Printing (HTML) • surveytable

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:

library(surveytable)
set_survey(namcs2019sv)

Variables	Observations	Design
Survey info {NAMCS 2019 PUF}
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")

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
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.

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

for (vr in c("AGER", "SEX")) {
  print( tab_subset(vr, "MAJOR", "Preventive care") )
}

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient age recode (Major reason for this visit = Preventive care) {NAMCS 2019 PUF}
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	28.0
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	10.0	1.7	6.9	13.8
N = 1568.

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient sex (Major reason for this visit = Preventive care) {NAMCS 2019 PUF}
Female	1,014	139,091,345	11,844,812	117,664,165	164,420,512	62.2	2.9	56.2	68.0
Male	554	84,532,326	10,593,549	66,039,112	108,204,272	37.8	2.9	32.0	43.8
N = 1568.

Create HTML or PDF tables

Using a Quarto document, you can create tables in many different formats, such as HTML or PDF. Here is a straightforward example of what a Quarto document might look like:

---
title: "My tables"
author: "Me"
format: html
---

# Welcome 

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

```{r, results='asis'}
library(surveytable)
set_survey(namcs2019sv)
```

# Tables

Take a look at this table:

```{r, results='asis'}
tab("AGER")
```

Note the format setting, which specifies that this document will create HTML tables. Also note that you do have to add the results='asis' argument to the code chunks that print tables.

Print using various table-making packages

Use the output argument of set_opts() to select a table-making package. By default (output = "auto"), surveytable automatically selects a package depending on whether the output is to the screen (huxtable), HTML (gt), or PDF (kableExtra). You can also explicitly select one of these packages.

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

It allows you to generate tables in the way that you prefer.
It allows you to print those tables to a variety of destinations, including the screen, HTML files, or PDF files.

`huxtable`

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 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")

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
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")
```

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
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")

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

Here is HTML:

```{r, results='asis'}
tab("AGER")
```

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
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`

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

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

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")
```

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
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.

Generate unformatted output

Some analysts might wish to compare the output from surveytable to the output from other statistical software, such as SAS / SUDAAN. In this situation, set_opts(output = "raw") might be useful. This command tells surveytable to print unformatted and unrounded tables.

set_opts(output = "raw")
#> * Generating unformatted / raw output.

tab("AGER")

#> Patient age recode {NAMCS 2019 PUF}
#>               Level    n    Number       SE        LL        UL   Percent
#> 1    Under 15 years  887 117916772 14097315  93228928 149142177 11.376609
#> 2       15-24 years  542  64855698  7018359  52386950  80292164  6.257277
#> 3       25-44 years 1435 170270604 13965978 144924545 200049472 16.427706
#> 4       45-64 years 2283 309505956 23289827 266994092 358786727 29.861131
#> 5       65-74 years 1661 206865982 14365993 180480708 237108637 19.958428
#> 6 75 years and over 1442 167069344 15179082 139746193 199734713 16.118849
#>          SE        LL        UL
#> 1 1.3108198  8.913995 14.238468
#> 2 0.5933708  5.138530  7.534097
#> 3 1.1296060 14.254174 18.787872
#> 4 1.3662053 27.185465 32.643562
#> 5 1.2288913 17.580833 22.505589
#> 6 1.2673229 13.689540 18.789681
#> N = 8250.

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

Save tables and charts to an Excel workbook

Before using Excel printing, please be sure to install these packages: openxlsx2 and mschart.

To save tables and charts to an Excel file, turn on Excel printing with set_opts( output = "Excel", file = "my_workbook" ). Set the file argument to the name of an Excel file.

set_opts(output = "excel", file = "my_workbook")

#> * Printing to Excel workbook my_workbook.xlsx.

Generate some tables:

total()
#> * Printing Total {NAMCS 2019 PUF} to Excel workbook my_workbook.xlsx.
tab("AGER")
#> * Printing Patient age recode {NAMCS 2019 PUF} to Excel workbook my_workbook.xlsx.

To turn off Excel printing, set the output argument to a value other than "Excel", such as "auto":

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

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.

Save the tables

Save to a CSV file

All tabulation functions have an argument called csv. Use it to specify the name of a CSV (comma-separated values) file, like so:

tab("AGER", csv = "myfile.csv")

Level	n	Number (000)	SE (000)	LL (000)	UL (000)	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
Under 15 years	887	117,917	14,097	93,229	149,142	11.4	1.3	8.9	14.2
15-24 years	542	64,856	7,018	52,387	80,292	6.3	0.6	5.1	7.5
25-44 years	1,435	170,271	13,966	144,925	200,049	16.4	1.1	14.3	18.8
45-64 years	2,283	309,506	23,290	266,994	358,787	29.9	1.4	27.2	32.6
65-74 years	1,661	206,866	14,366	180,481	237,109	20.0	1.2	17.6	22.5
75 years and over	1,442	167,069	15,179	139,746	199,735	16.1	1.3	13.7	18.8
N = 8250.

Open this CSV file in Excel or your favorite text editor or spreadsheet.

Save to an R data file

Use the built-in saveRDS() function to save a table to an R data file:

tab("AGER") |> saveRDS("myfile.rds")

You can later load this data file back into R. To print the table, just load the file, like so:

readRDS("myfile.rds")

Level	n	Number (000)	SE (000)	LL (000)	UL (000)	Percent	SE	LL	UL
Patient age recode {NAMCS 2019 PUF}
Under 15 years	887	117,917	14,097	93,229	149,142	11.4	1.3	8.9	14.2
15-24 years	542	64,856	7,018	52,387	80,292	6.3	0.6	5.1	7.5
25-44 years	1,435	170,271	13,966	144,925	200,049	16.4	1.1	14.3	18.8
45-64 years	2,283	309,506	23,290	266,994	358,787	29.9	1.4	27.2	32.6
65-74 years	1,661	206,866	14,366	180,481	237,109	20.0	1.2	17.6	22.5
75 years and over	1,442	167,069	15,179	139,746	199,735	16.1	1.3	13.7	18.8
N = 8250.

Suppress printing

There are times when you might want to prevent the tabulation functions from printing tables. If you are saving the tables to a CSV file anyway, you might not need screen printing.

As mentioned above, if the tabulation functions are called from within a loop without using the print() command, they won’t print.

An easy way to suppress printing when the tabulation functions are called from the top level is to assign the output to some variable. For example, this will save the table to a CSV file, but won’t print it to the screen:

tmp = tab("AGER", csv = "myfile.csv")