continuous.Rmd

---
title: "PhyloWizard Continuous"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
```


```{r, echo = FALSE}
# hidden from user, these are the functions to provide the summaries

summary_stats <- function(data) {
  ave <- mean(data)
  medi <- median(data)
  stand_dv <- sd(data)
  range_upper <- max(data)
  range_lower <- min(data)
  return(list(ave = ave, 
              medi = medi, 
              stand_dv = stand_dv, 
              range_upper = range_upper,
              range_lower = range_lower))
}

num_obs <- function(data) {
  length(data)
}

data_normal <- function(data) {
  
  shap_test <- shapiro.test(data)
  return(shap_test$p.value)
}

density_plot <- function(data) {
  
  d <- density(data) # returns the density data 
  plot(d, main = "Density Plot of Data")
  polygon(d, col="darkolivegreen3")
}

hist_w_normal <- function(data, char_name = NA) {

  #histogram
  h <- hist(data, col="grey", xlab = char_name, 
            main = paste("Shapiro-Wilk test of normality P-Value:", round(data_normal(data), digits = 3), sep = " "))

  #normal distribution based on mean and sd of data
  xfit <- seq(min(data),max(data),length=40) 
  yfit <- dnorm(xfit,mean=mean(data),sd=sd(data)) 
  yfit <- yfit*diff(h$mids[1:2])*length(data) 
  lines(xfit, yfit, col="black", lwd=2, lty = 2)

  legend("topright", 
    legend = c("Data", "Normal"), 
    col = c("grey", "black"), 
    pch = c(19, 19), 
    bty = "n", 
    pt.cex = 1, 
    cex = 1, 
    text.col = c("black", "black"), 
    horiz = F)
  
}

```


## Let's make up test data 

Here we go!! 

```{r}
test_data <- rnorm(n = 100, mean = 65, sd = 10)
```


```{r, include = FALSE}

summary <- summary_stats(test_data)

```

Your data has a mean of `r round(summary$ave, digits = 2)`, median of `r round(summary$medi, digits = 2)`, and standard deviation of `r round(summary$stand_dv, digits = 2)`. The data range from `r round(summary$range_lower, digits = 2)` to `r round(summary$range_upper, digits = 2)`.

The plot below shows the distribution of your data compared to a normal distribution with the same mean and standard deviation. The Shapiro-Wilk test of normality compares your data to a normal distribution. A significant p-value indicates that your data are significantly different from a normal distribution. 

```{r, echo = FALSE}
hist_w_normal(test_data)
```