bivarian

Generates a HTML output with p values for bivariate analysis

https://github.com/andresfloresg/bivarian

Repository

Generates a HTML output with p values for bivariate analysis

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# BiVariAn 



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[![CRAN status](https://www.r-pkg.org/badges/version/BiVariAn)](https://CRAN.R-project.org/package=BiVariAn)
![CRAN Downloads](https://cranlogs.r-pkg.org/badges/grand-total/BiVariAn)









# Overview

The purpose of 'BiVariAn' is to facilitate bivariate analysis by integrating functions that allow the results of such analyses to be represented graphically and in text.

Currently the package can generate summary tables of analysis data for: Analysis of continuous variables across two groups using Student's t-tests (OR Welch's t-tests according to homogeneity of variances), Mann-Whitney U; analysis of dichotomous variables using Chi-square and Fisher's exact test; analysis of normality of raw data using the Shapiro-Wilk test.

It is also possible to generate graphs automatically with or without labels on the variables with the possibility of choosing a theme from the 'ggplot2' package.

Currently under development is the option to generate a generic interpretation of the statistical tests, temporarily available only in Spanish, but there are plans to include more languages.

In addition to bivariate analyses, regression analyses with predictor selection using the stepwise backward method for linear, logistic and Firth penalized logistic regression models can be performed in an automated manner.

Note: The use of the package does not replace individual customized analysis, but rather streamlines the process of producing the results. We encourage users to perform statistical analyses based on the recommendations for each type of study.

## Installation

You can install the development version of BiVariAn from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("AndresFloresG/BiVariAn")
```

Or you can install the official CRAN version with:

``` r
install.packages("BiVariAn")
```


## Example

Loading the package

```{r, warning=FALSE, message=FALSE}
library(BiVariAn)
```


Render an automatic Shapiro-Wilk's table of a simple dataset

```{r, warning=FALSE}
auto_shapiro_raw(cars)

shapiro.test(cars$speed)
```

Return Shapiro-Wilk's results as a dataframe

```{r}
auto_shapiro_raw(cars, flextableformat = FALSE)
```

Render an automatic Shapiro-Wilk's table of a more complex dataset

```{r}
# Load riskCommunicator to access Framingham dataset
library(riskCommunicator)
```

```{r, message=FALSE}
# Load dplyr to select specific columns
library(dplyr)
data(cvdd)
```

For shapiro.test, sample size must be between 3 and 5000

Let's select only 300 observations (arbitrary)

```{r}
set.seed(081224)
ex_sample<-slice_sample(cvdd, n=300)
```

Now, let's select specific columns from the database

```{r}

auto_shapiro_raw(ex_sample %>% select(TOTCHOL, SYSBP, DIABP, BMI, HEARTRTE))
```

Common use of shapiro.test

```{r}
shapiro.test(ex_sample$TOTCHOL)
```

Return the same Shapiro-Wilk's results as a dataframe

```{r}
auto_shapiro_raw(ex_sample %>% select(TOTCHOL, SYSBP, DIABP, BMI, HEARTRTE), flextableformat = FALSE)
```

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