surveytable is an R package for conveniently tabulating
estimates from complex surveys.
- If you deal with survey objects in R (created with
survey::svydesign()), then this package is for you. - Works with complex surveys (data systems that involve survey design variables, like weights and strata).
- Works with unweighted data as well.
Preliminaries
Concepts
There are two important concepts that we need to learn and distinguish:
- A data frame is a standard way of storing data in R. A data frame is rectangular data. Variables are in columns, observations are in rows. Example:
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosaA data frame, in an of itself, cannot represent a complex survey. This is because, just by looking at a data frame, R does not know what the sampling weights are, what the strata are, etc. Even if the variables that represent the sampling weights, etc, are part of the data frame, just by looking at the data frame, R does not know which variable represents the weights or other survey design variables.
You can get a data frame into R in many different ways. If your data
is currently in a comma-separated values (CSV) file, you can use
read.csv(). If it’s in a SAS file, you can use a package
like haven or importsurvey. If it’s
already in R format, use readRDS(), and so on.
- A survey object is an object that describes a
survey. It tells R what the sampling weights are, what the strata are,
and so on. A data frame can be converted into a survey object using the
survey::svydesign()function; if a survey uses replicate weights, thesurvey::svrepdesign()function should be used.
Generally speaking, you only need to convert a data frame to a survey
object once. After it has been converted, you can save it with
saveRDS() (or similar). In the future, you can load it with
readRDS(). You do not need to re-convert a data frame to a
survey object every time.
NAMCS
Examples in this tutorial use a survey called the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF). NAMCS is “an annual nationally representative sample survey of visits to non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists.” Note that the unit of observation is visits, not patients – this distinction is important since a single patient can make multiple visits.
The surveytable package comes with a data frame of
selected variables from NAMCS, called namcs2019sv_df
(sv = selected variables; df = data frame).
The survey object of this survey is called namcs2019sv.
namcs2019sv is the object that we analyze. You really
only need namcs2019sv. The reason that the package has
namcs2019sv_df is to illustrate how to convert the data
frame to the survey object.
More concepts
When importing data from another source, such as SAS or CSV, analysts should be aware of the standard way in which variables are handled in R.
- Specifically, categorical variables should be stored as
factor. - While true / false variables could be stored as
factoras well, some programming tasks are easier if they are stored aslogical. - Unknown values should be stored as missing (
NA). If a variable contains “special values”, such as a negative value indicating that the age is missing, those “special values” need to be converted toNA.
Variables in namcs2019sv_df are already stored
correctly. Thus,
-
AGER(patient’s age group) is afactorvariable; -
PAYNOCHG(which indicates whether there was no charge for the physician visit) is alogicalvariable; and -
AGE(patient’s age in years) is anumericvariable.
Create a survey object
As seen below, tables produced by surveytable are
clearer if either the variable names themselves are descriptive, or if
the variables have the "label" attribute that is
descriptive. In namcs2019sv_df, all variables already have
the "label" attribute set. For example, while the variable
name AGE itself is not very descriptive, the variable does
have a more descriptive "label" attribute:
attr(namcs2019sv_df$AGE, "label")
#> [1] "Patient age in years (raw - use caution)"Documentation for the NAMCS survey provides the names of the survey design variables. Specifically, in NAMCS,
- cluster ID’s, also known as primary sampling units (PSU’s), are
given in
CPSUM; - strata are given in
CSTRATM; and - sampling weights are given in
PATWT.
Thus, the namcs2019sv_df data frame can be turned into a
survey object as follows:
mysurvey = survey::svydesign(ids = ~ CPSUM
, strata = ~ CSTRATM
, weights = ~ PATWT
, data = namcs2019sv_df)Tables produced by surveytable are clearer if either the
name of the survey object is descriptive, or if the object has the
"label" attribute that is descriptive. Let’s set this
attribute for mysurvey:
attr(mysurvey, "label") = "NAMCS 2019 PUF"The mysurvey object should now be the same as
namcs2019sv. Let’s verify this:
all.equal(namcs2019sv, mysurvey)
#> [1] TRUEWe have just successfully created a survey object from a data frame.
Begin analysis
First, specify the survey object that you’d like to analyze.
library(surveytable)
set_survey(namcs2019sv)| Survey info {NAMCS 2019 PUF} | ||
| Variables | Observations | Design |
|---|---|---|
Check the survey label, 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.List variables
The var_list() function lists the variables in the
survey. To avoid unintentionally listing all the variables in a survey,
which can be many, the starting characters of variable names are
specified. For example, to list the variables that start with the
letters age, type:
var_list("age")| Variables beginning with ‘age’ {NAMCS 2019 PUF} | ||
| Variable | Class | Long name |
|---|---|---|
The table lists
- variable name;
- class, which is the type of variable; and
- variable label, which is the long name of the variable.
