# KnitR/dice.Rmd

---
title: "Descriptive Statistics of 10000 dice rolls - a simple KnitR example"
author: "Martin Papke"
date: "22 August 2018"
output: pdf_document
---

{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE) library(knitr) library(readr) library(dplyr) library(ggplot2)  # A simple KnitR example ## Data import In this document we aim to show how KnitR can be used to gerenate a report or an article containing statistical data and how the R code can be integrated within the document. As example data, we use 10000 dice rolls contained in the file *dice.csv*. As usual in R we could load the data with {r loaddata} # data <- read.csv('dice.csv', stringsAsFactors=FALSE) # dice <- as.numeric(data$X3)

To give a standalone example here, we use R's feature to generate random numbers

dice <- sample(1:6, 10000, replace=TRUE)


## Statistics

Now we can do some statistics
 {r statistics}
dicemean <- mean(dice)
dicemedian <- median(dice)

So, the mean of our dice throws is $\bar x = r dicemean$ and the median is r dicemedian. We
know count the absolute frequencies of the dice results:
{r statistics2}
dicetable <- table(dice)

We obtain the results
{r table1, echo=FALSE}
kable(dicetable, caption='Dice results')


## Plots
In KnitR, plots can be done into the document, just call the usual R plot command
{r plot}
xy <- data.frame(dicetable)
ggplot(data=xy, aes(x=dice, y=Freq)) + geom_bar(stat="identity")


## Some data manipulation
We now combine each two dice throws into one, hence we get 5000 samples of two dice throws.
{r combine}
dicetwo  <- dice[seq.int(0,10000,2)] + dice[seq.int(1,10000,2)]
twotable <- table(dicetwo)

As the result of the first two throws were $(r dice[1],r dice[2])$, the first entry
of *dicetwo* is $\texttt{dicetwo[1]} = r dicetwo[1]$.

Finally, we look again at a plot
{r plot2}
xy <- data.frame(twotable)
ggplot(data=xy, aes(x=dicetwo, y=Freq)) + geom_bar(stat="identity")