf)
            , c("0", "1", "2-3", "4-10", "??"))
    tab("Number of chronic conditions")

Number of chronic conditions (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | 121 | 140 | 19 | 106 | 184 | 15.2 | 2.0 | 11.5 | 19.6
1 | 189 | 180 | 17 | 148 | 218 | 19.5 | 1.9 | 16.0 | 23.5
2-3 | 446 | 444 | 23 | 400 | 492 | 48.3 | 2.4 | 43.5 | 53.1
4-10 | 148 | 156 | 17 | 125 | 194 | 16.9 | 1.8 | 13.5 | 20.8
N = 904. Checked NCHS presentation standards. Nothing to report.

# Figure 3

- This figure shows the percentage of residents who need help with one of the activities of daily living (ADLs).
- In addition, it shows the distribution of residents by the number of ADLs with which they need help.

Here’s a table for `bathhlp` (help with bathing):

    tab("bathhlp")

Type of assistance resident needs to bathe (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
MISSING | 22 | 10 | 2 | 6 | 17 | 1.1 | 0.3 | 0.6 | 2.1 |
NEED HELP OR SUPERVISION FROM ANOTHER PERSON | 551 | 581 | 25 | 534 | 633 | 63.3 | 2.3 | 58.7 | 67.7 |
USE OF AN ASSISTIVE DEVICE | 11 | 7 | 2 | 3 | 15 | 0.7 | 0.3 | 0.3 | 1.5 | Cx
BOTH | 127 | 113 | 15 | 87 | 148 | 12.4 | 1.6 | 9.4 | 15.9 |
NEED NO ASSISTANCE | 193 | 207 | 18 | 173 | 247 | 22.5 | 2.0 | 18.7 | 26.6 |
N = 904. Checked NCHS presentation standards: Cx: suppress count (and rate).

This variable has multiple levels.

- Several of these levels correspond to a resident needing help,
- One level (`"NEED NO ASSISTANCE"`) = does not need help
- One level (`"MISSING"`) = unknown

We want to show (resident needing help) as a percentage of knowns only (that is, excluding the unknowns).

To do this, convert the variable to having 2 levels (needs help / does not need help) plus `NA` (for unknown); then use the `drop_na` argument to base percentages on knowns only.

    for (vr in c("bathhlp", "walkhlp", "dreshlp", "transhlp", "toilhlp", "eathlp")) {
      var_collapse(vr
        , "Needs assistance"
        , c("NEED HELP OR SUPERVISION FROM ANOTHER PERSON"
          , "USE OF AN ASSISTIVE DEVICE"
          , "BOTH"))
      var_collapse(vr, NA, "MISSING")
    }

    tab("bathhlp", "walkhlp", "dreshlp", "transhlp", "toilhlp", "eathlp", drop_na = TRUE)

Type of assistance resident needs to bathe (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Needs assistance | 689 | 702 | 25 | 654 | 752 | 77.2 | 2 | 73.1 | 81.1
NEED NO ASSISTANCE | 193 | 207 | 18 | 173 | 247 | 22.8 | 2 | 18.9 | 26.9
N = 882. Checked NCHS presentation standards. Nothing to report.

Type of assistance resident needs for locomotion (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Needs assistance | 622 | 625 | 24 | 578 | 675 | 68.9 | 2.3 | 64.2 | 73.4
NEED NO ASSISTANCE | 253 | 281 | 22 | 241 | 329 | 31.1 | 2.3 | 26.6 | 35.8
N = 875. Checked NCHS presentation standards. Nothing to report.

Type of assistance resident needs to dress (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Needs assistance | 527 | 561 | 25 | 513 | 614 | 61.7 | 2.3 | 57.1 | 66.2
NEED NO ASSISTANCE | 355 | 348 | 22 | 308 | 393 | 38.3 | 2.3 | 33.8 | 42.9
N = 882. Checked NCHS presentation standards. Nothing to report.

Type of assistance resident needs to transfer in/out of chair (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Needs assistance | 463 | 464 | 24 | 418 | 515 | 51 | 2.4 | 46.1 | 55.8
NEED NO ASSISTANCE | 420 | 446 | 24 | 400 | 496 | 49 | 2.4 | 44.2 | 53.9
N = 883. Checked NCHS presentation standards. Nothing to report.

Type of assistance resident needs to use bathroom (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Needs assistance | 437 | 443 | 24 | 398 | 493 | 48.7 | 2.4 | 43.8 | 53.5
NEED NO ASSISTANCE | 447 | 467 | 25 | 421 | 518 | 51.3 | 2.4 | 46.5 | 56.2
N = 884. Checked NCHS presentation standards. Nothing to report.

Type of assistance resident needs to eat (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Needs assistance | 257 | 240 | 21 | 200 | 286 | 26.3 | 2.3 | 21.9 | 31.1
NEED NO ASSISTANCE | 628 | 671 | 26 | 622 | 724 | 73.7 | 2.3 | 68.9 | 78.1
N = 885. Checked NCHS presentation standards. Nothing to report.

Now, go through the “advanced variable editing” steps – very similar to Figure 4 – to count how many ADLs were present.

    rccsu2018$variables$num_adl = 0
    for (vr in c("bathhlp", "walkhlp", "dreshlp", "transhlp", "toilhlp", "eathlp")) {
      idx = which(rccsu2018$variables[,vr] %in%
        c("NEED HELP OR SUPERVISION FROM ANOTHER PERSON"
          , "USE OF AN ASSISTIVE DEVICE"
          , "BOTH"))
      rccsu2018$variables$num_adl[idx] = rccsu2018$variables$num_adl[idx] + 1
    }
    set_survey(rccsu2018, mode = "NCHS")
    ## * Mode: NCHS.

Survey info {RCC SU 2018 PUF}
Variables | Observations | Design
--- | --- | ---
83 | 904 | Stratified Independent Sampling design svydesign(ids = ~1, strata = ~pufstrata2 + su_facid, fpc = ~pufpopfac2, weights = ~suwt, data = d1)

For generating the figure, create a categorical variable based on `num_adl`, which is numeric.

    var_cut("Number of ADLs", "num_adl"
            , c(-Inf, 0, 2, 6, Inf)
            , c("0", "1-2", "3-6", "??"))
    tab("Number of ADLs")

Number of ADLs (knowns only) {RCC SU 2018 PUF}
Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | 131 | 114 | 12 | 92 | 142 | 12.4 | 1.3 | 9.9 | 15.4
1-2 | 218 | 249 | 22 | 209 | 297 | 27.1 | 2.2 | 22.8 | 31.8
3-6 | 555 | 555 | 25 | 508 | 606 | 60.4 | 2.4 | 55.6 | 65.1
N = 904. Checked NCHS presentation standards. Nothing to report.