Common classes are factor (categorical variable),
logical (yes / no variable), and numeric.
Tabulate categorical and logical variables
The main function of the surveytable package is
tab(), which tabulates variables. It operates on
categorical and logical variables, and presents both estimated counts,
with their standard errors (SEs) and 95% confidence intervals (CIs), and
percentages, with their SEs and CIs. For example, to tabulate
AGER, type:
tab("AGER")| Patient age recode {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
The table title shows the variable label (the long variable name) and the survey label.
For each level of the variable, the table shows:
- the estimated count, its standard error, and its 95% confidence interval; and
- the estimated percentage, its standard error, and its 95% confidence interval.
Low-precision estimates. Optionally, the
tab() function, as well as the other tabulation functions
that are discussed below, can automatically identify low-precision
estimates using algorithms developed at NCHS. For counts, rates, and
percentages, the functions flag estimates if, according to the
algorithms, they should not be presented, should be reviewed by a
clearance official, or should be presented with a footnote. If no
estimates are flagged by the checks, the table has a footnote that
indicates this. If the checks do identify an estimate, that is denoted
in an additional column and in the table footnote.
Turn on this functionality using any of the following:
set_opts(lpe = TRUE), set_opts(mode = "nchs"),
set_survey(*, mode = "nchs"), or
options(surveytable.find_lpe = TRUE).
As an example, let’s tabulate PAYNOCHG:
tab("PAYNOCHG")| Expected source of payment for visit: No Charge/Charity {NAMCS 2019 PUF} | ||||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags |
|---|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards: Cx: suppress count (and rate). | ||||||||||
This table tells us that the estimated number of visits in which there was no charge for the visit has low precision. Intuitively, we can see that the CI for this count estimate is very wide, indicating high uncertainty.
The CIs that are displayed are the ones that are used by the NCHS presentation standards. Specifically, for counts, the tables show the log Student’s t 95% CI, with adaptations for complex surveys; for percentages, they show the 95% Korn and Graubard CI.
Drop missing values. Some variables might contain
missing values (NA). Consider the following variable, which
is not part of the actual survey, but was constructed specifically for
this example:
tab("SPECCAT.bad")| Type of specialty (BAD - do not use) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
To calculate percentages based on the non-missing values only, use
the drop_na argument:
tab("SPECCAT.bad", drop_na = TRUE)| Type of specialty (BAD - do not use) (knowns only) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 6600. Checked NCHS presentation standards. Nothing to report. | |||||||||
The above table gives percentages based only on the knowns, that is,
based only on non-NA values.
Multiple tables. Multiple tables can be created with a single command:
tab("MDDO", "SPECCAT", "MSA")| Type of doctor (MD or DO) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
Entire population
Estimate the total count for the entire population using the
total() command:
total()| Total {NAMCS 2019 PUF} | ||||
| n | Number (000) | SE (000) | LL (000) | UL (000) |
|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | ||||
Subsets or interactions
To create a table of AGER for each value of the variable
SEX, type:
tab_subset("AGER", "SEX")| Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 4609. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 3641. Checked NCHS presentation standards. Nothing to report. | |||||||||
In addition to giving the long name of the variable being tabulated,
the title of each table reflects the value of the subsetting variable
(in this case, either Female or Male).
With the tab_subset() command, in each table (that is,
in each subset), the percentages add up to 100%.
The tab_cross() function is similar – it crosses or
interacts two variables and generates a table using this new variable.
Thus, to create a table of the interaction of AGER and
SEX, type:
tab_cross("AGER", "SEX")| (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
While the estimated counts produced by tab_subset() and
tab_cross() are the same, the percentages are
different.
- With the
tab_subset()command, within each table (that is, within each subset), the percentages add up to 100%. - On the other hand, with
tab_cross(), the percentages across the entire population add up to 100%.
Tabulate numeric variables
The tab() and tab_subset() functions also
work with numeric variables, though with such variables, the output is
different. To tabulate NUMMED (number of medications), a
numeric variable, type:
tab("NUMMED")| Number of medications coded {NAMCS 2019 PUF} | |||
| % known | Mean | SEM | SD |
|---|---|---|---|
As before, the table title shows the variable label (the long variable name) and the survey label.
