---
title: "A Test File"
author: "Friedrich Leisch (adapted by Mark Clements)"
output:
  html_document:
    toc: TRUE
---

We load the `ascii` package and set the output type as `"pandoc"`.
```{r setup}
library(ascii)
options(asciiType="pandoc")
```

A simple example: the integers from 1 to 10 are
```{r integers,results='asis'}
ascii(1:10)
```
```{r testing, include=FALSE}
print(1:20)
``` 
<!-- the above is just to ensure that 2 code chunks can follow each other -->

We can also emulate a simple calculator:

```{r}
1 + 1
1 + pi
sin(pi/2)
```

Now we look at Gaussian data:

```{r, results='asis'}
library(stats)
set.seed(12345)
ascii(x <- rnorm(20))
ascii(t1 <- t.test(x))
```
Note that we can easily integrate some numbers into standard text: The
third element of vector `x` is `r x[3]`, the
_p_-value of the test is `r format.pval(t1$p.value)`.

Now we look at a summary of the famous `iris` data set, and we
want to see the commands in the code chunks:

```{r, results='asis'}
data(iris)
ascii(summary(iris),header=TRUE)
```

```{r,fig.cap="Pairs plot of the iris data"}
library(graphics)
pairs(iris)
```


```{r,fig.cap="Boxplot of sepal length grouped by species"}
boxplot(Sepal.Length~Species, data=iris)
```

Finally, we test the new `asciiCoefmat` function:

```{r, results='asis'}
library(stats)
x = y = 1:10
y[1] = 5
lm(y ~ x) |> summary() |> coef() |> asciiCoefmat()
```

```{r}
library(stats)
x = y = 1:10
y[1] = 5
lm(y ~ x) |> summary()
```
