# twoxtwo

The `twoxtwo` package provides a collection of functions to display, summarize, and analyze data in two-by-two contingency tables. Statistical analysis functions are oriented towards epidemiological investigation of exposure/outcome relationships.

## Installation

``````## install.packages("devtools")
devtools::install_github("vpnagraj/twoxtwo", build_vignettes = TRUE)``````

## Features

• `twoxtwo()`: Construct `twoxtwo` object
• `odds_ratio()`: Estimate odds ratio and confidence interval
• `risk_ratio()`: Estimate risk ratio and confidence interval
• `risk_diff()`: Estimate risk difference and confidence interval
• `fisher()`: Perform Fisher’s exact test
• `chisq()`: Perform Pearson’s chi-squared test
• `arp()`: Estimate attributable risk proportion (ARP) and confidence interval
• `parp()`: Estimate population attributable risk proportion (PARP) and confidence interval
• `ein()`: Estimate exposure impact number (EIN) and confidence interval
• `cin()`: Estimate case impact number (CIN) and confidence interval
• `ecin()`: Estimate exposed cases impact number (ECIN) and confidence interval
• `summary.twoxtwo()`: Summarize `twoxtwo` object
• `print.twoxtwo()`: Print `twoxtwo` object
• `display()`: Render `twoxtwo` table contents as a `knitr::kable`

## Usage

### Example

First load `twoxtwo` and `dplyr` to help prep data:

``````library(twoxtwo)
library(dplyr)``````

Next create a object with S3 class `twoxtwo`. For this example, use the `twoxtwo::titanic` dataset. Note that “exposure” and “outcome” variables must each be binary variables:

``````crew_2x2 <-
titanic %>%
twoxtwo(.data = ., exposure = Crew, outcome = Survived)

crew_2x2
# |         |           |OUTCOME      |OUTCOME     |
# |:--------|:----------|:------------|:-----------|
# |         |           |Survived=Yes |Survived=No |
# |EXPOSURE |Crew=TRUE  |212          |673         |
# |EXPOSURE |Crew=FALSE |499          |817         |``````

The `twoxtwo` class has its own `summary.twoxtwo()` method that computes effect measures (odds ratio, risk ratio, and risk difference):

``````summary(crew_2x2)
#
# |         |           |OUTCOME      |OUTCOME     |
# |:--------|:----------|:------------|:-----------|
# |         |           |Survived=Yes |Survived=No |
# |EXPOSURE |Crew=TRUE  |212          |673         |
# |EXPOSURE |Crew=FALSE |499          |817         |
#
#
# Outcome: Survived
# Outcome + : Yes
# Outcome - : No
#
# Exposure: Crew
# Exposure + : TRUE
# Exposure - : FALSE
#
# Number of missing observations: 0
#
# Odds Ratio: 0.516 (0.426,0.624)
# Risk Ratio: 0.632 (0.551,0.724)
# Risk Difference: -0.14 (-0.178,-0.101)``````

Individual measures of effect, hypothesis tests, and impact numbers can be calculated using the `twoxtwo` object. For example:

``````crew_2x2 %>%
odds_ratio()
# # A tibble: 1 x 6
#   measure    estimate ci_lower ci_upper exposure         outcome
#   <chr>         <dbl>    <dbl>    <dbl> <chr>            <chr>
# 1 Odds Ratio    0.516    0.426    0.624 Crew::TRUE/FALSE Survived::Yes/No``````
``````crew_2x2 %>%
chisq()
# # A tibble: 1 x 9
#   test      estimate ci_lower ci_upper statistic    df   pvalue exposure outcome
#   <chr>     <lgl>    <lgl>    <lgl>        <dbl> <int>    <dbl> <chr>    <chr>
# 1 Pearson'… NA       NA       NA            46.5     1 8.97e-12 Crew::T… Surviv…``````

Note that data analysis can also be performed without first creating the `twoxtwo` object:

``````titanic %>%
odds_ratio(.data = ., exposure = Crew, outcome = Survived)
# # A tibble: 1 x 6
#   measure    estimate ci_lower ci_upper exposure         outcome
#   <chr>         <dbl>    <dbl>    <dbl> <chr>            <chr>
# 1 Odds Ratio    0.516    0.426    0.624 Crew::TRUE/FALSE Survived::Yes/No``````
``````titanic %>%
chisq(.data = ., exposure = Crew, outcome = Survived)
# # A tibble: 1 x 9
#   test      estimate ci_lower ci_upper statistic    df   pvalue exposure outcome
#   <chr>     <lgl>    <lgl>    <lgl>        <dbl> <int>    <dbl> <chr>    <chr>
# 1 Pearson'… NA       NA       NA            46.5     1 8.97e-12 Crew::T… Surviv…``````

### Vignettes

The package includes vignettes to describe usage in more detail.

For details on the `twoxtwo` data structure and demonstration of basic usage:

``vignette("basic-usage", package = "twoxtwo")``

For formulas and examples of how to calculate measures of effect:

``vignette("measures-of-effect", package = "twoxtwo")``

For information on hypothesis testing functionality in the package:

``vignette("hypothesis-testing", package = "twoxtwo")``

For formulas and demonstration of attributable fraction and impact number calculations:

``vignette("af-impact", package = "twoxtwo")``

## Contributing

Please use GitHub issues to report bugs or request features. Contributions will be reviewed via pull requests.