The table shows the percentage of values that are not missing (not
NA), the mean, the standard error of the mean (SEM), and
the standard deviation (SD).
Subsetting works too:
tab_subset("NUMMED", "AGER")| Number of medications coded (for different levels of Patient age recode) {NAMCS 2019 PUF} | ||||
| Level | % known | Mean | SEM | SD |
|---|---|---|---|---|
Perform statistical hypothesis testing
The tab_subset() function makes it easy to perform
hypothesis testing by using the test argument. When the
argument is TRUE, a test of association is performed. In
addition, t-tests for all pairs of levels are performed as well.
Categorical variables
Consider the relationship between AGER an
SPECCAT:
tab_subset("AGER", "SPECCAT", test = TRUE)| Patient age recode (Type of specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 2993. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Patient age recode (Type of specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 3050. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Patient age recode (Type of specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 2207. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Association between Patient age recode and Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |
| p-value | Flag |
|---|---|
| Pearson’s X^2: Rao & Scott adjustment. *: p <= 0.05 | |
| Comparison of all possible pairs of Patient age recode (Type of specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Patient age recode (Type of specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Patient age recode (Type of specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (Patient age recode = Under 15 years) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (Patient age recode = 15-24 years) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (Patient age recode = 25-44 years) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (Patient age recode = 45-64 years) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (Patient age recode = 65-74 years) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (Patient age recode = 75 years and over) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
According to these tables, there is an association between physician specialty type and patient age. For instance, for patients under 15 years, there is a statistical difference between primary care physician specialty and medical care specialty. But for older patients, such as in the 45-64 age group, there is no statistical difference between the two specialty types.
As another example, consider the relationship between
MRI and SPECCAT:
tab_subset("MRI", "SPECCAT", test = TRUE)| MRI (Type of specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} | ||||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags |
|---|---|---|---|---|---|---|---|---|---|---|
| N = 2993. Checked NCHS presentation standards: Cx: suppress count (and rate). | ||||||||||
| MRI (Type of specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 3050. Checked NCHS presentation standards. Nothing to report. | |||||||||
| MRI (Type of specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} | ||||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags |
|---|---|---|---|---|---|---|---|---|---|---|
| N = 2207. Checked NCHS presentation standards: Cx: suppress count (and rate); Px: suppress percent; Pc: footnote percent - complement. | ||||||||||
| Association between MRI and Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |
| p-value | Flag |
|---|---|
| Pearson’s X^2: Rao & Scott adjustment. *: p <= 0.05 | |
| Comparison of all possible pairs of MRI (Type of specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of MRI (Type of specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of MRI (Type of specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (MRI = FALSE) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) (MRI = TRUE) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
According to these tables, there is no statistical association between MRI and physician specialty. For each of the 3 specialty types, a minority of visits have MRI’s. For the visits with MRI’s, there was no statistical difference between specialty types.
As a general rule of thumb, since there is no statistical association
between MRI and physician specialty, presenting this tabulation would
not be particularly interesting, especially since the subsetting
decreases the sample size and therefore also decreases the estimate
reliability. Instead, it would generally make more sense to just
tabulate MRI without subsetting by
SPECCAT.
Numeric variables
The relationship between NUMMED and
AGER:
tab_subset("NUMMED", "AGER", test = TRUE)| Number of medications coded (for different levels of Patient age recode) {NAMCS 2019 PUF} | ||||
| Level | % known | Mean | SEM | SD |
|---|---|---|---|---|
| Association between Number of medications coded and Patient age recode {NAMCS 2019 PUF} | |
| p-value | Flag |
|---|---|
| Wald test. *: p <= 0.05 | |
| Comparison of Number of medications coded across all possible pairs of Patient age recode {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
According to these tables, there is an association between the number
of medications and age category. NUMMED is statistically
similar for the “Under 15 years” and “15-24 years” AGER
categories. It is statistically different for all other pairs of age
categories.
Finally, let’s look at the relationship between NUMMED
and SPECCAT:
tab_subset("NUMMED", "SPECCAT", test = TRUE)| Number of medications coded (for different levels of Type of specialty (Primary, Medical, Surgical)) {NAMCS 2019 PUF} | ||||
| Level | % known | Mean | SEM | SD |
|---|---|---|---|---|
| Association between Number of medications coded and Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |
| p-value | Flag |
|---|---|
| Wald test. *: p <= 0.05 | |
| Comparison of Number of medications coded across all possible pairs of Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
According to these tables, there is no association between the number
of medications and physician specialty type. NUMMED is
statistically similar for all pairs of physician specialties.
