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.

To install the stable release from CRAN:

install.packages("twoxtwo")

Or to install the development release from GitHub:

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

• 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

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…

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")

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