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Printing (HTML)

Source: vignettes/Printing-HTML.Rmd
Printing-HTML.Rmd

Basic printing

If a tabulation function is called from the top level, it should print out its table(s) on its own.

As usual, first, let’s start up the package and pick a survey to analyze:

Survey info {NAMCS 2019 PUF}
Variables Observations Design
33 8,250 Stratified 1 - level Cluster Sampling design (with replacement)
With (398) clusters.
namcs2019sv = survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT
, data = namcs2019sv_df)

Now, when a tabulation function is called from the top level, it prints. You don’t need to do anything extra.

tab("AGER")
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20   1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

If that tabulation function is called not from the top level, such as from within a loop or another function, you need to call print() explicitly for it to print. For example:

for (vr in c("AGER", "SEX")) {
  print( tab_subset(vr, "MAJOR", "Preventive care") )
}
Patient age recode (Major reason for this visit = Preventive care) {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 300 50,700,892 8,555,609 36,351,714 70,714,146 22.7 3.5 16.1 30.4
15-24 years 121 18,196,389 2,888,616 13,246,305 24,996,296 8.1 1.2 5.9 10.9
25-44 years 370 50,573,223 6,834,740 38,749,084 66,005,455 22.6 2.5 17.8 28  
45-64 years 355 53,804,610 9,477,599 37,982,129 76,218,371 24.1 3.2 17.9 31.1
65-74 years 225 27,985,400 4,668,693 20,072,754 39,017,198 12.5 1.8 9.2 16.5
75 years and over 197 22,363,158 3,804,827 15,925,231 31,403,678 10   1.7 6.9 13.8
N = 1568.
Patient sex (Major reason for this visit = Preventive care) {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Female 1,014 139,091,345 11,844,812 117,664,165 164,420,512 62.2 2.9 56.2 68  
Male 554 84,532,326 10,593,549 66,039,112 108,204,272 37.8 2.9 32   43.8
N = 1568.

Create HTML or LaTeX tables

To create HTML or LaTeX tables from an R Markdown notebook or a Quarto document, add the results='asis' argument to the code chunk, like so:

```{r, results='asis'}
tab("AGER")
```
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20   1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

You can change the package that surveytable uses for printing. surveytable comes with code for using one of these packages: huxtable (default), gt, and kableExtra. There is also the option to automatically select one of these packages depending on whether the output is to the screen (huxtable), HTML (gt), or LaTeX (kableExtra). In addition, you can supply custom code to use another table-making package.

Changing the table-making package has a couple of uses:

  • Use as_object() to generate an object from your favorite table-making package, further edit this object using that table-making package, and then finally print it, so the table looks exactly the way you want it to look.
  • Print to destinations other than the screen, such as HTML or LaTeX.

huxtable

By default, surveytable prints using huxtable. If you’ve changed this, you can always go back with:

set_opts(output = "huxtable")
#> * Printing with huxtable.

This is what printing to the screen looks like.

tab("AGER")
#>                                     Patient age recode {NAMCS 2019 PUF}                                     
#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │
#> │ years       │       │             │            │             │             │         │     │      │      │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
#> │ and over    │       │             │            │             │             │         │     │      │      │
#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
#>   N = 8250.

To create HTML or LaTeX tables from an R Markdown notebook or a Quarto document, add the results='asis' argument to the code chunk, like so:

```{r, results='asis'}
tab("AGER")
```
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20   1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

gt 

set_opts(output = "gt")
#> * Printing with gt.

With gt, printing to the screen and to HTML look the same. Here is what printing to the screen looks like:

tab("AGER")
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20.0 1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

Here is HTML:

```{r, results='asis'}
tab("AGER")
```
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20.0 1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

kableExtra 

set_opts(output = "kableExtra")
#> * Printing with kableextra.

We have not implemented screen printing with kableExtra yet. Try one of the other packages.