As a general rule of thumb, since there is no statistical association
between the number of medications and physician specialty, presenting
this tabulation would not be particularly interesting, especially since
the subsetting decreases the sample size and therefore also decreases
the estimate reliability. Instead, it would generally make more sense to
just tabulate NUMMED without subsetting by
SPECCAT.
Categorical variables (single variable)
To test whether any pair of SPECCAT levels is
statistically similar or different, type:
tab("SPECCAT", test = TRUE)| Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Comparison of all possible pairs of Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} | |||
| Level 1 | Level 2 | p-value | Flag |
|---|---|---|---|
| Design-based t-test. *: p <= 0.05 | |||
According to this, surgical and medical care specialties are statistically similar, and are statistically different from primary care.
Calculate rates
A rate is a ratio of count estimates based on the survey in question
divided by population size, which is assumed to be known. For example,
the number of physician visits per 100 people in the population is a
rate: the number of physician visits is estimated from the
namcs2019sv survey, while the number of people in the
population comes from another source.
To calculate rates, in addition to the survey, we need a source of
information on population size. You would typically use a function such
as read.csv() to load the population figures and get them
into the correct format. The surveytable package comes with
an object called uspop2019 that contains several population
figures for use in these examples.
Let’s examine uspop2019:
class(uspop2019)
#> [1] "list"
names(uspop2019)
#> [1] "total" "MSA" "AGER" "Age group" "SEX"
#> [6] "AGER x SEX" "Age group 5"The overall population size for the country as a whole is:
uspop2019$total
#> [1] 323186697Once we have the overall population size, the overall rate is:
total_rate(uspop2019$total)| Total (rate per 100 population) {NAMCS 2019 PUF} | ||||
| n | Rate | SE | LL | UL |
|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | ||||
To calculate the rates for a particular variable, we need to provide
a data frame with a column called Level that matches the
levels of the variable in the survey, and a column called
Population that gives the size of the population for that
level.
For example, for AGER, this data frame as follows:
uspop2019$AGER
#> Level Population
#> 1 Under 15 years 60526656
#> 2 15-24 years 41718700
#> 3 25-44 years 85599410
#> 4 45-64 years 82562049
#> 5 65-74 years 31260202
#> 6 75 years and over 21519680Now that we have the appropriate population figures, the rates table is obtained by typing:
tab_rate("AGER", uspop2019$AGER)| Patient age recode (rate per 100 population) {NAMCS 2019 PUF} | |||||
| Level | n | Rate | SE | LL | UL |
|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||
To calculate the rates for one variable (AGER) by
another variable (SEX), we need population figures in the
following format:
uspop2019$`AGER x SEX`
#> Level Subset Population
#> 1 Under 15 years Female 29604762
#> 2 15-24 years Female 20730118
#> 3 25-44 years Female 43192143
#> 4 45-64 years Female 42508901
#> 5 65-74 years Female 16673240
#> 6 75 years and over Female 12421444
#> 7 Under 15 years Male 30921894
#> 8 15-24 years Male 20988582
#> 9 25-44 years Male 42407267
#> 10 45-64 years Male 40053148
#> 11 65-74 years Male 14586962
#> 12 75 years and over Male 9098236With this data frame, the rates table is obtained by typing:
tab_subset_rate("AGER", "SEX", uspop2019$`AGER x SEX`)| Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} | |||||
| Level | n | Rate | SE | LL | UL |
|---|---|---|---|---|---|
| N = 4609. Checked NCHS presentation standards. Nothing to report. | |||||
| Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF} | |||||
| Level | n | Rate | SE | LL | UL |
|---|---|---|---|---|---|
| N = 3641. Checked NCHS presentation standards. Nothing to report. | |||||
Create or modify variables
In some situations, it might be necessary to modify survey variables, or to create new ones. This section describes how to do this.
Convert factor to logical. The variable
MAJOR (major reason for this visit) has several levels.
tab("MAJOR")| Major reason for this visit {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
Notice that one of the levels is called
"Preventive care". Suppose an analyst is only interested in
whether or not a visit is a preventive care visit – they are not
interested in the other visit types. They can create a new variable
called Preventive care visits that is TRUE for
preventive care visits and FALSE for all other types of
visits, as follows:
| Preventive care visits {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
This creates a logical variable that is TRUE for
preventive care visits and then tabulates it. When using the
var_case() function, specify the name of the new logical
variable to be created, an existing factor variable, and one or more
levels of the factor variable that should be set to TRUE in
the logical variable.
