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

Begin

Begin by loading the surveytable package. When you do, it will print a message explaining how to specify the survey that you’d like to analyze.

library(surveytable)

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

set_survey(rccsu2018)
## * Mode: General.

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_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 {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 {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	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 {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 {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
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 {RCC SU 2018 PUF}
Level	n	Number (000)	SE (000)	LL (000)	UL (000)	Percent	SE	LL	UL
FALSE	674	674	24	628	723	73.3	2.1	68.9	77.4
TRUE	143	160	18	128	201	17.5	1.9	13.9	21.5
<N/A>	87	85	11	64	111	9.2	1.2	6.9	11.9
N = 904. 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	45.5	76.7	Px
TRUE	30	34	8	20	58	38	7.6	23.3	54.5	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 {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	2.5	38.9	49.1
TRUE	481	498	26	449	552	54.2	2.6	49	59.3
<N/A>	26	17	4	10	28	1.8	0.4	1.1	2.9
N = 904. 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
TRUE	481	498	26	449	552	55.2	2.6	50	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	70.5
TRUE	340	304	19	268	344	33.7	2.1	29.5	38
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"

Create a new variable in the variables data frame (which is part of the survey object).
Call set_survey() again. Any time you modify the variables data frame, call set_survey().
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 {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	11.5	19.6
1	189	180	17	148	218	19.5	1.9	16	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 {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	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 {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.