Here is HTML:

```{r, results='asis'}
tab("AGER")
```
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20.0 1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

auto

This option automatically selects one of the above packages depending on whether the output is to the screen (huxtable), HTML (gt), or LaTeX (kableExtra).

set_opts(output = "auto")
#> * Printing with huxtable for screen, gt for HTML, or kableExtra for LaTeX.

Screen output (this should use huxtable):

tab("AGER")
#>                                     Patient age recode {NAMCS 2019 PUF}                                     
#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │
#> │ years       │       │             │            │             │             │         │     │      │      │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │
#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
#> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
#> │ and over    │       │             │            │             │             │         │     │      │      │
#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
#>   N = 8250.

HTML output (this should use gt):

```{r, results='asis'}
tab("AGER")
```
Patient age recode {NAMCS 2019 PUF}
Level n Number SE LL UL Percent SE LL UL
Under 15 years 887 117,916,772 14,097,315 93,228,928 149,142,177 11.4 1.3 8.9 14.2
15-24 years 542 64,855,698 7,018,359 52,386,950 80,292,164 6.3 0.6 5.1 7.5
25-44 years 1,435 170,270,604 13,965,978 144,924,545 200,049,472 16.4 1.1 14.3 18.8
45-64 years 2,283 309,505,956 23,289,827 266,994,092 358,786,727 29.9 1.4 27.2 32.6
65-74 years 1,661 206,865,982 14,365,993 180,480,708 237,108,637 20.0 1.2 17.6 22.5
75 years and over 1,442 167,069,344 15,179,082 139,746,193 199,734,713 16.1 1.3 13.7 18.8
N = 8250.

Advanced printing

The proper approach

Advanced users can add functionality to use any table-making package that they want. For more information, see help("surveytable-options").

The “quick-and-dirty” approach

The tabulation functions return either:

  • for a single table, a data frame, with certain attributes set; or
  • for more than one table, a list of such data frames.

You can convert a single table to a data.frame with as.data.frame(), like so:

tab("AGER") |> as.data.frame()
#>               Level    n    Number       SE        LL        UL Percent  SE
#> 1    Under 15 years  887 117916772 14097315  93228928 149142177    11.4 1.3
#> 2       15-24 years  542  64855698  7018359  52386950  80292164     6.3 0.6
#> 3       25-44 years 1435 170270604 13965978 144924545 200049472    16.4 1.1
#> 4       45-64 years 2283 309505956 23289827 266994092 358786727    29.9 1.4
#> 5       65-74 years 1661 206865982 14365993 180480708 237108637    20.0 1.2
#> 6 75 years and over 1442 167069344 15179082 139746193 199734713    16.1 1.3
#>     LL   UL
#> 1  8.9 14.2
#> 2  5.1  7.5
#> 3 14.3 18.8
#> 4 27.2 32.6
#> 5 17.6 22.5
#> 6 13.7 18.8

Alternatively, you can pass this data frame to your favorite table-making package. This example passes it to gt:

set_opts(count = "1k")
#> * Rounding counts to the nearest thousand.
tab("AGER") |> gt::gt()
Level n Number (000) SE (000) LL (000) UL (000) Percent SE LL UL
Under 15 years 887 117917 14097 93229 149142 11.4 1.3 8.9 14.2
15-24 years 542 64856 7018 52387 80292 6.3 0.6 5.1 7.5
25-44 years 1435 170271 13966 144925 200049 16.4 1.1 14.3 18.8
45-64 years 2283 309506 23290 266994 358787 29.9 1.4 27.2 32.6
65-74 years 1661 206866 14366 180481 237109 20.0 1.2 17.6 22.5
75 years and over 1442 167069 15179 139746 199735 16.1 1.3 13.7 18.8

The reason that this is the “quick-and-dirty” approach is that the output it creates is not as nice as conventional tables, described above. The output does not have table title (which has important information about the variable and the survey), table footer (which has important information about sample size and low-precision estimates), and it does not format the estimates. Nevertheless, there could be situations in which this approach is helpful, such as

  • extracting an exact value from a table using as.data.frame(); or
  • quickly using your favorite table-making package.