Thus, if an analyst is interested in surgery-related visits, which
are indicated by two different levels of MAJOR, they could
type:
var_case("Surgery-related visits"
, "MAJOR"
, c("Pre-surgery", "Post-surgery"))
tab("Surgery-related visits")| Surgery-related visits {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
Collapse levels. The variable PRIMCARE
(whether the physician is this patient’s primary care provider) has
levels Unknown and Blank, among others.
tab("PRIMCARE")| Are you the patient’s primary care provider? {NAMCS 2019 PUF} | ||||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL | Flags |
|---|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards: Cx: suppress count (and rate). | ||||||||||
To collapse Unknown and Blank into a single
level, type:
var_collapse("PRIMCARE", "Unknown if PCP", c("Unknown", "Blank"))
tab("PRIMCARE")| Are you the patient’s primary care provider? {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
Convert numeric to factor. The variable
AGE is numeric.
tab("AGE")| Patient age in years (raw - use caution) {NAMCS 2019 PUF} | |||
| % known | Mean | SEM | SD |
|---|---|---|---|
To create a new variable of age categories based on AGE,
type:
var_cut("Age group"
, "AGE"
, c(-Inf, -0.1, 0, 4, 14, 64, Inf)
, c(NA, "Under 1", "1-4", "5-14", "15-64", "65 and over"))
tab("Age group")| Age group {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
In the var_cut() command, specify the following
information:
- name of the new categorical variable;
- name of the existing numeric variable;
- cut points – note that the intervals are inclusive on the right;
and
- category labels.
Be cognizant of any “special values” that the numeric variable might
have. In some data systems, negative values indicate unknowns, which
should be coded as NA. That’s what we do here – any value
between -Inf and -0.1 gets coded as missing
(NA). Though in this particular data, there are no unknowns
and no “special values”.
Check whether any variable is true. For a series of
logical variables, you can check whether any of them are
TRUE using the var_any() command.
A physician visit is considered to be an “imaging services” visit if
it had any of a number of imaging services ordered or provided. Imaging
services are indicated using logical variables, such as MRI
and XRAY. To create the Imaging services
variable, type:
var_any("Imaging services"
, c("ANYIMAGE", "BONEDENS", "CATSCAN", "ECHOCARD", "OTHULTRA"
, "MAMMO", "MRI", "XRAY", "OTHIMAGE"))
tab("Imaging services")| Imaging services {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
Interact variables. The tab_cross()
function creates a table of an interaction of two variables, but it does
not save the interacted variable. To create the interacted variable, use
the var_cross() command:
var_cross("Age x Sex", "AGER", "SEX")Specify the name of the new variable as well as names of the two variables to interact.
Copy a variable. Create a new variable that is a
copy of another variable using var_copy(). You can modify
the copy, while the original remains unchanged. For example:
var_copy("Age group", "AGER")
#> Warning in var_copy("Age group", "AGER"): Age group: overwriting a variable
#> that already exists.
var_collapse("Age group", "65+", c("65-74 years", "75 years and over"))
var_collapse("Age group", "25-64", c("25-44 years", "45-64 years"))
tab("AGER", "Age group")| Patient age recode {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
| Age group {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
Here, the AGER variable remains unchanged, while the
Age group variable has fewer categories.
Save the output
The tab* and total* functions have an
argument called csv that specifies the name of a
comma-separated values (CSV) file to save the output to. Alternatively,
you can name the default CSV output file using the
set_opts() function. For example, the following directs
surveytable to send all future output to a CSV file, create
some tables, and then turn off sending output to the file:
set_opts(csv = "output.csv")
tab("MDDO")| Type of doctor (MD or DO) {NAMCS 2019 PUF} | |||||||||
| Level | n | Number (000) | SE (000) | LL (000) | UL (000) | Percent | SE | LL | UL |
|---|---|---|---|---|---|---|---|---|---|
| N = 8250. Checked NCHS presentation standards. Nothing to report. | |||||||||
set_opts(csv = "")
#> * Turning off CSV output.If the tabulation functions are called from within an R Markdown
notebook or a Quarto document, they produce HTML or PDF tables, as
appropriate. This makes it easy to incorporate the output of the
surveytable package directly into documents, presentations,
“shiny” web apps, and other output types. See
vignette("Printing-HTML") for details.
Finally, the tabulation functions return the tables that they
produce. More advanced analysts can use this functionality to integrate
surveytable into other programming tasks.