README.Rmd

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# Survey table: formatted survey estimates

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`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.

* The `surveytable` package provides short and understandable commands that generate tabulated, formatted, and rounded survey estimates. 
* With `surveytable`, you can
  * tabulate estimated counts and percentages, with their standard errors and confidence intervals,
  * estimate the total population, 
  * tabulate survey subsets and variable interactions, 
  * tabulate numeric variables, 
  * perform hypothesis tests, 
  * tabulate rates, 
  * modify survey variables, and 
  * save the output. 

* Optionally, all of the tabulation functions can identify low-precision estimates using the National Center for Health Statistics (NCHS) algorithms (or other algorithms). 
* If the `surveytable` code is called from an R Markdown notebook or a Quarto document, it automatically generates HTML or LaTeX tables, as appropriate. 
* The package reduces the number of commands that users need to execute, which is especially helpful for users new to R or to programming.


## Installation

Install from CRAN:

``` r
install.packages("surveytable")
```
or get the development version from GitHub:

``` r
install.packages(c("remotes", "git2r"))
remotes::install_github("CDCgov/surveytable", upgrade = "never")
```

## Documentation

Find the documentation for `surveytable` here: https://cdcgov.github.io/surveytable/

## Example

Here is a basic example, to get you started. 

1. Load the package:

```{r, message=FALSE}
library(surveytable)
```

2. Specify the survey that you wish you analyze. `surveytable` comes with a survey called `namcs2019sv`, for use in examples. 

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

3. Specify the variable to analyze. In NAMCS, `AGER` is the age category variable:

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

The table shows:

* Descriptive variable name
* Survey name
* For each level of the variable:
  * Number of observations
  * Estimated count with its SE and 95% CI
  * Estimated percentage with its SE and 95% CI
* Sample size
* Optionally, the table can show whether any low-precision estimates were found

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